 Let's see. Hello. Well, hello, everybody. Thank you for coming. We are super lucky because, actually, the day is pretty nice. You cannot imagine what happened to me like two days ago. When I saw this blizzard coming, I thought that it was going to be a massive problem for you to be here, but fortunately, you are here. I'm very dry, so that's fantastic. So welcome to the second edition of the symposium of blockchain for robotic systems and AI. This second edition, which is this year's edition, is a little bit like the extension of the first year like edition that we did only in the combination of robotics and blockchain technology. Before we can get into stuff, let me do a little bit of housekeeping. So you know that if you check the agenda of today, this is like one day symposium, and it's divided in several sessions. So the first session is going to be a keynote session. So there's going to be experts in the field of robotics, of blockchain technology, of data sharing, of privacy, of security that are going to talk for 30 minutes. I totally encourage you to see all the keynotes, because they are amazing. The speaker lineup is amazing. Then we are going to have lunch, and then we are going to complement this first session with the second session with only papers. So we receive around 20 submissions for papers, and we accepted six. So we had a 30% acceptance rate, and the accepted papers are going to present. And you will see that there's a nice combination of robotics of AI with the world of crypto, with the world of blockchain. So I totally encourage you to attend. This is going to be complemented by a third session, that this is like new for this edition, in which we are going to talk about industry. So we are not going to remain only in the academic world. We are going to talk about how industry is interested in this combination, and what do they need, like from this synergy. And finally, at 4.30, we are going to have a workshop about computational law. So lawyers that are interested in the world of technology are going to break their perspective about how we can enforce smart contracts and blockchain technology for legal procedures. And at the end, so we are going to have a Boston blockchain meetup gathering here. So if you are interested in what's going on in the Boston ecosystem, please attend. But finally, we are going to have drinks and a pub nearby. So if you find a nice connection, and you want to talk more, and you want to ask questions that you couldn't ask during the session, please go to the mid-haul. And then with a beer, talk to your preferred speaker or paper author. Yeah, that being said, yeah. So we received 20 initial submissions, as we said before. We got six papers accepted. So we have a 30% acceptance rate. We got an increase in the page views of the call of papers. So this is something good. So we are still small, but we are growing every year, which is nice. So the community is growing. And we have an amazing lineup of reviewers. For these papers, the reviews are still ongoing. So we are still going through the rounds of reviews, especially for the papers that needed more changes. But yeah, but we want to thank these people because they dedicated a lot of time in reading the manuscripts. So the accepted manuscripts after the rounds of reviews are going to be published in the frontiers research topic. So this is pretty cool. We got the previous editions and proceedings published in Ledger Journal, which is pretty interesting. It's the first journal about blockchain and cryptocurrencies. So we know that this edition got a lot of views and some citations already, so which is nice. And yeah, with this, I would like to start a little bit like the intro of this talk. So we are all here because we care about technology, and especially about how robotics and AI is going to impact the world. And you know that this is a difficult topic, right? Because there's many people talking about many different things about this world. So there's people saying that AI and robots are the beginning of the doom, right? There's other people that are saying that, for example, no, robots are going to free us from the work that we don't want to do, right? So there's a lot of complex views on a very complex topic, right? But I would like to give you some kind of a vision over this complex topic. And I will start with my own story, right? So one of these guys here was my PhD advisor, you know? So the other is his robot clone, right? So let me give you some context, right? So I did my master in my PhD in Japan in Osaka University. And one of these guys was my supervisor, right? So one of these guys is called Professor Hiroshi Ishiguro, right? And then he was very interested in how we can be in two places at the same time, yeah? He really wanted to explore this concept of telepresence, right? So this comes from the fact that Professor Hiroshi Ishiguro was a very successful professor in Japan. And he was hired to give lectures like in Kyoto, in Tokyo, in Osaka, right? And he realized that he was spending a lot of time near traveling, traveling to the places that actually he really didn't want to be, you know? So he realized that, OK, what happens if somebody could represent me in that place? What happens if I would be able to send a proxy, right? So he decided to build a very realistic robot, you know, and send it to the places he didn't want it to be. This, what seems like pretty fringe, right? Makes more sense when you see this. So what you are seeing here is basically the two together, right? And you can see the robot as a very, very expensive Skype client, right? In the normal teleoperation-like mode, the robot is sent to the place that Professor Ishiguro doesn't want to be, right? And he just operates the robot through a sensor of cameras. So it's basically what will happen if you see a very realistic image of somebody that talks like that person, that moves like that person, and reacts like that person. But it's not that person because that person is not there. So in a certain sense, he wanted to transfer that presence, right? So let me tell you a story about that. So of course, Professor Ishiguro didn't want to be in the place that the robot went eventually, right? But somebody had to move the robot, right? Yeah, so that was me, right? That was not only in Japan. It was basically worldwide, right? Like so for a really, really long time, you know, I had to basically travel with a human torso in my luggage. So every security guard in this world knows my face, right? But yeah, but let me tell you something more, you know? So the problem with this is the fact that, for example, the future of robotics for Professor Ishiguro was the fact that we will have a very complex robot, right? That will represent us, you know? One very complex robot that is very expensive, like hard to repair, et cetera, right? For me, when I was doing my PhD, I realized that that was not my vision of the future of robotics, you know? My vision of the future of robotics was more distributed, more decentralized. So I thought that through making robots very simple, very easy to repair, right? But putting them in big groups so they can collaborate and cooperate among them, you can make very complex tasks. You can achieve very complex tasks. And at the same time, you know, you can just basically have a lot of like nice properties that one very complex robot doesn't have, right? So what you're seeing here is a very easy example of what is a swarm of robots, right? So this is like a mini swarm of robots doing something called foraging. So basically robots, what they do is that they self-organize in order to find these 3D tokens, right? Like in this football field. So these three tokens could represent whatever, could represent resources, could represent data, could represent people, right? And what they do basically is that these robots, in a very decentralized, there's no boss here. There's no command and control like operation. Find these 3D tokens and then they put them into the nest, yeah, which is the center of the field. So these nest simulates like a human society, for example. So once the robot puts like a token, the robot gets a recharge in the battery. So it gets a reward, right? So robots start like to self-organize in a decentralized way in order to keep on doing this thing for a really long periods of time, right? And achieve sustainable behavior. So now that you see this, you think, okay, why is this useful? Why we care about this? Well, so if you take like the token and you put like people in a city and then you take out like the center of the field and you put an airport and you make the robot like a self-driving car, then you can start seeing like the use of this, right? The interesting thing about these systems is that since they are decentralized and there's no single point of failure, I can break one robot or two robots or three robots and the others will cope, yeah? So this system is robust, has fault-tolerant capabilities, you know, by default by design, which is very interesting for this kind of like new public infrastructure that we are trying to envision, right, with robotics. But, you know, there's another problem here. When I finished my PhD, I realized that like the world of distributed robotics, the world of like swarms, you know, like is very polarized, you know? So there's like people like trying to like do research on the theory, on the basis of these like emergent properties that these systems have, right? And then they are focused on these. But there's also other people that basically says, no, I mean, these systems are going like to deliver Amazon packages, you know, like in five years and we're going to have swarms of self-driving cars in cities, you know? And so I realized that these two communities are very far apart and there's nobody trying to breach, you know, these like two things, you know, these like two big visions, you know, of this world, right? And this is basically because there's many things that we didn't tackle in order to make these systems which have good capabilities work and unbiable, right? So some of the problems that I realized that we have for these systems is basically that we don't have any security standards for these systems, right? Like so we realized that they have good properties, but like what happens if at some point in time some of the robots, you know, get hacked or some of the robots, you know, start like to misbehave, yeah? What will happen with these systems? Will remain falter and will remain like robust. There's also no good way to understand how these big systems, especially large, like swarms can get into agreements and get into consensus, right? For certain things. We don't have research like based on that, like that puts the grounds like for these systems and more particularly, you know, we don't have new business models like for these systems, right? So it's very hard like to breach the gap between academia and the industry. So one of the things that I realized like why we have like these problems is because we don't have good interfaces to these systems. So while in academia, we have a lot of research about how to do human to robot interaction with one human with one robot, right? We don't have good interfaces for doing human to big groups of robot interaction, right? And it's because as you scale these systems, the system gets more complex. So they are very difficult to audit. They are very difficult to become transparent, right? So we started like to see that this is like a need. You know, for example, you know, this is like an article that I found like one couple of weeks ago in which researchers like started to say, well, if we have like self-driving cars in New York, for example, and we know which cars for example are in Times Square, we can actually block part of Manhattan, right? And you might think that hacking like a self-driving car is very complex, but actually it's not. It's extremely simple, you know, like to do that. And there's been cases already of people, you know, like that got into trouble, like on the highway or driving, yeah? So what these article projects a little bit is we are trying to create new infrastructure based on robotics, but we are not covering like the holes that this is like providing. So do you remember three or four years ago this ransomware, you know, this thing that basically got into your computer and said, I'm going to encrypt your hard drive. And if you don't pay me like, I don't know, $1,000 in Bitcoin, you are not going to get your hard drive back. Well, that was cute, you know, but imagine that you are in your self-driving car, you know, driving 120 kilometers per hour in the highway. And at some point in time, there's a pop-up that comes like from the dashboard saying, I'm not going to break until you don't pay me $3,000 in Bitcoin. Things get slightly different, right? So, okay. So, you know, while we were envisioning this like combination, we tried like to do some research that basically tries like to tackle these problems, right? So this is a simulation that like we did in collaboration between the ULB and like at MIT and basically is how we got for the first time the idea of how a group of robots can self-police, you know, each other, you know, can monitor each other, right? So this is like a research done like by a PhD student called Volker Strobel, yeah, in ULB. And what you're seeing here is something very simple, right? So you are seeing like these robots, which are very, very, very simple, trying like to go around this checkerboard, you know, trying to sense, you know, the tiles, you know, the color of the tiles. And then eventually what they're trying to get is into unconsensus about what is the majority color, something super simple, right? So basically how this works is one robot goes like to a part of the checkerboard and says, oh, I've sensed that there is 30% black tiles and 70% of white tiles. So once you get, you bump with another robot, you exchange the opinions, right? And you get into a sub-consensus which emerges into a big consensus. And at the end, we all agree that black is the majority color or white is the majority color, right? Very simple stuff. Well, so what we did is we started like to simulate, you know, what will happen if you will introduce decent time robots. So robots are hacked to break the consensus, right? Start to lie or start like to simulate some kind of like problem that the robot has. And we realize that, well, we can just like compare, you know, the classical approach, the classical algorithms that we have like for consensus and a blockchain-based like approach. What this distinction makes is the fact that if, for example, we use the classical approach, right, you can get like messages around like this like swarm, right? But robots basically somehow believe, you know, what the other robots are saying, right? With the blockchain approach, you have like a record. You know, you get all these votes into like a blockchain that is recorded in every single robot and every time, you know, you bump into each other, you synchronize the blockchain, right? So what we saw is that, for example, in this graph, you have like two axes. The X axis is the number of like decent time robots, the number of like bad bots, right? And in the Y axis, you have the exit probability. So the amount of times that the robots concentrated the right color, right? So what you see in the classical approach is that as you start to include more bad bots, the success rate drops dramatically. What this means is that if you are in New York and you have like 300 like self-driving cars and then you hack, let's say, 10, right? Your success probability that you will have like a good system based on pure peer-to-peer communications and swarm like technology will drop dramatically. And it's because actually this peer-to-peer and this decentralization gives you really good things but also gives you a lot of open problems which is how you stop a lie, for example, right? But for example, if you use a blockchain approach and you start like to register, you know, like these boats, you know, like in transactions among the robots, you can start to find inconsistencies in the system. You can start to find the fact that actually if I told you that the majority color is white, right? And we register this and then I tell you that the majority color is black and you concentrate like this blockchain, you start to find that I got into an inconsistency. We all have the same controller but I'm starting to change my opinion in a very weird way. So you can assign me a reputation. If my reputation goes below a certain threshold, you can weed me out of the system and you can continue doing whatever you are doing, right? So what will happen if we add reputation to robots, you know, based on the data that they provide, right? So this is basically this idea, right? So with that, we can continue like the system, you know, we can continue the self-sustaining behavior of the system. But yeah, but this has a problem, yeah? Also, and the problem is this is like a very simple case, but what will happen if we are trying to do more complex actions? For example, sequential actions, actions in which the robots have to assemble stuff, they have to keep an order, right? Normally, what happens is that in these kind of actions, in order to maintain this like robustness and this fault tolerance, right? And these nice capabilities, we need to distribute, for example, the blueprint of what the robots need to do. So for example, if robots need to make like a bridge, for example, they need to understand, okay, so the piece number one should go here, the piece number two should go here, the piece number three should go here, right? And if I am broken, you can come and because you have the same plan, you can continue the plan as expected, right? But this also has a problem. The problem is if we all have the plan, right? Because we need to maintain this like capabilities, the fact that actually we have a lot of us with a lot of plans replicated also opens new holes. If I am an attacker and I want to know how to hurt that system the best, I just need to get one robot understand the plan, right? And then act accordingly. So what we envision is by exploring this blockchain like a space, do we have any tools, you know, within this like blockchain space that we can use in order to give a blueprint of the robot without actually giving the data? And it turns out that yes, we can do it, you know? So many of you might know the concept of Merkle Tree. A Merkle Tree is like basically a binary tree that instead of having a data like in the notes, you know, there is or decisions or whatever, you have hashes. So all the hashes are stored in the like lower level and then you encrypt like this information and you rehash it, rehash it, rehash it until you get into the root, right? So what we did in this research is trying like to substitute the normal transactions, you know, in the blockchain, A sends B1 Bitcoin, okay? With robot actions, yeah, that belong to a plan. So for example, here could be stuck piece number one, stuck piece number two, stuck piece number three, right? And then we can encrypt this information up to the root, right? And we can give that root to the robots, right? So how this works is the fact that like an operator designs the whole plan, right? In advance and says, okay, so in order to build this bridge, I need action one with piece number one, action two with piece number two, action three with piece number three. You encrypt all this information and then you give like this tree like to the robots. The robots with that tree do not know what they have to do because everything is encrypted, right? But they know that if at some point in time they find the correct combination of robot action and robot sensor input, let's say like that, if they are in front of piece number one and they say, okay, what should I do with piece number one? Should I stuck it? Should I move it? And then they find the right combination and this right combination ends up being the hash of the first leaf or the second leaf or the third leaf. They know they have to do that even though they don't know what the other actions really mean, right? So what, yeah, we try this with several like missions which could be projected in many other like things. But for example, I'm going to show you here, like so robots, so what we did is we encoded a maze like in one of these marco trees, right? So ones are like obstacles or walls, seers are empty spaces and the asterisks and the ad are the entrance and the exit, right? So what we did is we encoded this like tree and then we gave it to the robots and say, wander around at some point in time if you find like a place that belongs to the tree, just stop there, you know, become a wall, right? So robots do not know what they have to do and robots cannot infer any details about like the plan but they know that once like they find like a good action they just stop it, right? So at the end, like through a lot of like wandering around robots are able to make like the maze, right? The interesting thing about this is that if I now capture any of the robots and I say, what do you know about the plan? They do not nothing. The only thing they know is that, yeah, I know that just this hash with this hash is correct. It's part of the plan. So I just stopped here, right? So you cannot infer where the entrance or the exit might be which is very interesting for security reasons, right? So we try to project this and say, okay, instead of like a very simple maze, can we do large scale missions? So what will happen if we will encode the Millennium Falcon in a marco tree, right? So we somehow like conducted this research and we understood that is within reach of technology like to do that. So for example, encoding like the Millennium Falcon in a swarm of robots only requires like a 235 kilobytes of memory and 3.35 megabytes of communication among the robots. So even though with low computational hardware, we can achieve that. But now I would like to give you a final touch to this. One thing you know that like we talked about is the fact that actually there's missing new business models, right? Like for these like systems, right? So what I'm going to present you here is like something that will be presented in more detail in the paper or like presentation, but it's like something that I hope makes you think a little bit. So what you're seeing here is like a robot called Gakachu, right? And Gakachu is a Kuka arm that instead of like assembling robot parts is painting pictures. So in this case, what Gakachu is doing is like he's basically choosing like a kanji, a Japanese kanji, right? Like from the internet. And he's basically replicating it, right? With a brush and he's doing that. So the interesting thing about Gakachu is that once the robot is start painting, there's an auction that is starting. So there's like a live stream that captures like the robot and auctioners like in the internet through the like Ethereum blockchain can auction what they want to pay for that picture, right? So once the auction is over, there's a winner, right? Of the picture and the winner instead of like giving the money to the owner of the robot just gives money to the robot owns account, right? So the robot gets the funds of that process. The important thing about this is that those funds are used in order for the robot autonomously to buy everything that it needs to paint the next picture, right? So in a certain sense, with that money, the robot can basically buy like more campuses or like a more paint, the electricity, the internet like bill, right? To paint the next picture, right? So with this, what we are trying to understand is what will happen if like robots, instead of being just pure labor, get into the capital world, and what things will we can achieve with there? So maybe you know that like there was a guy here called Martin Trust. And Martin Trust, you know, was one of the pioneers of understanding this world of entrepreneur, right? A guy that basically is in the world of labor that moves to the world of capital, gets a little bit of capital and goes back to the world of labor in order to improve it, right? So now the same thing could happen like with robots, right? So robots like are pure labor in factories, but at some point in time could empower themselves like in getting some capital and then change their activities based on that, right? So this is like something that I just introduced, but like you will hear more like in the paper presentation, right? So this is the interface, but I think that like I am running out of time, so I prefer like you like to listen like about this like with Alex and his colleagues, right? So to finish and give you this like intro session, what we are seeing here is the synergy of like a different worlds that are set apart that normally do not like interact with each other, but now they can interact and they can create something bigger than them alone, right? So of course, you know, we are here because we are interested in the world of robotics, in the world of AI. The robots are here, the robots are coming. This isn't, in my opinion, and you can tell me about it, this is not us against the robots, it's us with the robots. And we need to find like ways in order like to coexist and benefit from this like a coalition. But of course, we cannot just leave them completely unattended, you know? We cannot be this reductionist that says, you know, more autonomy for the sake of autonomy, right? Autonomy comes with a price and we need to have a new interface to that autonomy, right? So 20 years ago, we didn't have like these like tools that could create digital trust, but now we have them, right? And in the combination of these like two worlds, you know, we can do something very powerful, but also we need to put it somewhere, you know? We can just not like leave it anywhere, you know? This needs like to be placed in a society like a framework in order like to make things in a good way, right? And make like for example, society is more like transparent, more greener, you know, like more efficient, right? And of course, you know, we are in the media lab today, so we care about how to deploy these systems, you know? How to move like from academia, like to real world applications. So this is like something I also encourage you like to think about. And yeah, and with this, I am over, you know, so this is done. So thank you very much. So I don't know if we are already like running late, you know, in the first presentation, but I would like to introduce you like to Professor Sandy Penland. He's the director of like Human Dynamics Group. He is one of the founders of this place and he's very, very, very interested like in data, like as you will see. And without further delay, I give you on the stage to him. Thanks. So thank you. Glad to see you're all here. This was a good turnout, nice day. So I titled the Blockchain Robotics and how we got here. We're referring to my group and me because I'm not known for robotics. So I thought I would sort of explain something which is why we're doing some of this now and where I hope it will go, okay. And also try to bring some threads from other lines of research together that are interesting. So what I am known for is wearable computing. So back in the early 90s, we did some of the first wearable computing, decorating humans with computers and sensors and stuff like that and it was great fun and it produced a lot of very weird looking people that got a lot of TV coverage and everybody said, I'll never wear that. So I worked with fashion schools who came up with things like this. So this is actually in the sort of early, early mid 90s where they were designing things that looked like iPhones back before there was wifi or cellular or telephones and the guy that did the little head mounted display there actually went on to do Google Glass and so forth. But it's this notion of a symbiosis between people and humans. And one of the things I did after this actually was work for Nissan where I designed the framework for their autonomous vehicle. And the goal there was to be able to have something that was a cooperation between people and the machine. And one of the real challenges of autonomous vehicles is that you're going to be in an environment where some of the other cars have no autonomy, they're just people. And other cars are by other manufacturers. And so you have this requirement to be cooperative without necessarily being able to talk to them in a sort of deep code to code sort of a way. And I think that's the type of system that we're gonna see more and more of where we have these wearable or autonomous vehicle elements, not necessarily robots the way we think about it, but we have to cooperate with them. And it's gonna be this mixture of people and machines cooperating to get something done and you wanna design the system as a whole. So one of the main things that we learned from doing this was that it's not about the robots and it's not about the wearable computers. It's really about the communication between them. It's actually not that difficult to build a lot of these things, at least first order, but it's very difficult to get them to coordinate and cooperate with each other. And this has been noticed before with respect to people. So this is a little quote from Adam Smith in the late 1700s. Everybody knows what the invisible hand is, right? So the invisible hand is a way of people and institutions cooperating with each other without conscious planning, the way Eddie was just talking about. Now in today's society, we tend to think of this as being something that's a market property, something that comes from the market. But that's not what Adam Smith said. What Adam Smith said is that it was peer to peer communication, local communication and local negotiation that determined the balance of services and the norms for cooperation. So not a global thing, a local emergent property. And that's really interesting because for a lot of reasons, local emergent properties are more robust to all sorts of things. They're less susceptible to corruption and attack of various sorts. So what we study, oh and actually Carl Marx said the same thing, just these two guys may be the only time they agreed, but there we are, right? So what we study is we study how you can get systems of communication, local communication, where you continually negotiate policies of action to get a desired overall system performance. And a good example of this is network bandits or distributed bandit problems. Bandits are things that are little autonomous elements, actors, that have a number of different policies, number of different options that they can choose from, but they don't know the rewards associated with each of the options that they can choose. And so they experiment to find something that is the best way for them to get along in their environment, sort of like people. And it's actually in biology, this is called foraging behavior. You see animals experimenting to find better food sources and things like that. So it's a mathematical model of that. And in distributed systems, what you have is you have the ability to observe or communicate with other people and that's good. It's a very powerful technique. You can imagine early humans, if I see you eat the blueberries and then get sick, I'm not gonna eat the blueberries. Almost no cost to me, just observing you and then learning from you. So this distributed learning. And there are optimal ways to do this learning. Thompson sampling is a classic one. There's some variations on it. Most of those are framed as single users though in an environment. There hasn't been nearly as much research on distributed things, although clearly there is. And so we're interested in that problem because that maps to these sorts of problems that Addy was just talking about. You'll have budgets of agents, we don't say if they're people or machines, that they have to learn from each other to coordinate actions that they take that have the best utility for all of them. Sounds pretty good. So some of the things that we're doing is we're focusing on fat-tailed environments. So one of the things that's wrong with a lot of machine learning today and a lot of estimation is that they implicitly assume concentrated distributions. Those noise models like normal models or something like that. But actually in these distributed systems you get cascades. You see me do something, you begin to copy it, he begins to copy it, five other people begin to copy you too, you get this cascade of behavior. Current techniques typically don't work very well with that at all. They go haywire in all sorts of ways. And so what we're doing is focusing on how can you build systems that are robust to this and actually can learn from these sorts of signals. Byzantine agents had talked about that a little bit. What happens when the agents are trying to mislead you, right? And this comes into different flavors. It may not be an intentional misleading, it may be just that they have a very different purpose than you do. So they take an action and report that this was a very good action whereas you would think that it's a very bad action. How do you detect this and compensate for it? Because this is certainly key to the sorts of problems. Privacy, so certainly with people you can understand how you don't want your personal data leaking out everywhere but the same is probably true of many situations with robots, particularly if robots are agents of people. And so how do you actually do this communication which provably preserves people's privacy? And unreliability, you can look at that as a form of Byzantine. It's just, you get screwy stuff happening and sometimes you have to be robust. So we do work in this area. I'm not gonna talk a whole about it except to sort of say that we have some very strong results in these areas. So for each of these cases, we find mathematical schemes that are asymptotically optimal, they're completely decentralized and robust and they're differentially private. So you wouldn't have thought you could do this. The key idea is that in these communication channels between agents, you're trying to model the distributions observed by the agents and you hold out outliers for later consideration. You say, well, that looks weird. I'm gonna hold that back and when I get more of them, I can then decide whether the guy's trying to trick me, whether this is a cascade or other sorts of problems. So there's nice mathematical ways to do this. So this is very new stuff. Abbey is the guy who's a PhD student who was like rocking on this. So if you're interested in the sort of math of it, I do, I'd point you at that. So hopefully that sort of gets you a framing of the types of things that we do and I can be happy to talk more about this. The other thing that we do in my group is we build software to support these sorts of things. So we build things that are blockchain systems that have off-chain data and methods of doing communication, auditing, and machine learning on top of them. And we've been very successful. This is me wearing a tie, something you never see. The president of the European Union invited me to lecture all of the ministers of the single digital market on how they should be handling data for privacy and localization and things like that. And that's basically, you now know the story. So blockchain, off-chain data, encrypt the whole thing all the time, and then do analytics on that. And so, and obviously we have papers and stuff about this. The sort of key insights are that in these sort of coordination systems, you don't want to share data. The moment you share the raw data, even if it's anonymized, you've doomed yourself. What you can do is you can share answers about your private data with other people. So for instance, for differentially private distributed bandit problems, it turns out that sharing means of sort of buckets of action. So different types of actions, you're gonna share the mean payoff, not the specific payoffs, yields a differentially private scheme. It's pretty good, because you can still get optimal convergence from that sort of a thing. And then the second thing is you have to log things on a blockchain so that you can go back and remember things accurately. And so that other people can query you if there's a problem. You can essentially debug things if you can show that there's a problem. You can then go up a level, get higher level permissions to be able to go back and look at the data you need to be able to figure out what's going on. And so we build stuff like that. Not gonna spend a lot of time on it. We're building systems for Senegal and Columbia that do this sort of thing that give you this auditing ability. It's pretty amazing. Anyway, so the bottom line on this is there's sort of two threads that we do. We'd be happy to collaborate with people. You'll hear a little more about this. One thread is building these blockchain systems for humans and human institutions, but nowadays that includes robots and autonomous actors of all sorts. One of the more hot areas in this is IoT types of things. So you're really, really dumb robots like your Nest thermostat and things like that. How do you actually guarantee that those are used in the correct way, aren't hacked, and maintain your privacy? So those are the sorts of questions. They're not very interesting robots, but they're very interesting IT and control problems, particularly when you talk about it as a distributed thing. Autonomous vehicles, of course, and so on. So hopefully we'll find good ways to work together. And I just wanted to welcome you and tell you why we're interested here. Okay, thank you. Say what? Yeah. Are you doing the annual meeting you were doing again? The algorithm? The annual meeting. The meeting you do every year. Oh, yeah. So the annual meeting, that thing there, we do one in Davos. So that's coming up in January. And then we do one here. That's sort of a big open one. And then we also have a meeting that is a much more technical meeting, which is for our sponsors and collaborators. And that includes, at this point, seven nations and half a dozen large corporations like Ernst and Young, Intuit, IBM, Swisscom, et cetera. So we have twice a year. And if you're interested, sure, why not? Okay, yeah. Yeah, so one of the big concerns that everybody has, right, is dominance of the tech world by a few players. And the whole reasonable data of AI and all that is about data. And so it really boils down to who owns and controls the data. And today, we have sort of come from this Wild West, which has allowed certain organizations to become very, very large. And it has all sorts of dangers, isn't it? I mean, I actually don't think Google or Facebook are gonna come and kill my children, right? That they might steal their wallets, but they're not gonna kill them. On the other hand, governments have access to this, too, and they might, they historically not behave themselves. The best idea we have at the moment, the one that I'm pushing, is that we call data cooperatives. So under the laws of the U.S. and most of the EU, you have these cooperative organizations which are owned by the members and are democratic. So like in Switzerland, you have MeGros, here you have a whole variety of community organizations that are typically called credit unions. And what these are is they're chartered by the government to manage the money, of course, but also the data, just sort of by accident, it's in the regulation of the members. So you can have something where a group of people have access to having a copy and controlling their own data. And just to sort of give you a sense of the power of that, currently a lot of websites have these terms and conditions, and so it says you can't use it for this, you can't develop it. Well, having something that's a legal representative of you overrides those. Wait, understand that as imagine that you're in hospital and you're like in a coma. Your lawyer could see your Facebook page regardless of what Facebook says, right? And keep a copy of all that data. Absolutely, just like no question. So can one of these cooperative sites, because it legally is you? Okay, so that's thing one. Thing two, like here in Boston or in any place, we have all these hospitals, they do treatments on you, they give you drugs, and nobody including them knows if they're any good, right? So when drugs get approved, everybody stops looking because they might see something bad, okay? So we take these pills and God knows what happens and we don't know if they interact and blah, I mean there's all sorts of stuff we don't know because nobody wants to look and they hide behind privacy law. So we can't share that with you, right? But if you had a cooperative, you have a right to your medical record. If we had 50,000 people who had their medical records here in Boston, we could, those 50,000 people could analyze their medical records, not giving them up. You still control them, but you agree as a cooperative to ask how what's the efficacy of this drug? What are the interactions? So the people could know, and once the people can know, it will happen. This is a political action, but a vehicle of political power to have sort of knowledge-based activity. And the key is is you have to have collectives of people because your data, my data, not very valuable, can't really get much of the way of insights out of it. But if we had 50,000 people in a town, we can tell if the government's any good, we can tell if the hospitals are any good, we can tell if the bank is behaving, we can tell if Uber's doing what it ought to do. I mean, you name it, you can go right down the list. And so this is a little bit like labor union battles were a century ago. A century ago, you had these big corporations that owned everything, same, right? The robber barons that they were called in this country. And they were exploiting workers because there were no other options. You worked for their wages or you didn't work, right? And so what they did is they banded together into cooperatives that are called later labor unions today, and as a cooperative, they were able to point out unfair practices and get companies and eventually government. Notice that the company's changed before the government changed, right? Okay, and I think the same thing probably needs to happen with data, but it's through this collective action. Sir, so people are very, so people are a sort of ongoing discussion. Most companies that are not Google or Facebook or Amazon are really interested in this because they're feeling completely cut out, right? Citizens, of course, feel cut out. Governments are worried because most of the data that they need to provide citizen services is not data that they own. So if you look at the sustainable development goals for the UN, most of those goals require having data from private entities like telcos and banks. Typically, that's not the situation in the world today. Some countries have made laws about this, but they don't have practice, regular practice about that. So that's one of the battles that's going on. And interestingly, companies are willing to give up their data enough to make government better, so give it to government to be able to do better management, but not all of the data. They don't want to give up personal data, just aggregate data. And that's actually enough for government. It's like census data. Companies are willing to contribute to the census, a rich census. And if you go to inequality.media.mit.edu, which is a one of our sites, you can see what you can do with that. It's really surprising, okay? Without any individual level data. Okay? So who's next? Ey yo. So okay, I think we're going to move like to the next speaker, right? So I think in this case, I would like to introduce Professor Marco Dario. So Marco is one of like the founders, I will say like of the swarm robotics like Phil, you know? He is well known in the community for developing the ant-colonization algorithm. And he directs the Iridia Lab in the pre-university of Brussels, which is one of the forefront labs for like a swarm and optimization, right? So without further delay, thank you Eduardo. So very nice to be here. Last time was 25 years ago or more. So I'm not a frequent visitor. So as opposed to Professor Pentland, I'm not an expert in anything else than swarm robotics. So the title of my presentation is to, we need a blockchain swarm robotics. So I will present first what is swarm robotics, what we do briefly, and then initial experiments that I have done with Eduardo and Volker on the user blockchain to make a swarm more secure. Not working? Okay, so I think you agree with me that in the future will be more and more robots. There are always already swarm of drones, logistic robots. In the future there might be nano robots or possibly the first deployment of a huge swarm of autonomous vehicle. Next slide. Next slide. Yeah, okay. So let me tell you something about swarm robotics. What is a robot swarm? What I call a robot swarm is a large number of autonomous robots that communicate in a peer-to-peer way through local interactions between them and with the environment. And they self-organize to solve some problems or to perform some tasks. And all these in absence of centralized control. Next slide. Okay. So we study swarm robotics out to design such systems. So that there is some collective behavior that results from all these local interactions between the robots and between the environment without any centralized controls. Next slide. Okay, so the problem I am interested in in the last 20 years approximately is how to control a robot swarm so that the robots cooperate to perform a task so that the robot swarm is scalable which means that you do not need to reprogram the swarm whether when you want more work done or less work done. And that the robot swarm is tolerant to these anti-robots. These robots that are malfunctioning or malicious. Next slide. Next slide. Next slide. Thank you. Okay, so most cases we use self-organization or no centralized control, local interaction, local communication. And this is very good because it's coherent with our goals of having a full and unscalable system. However, there is a problem that is known as the micro macro link problem. Next slide. So the problem is that our goal is to program the swarm. But we can only program the single robots, right? So how do we program this single robot so that we get the swarm to do what we want? Next slide. And the way we do this is by taking a behavior-based approach where we design and implement behaviors for the robots. And so for the single robots and then we test the behavior of the swarm in simulation and we repeat this cycle until we are happy with the results of our swarm, how the swarm, simulated swarm performs. And then we move to the test with the real robots. And we recycle again until we are happy with the final result. So the reason to take this approach is that we need, well, our goal is to program the real robot swarm. However, programming directly the robot takes a lot of time, can break the robot, can cause many type of problems, security problems also. And so it's better to, and faster and more efficient to work in simulation. However, once you get good results in simulation, most probably they will not carry over to the real robots for a number of different reasons. So you need to have the second cycle. Next slide. Okay, so there are many different collective behaviors that you might want to program in a robot swarm. There are specially organizing behavior like how they aggregate, how they form a pattern, how they can cluster objects and so on. There are navigation behaviors and there are collective decision-making behaviors. And the one that we are being focusing on in our blockchain for a robot swarm research is on consensus achievement. So next slide. So I was saying that the way that we program our robots is taking this behavior-based approach. And most of the time what we do is we program the robots using simple stochastic rules that are very often inspired by behaviors that you will serve in social insects or other social animals. Next slide. So just to give you a couple of examples. We have been working on self-organized search and retriever where a certain number of robots that you see there are self-organizing, searching the space for an object, the right object that you see there. And they manage to take over time different rows all in a self-organized way up to the moment in which one of these chains of robots reaches the object that has to be retrieved and the other robots use the chain to find the object and then to grasp it and to retrieve it to the goal location that is represented by the blue object on the far right. Okay, in a similar way. Next slide. We had done experiments with search and retriever in three dimensions where we have three types of robots that can move in the environment. They search for an object placed on a shelf and they self-organize to retrieve it. So now I want to show you briefly a video of these experiments so that you get a better feeling of what we are doing. For the next slide. You have to click. Swarmonide is a heterogeneous robotic swarm made up of three types of robot. The handbot is designed to manipulate objects. The handbot can also climb but needs help from other robots to move around. The footbot is a wheeled robot with a gripper. Using its gripper, a footbot can form physical connections with other footbots or with the handbot. The eyebot can fly and rapidly explore large areas. It can attach to the ceiling and provide environmental information to the other robots. In this film, the swarminoid is deployed to find and then retrieve a book. Here, the swarminoid has already partially explored its environment. As the eyebots search, successive eyebots attach to the ceiling, forming a connected network. Once an eyebot has found the book, the knowledge propagates back to the deployment area. The handbot then requests transport assistance from the footbots. Using the eyebot network, the footbots form a ground-based chain linking the deployment area to the book. The composite footbot handbot entity then follows this ground-based chain. A second handbot prepares for transport. The first footbot handbot entity has rotated and aligns with the bookshelf. While climbing, the handbot supports its weight with a cord attached to the ceiling. Actuated fans give the handbot control over its angle of rotation around the vertical axes. Swarminoid is a parallel distributed system. Parallel activity and redundancy increase its robustness and flexibility. The second footbot handbot could retrieve another book or act as a backup should the first footbot handbot fail. In this film, the Swarminoid retrieves a single book. However, the true value of the Swarminoid concept would manifest itself in parallel task execution scenarios and in unstructured environments. Future incarnations of the Swarminoid might be able to replace human workers in hazardous environments, perform search and rescue missions, or even conduct exoplanetary exploration. Okay, so this was to give you an idea of what we do with these robot swarms. And as you can imagine, even though everything here is self-organized, Eduardo was saying at the beginning that one of the reasons for war robotics research is that you get the system to that full tolerance because if one robot breaks down, the other take over. This is a little bit wishful thinking in the sense that it is true in principle, but then when robots break down, they create problems for the other robots. They misbehave, they create problems for other robots. So we need to find ways to increase the full tolerance of this system. So now to go closer to the main subject of my presentation, in the next slide, I will show you another short video in which I will explain how we do collective decision making with a swarm of 100 robots. And then I will move to the blockchain so you can start the video. In our research, we study collective decisions in swarms of simple robots. We take inspiration from the house hunting behavior of honeybee swarms. When house hunting, honeybees choose their new nest location in a self-organized manner. The collective choice they make is the result of simple interactions between the swarm members. In our artificial swarms, collective decisions are also the result of self-organized interactions between individuals. The kilobot is a small and cheap robot with very limited capabilities. It can move in a straight line or rotate in place around its center. The kilobot has only one sensor with which it can measure the brightness of the ambient light. It can also exchange three byte messages with neighboring robots. And when receiving a message, estimate the distance of the center. We consider a site selection problem to study collective decision making in a swarm of 100 kilobots. Robots are initially located in the nest, the area where robots exchange site preferences and take individual decisions. From the nest, robots can move either to the red or to the blue site. The goal of the swarm is to find consensus on the best site. In our case, the red site. The quality of a site is an abstract numeric value. We emulate the noisy robot's perception of a site's quality using infrared beacons placed under the arena surface. Such beacons broadcast noisy quality values. A swarm has made a decision when as a result of a decision making strategy, a large majority of robots have the same preference. We control the kilobot with a finite state machine that implements our decision making strategy. In the dissemination state, the robot is in the nest and its primary goal is to promote its current site preference. To do so, the robot repeatedly broadcasts its preference while performing a random walk. Before moving to the exploration state, the robot collects the preferences of its neighbors. It then applies the majority rule to update its preference which determines the site it will explore next. In the exploration state, the robot travels towards the chosen site. Once there, it randomly explores the area in order to estimate the site quality. Eventually, the robot returns to the nest and re-enters the dissemination state. In a way similar to honeybees, the effort each robot makes to promote a particular site is proportional to the quality of that site. Specifically, a robot promotes its preferred site for a time that is proportional to its current estimation of the site quality. This modulation mechanism introduces a positive feedback that over time leads the swarm to choose the highest quality sites. Self-organized collective decisions let the swarm overcome the limitations of its individual robots. Also, in this case, the system is in principle robust to robots that break down. But what happens if some of these robots start to send wrong messages? And so this is what prompted us to start studying malicious robots in the context of this type of problem. That is what Eduardo presented in the first presentation this morning. Next slide. Okay, so going back to the problem I'm interested in, as I said, my main interest today is to show you our initial research on how to make the robot swarm tolerant to Byzantine robots. Can you? Next slide. And this is work, as I say, done in collaboration with Fulcastrope PhD students in my lab and Eduardo Castello, the rare postdoc currently in my lab. Next slide. It's always clear that whenever, as soon as we have a robot swarm that is deployed in the real world, it will be subject to attacks. So there will be some guy that wants to create problems. So the, what we are trying to do is to see whether it's possible to control these swarms using smart, particular type of computer program that is implemented in smart contracts. So that they are robust, full tolerant to tampering with messages and to civil attacks. Next slide. Next slide. Okay, so I think everybody here knows what is a civil attack. Robots can create fake idea while working so that one robot can try to take over the control of the swarm by creating many fake IDs and that the tampering of messages is obvious. Next slide. So you know that the blockchain basically creates a trust and tamper proof system among untrusted agents, usually computers. What we are doing next slide is to use exactly the same approach using robots in place of computers. And the next slide, and we are doing this on in the context of the Ethereum framework. Next slide. Next slide. Okay, thank you. So the goal of the study is to first show that it's possible to write smart contracts that control the decision making of the robot swarm. So this was not done before, so it's important to check it. Then to show that the blockchain-based control make the robot resistant to this Byzantine robot that either tampering with messages or that try civil attack. And then to show that this blockchain-based approach outperforms other classic approaches. Next slide. So the collective decision problem base, this is the example that you have seen before this morning, sorry, two talks ago with Eduardo. We have a swarm that has to collectively estimate the frequency of tiles on the ground. So the ground is covered with white and black tiles. Compared to the example that was shown by Eduardo this morning, this is not the collective decision on which is the most frequent, but it's a collective estimation in which the swarm has to find out what is the percentage of white tiles in the environment. So the experiments are run in a simulation, but we have already everything in place to run experiments with the robots. We just still have to do them. Next slide. Yeah. So the blockchain-based approach that we use works as follows. The robots move randomly in the environment, so they explore the environment and they measure the frequency of tiles that they move over. So for example, what is their own local estimate of white tiles? And then every 45 seconds, they send their reading as a transaction that is added to the pool of unconfirmed transactions. And to do so, they pay a certain amount of tokens. We are in the Ethereum framework, so they pay 40 ethers. Next slide. Our robots, while moving randomly in the environment, they spend also all the time, they perform proof of work, they mine, and the first, as usual, the first robot to solve the puzzle and append the block to the blockchain and get a reward. Next slide. So when a block is added to the blockchain, its transactions are verified, the smart contract computes the means of the estimating the transaction after removing some outliers. And there, we use a very, very simple outlier detection mechanism. So the goal is the research is not to find the best possible outlier detection mechanism but to show that the system works. So the outlier detection mechanism that we use is just saying that everything that is 20% bigger or smaller than the current mean is an outlier. And then the robots that have the same transaction that are actually used for computing the average of the mean are paid back a certain amount of ethers that is bigger or equal to what they pay to participate. This is because only the transactions that were actually used to compute the mean, so not the outlier, are paid back. Next slide. So you already understand here that these mechanisms automatically take care of the civil attack because the robots to send transaction has to pay, but if it's a bad guy, it's malicious robot, it's sending transactions that are considered as outliers. It's not paid back, so it does not have money to perform the civil attack. So the approaches we are compared with are linear approximate consensus based on time linear approximate consensus. These are two classical approaches for computing using this estimate of the frequency in the distributed system. Next slide. They're quite simple. In the linear approximate consensus, the robots collect their own sense of readings and the estimates of neighbors for a period of 45 seconds and then they update their own estimate with the formula that you see there. And while moving around randomly, they distribute the estimate to other robots that happen to be in their neighborhood all the time. Next slide. The based on time version of this algorithm is basically the same except that the update of the estimate is done without considering the outliers. Next slide. So in our experiments, we use as an evaluation matrix the absolute error that is the absolute difference between the true value of the frequency and the estimate proposed by the robots, the consensus time and the blockchain size. Next slide. What you see in the next few slides is always this structure. You have the representation of the experiment there and then you have linear consensus, business time consensus and blockchain results. Here you see on the X axis the true percentage of Y times and then the absolute error on the Y axis. So what we see from these graphs is that all the approaches are similarly good at finding the estimate of the frequency of Y times. Even though the blockchain approach has slightly higher error. So these results basically show that it is possible to implement our system in apps and so on with anti-robots and that the performance is good enough. Next slide. So this is the same results where you have the actual percentage of Y times and the estimate. So the optimal solution is along the dotted line, dashed line. Next slide. So now what happens when we have a business time robots? So what we see is that as soon as the number of business time robot on the X axis is increasing, the absolute error increases a lot for both linear consensus and business time linear consensus. And so the curve here is a little bit misleading but what you have there are the box plots plus the percentage of outliers and the outliers is every time that the system was giving basically 75% error which is the maximum in this particular case. The system is estimating that all the tires are black. On the other side, when you look at the results with the blockchain, when the number of business time robots increases, the performance decreases a little bit but stays the absolute error remains low. Next slide. When we come to consensus time, while with the two classical approaches, basically there is no consensus time because since there are malicious robots that will always vote for the wrong outcome, there will never be consensus. On the other side or the blockchain, we can have a consensus and the time grows very, very slowly with the number of business time robots. It's also important that with the classical approaches, if you take, how do you know what is the results? You take one of the robots and you read what is its estimator, right? But you don't know which robots are business time and which are not. Basically you pick a random robot, you read a value but you don't know whether it's one estimated or it's one of the malicious. Differently with the blockchain, even the robots that are malicious, they share the same blockchain as the others. So you can read the swarm estimate even picking up random robots that is malicious. Next slide. I already say when a robot try to perform a civil attack the thing cannot work with the blockchain. So next slide that we see in the results sorry the thing cannot work with the classical approaches can only work. I was correct. The civil attack cannot work with the blockchain while it works very well with the other approaches. So you see in the graph, the absolute error, it grows very, very fast with the number of business time robots for the linear consensus and business time linear consensus while it remains quite low with the blockchain approach. Next slide. Last memory usage. Okay, as you know, one of the issues with blockchain is that the memory usage grows with time. So in our experiments what we have done we have measured how this growth is fast and we found out that at least in our experiment in the framework of our experiment this is very manageable because the size of a transaction is 148 bytes and even that the E-PAC robots have a storage capacity the E-PAC robots are the robots that we were using of 16 gigabytes last or approximately. But this is something that for sure has to be taken care in future research. Next slide. So in conclusion, what we have shown is that we can implement RoboZoom's behaviors using mark contrast within a blockchain-based context and that using the blockchain-based approach first the RoboZoom can achieve consensus even in the presence of business time robots because these business time robots are now identified and discarded from the computation of the estimate. We found that the absolute error in the estimate remains low with an increase in number of business time robots and that the RoboZoom is resistant to civil attacks. Additionally, what we have is that since all the happens is memorized in the blockchain the behavior of the RoboZoom can be audited in the future and analyzed. Next slide. So there are many, I think there are many, many problems and open challenges in this line of research. The first one is that well, when a RoboZoom moves around or the moment there is no guarantee that it remains connected all the time. So if two subswombs disconnect from each other they could grow different blockchain. And when these two subswombs connect again one of the two, the shorter one of the two blockchains will just be lost. So it's a lot of work that has been done by part of the RoboZoom that is somehow lost. So maybe we should ensure connectivity all the time or maybe there are other solutions we don't know yet. Another challenge is how to extend this to more challenges scenarios. As here we consider one smart contract one particular problem, simple problem. Can we extend this to a problem where there is more than one task? Another issue is that in our current RoboZoom all the RoboZooms are the same. So they all have the same computational power. And in the real world it may be that the subswombs are joined by RoboZoom of different heterogeneous systems RoboZoom of different capacities. So maybe that proof of work is not the best way to go and maybe that we should look at alternatives. And finally, what are the aspects of RoboZoom behavior that should be implemented in the blockchain framework and which should not? For example, in this case a collective decision is reasonable to think that the blockchain approach is okay but there might be a situation where you need a fast response to the activities of the robots and which is not compatible with the implementation of the blockchain. Next slide. Yeah, thank you very much. Any recent and specific you decide to use the Ethereum blockchain? Yeah, because when we started the research was the one that gave better support for implementing smart contracts. Yeah, but we are already starting to look into alternative frameworks, especially for the issues of using alternatives to proof of work. Yeah, there are a few now that are specifically based on IoT. A quick question. I think it was you addressed computational requirements towards the end. Comparing the linear approximation approach to the blockchain approach, is there been any comparative study in the total computational requirement? And how does it scale? You mean how? The total computational requirements for the whole entire system. Yeah, they're very similar. The problem with the blockchain is that you need more communication. You have to exchange more data. Just to confirm the robot swarms are not the ones contributing to the consensus process. Rather, they're just clients putting their data onto the Ethereum blockchain and then there are nodes on the Ethereum blockchain that does the maintaining of the consensus of the ledger. I'm not sure I understand. The way it works is that each robot is running is one node in the blockchain framework. It's running Ethereum. So each of the robots are also full nodes? Yeah. Yeah, they're mining all the time. But are they part of a private network? Yeah, yeah, it's a private. It's not the main Ethereum. So yeah, let's introduce the next speaker. So the next speaker is Thomas Arjano. So he's one of the first guys I encountered when I came here to MIT. And one of the first guys that helped me draft some of the initial papers in this field. So I'm really grateful to him. I think he's going to talk about identity, about data, about how to manage these kind of things. So without further delay, let's welcome the speaker. Thank you, Eddie. Thank you, Professor Marco. We got some famous names here, Fabio, Alexander. I met Alexander earlier. But thank you for flying into this beautiful, snowy Boston to witness the snow. But it's good. I think I recognize some of you guys from last year. We had the same conference here. So hopefully this will become kind of an annual thing because it's good to see what people are doing in other fields. We tend to focus on our own fields and we forget about everything else. So occasionally it's fun to see something like this because compared to your videos, my life is boring. It's just a whiteboard with a lot of mats. Crypto, there's nothing moving, nothing 3D. So today I want to talk about rethinking trusset computing, particularly in the context of IoT devices, blockchains, and so on. And my naive view of robots is that it's an IoT device that has intelligence. So it's not just a sensor that does one thing. It has capabilities. And therefore it's good and it could be dangerous. If you abuse a whole swarm of robots, you can get into serious trouble and you can cause a lot of fun. I'm thinking airports, roads, and so on. So blockchain technology is still nascent. And if somebody tells you that it's mature and you've got a production and please buy my coins or my tokens, run for the hills, if you're interested, I was reading this recent BBC article about one coin, a coin one, one of these Ponzi schemes. And the person who started all of it is now missing, wanted by several authorities. So just quick check on virtues of blockchains. What got people interested in Bitcoin in particular? So the idea was that you would have physically distinct nodes. So in the original Bitcoin, you put up a mining rig in your basement, a physical device. In order for all the devices to reach a particular state, shared state on the ledger, it would need to run some agreement consensus protocol. So this is proof of work. And they need to be able to do this independently of each other. Then there must not be interdependence between nodes within a blockchain system by definition. The fact that each of the nodes carry a complete set of transactions, and its sign and chain and so on provides tamper detection, as Professor Marco said earlier. And actually, well-defined limited operations. So I won't go there. One reason why Bitcoin is successful is because it has such a limited opcode. You kind of need three operations in Bitcoin, and you're done. It just runs. But in Ethereum, if you look at Solidity, it's a fully-fledged programming language. And you could do interesting stuff. You can do damage, like the DAO attack and so on. So when you look at swarms of robots, how do you know that those things flying in the sky, those things that are running in your house, are healthy? So what does healthy mean? So in the context of trusted computing, healthy means that the device is running the correct firmware, the correct software. It's using the correct hardware and so on. It hasn't been tampered with by anybody else. And you'd like to have visibility into this swarm, literally, could you have a screen where you have a yellow, green, red color for each of your nodes where red means you're not sure? What is it running right now? Could you have the ability for the nodes to report? Truthfully reports, it's stacked. When I say stacked, just imagine your drone being a complete PC board. Everything from the bootloader code, pre-bios, all the way to the kernel, everything, it needs to be reportable. So one of the key things, I think, is that the idea of a cohesion, the value of a swarm of robots, is dependent on this ability to report the node status and therefore the health of the entire population. And this is actually not just true in robots, but just in your boring enterprise network devices, enterprise IT and enterprise network guys want to know how healthy each of the nodes are, each of the routers within its domain. And so could we rethink how we use trusted computing technology to use the features there to feed into the decision making based on the policy so that when you ask for a robot to report on something, in addition to that something, you have the option to ask it to report its health. Has it been tampered with? Has somebody touched it? Has the firmware been updated? Has somebody tried to update the firmware? So a bit of history for those who are old enough. So back in the 80s, there was a very important set of documents coming out of the DoD. This one here is called the Orange Book, the whole series is called the, I believe it's called the Rainbow Book, but it defined the notion of a trusted computing base and more importantly a network trusted computing base. So it's literally verbatim total, the totality of protection mechanisms within a network system. And the whole point is that it needs to be able to implement and carry out a policy that you decide on. Right, now this is a TCSEC, that's 1985, that's a long time ago, this is long before virtualization. I think VMware did not exist, if you guys know VMware. Cloud computing was a dream, did not exist. So interestingly, some of these definitions are being revisited in the trusted computing group. So there's a group of vendors and service providers who have been working in this space for maybe 20 years, since 1999, trying to address some of these issues. The trust computing group had, so people talk about trust and trustless today pretty loosely, right? If you look at the coin desk, you look at some of these media, but it's not an easy matter. And in late 1999, 2000, the TCG came up with the following definition of trust, and think about it carefully. So it needs to perform a well-defined function. So think about the brakes on your car. Why do you trust this little thing when you're driving that it will stop the vehicle? I mean, have you thought about that, right? You just press it and it works. Why is that? And it works once, it works twice, it works five times, it works 100 times, it works 5,000 times, it works 10 years straight. So repeated operations of the same thing and without deviation, it generates social trust, a human trust in the function. But the function needs to be well-defined. A brake, another one is a door handle. A door handle is a very well-defined function. It can only go sort of L. Now if it does something else, if it does a 360, you usually panic, what, what is this, right? So those are the easy examples of how you define trust. The second property, and think about it chips, so when the TCG defined this, it was actually thinking of a chip set, TPM 1.0 chip and what features it needed to have. It needs to operate unhindered and shielded from external interference. So your car brakes need to work unhindered. You can't have a piece of carpet sticking out, you guys know about what happened with the Toyota case. The Toyota Honda, big lawsuit because it was this bad design. It needs cryptographic identity. So imagine you have a chip that's gonna be on your machines, on your laptops. It needs to be distinguishable from one another so that when a chip signs something, you know it's coming from that laptop versus the other PC over there. The fourth one is a difficult one. We kind of call it TCB dynamism, meaning that when you update the firmware or you downgrade or you remove certain features, it should not reduce the trustworthiness. So P4 is fairly difficult to create. So why am I talking about all of this stuff, right? So could these features also be inherent in robots? Could you have robots where this is just built in? You can use it or you could not use it. So imagine if your robot's a board, a C board essentially, and it comes with one of these TPM chips for free, it's about $2 each now. And it has all this capability. How would you use it to secure that particular robot and how can you build up that layers of trust such that you actually are able to use the features of all the robots as a swarm to a particular goal or to do a particular function? So one of the things we're looking at is extending this TCB as four properties, adding two more properties, group membership, when we think of a robot as belonging to a group, a swarm, how does it prove that? I belong to group A and not group B, right? We don't know how to do that today, but with this TCB model, you could. If a robot wants to join a swarm or leave a swarm, it needs to get permission to do that. Doesn't need to get permission from all the members of the group. Truthful attestation. So imagine, you guys know what SGX is, Intel SGX, you guys are in hardware, probably have heard. So this is the trusted computation, the enclave, secure enclave. What if each of your robots actually contains a secure enclave, meaning that you can do secure computation, that you can do everything, all the proof of work, hash computation, so on, within a trusted compartment, within the chipset. So if the robot could do that, you could ask it to report truthfully, not just the firmware and so on, but also its memory status, right? What's in the memory, right? And who put it there? So diagrammatically it looks like this, so you have a bunch of nodes, you have a bunch of robots out there forming a swarm. Collectively, it makes available these two additional functions as a collective so that you can have applications make use of it. The applications need not be aware of P1 to P4, and in fact it doesn't need to understand DP1 or DP2, right? So think about robots that have to carry a mission out in the field, take on military robots, right? So the same problem was already discussed 20 years ago. So imagine you have troops out in the field in a war situation, they're carrying backpacks, there's always one guy carrying the radio, right? How do you update the firmware of one of those boxes in the field? Requirements, you kind of assume things are gonna come back home, that the platoons are out there, okay? And you wanna somehow not do a flash, but do a firmware upgrade via satellite, right? This is almost the same problem, right? So this is kind of the stringent requirement some of these use cases. Could you ask individual nodes to do regular pings to each other each time reporting its status? So it's not just enough to say, hey, I'm here, I'm signing this thing using my keys, but who put the keys there? What's the provenance of the keys? Because then if you don't know the provenance of the keys inside the hardware, the signature is useless. When you do a consensus, you incorporate that, meaning that I will accept the proof-of-work results from a node if that node, if that robot accompanies it with its report of the internal status, memory status, and so on and so on. And so when I wanna confirm this, I need to also check the report, at the station to report the status of the robot. A governance, so when you have a group, a collective of robots owned by an organization, private organization, and there is governance. So typically in the PC world, when you buy a PC, it comes with firmware and hardware, software and so on, coming from different vendors. So there is, when let's say I buy a PC, it's got whatever BIOS version, whatever, 6.0, something. That list of components is called a reference manifest. Could each robot be given a reference manifest as defined when the robot left, the factory when the robot was being deployed? And how can you make sure that nothing has changed in that manifest? Right, and so governance here means that, okay, you can be part of our collective, you can join, but you have to have the same reference manifest as the rest of the nodes, the rest of the robots. That's kind of the end of my talk. This is, it is a bit, I admit, a bit sort of deep technology, it'd be complicated. If you wanna read the paper, it's on archive, a shorter version is gonna be in frontiers. Just take a read, if you're interested, reach out to me, my co-author there, Ned Smith is from Intel, again, we've been at this since 1999, so this is an old problem. The industry takes a long time to evolve, and so when I say blockchain is still in a nascent state, believe me it is, compared to the hardware and the software that needs to be used for the nodes of the blockchain. Okay, that's kind of it. Any questions? Who's next? Did I go too fast? Oh, you're gonna ask her, go ahead. Actually, I went all the way there, like so. So Thomas, I have one question. So I think it's very interesting that you revisit the world of robotics and especially distributed robotics from your end, right? So do you see, for example, any applications in which you see these two worlds combine, right? Like for example, do you see these happening more into household robotics, you know? Like for example, as Sandy was saying, in Nest with Nest or with Alexa, or you see these like more happening, for example, in a city level, you know, like we, for example, like still driving cars, you know, like a public infrastructure, all these things. So what are your visions about that? So definitely yes, particularly for high value assets. So there's a whole discussion about industrial IoT, what people are gonna use for the next generation nuclear reactors, whether it's the big reactors or the little modular ones, you know, those sensors need to report correctly. It needs to have these features. It needs to be able to measure environment and report unhindered, right? So I think going forward, there will be a lot of applications of this technology for what I call static IOTs all the way to very smart robots, right? And in fact, there is a group in the TCG, working on this, it's called the Cyber Resilience Group. So how do you create a future infrastructure that has resiliency against all these possible attacks? Yeah, definitely, maybe a house. So it's also price. Yeah. So it's kind of interesting, router vendors, and I mean, the big router vendor in San Jose, California, I shall not name, they consider that a $2 chip is expensive. And I think like, insane, this is national infrastructure and you think $2 is expensive for a $5,000 box, you know? So there needs to be a change in mindset also in understanding the value of the infrastructure and also the value of data that flows through the infrastructure. Okay, thank you very much. You didn't touch at all on the work, on blockchain-based identity systems, the standards work being done by the Worldwide Web Consortium and four or five related sort of groups of people. And it seems like there would be, from the blockchain sense, a great deal of overlap between what you're talking about in the IOT world and that work. How far along is that? So what happens next? Sure, so absolutely there is a connection. So IEEE has already a standard called device ID. It's a 802.11AR specification, which is now five, six years old and that's for a device identity, right? So ideally, so using our language, Adrian, a device needs to be able to produce an assertion about itself. And so the question is, what keys are being used to sign those assertion? Is there a key hierarchy and is there a key provenance, right? And so, you know, there's a number, so this whole identity problem has again exploded because people are interested in blockchain and cryptocurrency, digital assets, virtual assets and they have a key, right, a public key. But like, how do I prove to you that this is my public key and I haven't stolen it from Adrian? So there's a lot of fundamental revisits of problems that people kind of ignored 20 years ago right now. But yeah, this definitely ties into this whole, you know, blockchain-based identity work that's happening. There's a number of groups working on this, you know, right now. Yeah, so yeah, thank you for going through kind of the computing base. There are attacks where you modify that computing base temporarily and then recover so that, you know, it seems like in the long run, it's not like really... So the chipset up top, the TPM actually has registers inside, right, and so you can detect if the outer firmware has modified it and put back again, you can detect from the registers inside the TPM because every time you do something, the register updates. So you can actually grab the machine and run through its history. What you think should be the correct history and if there is a mismatch with the internal registers, then you know something's wrong, right? Now, the TPM hardware is a temporary system, meaning that it probably takes about a million dollars to scratch off and do like physical hardware attacks on that thing. So it truly has what is called shielded locations. Just like, you know, your smart card, SIM card is got the same technology, yeah, but it's low cost, again, under $5, ideally $2. But yeah, there is way, yeah, in the TCG, we talk about this every week, several working groups. How do you detect something like that? You know, clever manipulation of the firmware. Thank you, Eddie. Yeah, so I will introduce the next speaker. So the next speaker is Professor Fabio Buon Signoro. He's like a professor in Escuela Superior Santa Ana and also the CEO of Heron Robotics, right? So, let's welcome him. Thank you. Thank you for the kind introduction. So this long list of, yeah, it's just a way to say you that I'm involved in a number of research strategy and the rop mapping exercise at the European level. What I want to do today is to share some thoughts about and actually make a kind of ranking in favor of, so why blockchain matters to robotics? Because to this point is still something which is not controversial, but maybe not or the community is still convinced about that. And the outline of the talk is I will first give you some context from the point of view of robotics and the general context. Then I will talk about why P2P in general, in smart city and smart, whatever. Then in white matters in robotics applications, I will quickly give you a couple examples. Actually one, the first one was already showing a couple of talks before by Marco and Eduardo. And then I will do some final remarks. So the context, you know, we are having some impact on the planet to a point that we are significantly already creating problems to their most as is of our planet, to a point that we might be, and I understand this, we might be quite close to the tipping point. So to a significant change in our situation. In the meantime, this is what is actually going on in robotics. So if you look at this chart, you see that robotics is a very popular and growing field. And so it's, 9% is actually 10, more than 10% in the real data that you already have. But you see that you go to about 80 billions to know whether it is dollars of euros. But if you compare to the gross product of the planet economy, this is just 1,000. So very good if you are in robotics because it's a growing field. Very good, very nice idea to join the robotics field. But still is a niche. It's a small niche. So I think you have seen in many places very darker challenge movies, in particular in YouTube. From where you see that we can do marvelous things like opening a door, sitting on a chair and put your hands on a steering wheel in your car or operating a valve. We also so new know from that this is more or less the state of the art in the so-called mechatronic paradigm. Mechatronic means having a CPU where you run a complex state machine form of several parallel programs. And these programs operate your bioelectrical motors, operate your robot. This is what happens. So the movie is not starting but you may have seen that the... So what we open the door about one time in 20. So this is more or less the state of the art. And we do that by employing 10 people teleoperating the robot. So that's why many people think that we should go in the direction that Marco was showing for Swarm Robotics. So we probably needed to look at to more deep bio-inspiration. So because while our robots are all designed to top down, so you have a central brain managing formation, devising a plan, implementing the plan. In nature, we typically have the key word is emergence are emergence and self-organization. So we think that we should go a bit forward. And actually many people think, for example, in this flagship preparation program that we are pushing, that we should really reinvent robotics. But in the meantime, we have something that in Europe is called the industry 4.0, which means that a few significant advances in technology enabling technologies like internet of things. So you can put everything on the internet. Machine learning and deep learning now work before they didn't. And computer vision, if you don't have too many objects also work. So this is enabling a complete reinventing of all the manufacturing processes, which is not a small thing because now we typically have steel mass production. So we talk about versatile production. So you can order your car, but both cars are one equal to the other. So you have some options, but you cannot perfectly customize your car. You have options with dresses much more than in the past, but still you cannot have your own jacket, your sides and your preferred colors. This in term of market is an extremely important issue because if you look at the business history, for example, the car industry, they have years where we earn a lot and years when they lose a lot. And this is the same in all industry because markets are turbulent. Actually, the key factor today for a company in service or consumer or if you think to the iPhone or the Tesla car, a product service because typically you sell a product with a service is to be fast, really adaptive because fashion changes. So if you have a stock of blue shirts and the fashion is now red, all your stocks has a value of zero euros. So this capability of reinventing industry in such a way with words like craft, craftwork, a craft shop is actually critical for this asset. And so this free enabling technology together with, for example, force control, I've applied it to all these new kind of robots. Some that we know already, some that we hadn't before and also some interesting application. Another interesting thing, which is related to the introduction is that a bottleneck in sustainable production. So because there are things that people have been talking about for a lot of time, one is mass customization, what I was quoting before. The other is sustainable production. We all know that if these new billions of consumers adopt our Western lifestyle, we don't have enough resources. Someone say we need two planets, someone says eight planets, maybe mining the asteroids, but we know that. So a good side effects of these technologies is that we can recycle materials because before you had, I give you an example again of cars. You have every minute, you have a new car coming out of a production line. At the end of life, you should take the same car and dismantle it today, still today, despite having these new technologies, you do it by hand. There is no competition, so you have no way to, but if you have vision and if you have force control, you can disassemble cars. So this means that one side effect of these new technologies is that sustainable production, which has been preached for many years, is now possible. So I go faster. This is related to some work that we did. This is typically giving two hands and giving eyes and force control to a standard operator. But, and now we enter in why this matters now because this is from the World Economic Forum and the Ellen MacArthur Foundation about circular economy. And they actually dream of what? Having a satellite-based cloud network managing the whole reverse logistic networks. Because now, I think most of you know, we have a very complex logistic network, supply network, which gives us our products. So a car is actually manufactured by several hundreds of suppliers connected into a network. And that's all, so it is disposed at the end of life. In the circular economy paradigm, at the end of life, you should have a reverse logistic network. So you should do the same things in reverse. And the idea is this, no? So to have a big centralized system managing reverse logistic. And you see some problem here. Apart from, so it's really scalable. So we can really think of having such a network at global level. It is not distributed. This leads to some political and financial issues. So excessive concentration of few hands of power and capital. It's really secure. Cannot be tampered. And who owns the data? You know that there is a lot of discussion about the fact that now, actually, you don't need money. You need data, because if you have data, you can make money. A friend of mine said that we are transitioning from capitalism to dataism. So you should not focus about money. You should focus about ordering as many data as you can. Okay, I make this story. It's really ubiquitous because it applies to agriculture. We may decline the same discussion for food supply. And so are also constructions. So what can happen in 10, 15 years? Okay, we should have some new technologies. So things like swarm robotics might become a measure for application. I mean, not an interesting topic for a search. We will have quantum computing. We will have workable neuromorphic computing. So actually, we should go towards a system where swarms, a hierarchy of swarms and networks is the real backbone of the system. And another thing is that we are transitioning from browsing, so an internet that we mainly use, the distributed network that we use to share information as a distributed network that we use to share to computation. Something like this, no? So maybe you don't anymore, we'll have car factories because you will have the car like in this German case of a 3D printer bike. Someone close to your house will assemble from downloading from the internet the designs, your cars. On the other end, we still have someone building chips or building 3D printers. And the same for energy. We will have your sonal partner over your roof, but we may at some point also have a very big centralized fusion power facility. This is ITER. So what we can do now in short is semi-structured environment, network of connected agents. So I have no time to enter into the issue of self-driving cars. Maybe they are not ready for real autonomy, but if you have a city infrastructure which connects the cars and supplement the lacks of insights of the cars, you can have it probably already now. And so we need two instrumental environments. And this goes to the core issue, which are the smart cities. And yeah, so what's from my standpoint as a smart city is a huge collection of many robots, many AI, all interacting. And no, because I don't know the story, so about everything will be connected. A lot of things will be automated by robots and by AI. So and here we come because they are a great example of smart cities projects around the world. And these projects typically are implemented by choosing a main supplier, which one is typically in the West, one of those companies, and to give the keys of the city, the data of the city to that supplier, which is equivalent. And this has some, so there is an alternative to this because if you think about it, you are going to robotize everything, whether or not we reach this level of deep bio-inspiration solution. But even with the current technologies, we can instrument all of Boston to allow cars going around or keep moving parcels, people, and whatever, moving things from the factory. This means that with the current technology, assuming that it is scalable enough, we needed to move all the, we need to centralize everything. So if you think that today, Amazon is big, tomorrow it will be 10,000 times bigger. So this is because you remember the chart that I've shown at the beginning. So today robotics is 1,000 of the economy. So AI is a bit bigger, but we are still there. So if you think that this is going to become ubiquitous to permeate all the system, all the entities in this space are going to grow by, if not 1,000 times, 100 times or 10 times. So we know that there is a solution, PR to PR, right? So why it matters from robotics? It matters for robotics because it provides, and we have seen an example about that, distributed secure transaction ledge. Allows to distribute data, and you see that is a problem, because I emphasize it because it's more, if you wish, popular aspect of centralization of power, but do you really think that we can grow the current clouds to the level that we can really manage this complexity? I'm honestly, I'm not so sure that it is feasible. So it could, there could be a technical limit before all the consideration. For example, in Europe, why Petland was invited in Europe? Because in Europe, there is this concern that European data are going to the US, right? And the blockchain distributed approach to data ownership is seen as a possible solution, right? And then distributed computing that, but here is probably a core of a point that I want to make, because, so do I really need a blockchain in robotics? So if a single robot, maybe no, I could just create an RSA encrypted connection to a server, like with a home bank, right? It works, it's stable. If I have one to 10, 10 and, okay, it's still, so we have seen, Marco was saying before, so the amount of computation and communication, especially communication and data exchange is probably still a bit, so you need to balance it with respect to the benefits, because again, you may have a 10 point to point connection to a central server. The point is that it seems that we are adding to that towards millions of interacting agents. And can we really think of managing those millions or 10 of millions of independent intelligent agents with a centralized cloud computing infrastructure? Probably not. So the point is about, so the centrality is actually one of the important topic is the network science, yeah. So what people is doing with blockchain, because what I want to say is that, so there is an area of swarm robotics where you probably don't need to use the blockchain. So it can be interesting as a puzzle, but it's not real. But since we are adding to very big swarm, hierarchical swarm of heterogeneous things, even managed by heterogeneous companies, it seems that we needed to study the problem. So big swarms could have a different set of rules to follow when small worms. So what people is doing, security, we have seen an example about that. I think it is interesting to, and someone is doing some experiment about that to use a platform like Ethereum to distribute computation. So it's like, because if I don't want to use a remote cloud, I needed to have some mechanisms to distribute among many PRs the computation. Ethereum is low so far, but if I could make it quicker, it could, then I probably will need a market infrastructure because if you think about the city of tomorrow, not as, let's assume that I am lucky and as fabulous city, so there is a big cloud provider managing your city. But if you think that you have different actor, and this is advisable for many reasons, you will need a kind of market because we're living a market economy. And also markets are very efficient resource allocation mechanisms. So apart from the fact that we are political, so the first speaker, and I've shown that Marks and Adam Smith many times say, said similar things, but there has been some polarization in the latest times. But so we needed to manage markets. And so the blockchain provides tools for that. And then there is something, some work that we are doing together with Alexander about environmental monitoring. I started with the fact that we have serious sense that we are overloading, so we are a bit stressing our environment under many respect. And the typical issues, that typically you have public agency or O and G agency taking samples and telling you what's the health of the environment. Having a transparent third party provided, for example, by distributed ledger to take samples of the environment can have a societal value. Because at this point you can't trust the results because in all countries, citizens are many times suspicious about what the public entities are really doing. So you see the final results. You cannot do, this is if you wish is not the analogous but with the next type of citizen science because you also certify that the data that you are using are secure. I think that from the scientific standpoint is very interesting to see how you can merge market mechanisms with swarm mechanisms. So because swarm are one thing and markets are another thing but both rely on many agent interaction. And I was talking about a supply chain. So if you want an open supply chain, for example, I've shown an example of a local 3D printed bike. So now you have, but of course you need a supply network but now supply networks are, supply chains are top down. So you have a big assembly company, for example Kawasaki, which have its own hierarchical structure of suppliers going down. So if tomorrow you want to start your bike business, not selling bikes but making and selling bikes, personalized, customized for your friends, you needed to have a supply network which is really a network, not a hierarchy working for Kawasaki. And this requires to somehow to unbundle these kind of chains and create a build them as distributed agent structure. A multi-agent, of course, you can move the same logic to intelligent chatbot now. Yeah, I bought a, you can have event registry. So how you know that an event really occurred? You need to make, you can manage a birth registry of robot. Because if you think about this big smart, so what I think is that we are all preaching, we are all, all we people in robotics and they are preaching about the smart cities, but I see huge problems to manage a smart city with current technology. And before, if you don't suggest me something better, I think that the swarms and the blockchains are, it's the only thing, the first thing that they think about, because really I think that the complexity of smart cities as being a bit underestimated. And then, okay, in the future we may have some, and now I have a problem that we have seen before. Okay, next slide. Okay, so example, one you have seen already. So, I'm sorry. The second example is about the idea of building a market. So this is an example of clearly taken by Alexander and other people work. We need to implement something like a market. So if I want a city where I don't have the owner or the data owner of the city, I will need to be able to manage transactions among different operators. Then you can call it money or not, but you need to manage data. Remember data is, so maybe it's not even more money, but you needed to manage the economical transaction because if there are two actors providing parking, automated parking services, and automated the self-driving Uber service, the self-driving Uber may have to pay a toll or a parking. And we'll have to interact with the agent managing the parking lot. If this system is automated, I will need to automate a market transaction. So I buy the service and I give you money or tokens or whatever, but this is, and they are example of this. So you will need to implement the contract and we know that, for example, with the Ethereum network, you can associate a piece of program that can manage a program too. So, final remarks. I focus on this, but it could be any, right? Even a simpler electrical car is made of hundreds of components. The iPhone has less components or smartphone has less components, but still has a lot of components. And still you have hundreds of suppliers involved. If I wanted to allow the local-produced smartphone or the local-produced car or bike, I needed to handle what are currently hierarchical, pyramidal, supply chain, parallel, and one going to Kawasaki, one going to Ducati, one going to BMW if you talk about bikes, but could easily be, and so we, for example, typically when you show this, you use this kind of picture, but this is for human readability because we should have hundreds of nodes and the interrelations are much more complex. So, yeah, I see it doesn't fit very, but what, so this is what I already was anticipating. If you look at how supply chain today are managed, they are managed by typically what is still called the electronic data interchange and they use the internet, which is highly distributed, but they are not distributed. So this is even from a poor conceptual standpoint is a bit confusing. So where is the problem that I see? There is also a societal problem because we have a society which, because for example, I've tried to sell this idea of a blockchain based supply chain to some company, right? Well, the problem is that they are not really used to this mindset of having a distributed supply chain. So the main problem that you see is the cultural issue of, but we are working, we are used to work in a different way. Apart from the technical issues that we can have, we also have this problem because I mean, the industry is already managing very complex networks with very huge investments and involving a lot of people. What we are proposing, it's changing what they are doing. The reason why I wanted to tell something about the turbulence of the markets is that it's very mature this kind of change because we already have a system which is working below the possible effectiveness and efficiency level. Because we have these demands which go up and down very, actually, in a turbulent way, but we still have a very rigid supply system. And so really very short, making a very short, we can have a really distributed and scalable organization. And we have this new field if you wish of network robotics with many, many robots, so in the millions or up to 10,000, 2000 and above where you need market, you need data interchange, you need distributed ledger and just to finish, yeah, so these are, to my knowledge, the workshop that has been organized about blockchain or the interface blockchain and robotics. And I can tell you, more or less, this is the size of each one. So it's three, so someone told before that blockchain is nascent field, blockchain and robotics is an embryonic field, but I think there is a convincing case for looking on this interface. And so I think, so I could bore you by entry. So actually the summary of topics that I did before is taken from browsing this, what has been shown at this workshop one, and the major things coming out, they already put the things and also this edited book which is gone, so thank you. Hi, my name is Harry. Yeah, so I think you mentioned something about market being a very efficient way to allocate resources. And so I think actually market is just a very quick way to allocate resources, but there's no guarantee that the allocation is gonna be very just or fair or socially equitable. And so if you have, let's say, a blockchain algorithm that is used to manage a market, how can you make sure that you don't have something that is very similar to 51% attack? So let's use your example of the Uber market. So let's say you have a market, so let's replace Uber with ambulances. All right, so you have a market for ambulances that is managed by a blockchain algorithm. How can you make sure that people from the rich district of the city cannot abuse the system, like use a lot of competition power to create a 51% attack to always get the ambulances for themselves? Very good question. Of course, I have half an hour, so I could, this was another interesting aspect of the issue. I mean, markets are social constructs. So when we talk about natural markets, we typically, they are not natural, but they come from a kind of stratification of habits over time. And we know that market create inequality. I would say it's one of the typical bad effects of markets. Yeah, when you talk about a blockchain for doing something, for example, for managing the city, the difference with the natural market is that it is an artificial market. So you've right the rules. And while the rules of a real market are customary, so we used to do in that way, and this is also true in the stock exchange, algorithmic rules in an artificial market are algorithmic. So a nice topic of research. When I was talking about research on the interface, between, for example, SMORM, meaning as big multi-agent systems physically embedded, is that you may study the rules which lead to a more equitative approach because just to go in, to be a bit, not believing, but compare the Soviet Union to the People's Republic of China. The Soviet Union had a top-down, so they had some market locally, but typically was top-down planning. So the assumption was the central planner knew, knows the future. And this was the mistake. What they are doing now in China is that they mix planning with markets. So because markets are an allocation mechanism, the problem of managing market in such a way that they benefit all of the most, the biggest number, it's another, I didn't touch intentionally the political implications, but it's true. I think that an advantage of having an artificial market is that you can design it and you can simulate it. But then we will have a nice topic of the convivance coexistence of a natural market from the neighboring markets to the stock exchange, and the artificial market, by the way, the stock exchange is, I think, 60, 70% of the transactions are already automated. So it's already something which is similar to an algorithm that you run to see what you have to do. No, I see your point. So as we need to be vigilant about privacy issues, these kind of things, we also needed to be careful about how we design these new markets, because it's true. You might end up in a Blade Runner-ish nightmare, right? With a few rich people. So this is true with robotics and AI, because robotics and AI, I multiply by, say, 100 times the productivity of one hour of human work. This can be used in many ways. I hope I answered your question. So it's, yeah? You mentioned earlier about citizen transparent environmental monitoring being different from citizen science. Could you speak a little bit about that? Yes. Alexander will give you more details, but the essence is that if you have a distributed peer-reviewed way to check, for example, if the samples taken by a robot are authentic, so are really those samples. So is an application, if you wish, or what have been shown by Eduardo and Marco before? So you can avoid that fake data are entered into the data chain. And so you can, and so actually, I see this big potential in having citizen doing science on data peer-reviewed by citizens. So I was talking about, because you can use these technologies in many, like any technology, you can use in many ways, and you can develop different blockchains with different purposes. So for example, this is a kind of blockchain photo to give more power to the people, power on the data, because as told before, if data are the real wealth, giving the ownership of the data to the people, to distribute it, it's a positive thing. So you may, I don't want to go too far with this, but you may also think about, for example, electronic vote, right? I'm a big fan of electronic vote because I think it's idiot to move physically. But you know it, it's not so difficult to tamper an electronic vote. So this, again, could be an application. I didn't want that because I wanted to stay to the topic of robotics, but, and then you have all these strange things. But if you have these robots as a service, who can, how you can buy this service? Who can buy the service? And again, you have the potential for inequality, but you also, actually I am a dreamer of a post-scarcity society. You also have a possibility to, okay, it's a long discussion, I think we can, but I hope, so the idea is that to have not only the citizen elaborating the data, but also the data, the citizen overseeing that the data are not tampered, which is, okay. No worries, so let me introduce like the last speaker of this session. So he's like Professor Kapitonov, like he's an associate professor from ITMO University and also like a pioneer in this field. So yeah, without further delay, let's start your presentation. Yeah, thanks a lot. Yep, please drop it. I will bring my little friend with me. Just put it here. Okay, today my talk will be about more futuristic scenarios of application for decentralized technologies. Because right now there is several really good, stable solutions for information exchange, data storageing, communication, and so on. And right now it can be implemented to the real tasks and it's really scalable for purpose of smart city and citizens inside smart city. Okay, let's go further. Just short information about myself. I'm an associate professor in ITMO University in St. Petersburg and working with robotic things already eight years and mainly focusing on mobile robotics and last four years I'm working with decentralized technologies. We starting with Ethereum blockchain when it was launched and we mined one of the first thousand block inside that was really amazing for us. And after that we found that smart contracts and other peering technologies that makes possible execution of the source code in distributed ledger. It's really useful and it can be used and applicable for robotic stuff. There is the main idea what we found and we start to develop it. Okay, and what we found that's really distributed things peering communication is needed not in all spheres because there is a lot of disadvantages but advantages of course will solve the specific problems and work can be really useful. So we have to be able to do that. Work can be really useful. Of course the first thing it's a direction related with sharing economy because sharing economy right now there is the really good way how to maximize of the utility of some cars, stuff, equipment and you can see that many companies already going in this direction for example in aviation they already selling the engines for the planes not like the solution but like the service. The companies who are buying this plane they're paying for the time of the engine not for the engine and the same things I think it will be with the cars, with the industrial equipment there is way on the way it will come soon everywhere. The next thing it's crowdsourcing, crowdfunding stuff that's shown really powerful things for us to solution different crazy ideas and implemented to the real world. And the last thing but not, it's really important thing public assets management. That's what's right now executed by governance mainly all the public assets management and sometimes that's not so easy to make some really fast changes in the sphere. It's a big problem that we should solve and the pyrotechnologists will come there to make it more transparent, more immutable and to speed up the sphere. Okay, where is all those things as really needed? As we talked previously, we found such sphere it's environmental monitoring. Environmental monitoring right now it's really weak point from the site of transparency from the site of immutability of the data and participation of the citizens inside this processes related with environmental monitoring is really needed. It's really important that every person should be involved in the process to collect in the data, to provide in the data sharing it with the participants. But other task it's analyzing of this data interpretation of this data. It's can be of course made with the collaboration of the big laboratories, resource centers and so on. Right now there is a several projects mainly it's popular in Europe when the governance try to share the liability of data sourcing, crowdsourcing of the data about in the environment like CO2 dust, the solar energy, value, water quality and so on. There is all those tasks going forward and the citizens involving in the process to measure the data about the quality of the water and so on. And there is like a presented the main projects what I found especially for the soil for the air, for the water and the common platform for aggregation of this data and showing the interpretation of that information. Here is in this process it's really needed in peering technologies. Because how can you trust the information when you're for example sending your measure, your data you're sending to the server and after that you're getting the average level of the dust in the city. That's usually how it works. But the average level it's kind of tricky thing because for example during the day the level of the dust in the air is really high. But the night, but nobody on the street, of course it's not so important. At the night of course it's much more low and average level is okay. But we are getting the biggest part of the day where the high range of the dust inside air. And this process should be fully transparent for citizens. How the data collect, where it's storage and how it's processing. It should be all those steps should be transparent and clear for every participant of the city or for every citizen. And of course we are starting from the stationary sensors that can be placed on the house, on the roof, can be mount on some vehicles and buses. But sometimes it's not enough to get the data only from the single point where this sensor located. Sometimes you need to reconstruct some dynamic field of the pollution of the dust or some gas and estimate where it's going, how the flows working in that area. There is the task especially for robots and mobile robots and robotic systems in my opinion, they can be like the defender of the nature. They can connect us with the nature and explain to us what's happened, how it's going in the real time. And this is a really interesting thing when you can get more data about what's happened in the environment, how it's working and what will happen if you're decided to make this or this way. I think the autonomous systems and robots can connect us with the nature and give us the common picture, big picture of this area. And here just a couple demonstration how we implement the mobile robotics with steering technologies to collect the data about environment. Here is, you can see the drone, it's flying out of the city. And you can see here the land field in the top corner. This is the really big gray area about the relates with the garbage, the value of the land field, how many tones are really inside it. There is quite difficult to estimate it. But using the mobile robotics and the sensors networks, we can get the real information about such things. For example, on this video you can see there is lakes around the land field and it looks like not well and it smells like really bad. That's things can be solved, the monitoring of all that things can be solved with autonomous systems and robots. The next task, it's a water monitoring. We made the water drone to collect the information about the water quality inside Volga River, one of the biggest river in Russia. And we found really interesting scenarios when you're showing the quality of the water not in average level, but you're searching and estimating the flows of the pollution, you can find the initial point where the polluted was dropped inside the water and so on. And the discussion inside society is started about, okay, who did it? And the companies, of course, they're trying to say no. That's not mine, that's not mine. But the next step, somebody will ask, okay, please provide me such monitoring system. I want to be clear, I want to show that I'm not polluting environment. This is the next step. And it's how it can be in the future when the companies will connect to this system. One of the really, the next big thing, what I want to just show you and try to explain, try to imagine that, for example, we have a forest equipped by such system with a sensor. It can measure the CO2 or O2 and other consistent of the air around. And in this case, such system, forest plus autonomous system can be like a separate agent. And okay, if it's separate agent, we can make a deal. Okay, forest, for example, landing the land, he's a land field. And saying, okay, I'm renting this land and giving you O2, getting the CO2, providing the place for animals and so on. And we can estimate the economical value for such actions. And in this scenario, the real nature, part of the nature can be like an agent in the economical sphere. It's really, really weird. It's amazing for me, for example, definitely. But I like this idea when I can directly communicate with the nature in the economical way and not only. Just a short two topics that I want to discuss just a little bit more. This is the picture from the publication of the Cornell University. And they start discussion about, okay, in the future, we will see the problem that the economy of robots, because Fabio already told that the big part of transactions already made automatically. And can you imagine the really big value of this automatic economy and the real human economy when we are exchanging the money to each other, peer-to-peer. And there is a problem, because the human economy will be really small, really small. The robotics part will be much more bigger. And if we will see some waves, some oscillations in the robotics economy, it can be dangerous for human economy, because it's much more smaller than robotics one. And what the solution, what I found, for example, for that scenario, it's described in the book, Post-Capitalism. We should maximize the communication between each other and tokenize it. I mean, if I'm discussing something with you, I should pay to you. If you're asking something to me, I should pay to you or you should pay to me to maximize the value of the human economy. It's some weird things, but it's one of the way how to make human's economy much bigger than robotics one. Of course, right now, there is not only such aspects, economical aspects, discussing with our colleagues in the Scientific Society. But there's a lot of things, and that's all described in ethical, allied design for robotics researchers, for robotics sphere. There is a lot of things starting from the rules of the communication with AI and finishing the some intimate relations between the robots and robots and humans. There is a really interesting topic for discussion and all these things described here. One of the idea that's proposing, in this ethical, allied design book, it's creating the distributed networks for different AI and robotics solution, because right now, it's really needed. We need to ensure that any actions that can be from the side of the artificial intelligence and it shouldn't be in the single point. Single point for decision, it's dangerous for us. Okay, I think about the futuristic things, that's all, but just one more moment. I want to show you one demo. I hope it will be good. Yeah, please, Vitaly, can you assist me? This is the demo. I just want to show you how the possible to apply the simple car, simple robots can be connected with the peering technologies. And here is inside this technological background of this demo, there is a lot of peering things, starting from the peer-to-peer communication, transactions, IPFS, information storage, and finishing the robots operating system, robot software that's also needed to control some aspects of this work. Okay, just let me put it. I hope we will get the transaction for this robot, but there is the process, what I want to show you. At first, here is existing, do you know the interplanetary file system technology? Who knows, just raise your hand. Okay, there is like one third of the audience, it's really good. Interplanetary file system makes possible to provide the free communication channels, peering channels for different purposes. And the next thing, interplanetary file system can storage the information inside in the peering distributed network. After that, if you're having the free layer for communication between the different parts, different robots, you can broadcast the information about the deals, what should be done inside this process. If you're finding in the IPFS layer, in the free layer, some matches that somebody asked for the work and someone else ready to make it. Oh, perfect, it works. And somebody ready to make it, that's, oh, just, yeah, yeah, yeah, just wheel out, but no, not a problem. It's rotated around somebody, I think the first, yes, the first rows saw how it works. And when it matches in a interplanetary file system, that's mean you can put the information to the blockchain because the transactions inside the blockchain, it's costly. And you can just broadcast in blockchains some information. It's really costly, but when it matches, it can be put it in the blockchain. That's the thing about the third part where smart contract has been created. And in our case, this smart contract collect the information about the rows back for this robot. There is a special instruction, what it should to do. And in our case, it should rotate on the time in 10 seconds, because the price, what I paid, previously it was 10 tokens. And look at this, right now, peering technologies is really simple. It's really simple applicable. It's really simple to use it. Just several months ago, MetaMask, for example, released the application for the smartphone. You can control the peering communication through your smartphone. That's amazing. That's why we should use it. We should apply it. We should promote it and collect much more information about all aspects of the environment and try to improve our world. Okay, thank you. Any questions? If somebody wants, we can discuss after the demo and I will show the details if you want. If nobody have a question, you. Oh, yeah, please. So, I have two questions. The first one is, well, the first is a remark and the second is a question. So, the first one is, I really love the concept that you have about nature as an agent in this kind of network or this symbiosis with robots. Especially because we don't tend to think this, but we live in a transactional society. We live in a contractual society. Because I am an agent in a society and the state can regulate that. I can pay my taxes. These taxes somehow get me benefits, et cetera, et cetera, et cetera. So, maybe because nature is not an agent in this contractual society, we tend to not respect that agent because we think it's ethereal, it's eternal, but actually it's not. If we'll be able to have this interaction, it would be way better. So, I really love this example. The second thing is, could you explain a little bit more in more detail what just happened with this robot? How was the flow of information? What happened in the back end? How did this go to the robot? How did it move, et cetera? Okay, it takes several minutes, but Vitaly, can you change again for the browser? Yeah, here is... This is decentralized applications, application for this scenario, what I showed you, to send the robot liability to make the work. Using this decentralized application, you're communicating with IPFS layer, Interplanetary File System layer, where you're broadcasting the proposal that I'm ready to pay for this work, and you're providing the description of this work. Description of this work according to the Robot Operating System software in the ROSBAC file, and when it matches, then somebody tells you that I'm ready to make it. It matches, and sending to the Ethereum blockchain to mine the smart contracts inside the blockchain. Here is the link. For example, you can view the contract. Vitaly, I'm definitely asking you to save this contract, because I hope it will be historical. And when the smart contract puts it inside the blockchain, it starts executing the Ethereum virtual machine matching the addresses of the robot and address of the payers who paid the tokens. And start executing the smart contract, sending the hash of the ROSBAC file, what was put in the IPFS to Robot. Robot uploading this ROSBAC file. This is the description of what it should do. And after that, Robot executed. And after execution, it's uploading the final result, the ROSBAC, because when it's rotating, it's recording the ROSBAC file and putting it back to the IPFS, providing it to the next transaction to finalize the smart contract. This is a flow that I showed right now. Thank you a lot. So this was the final speaker for this session. Now we have lunch at the other side of this wall. So please enjoy it. We'll be back here at 1 p.m. with the paper presentations, so don't miss them. Until then, enjoy lunch. Thank you. Check one, two. Check one, two. Check, check. Check, check. Hey, hey, one, two. Check one, two. Check, check, one, two. Check one, two. It's a great view here. It's hard to speak in front of this view. To stay one day out. It's hard to speak in front of this view. Yeah, it's amazing. Okay, thank you so much for your attention. And stay here for the first talk in this session. So my name is Fabio Petrioli. I'm an associate professor at UCCAC, University of Quebec at Chico Timi. And we started work with my new PhD students, especially Marcella, did his master in robotics and moved to Canada to work with me in software engineering and robotics. And you say, oh, there is a great symposium about blockchain and robotics. Maybe you can share something and you prepare this work to share with you today. Just to say it's a really preliminary work yet in progress. But you have some insights that you would like to share, okay? So just to present, Quebec is a huge province in Canada. You run so far in the north. It's a good place to visit. You have the opportunity to visit. Take a flight. There is an airport. It's far, but there are not just bears there. There are flies also to come. So it's a beautiful place. It comes from small campus, but so kind. I do say. And you are trying to organize a team, a research team around software, software engineering in general. You do an acronym, smart seeds. Software is my art. Say software is more art than technology sometimes. And because it's hard to understand this phenomenon, you try to do it together. Try to organize this stuff. So finally, we start to discuss and say why people in Marcella start to discuss and why people... It's hard to use robots yet because industrial, traditional robotics is everywhere in the industry, traditional. But it is cost-efficient. Why is not everywhere yet? And you can say in the discussion because the safety, okay, in general, is not the safety to share. So there is the point that you have discussed that safety is something that you have to improve to put robotics everywhere. So one discussion, one option to improve the reliability in the safety maybe is blockchain. That's a discussion that you are trying to motivate this work. So what's our goal to try to identify and so the state of art on blockchain and robotics, okay? A little bit of point of view of the distributed systems and software. So our goal is try to identify, classify, evaluate the work on this topic. So how you did that, you do a systematic review. Yes, we read a lot and try to find the papers following traditional guidelines in systematic review in software engineering, okay? That's in the use of, you organize research questions and such strategy and blah, blah, blah, that you know probably and you try to show you some research questions as what are the main challenges that you can discuss, the main approach in blockchain, benefits and limitations, okay? So what you did, you propose a research string, search string in traditional libraries, okay, as ACM, IEEE Scopus, and you compose 89 papers and automatic search on blockchain and robotics, okay? That's you put these keywords and there is 89 papers and also we put together the papers manually from the first symposium, okay? First version of the symposium to try to collect more papers and after filtering the criteria seriously there is able to answer our research questions, you select 14 papers to analyze, okay? So I can show you and that's our first, maybe small but in progress contribution, a catalog of papers on this topic so that you can continue and use this work. And one important for me is it's really, really extremely new topic, okay? It's 80, 26, 27 and 19, okay? It's really, really, really recent topic. So you are in the trend, that's you can say. There's not a lot of but in progress there's a couple starting to come and different people are probably here working on. So what's the opportunity that you started to make mapping on this and you analyze these challenges and so it's not a surprise the majority of papers discuss about the challenges on communication, okay? How to robots work together and how communicate and a couple of seven from 14 papers work on collective decision. It's another important challenge discussed in this topic. So communication but also communication every time in point of security. This is the main point that you can realize and the distributed decision making algorithms is something really pervasive I can say in this work, okay? And we try to show you some quotations in really new paper. The usage of blockchain paradigm on embedded systems for distributed multi-agent robotics is still uncommon. This is important for me when I see the announcement for this event here say, wow, it's great blockchain and robotics. And first time is no meaning because blockchain is something for no real time system if you can think. But in fact, people starting to use this technology to improve the systems as robotics and however the limitations of embedded hardware is yet an issue to support this topic because as you probably know, blockchain is an intense proof of work. So this is an issue that you can work together to use blockchain in robotics systems. Okay? So also the discussion of using blockchains to intrusion detection this is one topic to discuss what's the main approach for what's the people are trying to use blockchain technology in robotics. Not surprised to is the smart contracts that you have an example in the first last presentation here uses smart contracts to organize the tasks in blockchain context. Okay? But the majority of approaches is smart contracts that is the point that people are focused on in blockchain. Okay? So blockchain approach has a potential enabling to build security and scalable distributed control systems device such as a robot in IoT environment. Okay? Some people are trying to focus and say it's a path. There's a potential to use. So the use of blockchains allow providing mechanisms for ensuring the truth, durability to storage and no repudiation from the information. Okay? There's new papers that can share this kind of discussion. Okay? Here talking about also this couple of work discussing about Lego Lego and safety regulations also in blockchain. Okay? Especially in swarm operation. However you can also discuss some traditional issues in blockchain and especially in robotics. And latency is the most important one Okay? It's a traditional problem that become probably the most significant one of the most important issue that in literature identify to use this technology in robotics systems. Okay? So with this consequence of the communication and swarm and large quantity of robots or to deploy this network this is not a simple task too. So this is another quotation about communication that you know. Okay? So just to finish some a couple of discussions that you prepared and to highlight. So regarding analysis studies mobile robotic systems more specifically swarm robotics is a predominant robotic to use a blockchain technology. Okay? And the integration of blockchain and robotic systems could be in fact a key series progress in the field of robotics. Okay? People are trying to observe that and you are here just to put together this idea probably. Okay? It is probably a huge impact in the economy that you discuss and it is clear in the literature to confirm that. So just to share some maybe points or recommendations that you can discuss maybe to create some metrics, parameters, parameters to evaluate this practice especially the point of view is security. Okay? Methods to compare blockchain and other different methods in distributed systems. Okay? And also requirements specifically requirements to put together robotics and blockchain. It is work to do it's something that you imagine that in the progress. Okay? So that's it. Some kinds of attributes in distributed systems for robotics. Also maybe is a kind of my research on software engineering how to put some software engineer aspects around blockchain and robotics. Okay? So I really appreciate your attention. I hope this as I said is a work in progress. You will continue to improve to the next version and work hard to publish a really good paper. And thank you so much for your attention. Okay? My name is Renita Moremi and I am associate professor at the University of Dallas. Today I'm going to talk about my research on a blockchain framework that's used for social robots and the means for analyzing this framework will be a mathematical concept called shift theory. So I apologize that I'm using 3D or motion graphics. It's mostly mathematical work and due to time constraints I've skipped the mathematical equations as much as I can and just simple visuals to show how shift theory really works. Before I go into the framework for social robots, I want to speak a little bit about the motivation for this problem. So the well start as a traditional conundrum in decision making. We all know of decisions we have to make in our lives, personal and professional, trivial and non-trivial. And one of the biggest hindrances is imperfect information. We often do not have all the information we need and that could relate to games, it could relate to where do you apply for college or how do you put in a job application, we simply do not have all the information we need. And in the absence of perfect information or rather in the presence of imperfect information we adopt an action and that action might have, may or may not have suboptimal outcomes for us. And these effects are compounded when we're working now not as an individual player but as a group. So there's this swarming behavior, this collective behavior where a bunch of people like us all make suboptimal decisions based on the imperfect information that we all have. The second impediment to good decision making is irrationality. Decades ago Kahneman and Tversky studied how people did not always do the right thing, especially when talking about outcomes of a game. So if you have the choice between getting $10 and $50 over a series of studies in various domains they found that people were consistently choosing the $10 outcome. And that's because they simply did not understand the mechanics of the game or rather their perceptions of the utility from the game seemed to be that choosing the $10 would be more profitable when in fact it was not. And so at Kahneman and Tversky did is they turned the traditional model of utility rising from economic games over and they said humans are not always rational. We have here an irrational actor who does not have good information who is behaving in such a way that the outcomes are suboptimal and often what plays into our decisions is biases when we do not have good information about a circumstance we rely on patterns that have worked well for us in the past or have worked well for others. And so we rely on these biases which are really shortcuts to making good decisions. Now I'll move on to the current conundrum in decision making. So for the past few minutes I spoke about the traditional conundrum. The current conundrum is that we have enabler technologies this trifecta of artificial intelligence machine learning and massive data sets and really good algorithms that parse through these data sets and these have tilted the scale of rationality in favor of the machine. We are all aware of recent advances where the robotic version of jeopardy or chess or alpha go defeated the world champions in those games when those games were played and they continue to improve. And what has happened is at the heart of this we have a game between two unevenly skilled players. On the one hand we have a robot a robotic version of the software that can perform one thing really well. So the alpha go robot just plays alpha go really well and you would not expect it to pick up a block. On the other hand we have a human who has imperfect information in a range of applications. So not perfect information in alpha go but a range of applications. And these things fairly well in many different domains. And it really because the scale has tilted in favor of the machine that is because the human has usually limited recall of the past is short-sighted in the future and even the current we are plagued with imperfect information. So what works for these robots? And why do they usually have the upper hand in these complex environments? It's because they do two things well. They reduce imperfect information because they can store everything in the cloud and they have access to these large data sets which we humans cannot. It's beyond the range of normal human cognition. And they're also usually not irrational. Given appropriate code it will always choose the best outcome which cannot be said for human beings all the time. And these algorithms are not just gaming versions. We see them combing through data sets of demographic information, voting polls, SAT results and they're working well. It helps when these algorithms help detect cancer faster. They help us correlate income to majors and in general they're able to predict the future well. Let's say stock prices, medical trajectory of certain illnesses. But the problem comes when we think of these devices or these robots as not just our helpers but their co-equals with us. And through this morning we heard various talks about robots working with humans becoming agents to interact with nature. So we're moving in that direction. And this is not really any more in the realm of a sci-fi future. We already have smartphones that are talking assistants and they keep the company of our elders. They entertain my children for sure. And when I was here last year I learned about a drone that has its own social media page. It orchestrates pickups and drop-offs. So we're looking at a future where we have a society that is inhabited co-equally with robots and humans in both virtual and physical spaces. And in such a scenario it is a problem when the robot always has the upper hand. If you're thinking of a robot as a co-equal member of society it's hardly that it knows everything in all situations. Who would want to play a game with an entity that always wins? Or to socialize with a robot that knows the answer to every question. I mean at least speaking for myself we all have these memories of conversations or quarrels with a friend that passes significant others. We're ten years down the lane and everything was said and there's no perfect information about who said what and we just let it slide. But that is not the case with the robot. The robot just knows what was it who said it along with the time stamp. So the work that we're proposing for social robots is to make them more social. It's not just in the form of aesthetics. There is a lot of fantastic work then around robot skins and appearances texture but the work around human empathy. I mean much of human society is made up of these glues of empathy where like-minded likely able individuals bond over common goals concerns challenges. So to make the robot more human in that sense. Now I'm not advocating for a robot who behaves with imperfect information and is irrational all the time. And that's where we bring in the concept of smart contracts. So I proposed framework and there's three things. The first is to have these tunable parameters both for imperfect information and irrationality. So for example a robot that's talking to a child or playing with a child has a certain level of imperfect information and irrationality and then when the same robot is used in a different application it has better information and exhibits a more serious demeanor if you will. And these classes are then embedded in smart contracts. Now smart contracts work well on the blockchain and that's not the only reason we pull the blockchain in here. It's distributed it's trusted and it's immutable. Now for analysis of all this. A common concern that was raised in this morning's talks was that the blockchain is limited in how much it can do. We have all this data that's coming up and analysis of these data sets is hard with attributes that maybe have different features. It's easy to represent an interaction or a transaction between entities up to three dimensional space that's good for us humans with algorithms we can go up a few more dimensions but imagine a transaction where you're recording I use the word transaction to record any interaction. So if there is a transaction between a robot and a child you're going to decipher sentiment emotion, facial expressions the content of the text being spoken all these features quickly add up and if I were to represent this as individual points it looks like a very dense point cloud and what happens in dense point clouds is you miss important features you miss components such as tunnels or voids in the graph you miss features about clusters and anomalies because it's just a very dense point cloud and analysis is difficult and that's where a fairly new mathematical concept developed around the turn of the 19th century called shift theory comes in shift theory actually has its root in algebraic topology and applied category theory. So this is the contribution of this work unifying framework for social robots analyzing shift theory and made more social or less social based on certain tunable parameters stored in smart contracts. This is the premise for sheaves on the left you have a line an edge which is a one simplex a simplex is a building block and then I have a two simplex the three simplex is the tetrahedron and n simplex in general is a convex hull and this is the extent of all the math that I will put up in the slides there is more in the paper than in free to talk with anyone interested about the details of this but algebraic topology really looks at the shape of spaces and what it does well is it understands patterns and the structure of spaces when it comes to things like graphs and by visualizing the block chain as a structure like this it becomes easy for us to find patterns faster. More and more applications are envisioned to use block chain there are some really nice visuals in the talks this morning but one example that I put up is that of fraud detection fraud detection usually depends on a range of characteristics patterns in location buying behavior the amount of the transaction and envisioning this as a blockchain of sheaves makes it easier to find out anomalies a sheave is simply a gluing of structures that has certain local properties and global properties and those local properties are enough to determine the global properties and that's where the power of faster computation comes in. Some more motivation for using algebraic topology last year at this symposium I proposed this framework for storing social network data on the blockchain and that also is massive amount of information lots of statistics online about how many tweets post shares that are generated every day and all this information and studying this with traditional graph theoretic tools becomes cumbersome so the default approach now is just to collect, harvest all the data and store it and analysis comes at a later time so one might argue that why do we use sheave theory there are some tools like principle component analysis that can help us study the structure of these interactions and use a limited they do not work well on manifold or curved spaces but sheave theory does really well on end dimensions which is harder for us humans to visualize but it's very forthcoming and very powerful using sheaves blockchain transactions have there is one other paper that I'm aware of that studies the blockchain using sheave theory to do work concerning distributed consensus protocols regardless there is other work purely from the field of mathematics that looks at sheaves and they study things like eccentricity and verdict significance simply put they're studying how well a node is integrated in the graph or how isolated it is how does that translate for us it helps us find anomalies and outliers and hubs and clusters there are tons of interesting things about transactions again transactions is a term I use loosely for interactions smart contracts usually smart contracts have underlined clauses just like contracts in a non-blockchain world where upon the fulfillment of certain clauses an action takes place so in terms of a graph you can depict it this way but when these conditions are executed another edge is formed so these smart contracts have the potential to balloon very fast because all these smart contracts lead to more actions which probably have more smart contracts embedded in them and so very quickly we're looking at very dense point clouds which are difficult to analyze in traditional tools and this scenario is further complicated by the use of IoT devices these devices latest statistics say 20 billion 22 billion by the end of next year so if these devices and various virtual agents are on the blockchain that results in computationally intractable techniques for analyzing the data produced by those devices this is an analogy of sheeps think of it as a sheaf of wheat a sheaf is made up of stocks what I have is a stock the power of sheaf theory comes in because it's so versatile it's applied to categories of objects and these objects can be heterogeneous these objects are also modular and the categories are modular so those of you in the audience who are familiar with object oriented programming might get that concept a little you might find it a little more relevant you can have objects of various elements so right now I have a stock of blocks and the common spine can be depicted as the spine inside that stock each of those blocks are analogous to the seeds inside or the germ inside every stock if we put together a bunch of these stocks and bind them with a smart contract then we have a sheaf this is another example on the left I have a single block depicted as a stock on the right I have an entire blockchain depicted as a stock so immediately you see the versatility it's not limited to a single kind of object to be linked within a stock and the individual blue circles they represent nodes each of these nodes can be transactions with those green circles are features so features recorded about a transaction these could be sensors that are monitoring various data points and then whatever is the common linking point usually is the hash that links them that comprises the stock in these diagrams here are some applications the one on the left is a blockchain stock for logistics and tracking you see now the structure of a sheaf where you have multiple stocks bound together these these are bound by smart contracts which are a function of the imperfect information and irrationality these can be tuned on the right we have a blockchain invasion for a pursuit invasion game where we have maybe a dominant robot and a slightly less skilled robot so heterogeneous robotic applications and on the bottom we have something like a swarm robotics behavior where multiple robots are measuring different things and they are all bound by the smart contract the applications as we mentioned in swarm robotics in smart social robots also in consensus applications consensus applications could be related to voting mechanisms and crypto wallets and exchanges can also be analyzed very well using sheaf theory we have central nodes that are behaving in different ways all of this information can be captured and analyzed very fast using sheaves and then stored on the blockchain and certain challenges and limitations that are not just unique to our framework they are the result of bringing together disparate disciplines one is quantum computing it helps and it hinders once the technology can be embedded in regular robots it of course leads to a very fast robot and therefore a very slow human but on the other side all of this if it's on the blockchain key management is a big issue and quantum cryptography can lead to issues in the provenance of the key and how strong it is how fast it can be broken so that is one challenge that affects such frameworks that is currently work done in post quantum computing that work is still emerging that produces ledgers that are resistant to quantum algorithms and so those will help this challenge the second one is that of regulation at its core blockchain has the concept of distributed computing so no one really owns who goes first how the consensus is formed it's a natural organic way based on mathematical constructs but in social robots or in smart contracts it might be necessary to have some kind of oversight for example if a robot is deployed to understand the prognosis of a patient it's helpful to decipher intent and emotion and these when not done well might lead to catastrophic consequences especially when things are not very black and white for medical conditions another common challenge is that of diverse blockchain environments there are so many environments used across so many applications and it becomes a challenge when we want a seamless way to interact with all our devices so that is another limitation and information overload blockchain in itself does not stood a lot and therefore tools such as sheaf theory are critical in reducing the amount of information that we gather and storing that on the blockchain so in conclusion this work brings together smart contracts that are tunable that behave differently in different environments and these smart contracts are created with the sole purpose of making these social robots more social or human imperfect information and irrationality will be the tunable parameters here and the analysis of all these interactions is done using sheaf theory which is a computationally efficient tool to analyze large volumes of data and finally storing all of these on the blockchain not just because of smart contracts but also because of the inner and trusted distributed nature of the blockchain for future work we are looking at elements of how AI technologies specifically the Boltzmann algorithm can help in looking at sheaf theoretic implications for analysis and a broader work is social justice through algorithms which we haven't hashed out yet but we are still in the initial stages so that is it for my talk and we have questions good afternoon my name is Jorge Peña Geralta and I'm here with Tommy Versterlund we are coming from University of Turku in Finland and I'm going to be talking about utilizing blockchain technology for managing collaboration in heterogeneous robotics swarms but before that just a brief introduction to what our research is about in this area we are working somehow in the intersection between robotics and blockchain technology and we are working in the intersection between robotics, cloud computing so IoT domain and in this direction our research call is to design methods that enable truly autonomous reconfigurable robotic swarms and we think about this from the point of view of how it works with cloud computing you can take someone else's application and without knowing exactly how it is implemented you can deploy it in a set of servers even to some extent in a distributed way so what if we could do the same with large robotic swarms heterogeneous what if you could take a set of robots with potential different capabilities and you could take an application that somehow is coded in a way that is abstracting these capabilities so doesn't necessarily know about the specific hardware or specific sensing capabilities of these robots and you could just deploy it and there could be a distributed consensus system that could take into account energy constraints that could take into account how to distribute the computation and how to distribute the tasks so that this is not given to the developer of the application this is a long-term goal and before explaining how or what can be the role of the blockchain in this idea I want to say or let me set the specific research questions that we want to answer in this paper and specific problem settings so I have said that we are talking about heterogeneous robotic swarms but what does heterogeneous mean so for us heterogeneous in this setting first means variable operational parameters so it means robots that interact with environment in different ways robots that can operate in the air, drones, robots in the ground, cars or why not robots in the water autonomous boats and by robots we mean in general any sort of autonomous agent in this context but it's also second variable and we have different sensing capabilities so robots that understand the environment or agents that understand the environment in different ways with different sensors they can have a geometric understanding they can have a semantic understanding or any kind of sensor suit and last they have variable processing power so how can we take this into account robots we can have an autonomous car with almost a super computer on it but drones have heavy load limitations so we cannot put a big a lot of processors in there there's that limit and just from a conceptual point of view so where would this be this image only shows cars and one track but in general we could think about a smart city of the future maybe even the near future where we have self driving cars we have autonomous delivery robots which are smaller we have also delivery drones or we have automated trucks or logistics industry and what we want to answer or what we are trying to discuss about in this paper is how can we achieve consensus in a collaborative heterogeneous multi-robot system but potentially ad hoc so we don't need a priori what are these robots and by achieving consensus we mean achieve consensus in the collaborative effort so now we are reducing to the fact that we have multiple robots and they want to share data they want to share data to improve their situational awareness to raise their degree of intelligence or level of autonomy and to do this we have to promote high quality data so we have constraints in terms of for example bandwidth in the network so what data is to be shared which robot is going to send which data to whom and when we think about data is not only about the quality of data or a specific characteristics of data but it's also about the amount so what size of data are we sharing and this is not only in terms of the network limitations but it's also in terms of processing limitations of who is receiving the data so how can we take this into account in this collaborative or distributed consensus system and we believe that maybe not the whole answer but part of the answer might be or can be in blockchain technology so in this paper we are tackling the idea of modeling to some extent sensing capabilities and processing power of different robots with blockchain or with technology that is part of the blockchain stack and specifically we are proposing the utilization of proof of work or cryptographic proof algorithms in order to model computing power this is maybe something relatively new in robotics but this has already been used to some extent in mining pools for example in the Bitcoin network and this can be utilized also in robots with limited computational power because we don't require full proof of work we don't require robots to solve the whole to find the proper hash we can utilize partial proofs of work so we can have to some extent an estimation of the processing power of the different robots without being aware of what hardware they have just by knowing how much of this proof of work they can solve within a certain time and then in terms of data we can somehow utilize the blockchain to characterize and rank data to try to promote this higher quality data so in this sense we can leverage the immutability and the data integrity properties of a blockchain in order to have a history of data samples provided by different robots and one thing that I have to say at this point is that as it has been mentioned blockchain in the current state of the art is not ready for low latency high throughput real time systems and data exchanges so we are not proposing that all data is shared through the blockchain but we are proposing that robots are requested to share a sample of that data and store it in the blockchain these samples have to be significant enough so that other robots either at the same time or later operating in the same environment are able to find the same features so it could be features that are using in SLAM algorithms for example it can be a corner of a building or something that another robot can find and this data which we call SLAMs that are stored in the blockchain can be utilized and be compared when submitted by multiple robots so of course these takes times is not trivial is initial research so it's still ongoing but it has a lot of potential and this can be used not only to rank the data and to choose what is the best source of data but also to characterize it so it can be images it can be data it can also be thermal data so it's just a sample from any kind of sensors it can be only geometric data from example from a laser scanner so just points in space and from these all robots that have access to the blockchain they can get an idea of who is able to produce what kind of data what is the quality in terms for example if it's images you can think of pixels inside each of those buildings windows in the building or if it's geometric data about how many points within a certain area defining a corner or defining a window as well but this all sounds very nice how to deploy it so it's not trivial either or it's not clear and here we are putting two main questions which are from the point of view of deployment are we talking about when anonymous blockchain permission less where everyone can join this can raise a lot of issues from the point of view of accountability and trustability because we are talking about potentially safety critical situations these are autonomous cars autonomous robots that might be operating in the same place than people so having anonymous agents in this setting might not be ideal however we are talking about having a permission to blockchain which is managed by a trusted authority for example public infrastructure in a smart city or mobile network infrastructure so this kind of blockchain not only allows to manage identities and we have a set of parties that are maintaining even in the case of network loss that is something that has been commented by the public and the public and the public and if suddenly only very few robots are cooperating so here there's a problem there's a higher chance of attack there's a lot of security risks but if we have some public infrastructure or trusted parties and large enough number maintaining the blockchain that might be a solution for how to define this in the case of trusted infrastructure yes it can be started by these public authorities and it can be maintained by them and we can have a single blockchain but what to do in the case of ad hoc blockchains one option that we propose in the paper is that these might be used by private parties for example think of Tesla, Audi or any car manufacturer that might be doing this within their own fleet so it's a private blockchain but it's not a single one it's just generated whenever there is a high enough number of cars in a certain area so within some distance or in the same environment then one of them could automatically start this blockchain and they could start automatically collaborating without the drivers being even aware of that so it could be from a private private point of view and just to finish some discussion and conclusion so there's a lot of challenges in this sense we believe that there's also a lot of opportunities but there's been a lot of talk today about scalability about real-time computing about security and what happens even in these cases of safety critical situations if we have a 51% attack or if we have civil attacks then we also have the problem of identity management and accountability and especially because we are talking about autonomous robots that are operating with people so how do we make them accountable or who is accountable of what or how do we make sure that we know who did what or what was done wrong and in general data integrity the blockchain in this setting is not used as a cryptocurrency so the value of the cryptocurrency is mainly used to transactions but what would happen for example if a set of malicious robots are the only ones operating in a certain environment providing fake data and validating between themselves there are strategies or there should be strategies to find these outliers based on for example some words that have been presented today but it's not clear and we need to do more work in that direction and just to conclude so what we are proposing here is that blockchain could be a good fit and a good tool towards consensus in robotic systems this has been said a lot today as well we can use the immutability and that integrity of these distributed ledgers to manage and to classify the data provided by different robots but we can also utilize part of this technology to abstract the different resources that in robots we don't usually a robotic developer just knows what hardware or what software it's using but if we want to open these robotic swarms to a wider array of applications we need to be able to abstract these resources and generate new ways of interacting and controlling swarms of robots so Eduardo has said very well this morning we have to go beyond human-robot interaction and start thinking how to do human-swarm interaction and control and that's all, thank you very much any question? so good afternoon colleagues my name is Vitzali today that in the morning in the beginning of our symposium Eduardo mentioned the need for new business models on robotics market so that's what I'm going to talk today but first of all let me ask you a question how many of you have used an Uber in the past month? by show hands most of you did so sharing economy became really common concept in our everyday life in our economy and I think that the same way that these technologies has led to this uberization of relations between the customer and the service provider the advent of cyber-physical systems paves their way for a completely new business on robotics market and these business models are not just about fully automatic production lines in the enterprise I'm talking about universal access to robotics capabilities for small and medium businesses and thankfully researchers have already started to think of these new concepts and one of the most famous ideas is robot as a service model and the idea there is that it's service oriented architecture that integrates different types of robotic devices and the hallmark of this concept is that people refuse to buy the software hardware directly and instead they use the service this allows to reduce significantly the cost of adoption robotics and also the cost of operating the robots and let's go deeper into robot as a service research first the robot as a service platform consists of basic services that describe the functionality of the robot some user services that can be added standardized communication protocols like HTTP and the final is computing environment and database and the publications that we researched most scientists actually discussed that in the context of cloud computing so publications about robot as a service widely represented by Yeong Chang and his colleagues from Arizona State University they actually published the most cited publication on the topic and it was one of the first to use the term robot as a service and in the first publication they say that in robot as a service there should be the same functions as in service oriented architecture which are robot as a service unit for which this functionality is described the service which is the cloud provider and then the broker is an interactive shell that gives access to those functions in the cloud and then in the next research those same authors expanded their research and they identified a few narrow points of using centralized approaches for robot as a service platforms and they proposed to solve them by using standardization and redundancy and even though they discussed the disadvantages of decentralized systems they actually state that some decentralized elements are required to improve the reliability of centralized systems and then in their last publication they presented an architectural scheme for their solution with multiple levels and we thought that the most interesting was a presence of business layer which not only manages the fees for the services but also it looks for new business opportunities to improve the whole system in the long run and some other research on the topic were published which is robot web and we found it interesting because it was one of the first to use robot operating system and it gives us a lot of opportunities to integrate different types of robot and then in this publication they focused really on robot as a service application for emergency response use cases and the interaction interface they present here is really similar to publish a subscriber system in ROS so we found it also interesting so basically as you can see in academic environments most research on robot as a service is focused on cloud computing and after reviewing these publications our team decided that well the future of robot as a service needs to be revised and that is why we need to propose new architecture and that's why we did actually practical implementation of that so here it is we offer broad decentralization of robotic devices and we offer giving them economic autonomy the first decentralization allows to actually reduce the computational burden on the agent management system and that allows to connect more robust system overall secondly even though some components of economic components were discussed we think that we need to propose a new way so every device need to be able to create transactions on its own in order to achieve fully automatic analytics system and that is basically the only way we can evaluate the performance of business processes and in our case we use organize the work of robots market mechanism and the robot sends the supply message to the special channel and same as the client sends the demand message and then we use interplanetary file system to do that to collect the demand and supply messages and after there is a match between supply and demand an economic transaction is created in the form of smart contract in one of distributed registers we started working with Ethereum a while back but now we have a lot of support and substrate and the use of smart contract technology allows us to guarantee the execution of robots code and basically improve the high and have the high level of protection against malfunction and hacking and in previous publications some methods of identifying faulty nodes were discussed but specific security issues for robot as a service were not raised and neither were transparency issues so the use of distributed registries allows us to have transparency of transactions and allows us to take some functionality of the cloud server to those independent software nodes so we have independent software nodes and independent robots and then finally all authors agree that there is a need for standardized communication protocol in robotics and on our opinion this standardized communication protocol is robot operating system and we pass the command to the robot using a rosback file which describes the functionality of the robot so this is our vision of how robot as a service should be organized and I'm happy to discuss business models on robotics market further and I'm happy to answer your questions thank you for your attention sure what do you think this is like something innovative compared to the other people that are working on something like this could you tell us more about that exactly so we think of liability as basically the robot that needs to deliver something to the client so basically whenever a client requests a service and pays for it the robot is liable and by using those peering technologies that we just described we can give this economic autonomy to the robot so it has its own identity has its own wallet and it's able to create contracts so now we can make sure that when this contract is executed the liability is transferred automatically and we can keep track of that thank you you might have kind of answered it through this previous answer but if you didn't use the smart contract would there be other ways to do it and why smart contract more advantageous to the other ways well in software architecture there are a lot of different ways of how you can do things but I think that it is really important to think of robots as independent economic agents and identity of wallet and ability to create contracts that's by having this smart contract smart contract is essentially a native contract for robots that's how I see that it basically is an instrument for us to create a contract with a robotic entity thank you so nowadays we have a lot of applications of multi UAV drone systems from surveillance problems to search and rescue exploration inspection missions and more but how can we improve these applications hello everyone my name is Mario Gabriel I came from Isai Supero in Toulouse France and today I'm going to talk about the implementation of the blockchain in the surveillance problem but first let's define what is the surveillance problem that we're talking about so a surveillance problem that I'm going to talk about basically consists in a set of UAVs completely autonomous they must patrol a certain number of points of interest or POIs but this patrolling must be according to these two criteria must be completely unpredictable so no external entity can actually predict which drone is going to visit each POI for security reasons and it must be efficient as well and efficient for us it means that each POI must be visited as many times as he can so in a perfect system a POI would be always visited by a drone but this is not possible so because we have limited number of drones but we want to maximize this number of visits but how to integrate the blockchain but the algorithms that are developed and the papers that are published to solve this kind of problems they usually they admit that this data is always available the communication is 100% secure instantaneous they don't take into account the communication process between the drones and they usually they rely on servers and they rely on non distributed products and so this is a problem because they are not resistant to single-point failure and it would be very nice as well to have anonymity and transparency because if you want to visit as a service for example I have a drone and I want to rent my drone to a company that runs a patrolling service so I want this every transaction to be transparent but how can we connect these four elements together and we think that a blockchain is the best answer so regarding the decision-making algorithm how to decide how a drone decides which POI to visit we have two options either we go with the classical optimization problem but unfortunately for implementing on a smart contract this is too complex and too resource intensive we are talking about drones we are talking about card computers very small very limited on power so the other solution is game theory game theory is great because game theory has high efficiency and low complexity so this can be easily implemented on a smart contract and the way we thought about this problem was using an utility function basically this function takes as input the position of all the points of interest in the system the position of every drone in that particular moment and also the idleness of every POI which means the time it took since the last drone visit to every POI and then by minimizing this utility function a drone can decide which POI to visit next but then there is a problem and that is why how the system is sure that a drone visits a certain POI so we brought two solutions a proof of visit or a proof of work the proof of visit is intended for smart devices intended for electronic devices devices have computational power for example a beacon that can communicate with a drone can see that the drone is there and they can actually they both sign a transaction and publish it to the blockchain the drone visits a certain POI but for example imagine that I want to navigate my drones around the forest around the tree how I'm sure that my drone visits a certain tree this is very simple to implement as well by using a proof of location the proof of location can be implemented by proof of location service based blockchains that are available right now I'm not going into very deep into this topic because it is out of the scope of my presentation but is a topic that is very well known and there are many services that provide this kind of proof of location already so talking about the smart contracts which smart contracts do we need to apply in order to make the system work first we need to have the system manager this is not a smart contract it's just the entity that is running the system it can be a company it can be an individual or it can be a foundation for the open source project but they need to run three smart contracts and the first one is a subscription if I have a house and I want to basically to rent a service to patrol my house I need to pay some tokens and these tokens are included in the subscription subscription smart contract and then there's the decision smart contract and this smart contract is basically the implementation of the algorithm that I told you before to make sure the drone computes the correct POI to visit after because the drones can be for example owned by a company or can be collected from individuals we need to make sure that the drones go to the optimal point of interest and why because in the end we have a reward smart contract basically is paying to the drones every time they visit the POI either by a fixed fare or by a fixed bonus of the POI and because we are paying tokens to the drones a malicious zone could just fly around the closest POIs just to collect as much tokens as it can so that's why it's important to have the decision smart contract as well and in order to embed everything in the drones as I told you we're embedding these in the small card computers in the drones itself so we want all the communication to be handled by the blockchain and you want every UAV to run a blockchain node and this is how the system looks like we have a control a control box basically just to handle the flight controller and we have the navigation the navigation is just to decide for example from this point of interest to the one the navigation basically it handles the path and the decision box actually decides which POI to visit this is the implementation of the smart contract that I told you in the slide before and then you have all the communication that is handled by the blockchain in order to select a blockchain that works with these multi these UAV heterogeneous devices and so on it's very hard to implement it with a blockchain like for example in Bitcoin a regular blockchain because this is very CPU intensive and we are dealing with very small cards these cards cannot compute the cryptographic puzzles that are proof of works that cannot mind blocks as a very large cluster so our approach was to go with IOTA and IOTA they propose a very different approach it's not a regular blockchain it's a Tangle it's a directed cyclic graph and this graph has many interesting properties one of them is that it doesn't require miners because the way it works every new transaction indirectly is attached to two transactions so it directly approves two other transactions and indirectly all the other transactions that are connected to these two and it's already non-cryptocurrency we can buy IOTA tokens right now and since we are trying to implement a service they handle payments and decision we can implement both the smart contracts and the payments on the same network so this is very very interesting and applies very well to this kind of project another advantage of IOTA is the way they handle the partitions for example on a regular blockchain if we split the blockchain we cannot reattach it without deleting one of the sub chains in Bitcoin for example the chain that lasts is the longest but in IOTA we can actually do a partition and the way it works and the way that we propose is for example when you launch a campaign we launch in one of these transactions and we there settle every parameter of the campaign and then the nodes which are built in the drones they will only process the transactions related with that campaign with that mission on a sub-tangle and in the end of the mission we reattach this tangle to the mainnet so that indirectly every other node will approve the campaign and we don't actually waste resources of the drones approving transactions from IOTA mainnet during the campaign and in order to see if it's possible to run an IOTA node on a small card computer we have Odroid XU4 it looks like a kind of Raspberry it's very similar it's a bit more powerful but it's quite similar in terms of characteristics and in the X-axis you can see the transactions per second TPS and Y-axis the CPU and RAM conceptions these were using the software that the IOTA foundation provides it won't be the final software but the IOTA node because it's still in development they plan to launch it in April next year but currently they have a test software that has a module, a spammer that we use in order to have these graphs so basically for every for example for the case of 30 transactions per second we're running for a long time and then we took the average and in the end we obtained this quite linear behavior and this can actually run both the control flight control and the blockchain in the same card we also ran tests on our algorithm the the game theory algorithm that I showed you before this is an image in our lab we run with three drones on the left side we can see the POIs they are the basically we divide in a grid of 5x5 with 25 points of interest and you can see in blue there are the areas that were recently visited and in red the areas that are visited they were not visited in a long time and the drones cover this was basically to validate that the drones will cover very efficiently they won't last they won't leave like an area for a very long time without a visit so finally I would like to thank you very much for your attention feel free to ask me every question that you may have wondering on the iota network given that it can split like that and then come back together what actually defines the main net as opposed to a peripheral net if you can have lots of these forks but you are saying that they can come back together and then be recombined and that how is the main net defined relative to these other net so basically the main net is where the majority of the devices are connected and is the network supported by the foundation where the iota tokens can value actual dollars in this image is just a small partition on the huge network so here exactly only after we are touching we can we can pay tokens to the drones and they will value some money because in the partition itself let's call it a sub-tangle they won't value any money because they are not connected to the main net I was just curious about your experience in terms of trying to implement the UIV with iota compared to with Ethereum I think that one of the I think one of the benefits of iota is that it is supposed to be more lightweight and you will be able to put the node onto more of a like a sensor or a single board computer much more like because of the lightweight of it I was just wondering whether you also have tried this with Ethereum as well in terms of Yes, currently in our lab we are doing work with Ethereum and with iota as well and what we notice is that it is very very the iota is very iota friendly and for example you can run the card that we are using is 32 bit card and if you try to run Ethereum the majority of libraries they are not compiled for 32 bits they are not compliant to the ARM architectures and so on iota is way easier to implement and for example in order to run a blockchain like Ethereum we would need to somehow decrease the complexity of the proof of work to be able to run in these small card computers but due to iota design in the way it doesn't need any miners we don't need to actually trade security for being able to run the network only on the small card computers One more follow up is have you tried running like a client node on the UAB instead of using it as a full node Yes, we tried but the whole purpose and this was a decision that we took in the beginning was to try to have a completely independent network of nodes basically they run a full node embedded in their software they don't need any external computer and external servers to connect to the network because for example if you want to control a forest and you have no internet connection since you're doing a partition of the network the drones they can communicate with each other and then in the end we attach their information to the mainnet so we don't actually need to have the internet connection at all time which makes the system more flexible and operate basically in every scenario Yes, you can use the ground station but in our case we it was just a design decision we decided to go just with basically drones we wanted to be sure that it was possible to implement and we didn't need to rely on ground stations or any other equipment Maybe one of the best ways to conclude this paper for example is to give this analysis for example he was just asking about how IOTA could be compared to Ethereum especially because the previous works are based on Ethereum and what are the pros and cons because I also know that for example IOTA hashing function has been criticized in the past so it's good to have this comparison and if the advantages are way over the disadvantages maybe you can claim that there is a new way of doing experiments in this field, right? So that would be really nice Thank you for your question actually we are currently working on it but when we wrote the paper we weren't really sure if we were actually doing the comparison but we are right now we implement the Ethereum and we're waiting for in order to implement the final version of IOTA but yes we plan to publish in the final paper the comparison between the two networks and we're not claiming that IOTA is better than Ethereum but just in this case, particular case we think we believe that IOTA is the best option Thank you I want to ask just a last question. Can you give a brief explanation about the costs of IOTA how much cost the transaction? Actually there's no fee associated with every transaction because the way it works is sorry I see these light gray blocks they're the tip blocks and when you want to append a new transaction you connect your transaction to two tip blocks at least usually it's all these two transactions directly connecting and approving these two transactions so there's no mining process because basically you do a small part of the work there's no need for external miners so you append, you approve two transactions then another transaction comes and approves yours and when the network has a high number of transactions the cumulative weight and we'll increase the trust in your transaction and that's the way it works, that's why it doesn't have miners and it doesn't rely it's very scalable actually the performance increases with a number of new transactions and there's no fee associated with the transaction Thank you Dear colleagues, my presentation is the last one about touching about the articles that were already started to talk about the robot painter and I will explain more details in this presentation what we did and why the new approach what we are promoting today like Robopreneurs the separate individual economical agents it's a really interesting topic not only from the technical sites but also for researchers and the future development and ideas how our world will look like just a short outline I will explain all the steps how we are making the work for robot painter and show the decentralized applications and details about the tractor planning painting drawing and so on and show the several experiments what we did some ideas about how we develop this project in the future actually the common workflow right now looks like this we are starting from the auction we are opening the auction on the decentralized application about who wants to buy this picture there is a picture the first picture is defined it's what translated from the hashtags from some social networks like twitter instagram and so on after that we are collecting such several of these auctions results and checking what the price was for every picture and use it for the future steps during the auction robots start to paint the picture and people can vote by the coins what the price is fair for that picture after finishing the auction as I showed previously the smart contracts also created and the rossback file put it inside and the owner of this picture put it also in the smart contract and when the smart contract is finalized that means the owner should get his product his picture how we synthesize this initial source for the picture for the hieroglyph would be drawn on the paper the first implementation we did the twitter search with the hashtag because it sounds crypto and after that we are creating the list of the words that's related with the hashtag in twitter and choose randomly the keyword for that set of the words and after that translate it the next step it's creating the picture based on the image of the hieroglyph we convert hieroglyph to image and the next step we skeletonize it we find the bounds of this picture trying to clusterize it maximize this with open cv applications and several additional libraries and after that we are getting the path what path should be for the robot which draw in this picture and when we have the trajectory when we have the path we can follow it using usual approach as direct the manipulator tasker so on here is the software and hardware what we used for drawing and communicating with the blockchain what's connected with the auction mechanism and here is we sensing what's happening on the picture on the that we look at the high with the death camera and getting the image from the usual camera and trying to get the information is it okay is it correct following the trajectory or not we used the real sense here the next thing the information from the sensor going to the main planning software to computer or control for the robot control and after selection and image processing it's starting the trajectory execution there is as usual local task planner and task execution and there is a we used the KUKA robot for that there is a special software they developed to connected with the robot operating system it's special interface and this interface can give the direct control to the drivers of this robot here you can see how looks like the interpretation of the hieroglyphs for robot this is the scheme of the auction we are starting from the initial point and painter send the transaction that he started the process of the painting and you can participate in auction to make a bit for this picture and we are collecting the bits during the one day 24 hours and when it's finished we are choosing the maximum price for this image and finalizing the smart contract for that deal just covering all the information with the smart contract and creating the new liability smart contract and finalize it this is an interface web interface how it looks like soon we will have more experiments and I hope we will ask you to join to participate in this process to collect more data because right now it looks sorry oh does the video work no oh okay yeah that's what to show and here I want to give just a more explanation about this is a robot this KUKA for it's created for soldering mainly but we equipped it for painting I think it's it's not a bad thing for this robot to be a painter not a solder and here is demonstrated how the initial information about the keywords from Twitter collected and after that we just translate this word to the hieroglyph and putting it searching here for the bounds of this picture and after that when we finish the search we just sending the trajectory for robot and here is the process of the painting how it's look like during the painting process of participants making the beats in decentralized applications and who will give the maximum beat will get this picture right now we are thinking about not just just taking the hashtag and draw it we want to add some right now we are on the way to adding some AI elements to this robot and our topic it's robotics and AI and here we start to work with the information from Instagram and the process will be look like this we are posting to Instagram some picture and collecting information about who likes this picture and when we collect the information about who likes we are checking the areas of interest and based on the information from the interest area we are synthesizing the new the new word the new data and after we know the area interests for participants we can this visualization of such things there is like purple it's not relevant not so relevant topics but yellow it's one of the hottest one of the closest topic what is really what is important for users who like this picture who like this follow this Instagram and using this data and combine it with the data about the price from the previous auctions we can synthesize in different proportion some new words right now it's a words but in the future I think I hope it will be like a full picture okay that's all thank you a lot any questions if no coffee break oh yep what is the price the highest price was just let me 0.7 efforts yeah for the picture it covers all the expenses for the I mean for the drawing process not for the robot of course okay any more questions yeah oh okay so what I find most interesting about this research is the fact of course that you have a new loop in which like you put like the robot not only in the labor part but also in the capital but also like what I think is very very interesting is the fact that you could have like some autonomous entities you know for example with only one robot as the employee right so as you have like entities for example trading in the stock exchange with an algorithm and that is an entity per se now you can have corporations in which the only real employer is a robot right so we have seen many cases in which especially artists are trying to think about what happens if you as an investor yeah can put some capital upfront in order like to set up the system but then is the robot through doing good and understanding what are the trends and getting more benefits out of the action to get some benefits that could pay back this initial investing effort right so once this initial effort is paid at some point in time the robot can buy itself out right and and is interesting you know what comes like there you know so for example people here at MIT are also thinking like about this one of them is called DASA is a guy that like will come at like a five like in order to oh 430 sorry in order to give out some ideas about computational law right and how this thing could go right but yeah it's interesting like food for thought maybe you have a question sir about three weeks ago the US patent and trademark law has issued a in the federal register a list of 13 questions looking for information about how to apply a trademark law to intellectual property like for instance your drawings that are generated by autonomous systems yeah you should all contribute yeah yeah yeah yeah maybe we should be talking to these people yeah so okay I think yeah the paper section is over so now we have like some coffee I think that the industry like session will start at 3pm so we have like 15 minutes you know so yeah let's let's get back here 15 minutes thank you okay hi everybody welcome back from the coffee break I like the coffee break because after lunch you usually have a glucose down and so I know because I teach a lot of people and I at 2 o'clock 3 o'clock they start to right after lunch I'm going to be talking about a specific topic called digital assets or digital investment assets and discussing where this whole piece is going and how it's converging with artificial intelligence and our blockchain there's no robot talk here I'm not talking about robots maybe at the end I will tie it in to some robotics and we'll see so a quick a little bit about myself I've spent over 20 years in technology I teach graduate level blockchain artificial intelligence and machine learning at a bunch of different schools in New York City we actually flew and me and my team flew in this morning from New York City at Columbia NYU and CUNY on these topics I run a company called Chain House we do blockchain AI advisory we're building some products in the space I'm a coder I spend time at night coding I've been doing that for over 20 years we do in terms of our services we do education on blockchain we do events I run one of the largest blockchain meetup groups in New York City I'm going to show you some of that and we have a division called Phytoscent which is decentralized finance and we're building some products in that space one of them is one of the things I'm going to be talking about today which is digital assets and we're looking for co-founders if anybody's interested please hit me up these are some of our customers that we deal with these are some of the people that we've educated companies that we taught blockchain and AI and we just wrapped up a project with the World Bank we did a project where we took Haiti we're still in the process of doing that and put mango farmers on the blockchain we helped design and architect that system and we're also involved with a company that came out of MIT that is building a blockchain and AI company that's building a mortgage platform on DLT and then we'll move to mortgage trading on the DLT which is effectively a digital asset so those are the things that I'm involved with I'm also writing a book on the core of blockchain for O'Reilly which should be out sometime in maybe January-February I have about 80% of the book done and I run this is the meetup group if you're ever in New York City I run a fairly large where over 5,000 people very active blockchain AI and data science group the benefit for me for doing this is I get a ton of market intel we learn a lot about what other people are doing we have a bunch of events coming up we actually have an event on AI in about two weeks so if you happen to be in New York City or if you are, I did see an art presentation if you'd like to talk about presenting please let me know so I want to talk about I'm going to set some up front baseline and then I'm going to talk about digital investment assets where it's heading and where there may be commercial opportunities for people that are looking for commercial opportunities first in the blockchain space there's a significant amount of noise you have people that are posting things like this this is Nouriel Rubini he's a professor at NYU he's one of the leading economists in the US he called the 2008 crisis he called it 2006 and 2007 and this is pinned to his twitter account so blockchain is a failure and to some degree there is some truth to what he's saying and then maybe there is a little bit of attention grabbing here and that right? on the flip side you have people who are advocating a different picture or thinking that there's going to be a different picture and somebody called the pump who believed at one point in time that bitcoin would hit $100,000 by the end of this year and obviously that hasn't happened and then he says the secret though is that the prices don't matter I don't know what that means I think that's all that matters that's my view so yeah but so all of this is happening me, I as a business and I as a teacher and educator and as a professor and all these kinds of things I do I try to avoid this stuff and I try to find the diamonds in the rough like where are the business opportunities right? so we work with a bunch of different companies and we start to see patterns and trends around where there is money to be made so there is a quiet storm where people are adopting blockchain where blockchain is moving forward it's not very sexy but it's occurring and that happens to be in the enterprises right now so if you look at some of the news articles that have come out just BC just came out with this article they are going to be tracking $20 billion worth of digital assets and they are going to be using certain blockchain technologies I want to cover that a little bit and so that's not a small amount of money it's not a very large amount of money when it comes to GDP or market size but it's a significant indicator in terms of where things are headed and then there is another and you see here another FX transactions about $250 billion worth of FX transactions that's a drop in the bucket of total FX transactions if you look at the Bitcoin market cap that's about $130 billion and they trade about $20 billion a day so $25 billion and FX is not a big deal but this is just the beginning this is the starting point where the banks are starting to look at this and what are they using what kinds of technologies are they using and they are using blockchain but it's not typically the blockchain that you might automatically think these are some of the chains that they are using and these type of blockchains evoke emotions in people certain types of reactions and these are typically some of those justified but these are the blockchains and they are getting adoption and people are buying licensing fees paying licensing fees for or deploying out into production so there are systems that are going into production that are based on some of these now whatever your opinion is on Libro and things like that I try not to take it an opinion on any of these I just look at what the situation is and try to find what's factual and what's not factual around it but I think it's important to understand that some of the technical terms around blockchain are not translatable or understandable by business people right so you say immutable you say consensus you see these kinds of terms their eyes will start to glaze and what I try to do because we talk to a lot of executives they say blockchain is a platform for digital exchange and you know you saw double spend and all that kind of stuff economic agents who have incentives and disincentives who get involved with each other what those incentives and disincentives are whether it's proof of work whether it's your mining or things like that is a separate question and then the blockchain that we're specifically talking about is enterprise blockchain is more about mediation than disintermediation how do you get people to work together as opposed to getting rid of the middleman and so that kind of resonates with some people and then some people get religious on me and get angry but you know it is what it is so and then there's a battle between and I was here at the I spoke at the Bitcoin conference that was here at MIT six months ago and that was a bit of a ruckus because I said the permission world and the permission less world is almost indifferent they're converging so if you look at for example a blockchain called quarter DLT called quarter it's a quote-unquote permission chain but they have a permission less or a permissioned version of it that's open to the public so you can get permission into it as long as you identify who you are and things like that and if you're doing financial transaction that's useful because KYC, AML and all that kind of is baked in and this is happening everywhere if you look at Hyperledger which is donated by the Ethereum Alliance this is an Ethereum client designed to be enterprise friendly and it can be both permissioned and permission less in both use cases and you see this as a trend this is a trend we're going to start off as permission and we will expand and become permission less eventually what does that mean as things start to expand if you have a large number of participants in a permissioned system it effectively is a public system you just have to get permissioned into it like getting a library card and then the other question is of centralization even in the Ethereum world and the Bitcoin world and I've referenced the paper here and I'll give you a link to the slide deck later these worlds are still grappling with decentralization if you look at the paper here a ton of power is concentrated among a small number of blockchain participants so decentralization overall from a pure point of view from a purest point of view is still somewhat elusive somewhat of a pipe dream so it's kind of a shades there's shades of decentralization and decentralization and I personally try to avoid that debate which kind of happens quite a bit especially in academia people take really strong positions and focus on some of the things that really really matter here's another paper that refers to the trilemma of blockchain which is you can have only two of the three you can have either self-sufficiency rent-free meaning you don't pay for transactions or it's resource-efficient so there's no mining costs and things like that you can have only two of the three so which of those two do you pick depends on your use cases your business case so permission and permissionless world the two worlds are converging to the point of little indifference there's a little that is different between the two you've seen five years from now the public blockchain world will become a little bit more permission because of regulation and things like that and the permission world will become a little bit more open and it's kind of converging and what matters and especially in the things that we do is does this technology move civilization forward does it move a use case forward whether it's permission or permissionless or things like that does this move it forward does it solve a problem and then we adopt it so that's kind of our view now these are rates transactions rates for blockchains I think most people here are familiar with the transaction rates for Bitcoin and Ethereum relatively slow like XRP we saw IOTA today are significantly on the high side if you look at Visa's annual report and see what they do in terms of transactions this is their 2018 annual report 124 billion transactions for the year they're working at a different level compared to Bitcoin doing seven transactions per second and you do the math it's a drop in the bucket and their capacity is to be able to do 65,000 transactions per second and they hit those peaks during Christmas they hit those peaks so these public chains have these issues or I call them trade-offs there are some issues that you're willing to take for certain benefits that you're willing to get and sometimes you're trying to trade-off for other things whether you go for a permission chain or a permission less chain there are certain trade-offs that you make now if you decide to make certain trade-offs will you arrive at the conclusion that you want to build a platform around let's say using a permission chain you get certain benefits there are certain advantages that you get one of the advantages is to build rich and deep types of tokens or digital assets and that's what's occurring now we're engaged with some of these organizations and this is what I really cover maybe the next 10-15 minutes number one if we assume and there is economic theory and research that supports this if we assume that a supply can create a market if you have supply not demand but if you have supply that can induce the existence of a market if that's true and if transactions per second are correlated with capacity and capacity is correlated with supply positively correlated and if the size of a market indicates how much data is emitted by that market the more data that market would emit that's true and if we agree that where there is sufficient data AI comes if there is no data there is no AI if we agree to these things then sorry my clicker is not then we can say that there is the rate of AI adoption is correlated to capacity of a market so the more I can do the deeper the transaction the faster the transaction the less fees on the transaction the more data I will produce the more likely I can have AI come along and do something with that kind of data interact with that kind of data and this is where the financial world is heading they see exactly this I can build certain types of assets I'm going to talk a little bit about that and then I can start to employ certain type of AI and there is a breakdown that it's going to go down to four different steps of how the industry is moving forward some of those steps have already occurred and some of those steps we're still in the middle of and some of those steps we can clearly see that are coming so first I'll call it errors or steps whatever you want to call it is the proof of concept error we're kind of past that error now we're moving past that especially in the permissioned DLT space people are said hey we've done the proof of concept we see that this makes sense I now want to build something and I want to take it into production then error two is people start toying around with smart contracts and tokens in every talk that you go to any blockchain event it's pretty much about smart contracts and tokens and then the error three which we're moving into now the next two years is native digital assets and machine learning apply to that and then finally AI and I don't mean AI as some of the stuff that we saw today I mean AI that's directly embedded in the blockchain not external extremely external but directly involved in the blockchain the first error this is somewhere around 2015 2016 when it started we kind of ended quarter one 2019 this is when a lot of enterprises said hey we did our proof of concept in 2018 or we're wrapping up a proof of concepts or a bunch of proof of concepts and now we like what we see with these DLTs that we're using or these blockchains that we're using and we now want to move into production or we want to build a team around building stuff that's into production at that stage there was no AI machine learning needed for you to even say you wanted to bring machine learning into a project involving a POC would require extreme audacity like what are you talking about trying to figure out what blockchain is so there was no need for that there was no real data for you to apply we're now in the phase of adoption of low hanging use cases and I had to put acronyms there like supply chain trade finance and insurance these sectors are heavily investing in blockchains now enterprise blockchain and use cases and applications we see that happening because we're getting the calls we're getting the emails I got an email today from a major bank saying hey we need to can you come over and talk about DLTs important point at this stage tokens represent something a token represents an asset or represents a right or represents a utility and that's what we think of when we think of our tokens but that's not where tokens are going to stay and a lot of the DLT projects are around cost reduction how do I eliminate or reduce reconciliation costs which is hundreds of billions of dollars in cost that are hidden in most businesses because businesses think that's how you do business and those reconciliation costs can come out people are using spreadsheets they're rekeying stuff in all that kind of stuff at this stage AI and ML become applicable so hey this is something that we can start to combine how do I figure out certain things using I'm going to give some examples using let's say machine learning and token economics the third which we're kind of moving into the next couple of years we'll see especially the beginning of next year is native digital assets these are digital assets that used to represent things but now they are exactly those things so for example I'm going to give you an example like a credit default swap you may have a token that represents a financial instrument like a credit default swap but that token is going to go away the credit default swap itself will be born on the blockchain in fact I will start to be able to create my own types of financial instruments on the blockchain we call it smart contracts but it's beyond smart contracts it's smart contracts that are tradable themselves I'm trading those smart contracts tokens in the Ethereum world are just a number and an allocation table I've got an Ethereum address and I've got a number associated with that address and that's what a token is but tokens will shift from representing an underlying asset or underlying thing to becoming that underlying thing I will now create a digital asset I can design its economic behavior so this is the economic behavior and push it out on the blockchain at this stage AI and ML become a strategic competitive advantage this is where companies say I'm going to combine the two because anybody can create tokens anybody can create digital assets but now I need a sustainable advantage and this is where AI and ML will converge with blockchain and there will be tons of data at this point sitting on the blockchain as much as there's data on Ethereum a lot of it is garbage even on mainnet there's a lot of garbage data on Ethereum there's a lot of garbage data on Bitcoin the fact that you can extract that data and mine it is of little use because what are you going to do to predict the price of Ethereum but there's not a whole lot that you can do but if you have very rich trading data I'm creating all these assets and I'm trading all types of assets you have an enormous wealth of data that you can mine and do predictive stuff with so for example there's an organization called ISDA which is a standards body that creates standards for complex derivatives if you want to issue a credit default swap you would use a ISDA contract let's say it's a template you fill out the fields in the template and boom you have a credit default swap and they've just announced that they've created smart contract templates and you can start then you start to use this to create your own credit default swap purely and natively digital it doesn't refer to a contract that's trading on a traditional market it's not a token that refers to a contract that's on a trading market but it's itself a digital asset so it's digital first maybe not even paper at all and then you see semantic analysis of the legal documentation which is ultimately training AI and machine learning models to then eventually produce their own legal documents so the ability to do semantic analysis is the first step for these models to be trained to go reverse out and produce documents themselves this is from Gartner by 2025 we'll see $176 billion blockchain market by 2030 that's 3.1 trillion that's not from necessarily from public blockchains this is from enterprise blockchains and enterprise adoption and the movement of money and movement of value through enterprises which is significant right and this is going to be the ramp towards which was mentioned earlier the digital investment assets I can design these assets I go onto a screen it could be visual it could be programmatic I design the assets I design the assets economic behavior or I let an AI do it AI then tests the asset AI then runs the risk model runs Monte Carlo right and then I click a button called publish and I publish that asset on the chain and it gets traded right what happens then is exotics exotics are these esoteric financial instruments they're not the norm become the norm meaning that an enormous amount of financial creativity comes into the digital asset creation say hey I want to create an asset that is pegged against the treasury but I wanted to cap it here I wanted to go there I wanted to reference that I wanted to do this so I create this really interesting digital investment asset that has never existed I can do it rapidly and then if I like it I run it through my models and if I like it I publish it and it's out on the market fairly quickly right and then it's traded and then I can apply machine learning so I can I can put a letter of credit as a digital asset a digital investment asset a letter of credit I publish a letter of credit I can do predictive analytics on the letter of credit what's my risk on that credit when do I expect it to be cashed out by the the issuer the beneficiary all kinds of things I can start applying machine learning to that that's probably the next couple of years and then we move into this other probably a little scarier world right and where AI becomes economic agents so the AI start trading right and the AI are doing the risk analysis and even the AI is doing the designing of the assets based on the data of the counterparty that they might have on the blockchain right the idea of smart contracts go away because the term smart contracts is a bit of a misnomer it actually means nothing right and these smart contracts are basically AI agents they're plugged into the blockchain right because blockchains will extend themselves and be able to invoke or be invoked by external things or oracles and things like that and smart contracts start to become basically AI agents the blockchain will become like TCP and UDPs gets pushed down to the stack just basically part of life not something that we think of consciously like we don't think about the internet consciously anymore maybe 10 years ago we did and the need for AI shifts being a competitive advantage those who don't have AI and ML cannot even enter the market because the incumbents would be significantly stronger than the new entrants and we'll start to see that and what are the agents trading agents will be trading digital investment assets so the AI creates an asset it feels like there's a market it detects there's a market it structures the asset it runs the models on the asset it publishes the asset another agent AI agent purchases the asset and then it's traded how do we know that that will happen because it happens now is algo trading and high frequency trading is exactly what happens so you have algorithms that are plugged in they just don't happen to be using blockchain because it doesn't make sense to use a blockchain and there's no blockchain that can support the types of transactions per second that high frequency trading and algorithmic trading require for example in algorithmic trading the way that you do price discovery is you flash bids you bid and you pull the bid you put up a bid you pull the bid back down you keep doing that and until somebody nibbles that's how you find your price and that's not really possible on any blockchain today but once we get there in a couple of years the blockchains most amenable to doing that are these enterprise blockchains that can sustain high TPS rates will become the place where you can start to do this type of high frequency trading and then basically the trading are automated right and from there it's an easy step towards artificial intelligence for them for these agents to start trading based on economic data that they can collect directly from the blockchain so where is this all going we are going towards a world where AI will create assets and trade assets and these assets traded would be traded on a high speed blockchain right digital investment assets traded on a high speed blockchain right when that happens maybe 5 years from now 10 years from now, 15 years from now but that's the direction that we're headed and the blockchains that support that type of capacity are going to get there faster are going to get the AI adoption faster are going to need or require the AI adoption faster an example of AI trading is this project on github called Genotech basically what it does it creates random algorithms completely random takes a let's say you know yesterday's closing adds subtracts you know divides it by 5 random algorithms and each algorithm is associated with a single bot it spawns a million bots these bots trade the bots that do well perform well based on some benchmark spawn additional bots they mutate and they spawn additional bots the ones that underperform are killed off and the ones that are spawned off are then evaluated they can improve on their performance and so this kind of work has been ongoing for some time this project's been around at least for 5 years you'll start to see projects like this get on high speed blockchains I want to be able to trade native digital assets and I want these bots to find my alpha or my the profits above the market return rate and then also getting involved in the digital investment asset space or the digital asset space are central banks, central banks use these systems called RTGS and LVTS which is these core systems to reconcile and to net out and if they're also using blockchains then there may be possibility for AIs to plug into that as well and this is a statement from the WF on how central banks are starting to position the US Fed has kind of said we're not involved but I think they are but other central banks globally have started to look at and we're involved in advising a company that's involved with the central bank as well so conclusion DIAs represent a real commercial opportunity for blockchain and AI to converge with real impact, there's real money to be made and then my robot tie-in they'll show up at your door you can't make the margin call so that's all I have, thank you I take any questions if anybody has any questions I have the slides up there, the bit.ly thing yes thank you I actually have two questions the first one is more of a design question, I'm just trying to think about how do you piece together blockchain and AI together are you thinking more about using blockchain data to train AI models although if you have a lot of data in pentabytes of data on a blockchain it may not be very sustainable from the blockchain's perspective or are you using blockchain as some kind of secure storage for AI, for example model parameters or checkpoints of AI on to the blockchain I'm just thinking about how you thinking about putting those two together so it depends on what blockchain you use a petabyte for certain enterprises and how they store data is probably not a big deal so there are blockchains that can handle petabytes or a node that has petabyte data it's not a problem for certain blockchains but to your point so these AI would mine that type of data they've been an enormous amount of data to mine and then build models off of that and that doesn't mean that's the only source of data they would use but that would be a big part of that the problem with data in the academic world it's very easy to do and I know because I'm in the academic world it's very easy to create models off of clean data in the real world to get clean data is very very difficult but what blockchains allow you do like DLT is what they allow you to do and I think there's a point I put up there is you have cryptographically insured consistent data model right all of the nodes must agree that that's the data model and therefore that helps with the data quality thanks and my second question is more about the fourth era where AI agents is a norm I think one of the risks a lot of people have about AI is like really deep nerdy models that ends up becoming a black box that makes it difficult to debug I was just wondering like given a scenario let's say that you have an AI agent that's developed this very complex structured like exotic derivatives and then decides to then develop it into another AI agent buyer through some type of complex negotiation do you think that this could be a risk that this exotic derivative structure could become something that may not be explained more interpretable if it's been conducted by two very deep AI agents yeah I think that's a very good point that's a very fair point and I think that is a possibility so today when you design like you have financial engineers that design an instrument that takes a long time it can take six months, eight months to come up with it even longer than that and then you have to go through compliance and you have to go actually before that you go through risk models you do Monte Carlos and all kinds of stuff and then things still can fall through the cracks even though relatively speaking those models are simple compared to let's say what in a model that an AI produces or an instrument an AI produces there's these correlations all these correlations I'm going to produce this little asset here now this digital asset based on these correlations that I see in the market that this will produce alpha for me and can something slip through the cracks of risk models and not catch something and potentially become a financial contagion yes and we see that in the HFT is one bad algorithm can rip apart a trading floor I have a quick question you are tokenizing the assets assuming you're using some smart contracts is those smart contracts you created or you're using some kind of your C standard or what and if so if there are your own custom contracts are you using like open sepuline are you proving for yes so when I was so era three I was referring to or era two I was referring to tokens I meant it in a very generic way it could be RC 20 it could be RC 721 it could be any kind of things the point is that if you look at a token what a token is is a hash map it's a hash table it's basically the key is an Ethereum address yeah but my question is because you were saying you were creating it seems to me probably misunderstood that you were creating those contracts on the go and in my experience creating, automatically creating the contracts that are not standard contracts it's been tough to to have it right so they have no security issues and stuff because you're coming from the Ethereum world I do a lot of Ethereum so that is a problem in the Ethereum world like Zeppelin and you have to get audited and all kinds of stuff and they bust you up and say well this is the severity that's a medium severity but if you go into the DLT world you have a lot more tools because you're using the full strength of a well established programming language or Turing complete well established programming language and so the ability to create that and the risks are lower and there may not be a cryptocurrency involved like let's say Ethereum things are not native on the chain even though the digital asset is something of value that's traded but there may not be a cryptocurrency involved so I think it's the complexity around creating a smart contract in the Ethereum world is not for one to one translatable into the DLT world even though there are complexities there is as well so your point is correct there are complexities but it's not the same as let's say in the Ethereum world the Ethereum world is just one tiny mistake that some of the best solidity guys can't catch and boom you've locked up 10 million dollars worth of Ether and boom you're done in the DLT world if somebody does a bad trade you pick up the phone and call the guy and say dude I know who you are and that trade was bad and we're going to court so there's other types of circumstances around it yeah okay thank you thank you Eduardo by the way this is my first time in Boston I came to New York back in August and I must say that it is a lot colder now but there's definitely a traditional Christmas feeling because in Australia it's summer it's hot so it's great to be here my name is Emma Jane and I am part of the Australian based space technology company Aeroid Technologies today I want to talk to you about what I believe to be one of the greatest innovations in robotics and automation over the next few years machines are going to start playing a much bigger complex role in industries and each one of our lives I also want to look into the future and convey an idea of how self verifying communication protocols modularized swarm robotics and decentralized autonomous organizations are going to change where we are now let's start with what industries have achieved and what we know so far software is crucial for communication verification and connectivity lately lots of the research and development has been around applying secure immutable technology layers like blockchain most usage of DLTs in robotics focuses on maintaining a secure log serving as a ledger storing events and validating and publishing information to the network blockchain is still in its early development and yes although improvements have been made on individual blockchains integration into the real world is still in its early prototype stage so talking about applying distributed ledger technologies into robotics the main problem that is still yet to be solved is having a truly distributed and decentralized connectivity from the lowest layers so that all of the robotic components can verify and interact with each other removing the probability of single point of failure within individual systems imagine the main components of a robotic system we have vision control power distribution communication locomotion all checked and verified by a secure peer to peer communication protocol the most challenging aspects of robotics and system automation as you would know usually comes down to the sensors because they are never 100% reliable if you tell a robot to move 100cm most likely it is going to move 90cm or 105cm so we need a better way of validating that the output information is accurate and reliable so why is this so important well often it involves people's lives and hundreds of millions of dollars let me give you an example reliance on sensor information without proper verification cause the fatal crashes of the Boeing 737 max planes earlier in the year this cost the lives of around 600 people another example where the risks are higher and the chance of failure is much greater out of space in 1999 the Mars polar lander was a mission that took over five years worth of planning and work and cost over 300 million dollars crashed into the surface of Mars after a sensor received the wrong information causing the engines to switch off some 100 to 200 meters above the planet's surface an entire mission failed in a matter of seconds because of one wrong data feed from the sensor I can stand here and give you dozens of examples but these incidents these failures they slow down the progress in fact often they slow down the entire industry there is still a long way to go there hasn't really been any memorable or inspiring missions since the 1960s when the first astronauts landed on the moon I have always had a passion for space I remember begging my parents for a telescope for my 15th birthday I remember the day I got accepted into the space engineering program at the University of Sydney and I remember writing letters to NASA since I was 10 about my ambitions to become an astronaut since they never replied I promised myself that by the time I turned 21 I would be part of the space industry I am fascinated with the idea of applying emerging technologies into space to make a change and bring about new innovation technologies like decentralized verification for machine to machine communications and operations can give a greater level of autonomy this autonomy and its capabilities are vital for isolated outer space operations and missions if we are really thinking about setting up colonies operations in the next couple of decades then we need to start seriously looking at these technologies especially when also thinking about the communication relay issues that come without space so to give you a little bit of context here every 100,000 kilometers you go from earth there is a latency of 0.47 seconds between the ground and the spacecraft I know that this sounds insignificant just 0.47 seconds but latency creates bottlenecks for the moon there is a two-way time delay of 2.6 seconds and for Mars this latency goes up to 40 minutes in fact in the Mars rover missions because of this huge latency sometimes the rovers could literally only move a couple of meters in an entire day so we can't scale these missions with the current way that we are doing things imagine even having just a few rovers or some small pieces of machinery and equipment it is going to take weeks to months to actually achieve anything useful providing accountable autonomy self verification and cluster based communication capabilities is the only way that large scale operations like natural resource mining and human colonization can take place in outer space at Aeroid we are aiming to develop and apply a verification and communication layer to the machines at the most fundamental layer we are developing software for robotics and autonomous systems our goal is to create a scalable and compliance ready software platform for machine to machine communications and operations space innovations over the past have been focused around hardware for example SpaceX with reusable rockets I believe that space hardware innovation should go hand in hand with software so we are addressing one of the key needs of creating a stable and scalable software communication protocol for outer space missions and operations we have been working for the past six months we have been looking at lightweight and low power consumption blockchain protocols we have been understanding accountability and governance in autonomous operations and also verification for machine to machine communication methodologies according to our findings some of the software architecture used in space applications today is around 10 years old these systems are highly concentrated, they are highly centralized and as you would know this brings in the idea of single point of failures can limit the data transfer bandwidth and also leave the system open to lots of security vulnerabilities if we talk about sending payloads into space this costs tens of millions of dollars right now the cost of sending around 1 kilogram into space is around $20,000 I know that these costs are reducing with the use of reusable rockets but they are still very high so similar to reusable rockets why wouldn't we send payloads that are adaptable and reusable imagine self-adapting reusable robotic modules that can connect like Lego blocks to work together to perform different tasks achieve different goals far from earth without having to rely on any communication from earth at NASA for each space mission there is a dedicated operations team by this I mean that they have to allocate specific resources and people purely for the task of watching over the machines watching over the spacecraft performing checks and balances and monitoring the machine health this is hugely expensive it takes up a significant cost of the overall mission budget and also isn't feasible because sometimes these operations teams end up costing more than sending the spacecraft into outer space itself so we really can't scale we can't do a lot of things efficiently and properly if we are always relying on the ground over the next few years we are going to see a huge transformation in the commercialization of the space industry this will not be from NASA or private companies doing exploratory or discovery missions a huge focus will be on industrial engineering resource extraction defence and tourism so imagine if we could set up mining operations on the moon or an asteroid imagine multiple machineries robotics working together without having to rely on communication or verification on Earth or systems on Earth having secure self-governing machine to machine interactions and checks and balances will give a greater level of autonomy for the operations and also take off a huge load of pressure from these operations teams IOT robotics, blockchain machine to machine economies and the space industry have the potential to generate over 8 to 12 trillion dollars in economic value by 2025 so coming back to aeroid and our machine to machine communication technology we are also looking at incorporating the concepts and principles of DAO decentralised autonomous organisations and seeing how this can apply into a machine environment I'm sure that most of you are sort of well versed in the area of DAO's but just to reiterate in case some aren't DAO's looks at the idea of running a normal organisation but in a distributed and an autonomous manner through smart contracts and mechanisms so this brings in the idea of machines performing checks and balances on each other in terms of validation and verification think of it a bit like machines policing other machines but having the communication and the verification operate on a more cluster mechanism so now you have a hierarchical system in the network unfortunately I would love to sort of go into more detail with you of exactly the technology that we're working on and the specifics behind it but we are currently in the process of acquiring two patents and as you know it's sort of very touch and go with these things so I've had to restrict a lot of what I really wanted to say and I guess that is one of the reasons why my team and I are so excited because our technology doesn't just apply into the space industry but theoretically it applies into any machine to machine operation including IoT devices and industries like smart cities autonomous vehicles and supply chain logistics before coming to Boston I was actually in Dubai over there they are very future forward thinking in terms of making their city a lot more smarter a lot more connected they even have a government blockchain initiative and just last week actually they announced their blockchain policy strategy approach for 2020 to give you one example the rodent transport authority in Dubai they are looking at implementing autonomous vehicle infrastructure so that a driverless car in the city they can pick up drop off passengers collect payments pay for tolls pay for parking act as a monetary agent there are a lot of use cases like this emerging around the world and my team and I do some work in Dubai so we're excited to really see these technologies being properly implemented going back to Aeroid and our vision we really thought about how we can affect change with what we're working on how we can show something that's really important moving forward and also inspire and catalyze the future of innovation particularly space innovation so we made a huge commitment we made a very bold move we started the lunar industrial initiative 2021 the lunar industrial initiative is our plan to send four adaptable robotic modules to the moon in 2021 to demonstrate our proof of concepts of our software communication technology so I'm sure that most of you are wondering how are we going to execute this well there are three major stages stage one we are applying our software protocol into swarm robotics swarm technology we're currently working with the University of New South Wales robotics lab in Australia to refine and improve our protocol stage two we will be testing these adaptable hardware modules these custom built rovers in moon like environment test chambers so at Mount Strollos in Canberra in Australia there is a space testing facility and this is really good for understanding what changes what adjustments need to be made to the hardware and also our software protocol because I can tell you building robots for earth is very different than building robots for outer space as part of our initiative we also want these robots to demonstrate some proof of commercial viability so at stage three we are looking at incorporating aspects of project wild into our initiative project wild it is headed by Professor Andrew Dempster in Australia and also part of the Australian centre for space engineering and research the main goal of project wild is to eventually mine water on the moon right now they are looking at mining at the moon's polar craters particularly the south pole so why water well again in terms of commercial viability water is the most versatile natural resource that we found on the moon it won't only be used to resupply astronauts with oxygen water but will also be used for power generation and refuelling rockets one of the unique things about project wild is that they are taking learnings and methodologies from Australian mining sites so instead of sort of taking a space engineering approach they are looking more at current earth mining the Australian mining industry is very well established well advanced particularly in terms of the tools and the extraction techniques that they use for extracting and mining natural resources in remote isolated environments in the middle of Australia so as part of stage 3 we will also be mapping and modelling the specific course and details of the operation we know that mining water is a huge undertaking so we are also looking more closely at navigational operations for the swarm robots to perform because our software technology will give these robots better communication and better verification to send these robots to the moon we will also be partnering with a commercial space carrier like moon express we know that this is a huge feat so we are not alone we are working with the University of New South Wales we are working with members of the University of Sydney International Space Society and also the Asia Pacific aerospace consultants and a venture capital firm so if we pull this off this will be the world's first multi robot autonomous swarm operation on the moon this will be amazing this will be this will go down in the history books I'm sure that most of you are aware of the huge benefits of using swarm robots swarm technology instead of having one specific machine set for one task these benefits become even greater in rough terrain environments like the moon we're sending one robot for one specific operation isn't viable it brings in the idea of single point of failure again and also high costs the Apollo missions they cost around $600 billion in today's standards and project Artemis that is NASA's latest lunar mission that was just announced this year that's set to cost at least $30 billion so imagine if these adaptor robotic modules could perform mining operations on the moon and also solve logistical problems that will inevitably come from sending one piece of hardware or machine to the moon to perform one specific task these are the kind of technologies that are going to contribute to human colonization in outer space so if the problem is high costs time delays and single point of failure then adaptable reusable robotic modules and self verifying communication protocols is the way to go providing a decentralized self governing infrastructure for machines will give greater accountability will improve autonomy and will also enhance the communication capabilities we know how crazy how risky our dreams are so whenever anyone says that we are working on something that is precious too challenging too hard I always like to think that someone has to do it because deep down I believe that we are all explorers and we are all curious creatures it has been an amazing journey for me I am learning every day the challenges that I have to face but I love the energy and I love the inspiration my dream is to say that one day that I contributed to the space industry even if that is just 1% so it has been a pleasure speaking to you all Eduardo thank you so much for inviting me I know I had to keep it to a high level but I would love to speak to you more technically afterwards so please if you have any questions or feedback I would love to hear from you it is too crazy for questions it is more like a statement than a question so when you do it maybe you should make an announcement saying that you made this announcement here so we all remember that we were here when you made it in 2021 so I just have to say that you should push for that direction definitely the goals are very high but definitely somebody has to do it moonshot thinking let's have an applause for Emma consulting and deal with a lot of retail and CPG executives who daily ask you different things about blockchain I'll talk more about that once again just so that everybody remembers it as nice blockchain is immutable, decentralized, distributed than a synchronized database I won't spend too much time on this there has been in 2019 itself 2.7 billion in spend on blockchain solutions I'm not sure why this is not coming here and a total of 44% blockchain companies which reside outside of the US the major investments and the major experimentations happen in the financial services industry with retail just lags behind by about 5% a few of the statistics that are important in the retail industries is that they are 18% of retail and CPG companies who already started working they are all in the POC and experimental stage nothing to scale at the moment and about 9,000 projects every year over the next few years which is set to grow yearly by about 8,000 projects supply chain in the retail and CPG world has the most number of applications and the most number of POCs today a lot of new blockchain applications and use cases are rising supply chain for traceability because nowadays everybody wants to know whether their food is organic they want to know where it's come from they want to know whether the products they are buying is creating deforestation or not these things have a huge application with regards to blockchain which is possible and corporately focus on sustainability to be able to now do this the next thing which is very interesting is online marketplaces people want to know whether their products are being sold by 3 third party sellers who they are who is moving to price compression move which marketplace changes which reacts fastest and who is the first to move these are things which have huge applications which blockchain can be a fuse there is also others which is data processing and payments which I think everybody is aware of but these are some of the things which are upcoming and a lot of companies are focusing on them out of all the projects only 8% make it to finally production and are actually maintained and the reason is I would talk about the top 3 challenges in the retail sector with regards to implementing blockchain yes even though nobody in this room would confuse it there are a lot of executives who still confuse it with bitcoin there is a lack of understanding as to what blockchain really is to be able to actually convince the top management and of course everybody the fear of missing out everybody wants to implement it everybody wants to know what it is there are a lot of innovations in it but to be able to scale it it's not yet happening everybody in the every company has an innovation champion and if you ask anyone of them today they would say yes we have a pilot running in blockchain we are going to set aside budget for it in the coming years but that year has still not come there is also a lack of digital regulatory body which is in place and thus there is no network effects to be able to actually scale this there are too many different consortiums there is the IBM food trust which is taken into consideration with about 10 of the largest food companies there is the hyper ledger fabric and there are many others where in they all form different consortiums so to be able to actually get a scalable solution is difficult the main problem for implementing would be scalability in retail three things matter performance privacy and ease of use performance with regard to processing of payments and I think Jamil had given you a rundown of the transaction process so I won't go into that but I think the user is not even an average process is about 65,000 transaction per second so blockchain either Ethereum is not up to mark even the new other ones like Corda etc. still have to scale up to be able to process at that speed programmatic advertising the different nanosecond decisions that go into loading a page on Google with advertisements with the other companies and the other companies that actually get that all working takes a lot of computing power and energy which at this point we are not there yet privacy with respect to the new regulations of the European GDPR wherein we cannot store consumer data we have to be very careful about how we store it how we share it that is something which takes into consideration where blockchain can sort wherein they actually make a new business model about privacy and to be able to track anonymous users on Bitcoin and other chains so this is something that is between Microsoft, Intel and a few other companies wherein they use the trusted environment to be actually able to keep your data private once again ease of use switch which is a company which is an application called speed and to be able to it's like a virtual wallet to be able to actually use gift cards and select different gifts online it's basically helps you between transacting between traditional and cryptocurrency this is just lately in the market it's not up to scale but it's something wherein you sell direct to consumers and of course efficiency related to the energy and consumption of what we actually use a few internal obstacles that companies face whether evaluating the cost benefit analysis or whether convincing decision makers are some of the things that companies really consider today to be able to see it as an impediment to actually implement blockchain there are some use cases in POCs which are in production for example traceability McDonald's used it to be able to track with the IOT sensors the transportation of food so that when the temperature decreases when you're actually transporting chicken it would be sent to the through IOT sensors to your database and then to be able to actually track that so that you know and you reduce spoilage Walmart also did something similar to traceability from origin to a scale with regard to deforestation in that aspect loyalty programs is something that companies are also experimenting with Singapore Airlines is doing something with the hyper ledger fabric in which Microsoft is part of but once again these are all in the infancy stage digital identity this is something that is part of both financial as well as retailers are looking at with regard to having your own digital identity and to be able to use it creating your facial recognition or a thumb print this is also used in the retail sector when companies like beverage in the industry like they want to sell alcohol online and you need to validate the user with respect to age etc so this comes in handy a lot with regard to that Alibaba in China who is using I would say China is the leader in analytics and they are using they have actually piloted their online luxury pavilion called team all in where they actually use blockchain to track fake goods which is as you can see a 461 billion dollar industry in China being one of the most produced of fake goods so Alibaba is actually using a lot of their own cloud based blockchain services to be able to combat that the China is also one of the largest countries with respect to filing patents within blockchain as you can see about 49% come from China this is a real world use case which actually happened in which a large toy manufacturer they wished to have a blockchain based digital identity what we did was save their identity on the website, on the phone wherein it had a private and public key then they were able to scan a QR code once they log into a URL and then you could actually get through to whatever URL you want to this is within the enterprise and they didn't want to go public with it they just wanted to test it within the enterprise and then move to their suppliers and customers etc you would think this would be the solution that they had asked for the product architecture is basically based it's pretty simple we used a blockchain service and we also were able to exchange with the SAML tokens to be able to actually access the URL and provide through identification your access to the login page however once we presented it then the challenges came into place wherein they had more questions where do I have to maintain two separate systems is it cost effective can I access non-samal applications if I do that then why do I need blockchain is it really secure this is what I was talking about earlier I was talking about having a top-down approach or understanding and with respect to prioritizing security what we had to actually do was integrate this with an open source and actually redesign their VPN to be improving including only the blockchain which as you see is a small part but you actually have to do the tunnelling etc and then kind of basically replace your Cisco VPN with regard to that so this would be integration with other applications to actually be able to pilot blockchain there are a lot of technical innovations coming up there are a few things which people might have talked about something which is the blockchain as a service wherein companies can now just call an API rather and include it in their own networks there is hybrid blockchains wherein you can use public or wireless private you can have the transparency of a public blockchain but still have the security of a private blockchain a federated blockchain is different in the sense that there is a federation wherein few people are identified say from different companies or different people to be able to actually validate or give consensus to the data interoperability as many people have talked about the different types of blockchains that are available in the market so to be able to transact from one to the other and actually make a seamless transition for the public is something that is happening and people are working on it to be able to come back and it will happen in the future Ricardian contracts which is the new version of smart contracts for example it is actually a handwritten contract which people are readable and then converted into machine language through blockchain tags this is something that if for example the person is insolvent or the person the deal does not go through due to some reason this can then be tracked and it would not be executable which definitely is something better than what currently smart contracts have with regards to its limitations and finally stable coins stable coins would be the new bitcoin wherein people are trying to make it a little bit more stable so that it is not I would say it is not susceptible to price fluctuations there is a lot of work going into this to be able to have some kind of transparency with regard to the normal fiat money and stable coins currently so the enterprise wide blockchain shift and outlook for the future I would say would be that the consortiums would gain speed there would be a lot more but there would be a lot larger so to be able to actually come up with a scale application we are now in the POC stage and use case stage it would then move to MVPs they would be cross industry applications based on what we have just seen and there would still be a focus on payments trade and supply chain finance the key takeaways I would say integration with other devices interoperability and finally IOT devices and analytics to scale applications thanks any questions in your vision so I think that this session is very interesting because it gives the mindset of the industry in comparison to what is going on in the academia so you know very well what are the pros of systems like the blockchain but also how people in large companies think so do you agree with previous speakers that it's basically this non understanding between what these systems can give and how middle managers talk or their benchmarks what is basically hindering the progress of proof of concepts of new ways to understand this in the corporate world or do you think that is because definitely the technology is not mature yet there are too many gaps that we need to do more research on and we need to be more sure that it's not going to cause any problem so what do you think right I think specifically within the retail sector I think it's a combination of both so while people want to use blockchain and why we can use it to power many systems I think it has to be integrated as a holistic solution say for example the traceability would be like the main part wherein you can't really track it is from origin to the first stage of the farmers say to be example or if you want to track like palm oil etc so from the origin from the time the tree is cut to where it goes that's basically one part wherein it's very difficult to track and this is just an example so to be having a holistic solution say powered by blockchain or something that would kind of make people interested that would kind of give them what they want with respect to understanding so that they don't have to understand how it works they just have to know that this part of the traceability aspect of blockchain can actually give you a holistic solution and have your origin to be able to track easily so I think it's a combination which once a lot of more applications a lot of usability becomes available to consumers and other businesses this is going to take flight and really take off that's how I say it one of the things that you are saying right is my understanding in the whole day is that we also have a lack of interfaces right so we didn't talk like today about this but you know all these systems you know have like nice capabilities like for example with robots, with AIs like with data analysis, data science but we don't know which is the good interface to that system we don't know which is the Google of the blockchain right and somehow what you are answering me is like a little bit like that you know but the end like the farmer needs like a very, very, very engineer interface that doesn't that allows him like to do whatever he wants to do right but at the end like he doesn't need to understand what's going under the hood right right and then like I mentioned there was one company called switch which are now creating a user experience wherein you can actually like any other online website go and transact by a gift card selected so something which really doesn't make it complicated which is simple to use but in the back end you are really actually using the technology to be able like any other technology everybody wants a simple user interface but what happens is that you have to look behind whether it's analytics or powered by ML or NLP programming nobody really cares or wants to understand it so that's how you have to actually look at the maybe there's a new field coming like a human to blockchain interface human to blockchain interaction thank you thank you very much so finally we are going to close like this event like with a traditional law so I'm going to introduce like two guys one is Brian the other is DASA like they belong to the connection science like a group and also human dynamics and then they are experts like in MIT like about how to mix the world of like technology like cryptography like blockchain and also law which is like a big thing that we don't tend to like care you know but it's like a thing that really matters right so without further delay I'm going to start speaking of technology we're getting the clicker yeah so I'm of the two I'm DASA this is Brian I run something called law.mit.edu which has been the rapper for computational law research here at MIT and we're housed in the human dynamics lab Sandy Pentland's lab and we're about to launch a new publication the MIT International Law Report which you'll hear a little bit more about in a moment but this some research into robotics AI and law actually goes back on ways you'll hear toward the end of a presentation roots back in 2011 when we started modeling autonomous legal corporations and sort of a steady flow and we're finally at the point now with the launch of this publication where things are coming together so it's the perfect time to speak with you about it and we want to hear back from some of you your impressions and questions as well so with that Brian let's take it away so yeah we have this idea of like dows plus robots equals you know and what we've really been working on lately is this idea of automated and autonomous legal entities and so what that means is you know we have to kind of embed the legal thinking from the get go so we can optimize the protections of the legal system with these new forms of entities that people are coming up with because there are going to be all these different questions that start to arise so who owns the entity, who owns the IP how is anything produced how are any proceeds divided how are material sources who is capable of entering into contracts for a Dow like what happens if the Dow does something illegal what happens if the Dow goes bankrupt and all the other things that you don't really think about when you're setting something that's like really creative up it's mostly on the back burner and so we wanted to start showing why this type of thinking is important to like get in front of it's important to get in front of this type of thinking ahead of time instead of when everything hits the fan and so one of the kind of questions embedded here I think the way we phrased it is who is capable of entering into contracts for the Dow on behalf of or the behest of the Dow and that raises the sort of deeper almost philosophical question when if ever is a Dow capable of forming contracts itself when would or should or could a Dow be considered and treated as a legal entity itself and therefore capable of forming contracts enforcing contracts having them forced against it and so the answer to this kind of we break it down into a few different pieces but one of the key pieces that you were touching on there was the idea of legal personality rights the idea that you can have like a limitation of liability that creates a separate legal entity apart from yourself and gives you something that you can kind of hide behind in the form of liability limitation and then we get to the notion of investment securities contracts and intellectual property contracts and agency law and then toward and then we have some different use cases that we walk through and kind of talk about how they apply so this is the roadmap for the next like 15 minutes not for 2020 so generally like the container of legal personality rights requires a few of like these common ingredients so registration with the state identification of certain governance mechanisms like with a Dow that could be like the voting mechanisms or things like that the identification of the individuals who are in charge of administering the governance mechanisms certain states call this a custodian and then the legal protection within some narrow purpose so you have to be operating within some specific legal purpose and you only get these protections if you fit within that container and just to break this down a little so you know what we're talking about registration with the state speaks to like how many people have ever formed a corporation or an LLC so there's that step where somebody goes to the Secretary of State's office if it's in the United States fills out a form there's always a check involved they need their money and then they will like create the corporation for it you get a corporate ID and then it will show up along with who the directors are that's this registration process there is just I think it's noteworthy we had a kind of a law and technology conference here not long ago where one of the presenters was very interested in a different way of creating legal personality that doesn't require registration with the state initially and it's called the Massachusetts business trust so that just requires a trust agreement where the parties to the trust sign something and it's a legal entity although when we explored it it turned out you know in order to maintain its existence and the pay the taxes and to dissolve it you ended up having to do the registration with the state so it does seem like that first bullet is something that's within the scope of what an automated system with a capability we need to have in order to incorporate itself and another thing to point out I think at this stage is that some of these protections are at the state level and some are at the state level so in the US you know you'll have different state protections that are related to like business entity but you'll have federal protections that are related to like securities law so it's important to know which domain you're operating in in order to kind of like optimize for that domain we should probably disclose we're operating within within those different levels we're very much US centric for this conversation although as you'll see toward the end we've been dabbling and collaborating with people in Europe and other countries and so one of the most progressive states in this regard is Vermont they have a BBLLC statute a blockchain based limited liability company and with that you register with the state the state reviews are operating agreement to ensure the safety and access of the permission protocols you have a summary of that mission and purpose like I talked about and then there is some indication as to whether the BBLLC is fully automated or partially automated and then you specify the voting protocols and the way that this is played out so far with this organization called Dorg they set up their operating agreement they paid the fee they indicated what their different governance type was you can see decentralized ledger then they describe it and so I wanted to highlight that it's not showing up very well but yeah and then other states follow approximately the same recipe that I laid out so with Delaware the way that they got to the end result is a little bit different instead of having a standalone business entity they allow the use of electronic networks or databases to administer different of the existing corporate functions ownership books and records voting and one thing to note that's potentially interesting here is that Delaware corporate law permits the registration of series entities so if you have an entity that's nested like a thousand times as like these shell entities you can you can set that up theoretically in the state of Delaware one way that this is playing out is the LOW which is a legal legally compliant DAO for investments and specifically with them that would be another instance where you would have to also go through the the IRS's requirements for securities registration and I think all the members of this DAO have to be accredited investors so that's like an additional protection that you get because you're an investor in a company and so you have to meet these even more advanced even this more advanced special we should probably say the DORG is a project from kind of a civic hacking group in Brooklyn the LOW is a project of consensus and their open law spoke we've got a bunch of sites if you want to follow up on any of this and these slides will all be available I'll tweet a link out to them so that everybody can just use them and play around with them and then Wyoming has done something similar to what Delaware did they set it up so that certificate tokens could be issued instead of stock one of the interesting things that Wyoming has also done is they set up a special purpose depository bank for crypto transactions so now you have a specific place you can go where you can kind of fold these things using some smart contract framework as a way to keep assets and keep track of all your crypto assets essentially and one of the ways that this is kind of playing out is through lasso dow I believe they're somehow involved in co-working as well along with DORG but this kind of gets us into the next bit that I was talking about earlier with investments and securities this is governed by the IRS the big question here is something a utility token or SEC oh SEC sorry that was a mistake but the big question here is it doesn't meet the how we test is there any are you putting money into an entity with the expectation that people in that entity will do work on your behalf and give you some return so if so you need to make sure you're compliant otherwise the SEC can go after you and you can do something like what pocket full of quarters did where they sought a new action letter proving that they were utility instead of security and they're one of only two companies in the U.S. that have successfully beat like a no action letter so just curious who's sort of a no action letter okay so just FYI what we're talking about right now is you could just go and do something and believe or hope it's not a security and discover later that the exchange commission believes it is and then they can launch an enforcement action against you that's a bad day you on the other hand could get proactive with legally structuring things and that's very much the spirit of how we treat computational law is designing legal processes you know engineering law and legal processes up front and one of the mechanisms you could use in this case is called a no action letter and when you go to the regulator SEC if it's federal there's also state securities regulators and explain in detail what you're planning to do and ask them if they will basically issue a no action letter and they sometimes will and that will give you almost like a little safe harbor to do this like they know what you're doing in advance they agree that it's okay and sometimes they'll issue more of these when they want to encourage more experimentation like we had done a couple of years ago including creating like revolving loan funds and other investment vehicles part of the criteria for hacking teams was that they have lawyers it was a legal hackathon on their team and that they structure a request for a no action letter right with their code so describing and documenting what the code is who the investors would be what the protections and safeguards would be and so forth and here's an example of a company where they've actually got no action letter from the SEC and this is a good practice when you're creating your robot AI investment funds exactly the next kind of issue that we can run into and I think as a touched on something that's kind of like a theme is when you're more proactive you have more flexibility and so with regard to like the intellectual property rights you can set a lot of the stuff up by contract and a lot of the stuff is set up by contract a contract among the members a contract between the individual and the state some sort of letter that functions as the proxy of a contract between the people and the SEC for example and with intellectual property it's especially interesting because you can start dividing fractional ownership rights using ERC 721 tokens and then different people can programmatically verify that they own part of an entity or part of the intellectual property of an entity or however you want to slice it and that really gives people a more granular control over all of these things and it provides new opportunities that people hadn't had before and so in another context if you look at contracts and agency this is a typical example of what an agency relationship is you have a principal the principal has the agent do some task the agent goes to a third party to effectuate that task and they can either be acting with express authority, inherent authority or implied authority and basically what that means is I can say hey you are authorized to go to buy a bunch of watermelons and you have the actual authority to go buy the watermelons I can say you have the actual authority to go buy produce and so by buying watermelons you would have that inherent authority or you would have inherent authority to buy produce but if I said you can go buy furniture and you come back with a bunch of watermelons and you work at a furniture store or you work at a produce store you would kind of have that inherent authority of having me being at a produce store it would make sense that I could actually go and buy that and so the third party wouldn't have to like the third party wouldn't have to go after the agent or they wouldn't have the ability to go after the agent in that situation so just another example maybe even more familiar than furniture and watermelons is like a house so you've got a broker it's not uncommon to get a broker to sell your house the broker is a kind of a broker or an agent like literally a real estate agent so that word is what we're talking about and so you'll usually have an agency agreement with them and they're authorized to do certain things like go and solicit offers and maybe even do more than that maybe get pre-approvals on loans in some cases so they can go deeper you could structure it even more deeply to give them a like a a letter of kind of blank on the word but you can make them like what is it called it essentially certifies that you're authorized power of attorney thanks POA I was trying to do the letter it's like it's not a letter of attorney you can give them a power of attorney so they can actually do the closing documents for you so these are all like different degrees of authority that a principal gives an agent to act on their behalf for the third party and one of the reasons this matters in this context is when you've got let's say people that are operating a robot you know with AI that's maybe a DAO to do transactions whether it's an investment fund we'll show you some other interesting use cases like a publishing company or other things like that then that that entity is going to be interacting with third parties it'll be doing things and so we've got this sort of there's this whole ancient legal framework of agency law which looks like this and really what it comes down to is what are each of these parties what are their rights and responsibilities with respect to the other parties and a big question is like would the third party have known that this agent was authorized to show me the house but not sell me the house you know to get a furniture but not watermelons or both or one or the other so there's a question there and we'll talk later about some of the ways we think structurally this can be designed into a legal process to clarify it and have things run smoothly yeah because there's certain ways that you can start granting authority as among the members of the organization such that all of these things are very narrowly scoped and clearly understood so that you don't have the you know edge cases where things go terribly terribly wrong hopefully hopefully so Tord is another one we're seeing more with autonomous vehicles there are these questions about you know what happens if the autonomous vehicle decides to hit my car instead of run over the baby who's liable in this situation and what it gets back to is this idea of an accountability gap so basically like taking the the analogous situation for if a person was there and then figuring out where the liability would have been apportioned if you know the same thing happened so if instead of you know this was being driven by a person instead of an automated vehicle you know the liability wouldn't necessarily go to the person except if these five things happen and then you can point to those five things and kind of have a little bit more of a protection there indeed good enough for now we have so many slides let's keep moving so we're going to get to the fun part that was all kind of like background so that we could set up you know the things that are the most juicy so the use cases and the research history so I'll let you talk about CorpBot oh yes okay so I promised you there was history here is some history so is anyone here part of the firm Robot, Robot and Huang it's kind of a joke but it's also kind of real so Tim Huang is a very creative guy he used to hang around here at MIT and Harvard and he went to get his law degree at Bolt Hall at UC Berkeley and he's got this concept of how much of a law firm can you automate and he's really good with creating autonomous entities so in 2011 we collaborated on a project called CorpBot where we just wanted to create some code that would go to a secretary of state secretary of state's office and form a corporation and that would conduct a single business function like sell a book on Amazon upload a book to Amazon, create an account sell it, get some money and then like dissolve the corporation and so you know we made some progress I'd say but then we all kind of go, what did some other projects and we never really completed that one also it turns out it's harder to do that type of robotic process automation with legal with more nuanced legal and business requirements much harder than we thought but we've been working at least I've been working on this since 2010 I'd say and so if we fast forward a little bit in 2016 thanks to Blockchain we were able to make some more progress and so one of our collaborators at law.mit.edu and then we were both partly at the digital currency initiative for this project she wanted to do what she was calling a blockchain border bank basically like a community bank on the border of Haiti and the Dominican Republic to make it easier to get microloans and so we did what we could to model that turns out that was really hard especially with the banking law and everything so what we ended up doing was a revolving loan fund operating under Massachusetts law where at least I was licensed to practice and I understood what the forms were and could model it more and we could test it all the way through without going to Dominican Republic jail or something and so and so here's the this is basically the UML that we came up with in order to figure out how to do the loan application in an automated way figure out based on some criteria who would issue the loans to and make sure we had the balance on the fund and then receive payments and then give them a receipt and chuck it on the blockchain for every payment that we could show and then to finally provide the acknowledgement that the loan was paid off which is a big legal document under Massachusetts law that you want to be able to show and we modeled that pretty well but again it was a little hard to do the test all the way through the best way we could figure out how to do it without putting millions of dollars into creating financial institutions was to use PayPal to actually like and then receive the money and at that point we're like okay we figured out how to integrate our code with a PayPal API but give me a break like is that even a realistic test with the PayPal fees and everything so we needed to look further but we made a lot more progress on modeling the entire entity and let's go forward ah okay now we come closer to the present even this is a little outdated now but you want to talk to this one yeah so this is the fun stuff so we've been working together on this project like it's related to automated and autonomous legal entities for a while now and we co-hosted a workshop as it was remotely here and I was in Berlin with a bunch of the people at full node and one of the things that we wanted to walk through was you know this kind of it's pretty basic schematic for what all of the actions that would be required in order to create a publishing now so if you got a network of people together and you wanted to produce a book or something or produce different books or start hiring people to write for you what would that look like and so what we came up with was okay so there's a publishing now the publishing now in order to buy like this expressive book machine which is basically a printing press for books and then you kind of create this smart contract marketplace that allows you to pay logistics partners to pick up the books it delivers them to the people in the public the public can deposit money to receive books the deposits go towards the publishing proposal that's either confirmed or denied and then the book itself is printed and so one of the things that we wanted to see here was okay so what legal rights and obligations existed all of these different steps and one of the things that we kind of came up with and one of the insights that we've been really trying to drive home here is that there's a really strong need to narrowly scope exactly what a Dow is doing from a legal standpoint so that you don't run into any of the contractual issues the agency issues those issues of uncertainty where people might be out of money because one of the things that I glossed over before and should have mentioned is if you if you don't have one of these legal containers the United States and all these state governments they'll assume your general partnership which means they can go directly after you for whatever liabilities that the organization occurs so it's really important just to identify the you that's like all you all so your every member is jointly and separately liable that with a general partnership and what that means is like you know like member one has like extra Toyota that can be like impounded and member two has a vacation house and you know twenty thousand dollars and there's savings account member three has whatever you know like a painting they can go after everybody until until they've paid off the debt so general partnership and like is like ultimate liability exposure and if you're going to have a legal entity you don't want later someone to say oh your Dow was a general partnership it's better to get ahead of it and to use some of our open source code and to develop it so that you can select the entity and then engineer the legal relationships and roles according to the business model that you have in mind yeah and that's especially the case if you have member four who lives in a trash can screws everything up and does something wrong they get in trouble then you know you lose the vacation home and the all the good stuff notice this one by the way on the last one so this is an interesting hybrid where the Dow we played at different ways where the Dow was a legal entity itself and where the individuals where the Dow was more like a tool or a platform and the individuals maybe had a different corporation but there's people very much here so I'd call this an automated and significantly like I know some level of autonomous in this but there's actually humans doing the votes like do we like this book do we not like that book do we want to get in this market we want to push this we want to put more marketing behind this one or not and sort of choosing the distribution of their resources against selecting and then pushing the new materials and who they want to work with so this is a kind of a hybrid approach on the distribution at one end there's a completely automated entity and you know you could create partly automated entities to go and form a new entity then you could dissolve the partly automated entities there's different ways you could imagine getting to a completely autonomous entity that was nonetheless validly legal entity that's the far end of the scale most of our work here had come more practical where there's hybrid between existing businesses existing business models human beings very much in the loop are actually in the driver's seat but then disappearing a lot of the complexity and making things much more responsive to the strategic and tactical decisions you make because it's all encapsulated within a single unified like integrated legal entity so the you know you imagine the bookkeeping all the financials but also like the inventory and like strategy and operations HR when you encapsulate all that you can make we believe you can make decisions and adapt it more the closer the speed of thought and that you can manage and and be much more flexible be a much better form of business yeah and to that end one of the things that we're also doing right now is we're launching a new publication which is going to come out Friday the MIT Computational Law Report of which I am the editor in chief and as the executive producer and the whole goal of the publication is it's a little bit different than other publications one you know it's focused on law which is you know a new thing for MIT we're you know looking at ways that you can reimagine and re-engineer the law so that it functions more like a computational system so we have a lot of interest in learning you know what is bridging that gap look like but we also want to because it's not to because these aren't two disciplines that have traditionally been connected with one another we want to do some field so we want to have conversations about you know how these processes take place we want to convene people together and you know see what the good ideas are and then we also want to produce content and that content comes in a few different forms and this is where we're really excited about what we can do because the content is going to be traditional written articles but it's also going to be rich media so podcast video lectures about how to code something so that we can introduce some of these things but it's also even going to have like a data playground where you can upload a prototype of an app people can evaluate it comment on it deploy it themselves you know iterate it and the goal is to come up with better solutions that are accomplishing some of these goals that we've been talking about indeed so this is what we call at MIT pre-competitive research so this field as Brian said yet the people that were working with the companies law firms governments and others maybe some of you perhaps are interested to find solutions and to find design patterns that work evaluate them and then the next step would be you could choose if you want to invest in a start up or you know put something out there in the market so this is and one of the things you meant you didn't mention that I'll highlight in the data playground is reproducibility so it's hard to what we really want with engineering the law as a computational system is predictable legal results so you don't want always to be talking to lawyers and have them say well it depends well upon what exactly does it depend can we know that up front can we engineer a system to achieve predict more at least predictable legal results the answer is yes we can and that's what we're trying to do and we think actually using the scientific method and and the you know tried and true almost like cultural DNA of reproducibility at MIT for some of these experiments to see if we can get the same business legal and technical results against some test hypotheses and other test regimes we think that's important and that that's how we structured the data playground yeah and well and I think one of the other things that would be especially pertinent to this group here is we're going to have a podcast that's going to come out on Friday that's about the idea of legal primitives so borrowing from the idea of cryptographic primitives what are some legal primitives that we can come up with that we can really fine tune and allow people to kind of like containerize and take away with them so that they know exactly what they're getting in all these different circumstances legal primitives that you could be thinking cave men or barbarians think more like building blocks that are fundamental building blocks that you can compose together to create something so cryptographic primitives are like digital signature encryption aspects of dual key cryptography these well worn primitives that are reusable so we're looking to identify some of the legal primitives notice maybe the previous speaker spoke about identity a contract well digital signature there's some that may have good overlap with cryptography in fact but there's some others that are unique to law and so getting through some of the last of these oh that's actually the Berlin working group at the top that we did but yeah to kind of like accomplish some of these things we've been hosting these workshops where we're getting down here is one of the guys who came up with the BBLLC statue in Vermont so this is a drill down on the door and like a total demo this is like a drill down that went for almost an hour and a half on agency law so we're going to go through the DOWs to see map all the roles relationships rights and responsibilities of the parties and play them against scenarios this one was I think what was that one's probably contracts yeah that was the contract though this was the publishing DOW and there's a few more we kind of ran out of slide space but we do a lot of convening as an input to things that we're really excited about with this first release is we actually have a challenge so if anyone wants to contribute to this challenge we would welcome it but the idea is that we want to kind of build up this repository of people who are working to produce code that automates certain of these functions and so you know if you have if you're working on like some small piece of it maybe like you know you want to understand how to integrate like a voting mechanism one of these BBLLC's or if you want to go the other direction and figure out how you can just like automate something in a way that produces certificate tokens you know this would be a place where we would very much welcome like that sort of stuff and if there's any interest in staying kind of up with these things we have a computational law telegram channel where you can get involved and kind of spit around feedback and ideas and you know start populating the space together with us I believe that probably the best thing to do is to go to law.mit.edu click on contact or forward slash contact join the email list and you could have a more even more curated you know kind of updates of when we're doing things in communication the telegram channel is great like I live there but it can be a little chaotic for those of you that aren't used to like dense chatter on telegram the other I feel like we should say something else about this this okay so this challenge also is part of the release the first release of the publication which is our soft launch is Friday of this week and the theme of the first release is automated and autonomous legal entities so several of the articles are on that some of the projects are on that several of the podcasts are on that as well as other more foundational computational law themes Sandy Pentland's got the anchor article where he sets the big vision on what is computational law and that's that's amazing and and one of the things that I know that we want to work on with the conference organizer is actually modeling the legal entity aspect of like a robot arm that creates art this is a very it's sort of adjacent to a robotic and and Dow publishing company it's something it's more art but it's not different in kind in some ways it's a lot easier because the housing of the robot arm actually has a place where we understand how we can work with work the robot will do we think we do and then and so we can start to actually engineer against certain scenarios and hypotheses like what if the artist owns the art the robot arm is doing something or what if the consortium that has purchased the robot arm are considered the owners what if the robot incorporates itself and is considered the owner on and on so there's always sort of permutations on scenarios and when you have like you don't you can't understand law or legal outcomes in the abstract law can only be understood when applied to facts that's why lawyers say it depends because maybe they don't know all the facts yet so we think that this will be a great platform to basically engineer all of the relevant facts and then play it against different scenarios to see whether we're getting the expected results for the legal roles relationships rights and responsibilities and fundamentally the legal outcomes that we're that we're seeking to engineer so that's we hope that that will be one of the challenge results that we can hack together on and if you have interesting ideas on this well intersection of robots AI and law with respect to legal entities or more generally we would love to hear about them right now we have some time for some open discussion thank you any questions I just want to ask you thoughts around you know checks and balances when it's come to information coming into the lab I mean lately I've been quite curious and also interested in AI bias data with AI so how do you like what's your thoughts around you know sort of filter right information and also checks and balances of information into the system so in other words if you if you put garbage in you might get garbage out so how do you do the checks and balances on on your proposals can we go backwards on the slides a little bit I'll do that while you're talking so let me see if I've got this right the basic question is like if if you're set up so that you know there's some stream of data that you're trying to that you're ingesting as a decision making function in the internal governance of the Dow like what happens if you know that that data gets corrupted or something like that and you know it starts producing all of these like terrible outcomes and to answer that I would say you know you can start to there's certain things that you can do that would be modeled kind of after like high frequency trading algorithms you know so if there are certain amount of calls one direction or another that signals like something super volatile you could have it set up so that kind of just meters off or requires like somebody to look at it or the group to get together and to reach some sort of consensus before it can proceed forward so it kind of like you kind of have to create like a legal pause button on like what you're doing to ensure that you're not like going the wrong direction so that's that's one thing that comes to comes to mind there yeah and then I'm going further some of this comes down to just good old fashion information security and so you know if even on a high frequency trading platform if somebody hijacks it and gives it market mark information so that it starts buying other things of course that's probably crime and fraud but that is one way you can get corrupted information or garbage in to manipulate activity like through a direct access you gotta you know information security doesn't go away it's even more important with auto automated and especially autonomous systems to make sure that it's getting the inputs that are expected from the oracles or the other sources but then you also come down to and you also have to be thinking beyond like a direct attack whether the sources you've chosen are really appropriate so I think you've mentioned the word bias so that's a big question that I think in Sandy so for example if you couldn't like the this system for evolving loan fund the but damn you can't really see the swim lanes on this too well but imagine there are swim lanes it didn't come through our JPEG but there are decision points where all of the information for loan applications presented to a board and then they make a decision so the way this system was created is people log in and authenticate themselves and they have authorization to be like n of m approvers to issue a loan or to change the distribution like oh we're gonna be doing a little bit more high-risk loans and more microloans or that'll be the distribution so this sort of depends on two things one making sure that the authorized people are logging in to set the parameters and approval chains and sort of workflow points but number two are you gathering the right information on the loan application or on your from like other information that we assume we'd be getting from Bloomberg and other places just to look at what the distribution of the fund would be and this is you know basic business judgment and one of the things Sandy Pentland says in his what is computational law that we're releasing on Friday is the critical importance with the legal dimension of these systems of modeling them and then not forgetting about them assuming that all of your decisions initially were absolutely correct but instead monitoring and then adapting them so if it turns out some of the information is biased and you need other information you have to change the balance of how you're calculating the relevance or weight of different information that's coming in hone the model to make better decisions so there's less bias that you don't want then this is a this needs to be built into the design of the system so Sandy very much advocates the computational law systems everything from creating a statute to you know managing your contracts or other businesses business types of instruments that most of the action shouldn't be on the initial design phase but it should be on the design of continuous adaptation and in the information that might be perfectly good in 2020 may end up being biased and not particularly reflective of the key inputs in 2021 or 2022 the years 2022 and so you have to continuously hone and identify where the bias or the other inefficiencies are as you go with computational law systems I suppose with any system but we think this needs to be part of the DNA of computational law those are some thoughts on what you said I think what you said raises a lot more questions that we haven't got to and that maybe we can't conceive of today hey Adrian so one of the primitives maybe not in a sense you meant it is reputation narrowly reputation issues sort of cross over all of the decentralized AI stuff that we heard about earlier today in all the domains where does computational law impact reputation or vice versa in other words is there a narrow subset of projects that are already under way or aspects of the discipline that can be used that can be applied as a primitive to the reputation components I think there are examples out there now like Estonia's got the E birth certificate Zoos got the land registry that's on Ethereum and I think these different groups are starting to plot some points down on what the factors are of identity that you need to have in order to properly like indicate or authenticate what your reputation is and that you're able to do something and so I think those are some places to start and then as more and more entities not entities but governments and different players start to do this that will become a little bit more clear like you'll start to identify more of the general trends and be able to say okay we've seen that of all these places here the five most common features that you should look for and go from there yeah I think that's all good practice and then if we go even a little bit deeper into the question of is there something in there that might be illegal primitive so I'd say the first one would be something in the zone of identity itself like there's a creature like a human being or a corporation that has legal personality and we actually so we'll release on Friday this our podcast with Drew Hinkus just starting to identify what we think legal primitives may or may not be we weren't sure by the end of the hour whether identity would or wouldn't be a legal primitive there's different ways to look at it let's just hypothesize if we considered a legal primitive and that was there was consensus around that people created reusable building blocks maybe something like the outcome of N-Stick or something at the the kernel of an UMA identity or something like user-managed access identity then you could imagine constructing that primitive that concept of a primitive such that the identity has attributes that may be part of the definition of the primitive and some of those attributes may be authorization some may be other identifiers and some in fact may be things that adhere to it like reputation so you could imagine if identity were primitive and it were a basket of identity attributes some of them could you could have like a agnostic sort of like genericized thing that we call reputation attributes so at that level I guess there could be reputation that was like a legal primitive but honestly there's not even consensus among the few people talking about legal primitives about how this would play out with identity at all at this point or whether identity is appropriate for a legal primitive we're just really not sure at this point so I would encourage I don't want to be that speaker that puts the question that says a question with a question but I would love you to think about that Adrienne and talk to people about it and then talk to us about whether you think identity would be appropriate legal primitive and if so what the role of reputation or things that are sourcing from a third party adhering to the identity might be oh that was fast a little tiny bit the link between identity and reputation is a context and what's missing because we really don't have any technology or science around reputation worth anything much these days what's missing is introducing not worrying about digital identity and identity as a legal construct but rather introducing the principles that I think law and bring into defining the context in other words the adjudication of reputation is or the gaming of reputation or how do you control the gaming of reputation don't bother about the stuff at the low levels that we do in the self sovereign identity groups that's way too low level and in UMA as well but rather this issue of defining context in the legal sense in the adjudication or appeal etc thank you that's helpful sir did you switch seats to be in mic position just two questions a little bit futuristic right now we are facing with the situation when the supply chain and some processes is really long and if we are talking about autonomous system and fully automated processes sometimes it's hard to define in some cases something happened something going wrong it's hard to define who is who will be in charge to pay for that and in some juridic such situations really exist when the many participants in the process for example of death of the person and nobody can be blamed because there is really long chain there is a movie about that but will we see something like insurance funds for autonomous systems and robots and AIs to ensure that in any case of damages from the site of such systems all damages will be compensated I can start us off on that so the way you posed it I thought you posed the question really well but there is one word I might suggest we amend it's hard to know what happened and who is in charge but to get right to the real point who is accountable or responsible who is going to be left holding the bag if something goes wrong to look at that dimension of it what we want to avoid is an accountability gap okay some people in the early days of the DAO especially we are specifically attempting to achieve an accountability gap where the idea is something is going to happen and you can't touch us like we are not part of any jurisdiction and I think it is very questionable whether that is a beneficial or sustainable or desirable system at all but as we look forward from the law.mit.edu perspective we are looking at systems that operate well based on our social expectations and that are extrapolate forward today which includes accountability and so to me when there are human beings and other corporations utilizing automated autonomous systems as tools it is not a big change in terms of what is accountable what you need is attribution at that point so to whom do you attribute the act so as long as that is clear then everything else falls the same way without an automated system but now where it gets interesting I think closer to your question assumption is what happens when the system is kind of taking actions and causing consequences without human review or approval or even knowledge now we are in the fun zone so in my mind I believe that it is not just possible but essential when more likely these systems start coming online that a major part of the equation like required is that there be financial and other mechanisms to ensure there is no accountability gap and so if one must if all one has is the automated or autonomous system to hold accountable then therefore we must look at things like insurance bonds reserve funds and things that are proportional to the harm necessarily that may be required for the type of thing it is doing so if it is selling books that may be relatively low if it is doing munitions and nuclear weapons distribution it may be quite high and everything in between and so looking at the potential exposure of different business activities is a bit of a magical art more than a science but there is risk managers that may be an appropriate you know risk management kind of premium? well the premium will be an appropriate risk management capabilities to have for certain situations like is insurance appropriate and if so what kind of product and what would the premium be is do I need a bond do I need like a liquid reserve fund or other things like that or is there a common defense fund different ways to start to build in accountability but I'd say that it becomes essential and it ought to be built into the process of having fully autonomous systems that are capable of causing harm and so I guess my answer to your question like is this something that could be thought of I'd say like hell yeah in fact I think it must be thought of and it should really be part of the core design I think it's a good question I think it's related with the first one will we see something like open source license but for AI and robotics but not the source code but to open itself I mean what Eduardo said if the robot can buy itself if it's possible in the future there will be some license like this what would the license do like for example there is in the open source some license you are putting that I'm not the owner of this code no more I'm open to society and I'm not take the burden of the damages or any things but is it possible the same thing for robots and AI for example please be free so I think there's a couple of concepts there one of them is the concept of emancipation open source is sort of close but let's get really point blank on the target one could imagine one could structure like legal documents and business models and social arrangements where we deliberately intend for some code to be emancipated so it was owned at some point and at a certain point it sort of owns itself or it is independent it becomes autonomous or would say emancipated so like a young person can't form a contract when they're 12 years old by the time they're 30 years old they can one of the things that happens there technically legally it's emancipation a slave similarly cannot own property in fact they were considered property when the slave is freed they are emancipated so an emancipation type of event is one way we can see this happening another thing I would call is something I sometimes refer to as the broken leash and so like you've got this dog or this thing and it's on a leash and it seems to be going pretty well and then the dog kind of bit through or otherwise just like ran off and like the leash is out of your hands or it's been broken so now we've got this rogue AI going around that for all intents and purposes or maybe the leash is relinquished because the one the only person that created and owned it and operated at what is now dead or they went to jail or they don't feel like doing that anymore or would have you and so we can imagine conditions that would result in that I think the interesting question is in fact I go one step further in 2019 I would say I envision that this is inevitable that we will see these things develop in the next handful of years you know up to I'm not going to put a number but in the future in your lifetime and so then the question becomes okay how could an emancipated AI or robotic or purely software be a kind of wholesome healthy you know desirable legitimate creature on the terrain with us and so this starts to create questions about what types of requirements or constraints might be appropriate for that and this is a question that's I think it's just about time to have real realistic conversations about it it's still premature but it's not too premature to start thinking and talking about it I'm glad you asked. Yeah and I think too it gets back to like you know a few years ago there was that like who owns the intellectual property rights for the monkey selfie where the animal took the selfie of itself it's like who owns that and you know one of the things that you can look to in order to determine that ownership is like does a legal personality container exist for that entity so whether it's an animal in certain places animals do have legal personality rights you know I could imagine you know if there was some sort of registration process there was some sort of you know indicating of like what those voting mechanisms were the decision-making calculus that went into it you know you could have a legal personality container for robots you know what that actually looks like you know remains to be seen but I think we're getting incrementally closer and closer to understanding what the contours of it look like in the inner working and so one component an inner working that would show up on the contour where you could connect with it I think one thing would be something like a license plate you know even if it's virtual so that you could say ah this AI or autonomous entity belongs to you know ACME Corporation or belongs to Sandy Pentland you know it's a personal shopping bot thing so that AI when I look at the license plate the license plate's visible to others is emancipated good to know well then maybe before I conduct a deal with it I should then check does it have the standard insurance and bonding or does it not is it fully paid up am I doing something within the scope of its capabilities is there like a robot.txt file I could query at a standard API to find out more things about including it owning itself or being emancipated what its capabilities are and what my recourse and remedies would be if it all goes terribly wrong but I think what we really want to keep our eyes on is it all going beautifully wonderfully right so like some of these types of entities can be extraordinary for the innovation and the economic prosperity and the social issues that they can help us to resolve and help us to achieve some of these deeper goals and so what we really need to be doing now is fundamental engineering and sort of pre-competitive research and development on designing the types of containers so that we can get the best out of these capabilities while also maintaining reasonable risk kind of management and also maintaining our values intact and I think that pretty much brings us to the end of the session yes so do you have a final question fire take mine from my side the question is does already exist a framework which allows could allow automatic litigation because I think to see a situation like that you have an intelligent trading algorithm which has a beautiful idea of trades but this happens not to be legal and so another argument is to detect a wrong move and open a litigation so say this I don't want to pay you because I mean in certain context for example an autonomous car not wishing to pay a parking lot or in trading speed so real time litigation could be automated litigation might be an option but as long as it is legal so we have a framework we have a framework for that so legally it could be implemented and now to meet the litigation on a trading floor or on a parking lot thank you last question so you could set up a framework for that so a lot of times when you enter into agreements with banks or other parties there are arbitration you could also imagine an online dispute resolution process like what eBay has, what Amazon has and those are a lot more efficient than courts are and so if you had something like that set up when you're setting up one of these where you're setting up a DAO you kind of have a check box for this DAO prefers this sort of online dispute resolution but it will do any of the following online dispute resolutions you could have a situation where something happens and there's a goal to quickly expedite all of these legal processes and it can automatically run through one indeed and so just to play it out quickly so let's take the parking lot, that's a good one so you have an autonomous vehicle that's going around doing deliveries from Grubhub or something, it's got an hour between things and it's more efficient for it to park than for it to circle the block or two hours so it shows up at a parking garage it has a RFID chip or something so it can be identified and enter and knows where payment would go one of the things you could structure on those components and building blocks if I were a parking garage owner I might be part of a consortium that developed a standard that would ask can I pre-approve your credit card for the amount of time that you'll be staying here in advance and then if I did that but for some reason it didn't clear when it was time for the car to go I could have an agreement that it was capable of entering when it came into the garage that I can maintain possession of your vehicle until I get payment or something else and that's where we get into questions like recourse like okay so the credit card was pre-approved when I went to do the sale it didn't go through because you reach your limit or there's a charge back now if you can check up front as part of a data exchange interchange that it has a certain insurance it has a certain reserve fund at least you know you have recourse overall so you can let the car go so I think these are some of the types of design design patterns that we would need to look to fill the gap between where we're at now where the components we have which are close and where we'd want to be to have things operate in more of a fully autonomous way does that make sense but largely is built upon it just uses the existing systems and frameworks but we now need to sculpt like more APIs and add a little bit more to like the transaction codes than the business models in order to build out full use of the capability and so with that I think the full use of our capability is now expired Tony but like first of all I think we should like Brian and Dasa you know for this amazing last session so thank you guys thank you it was great like I learned a lot and also I think that this is cool that like we can say that law turns into a innovative field right yeah so it's a green space definitely definitely yeah so with this I would like to close like the event so it's been like a long day you know a lot of information right but I think we all learn something new like today so I hope to see you next year maybe here maybe in Europe maybe in Petersburg who knows but yeah but like before we leave you know I have to say a couple of things like first thank you thank you for coming here you know and thank you for showing up and like a question and do like the networking that you did like outside second thing is that we are going to start like the Boston blockchain meetup like group there's not much time left you know actually we have to leave at six because and the important message is this one we have like drinks plan in the mid-haul you know which is like the path you know like at like 10 minutes away from here so I hope to see you there and ask all the questions that you couldn't ask now or just like chat with a beer right so with these you know we close the event and thank you very much