 Hello, everyone. Welcome to another edition of Curiosity on Stage. This presentation is part of a series where we discuss new and emerging technologies and how they are affecting us as a people in Canada and also worldwide. My name is Michelle Makarski and I am the Science Advisor at the Canada Science and Technology Museum. For those of you attending with visual impairment, I'm a woman with shoulder length brown hair and brown eyes, and I am joining you this evening from my home office in the city of Ottawa, which is built on unceded Algonquin and Ishinabe territory. And lastly, before we begin, I would like to take a quick moment to thank the National Research Council of Canada for their support in making this series more accessible through translations, captioning, and transcriptions. So, Curiosity on Stage. Our goal here is to inspire thought. We rely on the insights of experts to get us thinking about topics in science and technology that have the potential to really shake things up and really fundamentally change our experience as humans. Certain technologies actually have the potential to revolutionize the very structure and nature of our society to transform our industry, our culture, the economy, and even our philosophies. If we take, for example, the agricultural revolution, it was driven by sciences and technologies like animal husbandry, irrigation, and the plow. The resulting food surpluses allowed our populations to grow into cities and then into states. And the fact that not everybody needed to worry 100% of their time about what food they were going to eat allowed certain individuals to specialize in things like politics, handicrafts, or art, which created the basis for our modern economy. If we fast forward to the industrial revolution, it was driven by machines like steam engines, which provided sources of power other than humans or animals. These new sources of power made industries more efficient and therefore their goods cheaper. Populations rose dramatically again, and those populations moved to cities, which urbanized our society. Now the information revolution, also known as the age of the internet. Here we have things like computers and TVs and mobile phones, which demonstrate the advances in electronics, computing, and communications technology that define this revolution. As these integrated systems of technology spread through society and take root, information, innovations and ideas diffuse far and wide, fundamentally changing once again our culture, economy, politics, and our personal philosophies on life. Today, it seems like we're in another technological revolution, an artificial intelligence revolution. In the industrial revolution, machines were able to replace much of the physical work being done by humans. Now, we're seeing AI, we're seeing with AI this ability of computers to take on the cognitive work of humans, things that, you know, at least historically required human intelligence to do. So as you'll see later in this presentation, AI is an extremely powerful tool. And as a result, it's spreading into every corner of industry, economy, and society. Now what makes AI so useful is it's very good at finding patterns in very large sets of data. Think satellite images of the entire planet, your DNA, or the world's financial records. Now financial professionals spend a lot of their valuable time in low cognitive tasks, like sifting through a whole bunch of financial transactions. Now wouldn't it be great if there was an AI system able to rigorously audit financial data and pick out the key areas that human professionals should investigate further? Well, today I am delighted to welcome John Coulter of Mindbridge AI, a company developed to do just that. Now John has held a series of roles with increasing responsibility at Mindbridge and currently serves as their Senior Vice President of Strategic Insights and Marketing. Before joining Mindbridge, John held leadership positions at IBM in brand management, product experience, and design. And he was a member of the team that launched IBM Watson Analytics. Before IBM, John was VP of Sales Operations for Clarity Systems, which was later acquired by IBM. So I know I can't hear you, but I hope you're all talking with me as we welcome John today to curiosity on stage. Well, thank you very much for having me. I really do think that it's an interesting time to be in the world when we start thinking of where and how our finances, our whole ecosystem goes as it relates to artificial intelligence. In fact, most of you probably already use artificial intelligence every single day. The idea and the concept of picking up a smartphone and asking the question of where you want to go, where you want to eat, how to get something, that's all based in the same logic that artificial intelligence was created. I'm really excited as well because I'm just down the road from the science and tech museum. I get to go there with my kids fairly often here in Ottawa or as often as we were able to before now, obviously with our annual membership, hopefully we'll be able to go again. And we live fairly close to the Aviation Museum, which is really exciting for them. So thank you very much to the team over at Ingenium for all their dedication to learning and things like that for kids of all ages. I still consider myself a kid. I do want to get to a point where you can ask me lots of questions, but there's some things that we need to do to get there. First, we need to talk about what is the artificial intelligence, how it's changing everything and where we go. The world is very, very different today than when I first started in industry back in the late 90s, early 2000s. And it's really accelerated in a clip that I don't think anyone could possibly have seen or experienced, right? I don't think we had full understanding of where things would go. And when you look at there's laws like Moore's Law, which is all about the ability for CPU or computer processing unit size to shrink but double in power every 18 months, like the leading indicator for computer scientists and geeks like myself in the late 90s to try to figure out how small will these things go. Well, it's gone so small and I'll show you a representation in a minute. It's gone so small that we can now process more information than we ever have humanly thought possible with computing. I think we all maybe assumed we'd get there, but we're doing it now and we're doing it at speed and scale. Without thinking about artificial intelligence and having been in the space for almost a decade now, I really do liken the transformation to be very much like the Industrial Revolution probably was. It wasn't about getting more horses, it wasn't about getting more steam into the rooms. It was getting all of these pieces and componentry to work together to automate a variety of things on a production line or to make the products bigger and faster. Artificial intelligence is that but sort of on an explosive scale that's greater than anything we thought. And Jensen Huang, who's the co-founder of NVIDIA, where a lot of the processing from a graphical processing unit, I'll throw in a couple of techie things for those that are really curious. There's a company called NVIDIA which processes, which creates most of the processing units for graphics cards that are used all over the world and mostly in AI. He really agrees with the same statement that it is going to be a national imperative. Canada is very, very lucky to have had a significant involvement from the Canadian government and from the provincial governments. We've got three centres of excellence for artificial intelligence across the world or across the nation, rather one in Montreal, one in Toronto and one in Edmonton. But we have an ecosystem of startups that is expanding past I think 550 now different startups across Canada delivering artificial intelligence. So you may work in it, you may see it, you may have it, you may be part of it. So we're going to do a bit of rapid fire for the next 20 minutes or so, keeping myself on pace to have us into a question and answer roundabout half past this webinar series. And we'd love to have the dialogue as much questions as you want. Feel free to start putting them in and getting your questions in. We will answer them for those that may be looking at this as a replay. Hopefully you'll find my contact information and you'll have Michelle's. I'm sure and you can email us to call us. We're happy to chat about this. But here's the basic agenda. I'm going to give you more basics about what artificial intelligence is. I'm going to talk about the human problem or whether there is a human problem. I'm going to talk about AI in our world today and some of the impacts. And then we're going to get into that Q&A. So how can I help you understand further? So if you think about it, we want to give you some of the basics. We want to give you some of the things that we see and that I personally would believe are going to be contributing factors to your ability to embrace AI. And then how is it actually affecting you? So the basics, what is and why are we talking about artificial intelligence? Well, a lot of people are quite surprised to know that artificial intelligence is actually almost a 70-year-old concept. John McCarthy first coined the term at a Dartmouth University symposium that was bringing together mathematicians, statisticians, mechanical engineers, and the like. And they came together and said, we've got to really start thinking about how we can automate things further. Imagine this is on the backs of the Industrial Revolution. It's a bunch of think tank members coming together and they started talking about artificial intelligence. They didn't really know what it was going to be, but they started to think about how to get there. And the first general-purpose mobile robot was actually developed and deployed. Shaky, he was developing and deployed. I'm not sure why it has a male connotation. But in 1969, so almost 14 years later, through that same time, there was actually a lot going on in artificial intelligence. The US Department of Defense put together a program to translate English to Russian, Russian to English. As you can imagine, during this time was the Cold War. And so the two, if you will, the two superpowers were having a challenge. And one of the biggest challenges was how could they communicate and converse effectively without having a cast of thousands or hundreds of people being part of the information chain. They wanted to have very much bidirectional conversation with each other. And so the Department of Defense put together a multi-million dollar project. They started translating English to Russian, Russian to English. This was an early concept of artificial intelligence. And it worked fairly well, except for the fact that it didn't understand co-localisms or unique things around a given, you know, part of a person's dialect. So it would take things, you know, like out of sight and out of mind into something into Russian, but then back into basically blind idiot. So concepts like that didn't quite work as well. And so we had all these stops and starts. In 97, for those of you that have been around as long as I have, might remember this. Deep Blue from IBM started playing chess earlier in the years preceding this. And there was finally a chess champion that was beat by a computer. 2002, we got our first robotic vacuum. I think most people will attest that don't like house chores like I do. Robotic vacuums sounds really great. But they were really basic. This is 20 years ago, folks, right? They are very basic. So we went through another couple of fits and starts and starts and fits and stops and starts. And we got to a point where we got the internet, we got a wide scale adoption of search language and search engines. And we ended up to today, where we've got Siri or Alexa or Google Home, you know, sitting on our desk just ready to do something for us. And that's a pretty big 70 year journey. But when you really look at the advancements and you think about what happened, it's really the last almost almost 15 years that have had the most impact to the world. And part of that is because of this. I mentioned computational power the amount of data that we're trying to process is quite significant when it comes to artificial intelligence as Michelle said in her lead in right. And I think for a financial professional to look at every single trade that's going on in business. And every type of transaction that's going on in a business and make sure that it's accurate and it's correct and the right funds are going to the right people to the right buyers or getting from the right suppliers, or customers rather. It can be a nightmare. And you can imagine that if you look at the very large machinery 250 megabits of storage was about 550 pounds so that's more than double me. I'm a little bit of a stocky build, and it costs over $10,000 to deploy that much storage. You look at today and you can buy a 256 gig, you know, micro SD card, it's under two grams in weight and some sometimes you get them for less than $30. So you can imagine this computational power is a big piece of how we got here. Right. Now, on some of the basics, I don't want to go too technical because I think that it's the concepts that will make sense, but there's actually a microcosm or a set of envelopes that actual AI systems fit into so an overarching artificial intelligence platform will have everything from natural to language processing statistical modeling, it will likely have machine learning algorithms it will likely have some basic rules, and very much scripted things going into it, and then you get into this very specialized area this this darker blue of deep learning and deep learning is really only the last 10 years or thereabouts. And this is what really drove such an radical expansion of investment in AI, because we were able to do things that were so unbelievable. Even 10 years prior, based on this confluence of computational power, smarts and engineering and the ability for us to develop new languages to do this. Now what do I mean by this and why is this interesting. So around the time I'm talking about there's a gentleman named Demi Hassabas. Demi is the co founder of DeepMind. DeepMind is an organization which also has some Canadian roots. Dr. Jeffrey Hinton, who's down at the University of Toronto is one of their members, one of the founding members of the team as well, and they did something extraordinary. They took that little niche deep learning neural network area and created something that had never been done before. It was called the Q learner. So I'm going to explain this for those of you that are as aged, hopefully as well as I am as well, who had an Atari 2600 back in the 80s to play video games. They took a video game. They basically put it on an emulator, and they taught a piece of software how to play a variety of games. They then converted that into its own level of coding think about and this is why that picture of the brain with all the synapses is there. Think about all the decisions you make when you're playing a game, whatever that game might be this is I believe space invaders up there on the screen. You know, you used to go across and you would hit the red button to to explode what whatever was in front of you. They converted all of those mechanics that you would normally think of doing into computer code and essentially got to the point where the system itself was able to take any game from the Atari 2600 and actually play it better and better and better and better than any human champion in less and less time it started out with about 18 months or sorry 18 hours to to successfully learn enough without a human intervention. Learn how to play the game without human intervention took 18 hours to win space invaders, then they went on and on and on and it got to the point where it was working at a speed of about every two hours it could learn a new game and be extensively better than anything else that had happened. So to me that's kind of crazy and wild and wacky and insane. Now Google, a company, a company most of us know or its parent company now also that did something interesting with DeepMind. They bought it. They bought it for a ridiculous sum of money, and they turned it into Google's cat detector. You might be seeing what are you talking about. So the Google cat detector, it's a very funny story. They took this idea of the neural net, and they pointed it at one of their assets, which is YouTube, and all of their cache of search websites, and it went through the process to detect cats, dogs, other animals and they essentially took that same code and made it better and better. You see this in your day to day life right when you go to a search platform like a Google, or you're maybe using a streaming service like a Netflix. When you start typing in information when you start asking for something, the responsiveness and the quality of the of what you're getting back is significantly high. So Google bought DeepMind, they created the Google cat detector but as a proof of concept to show that we have really taken things to a new level. So we are now in this new AI spring. It's been almost a decade that we've been in this spring or this resurgence. And I love the Forbes quote from when this all started. Artificial intelligence is the broader concept of machines, being able to carry out tasks in a way that we would smart. It gives us an umbrella way to start thinking about it, but it is quite complex and it is quite, you know, integral into having to get to these levels of information. So I'm going to fast forward a little bit I'm going to speed up a little bit onto some of this concept so why AI, because it does it performs complex and laborious tasks, it doesn't need to sleep. It doesn't have traditionally it won't have the same level of bias there's a whole bunch of reasons why we can pass huge amounts of data through it, and provide the agility to act on the other side of it. And it basically takes all of this complex data all this voluminous data, and it processes it in spite speeds that you couldn't put enough human beings on, and comes out with very interesting insights extracted very very quickly. And why AI, because we're at a point where the computational processing power is available, we have elements like storage costs going down, further and further and further we've got cloud computing, we've got all these areas, and we've got the Google detector, which is obviously broken a whole bunch of barriers into how do we create something that will do good things and identify the things that us humans want out of this complex data. I'm going to transition into the human challenges with AI, and sometimes people don't like talking about this, and the human challenges are some are based on our own DNA and our makeup and some of it is based on just the technology itself. So I'm actually going to go in reverse order here and let's start with privacy and I'm going to talk about privacy I'm going to talk about bias I'm going to talk about ethics, but we'll start with the black box of AI. One of the biggest human challenges that we have with with AI is that everyone is nervous about what that decision process looks like. And it doesn't matter whether it's in, you know, industries like my like the one that I work in which is with financial institutions with public accounting and financial record keeping, or whether it's in that business to a consumer bot that's helping you pick your next cell phone or cell phone plan, or whether it's in, you know, self driving cars, everyone is very worried about what is this box actually doing, and how do I get comfortable understanding what it's doing. And so we do try to make sure that the human side right that people side can get access to understand and be able to trust it. And that's one of the biggest challenges that we have with AI, it is not a human problem. It's not a problem that the AI system vendors the solution specialist need to continue to break apart. The second piece is bias. And what's really interesting is the last 20 years of developing and deploying and designing software for a variety of organizations around the world, you see human bias everywhere. When there's in some analytics tools that people have seen, you know, just think of this as as, you know, charts and graphs out of the data in enterprises. It's very common that you will insert your bias into the question you're asking, I want to know how much our revenue grew period over period. And that's why I'm saying all the places that we grew, I may ignore all the places that we didn't, because I'm already instituting a bias that I want us to grow therefore I want to validate that hypothesis that we're growing. Okay, interesting humans as as individuals, we have a significant amount of bias and I don't want to belabor this and I'm not trying to make this in any way, a statement of politics or policies or anything like that, but we have bias, and the bias in AI itself, it comes from the people implementing the AI or designing AI. So there's a lot of time spent around how do we do this, and how do we do this ethically and how do we work through this and I'll give you an example of the challenges we have, as it relates to our bias, and then how it blends in with the ethical conversation. Now you can probably assume based on this picture what I'm about to talk about. We have a self driving car. In Ottawa, we've got this wonderful program out in the Canada Research Park that is going on to have self driving vehicles. There's a few other programs across the nation that have been deployed. I think Ottawa might have been the first city that that actually actively deployed something and I think Toronto and a few others have followed suit, but we've got this issue of ethics with with AI and with self driving cars or the neural network that's working behind it. You can imagine, we've got a car coming down the road, and we may not have enough room for that car to go past things. In this case, I'll put people, again, not trying to be by biasing anyone in their thought process but just giving you the sense there's something in the way there's an A and a B. When we look at the biases and the way that people interpret the ethics of doing something, there's actually this is a fairly significant study has been done on this case where different parts of the world have different desires in terms of what they spare or what they protect. And so when we start thinking about building AI and when we start thinking about the ethics, are we thinking about all these types of environments that we have to work in. So when we look at these biases and these ethics, it can be based on age, you know, it can be based on on, you know, a level of gender, right. Obviously, there's, there's, there's folks in the middle that are, you know, that are non binary and identified differently. It doesn't matter that it's, it's the DNA based structure that we're talking about here, but there's different opinions as you can see by the screen. And certainly, just even in terms of our level of education, right, who, who would you sort of spare based on whether they've got one degree two degrees five degrees no degrees right. And again, very different and differently applied. So when you bring all these things together, you have this issue of, you know, starting with the black box, what did it actually do. There's biases in there and what type of ethical concerns have been driven into this and you can imagine AI and things like medicine, right, we want to get some of those things right. And so it comes to our last point about the human challenge, which is the privacy of all of this. It takes a significant amount of data to run amazing AI systems. And so therefore, what level of data are we willing to use to train our systems, and to create inferences, and to ensure that there's a lack of leakage in the overall system, right. How do we do this. Well today, every time you sign up for a new service, right, you get a little terms and conditions, and I'm sure that many of us don't read all of them all the way, and that's okay. But, you know, at the end of the day, we are selling a bit of ourself into these ecosystems of data that we want to have this personally identified information or PII, right. Then goes into these programs and I'm not picking on a single vendor here it's just they're going into the programs they're going into the programs that you're using. And those can, if not driven ethically, if not, including bias and when there's explainability, you can get a very good sense of how that personal information and how that privacy is affecting or the outcome. But at the end of the day, we had an issue a few years ago with a company called Cambridge Analytica, who has been deemed to have influenced outcomes in certain political spectrums. Because of the amount of personal identifiable information or PII that they were able to use and leverage and build into their bots that were communicating in information flow through the media system. Obviously, by having that information and that level of depth, it created an ability for them to be very targeted, and people feel that that is uncomfortable. I get it. So, let's talk about it in your world, we set the stage there's a bit of basics there's some really cool tech stuff that we're doing based on really amazing advances in the actual technology. But how is it affecting you and so we'll spend the last sort of six or so minutes. As we get ready for Q&A again if you have question and answers, you know throw them into to the chat window down at the bottom, or any of the other places that we have available to us, we're going to talk about how it affects you today and every day. Well, it starts with AI being everywhere. That was my very first slide if you remember. And the thing is is that we do in fact use it every day. I'm sure that not everyone has a smart TV and I'm sure that not everyone has a smart thermostat and I'm sure that not everyone has a smart car, but the reality is since about 2016, maybe 2015, every single vehicle driven at a certain level of quote unquote has been equipped with a variety of safety sensing componentry. These are all components that feed into an AI based system. So Toyota safety sense they've got this camera at the front that's that and a LiDAR system that's actually pushing out and gauging how close you are to that next car. The vehicle I drive which happens to be part of the GM family of cars has a counter forward collision countermeasure. You know Tesla is always in the news talking about their full self driving capabilities, which is really interesting because we don't have the right legal framework to actually enable all that where you can take your hands off the wheel and you know sleep. That's not there yet, but you're even using it in some very basic things. We're about to hit tax season for most of you, you know RS RSP deadline was the other day, you're probably getting ready to do your tax whether it's Turbo tax net file you file. You know there's dozens of these all of these programs and the people managing these programs if you're going into an H&R block or you've got an accountant. They are using AI they're using it to again try to support and help you call through all this data and help you make better decisions. And so I am very fortunate that I embrace this technology, a lot of people don't. I find myself fortunate though that I know enough that helps me protect myself as much as possible from these things like privacy ethics and biases. I'm hoping that out of this you will come away with okay I'm going to spend a little bit more time on that terms and conditions type type scenario now I wanted to put a huge shout out to the government of Canada. They really have been a forward thinking leader around around artificial intelligence and on how businesses can thrive and how we can can move forward together. I actually got something called the algorithmic impact assessment program, when it's essentially a way for you to understand how much reliance you should put on a given type of artificial intelligence, that's fantastic. And back in 2018 I believe it was a series of businesses, Mindbridge being the first tech business signed the Montreal Declaration, which is all around ethical design and development and deployment of of artificial intelligence. And, you know, again I think that's a real good testament to us being safe, but it really does now lead us into the final sprint. How does it affect you and your finances that was the poll right. The reality is it affects everything. And we already mentioned the whole tax and doing your tax returns and your filings, but there are so many other areas of your financial ecosystem that we need to talk about. So back in 2017 Toronto Dominion Bank or TV bought an organization called layer six, layer six is an artificial intelligence team that was building amazing programs for financial services community, and they have pointed all those members into internally to develop different tools and techniques. You may have seen if you're a TD user, you may have seen in their most current apps they've got this thing called, what do they call it again it's the spend alerts and the, I think it's called my spend, and it actually shows you how far, you know, above or below last month and what your trends are. There's elements of AI that are baked in there to try to help you figure out where you need to go. CIBC I believe it is has a program where they actually challenge you to save more right so they've actually as part of their app system when they go to pay a bill, it asks you if you want to you know push some to savings. But you know clear bank or well simple or some of these other great Canadian upstarts in the in the banking and wealth management space, where they are using AI to find the best product for you or the best investment for you. Even organizations like the Pope province to do Quebec or CDP Q. A lot of their investment thesis is now being driven based on a variety of analytical programs that are steeped in AI as as a CPP, which is our Canadian pension plan obviously, or, you know, other private pension pension holders. So it really does impact you all the time. And what's what's kind of interesting is the next stage of this, which is how AI and the white collar jobs transition. Now I come from a space in the last five years of working with corporations and working with public accounting firms who deliver on audits. Why is this important and why is audit, you know why am I mentioning this about how it impacts you. There are some major failures that have happened over the years for those that have been investors for maybe a couple of decades might recall the Sarbanes Oxley act that was enacted in the United States. That was as a direct result of big failures like Enron, WorldCom and Tyco. These were big malfeasances can, you know, composed of senior leadership in those businesses, actively, actively hiding money moving money doing very strange accounting things, and it made the companies look bigger than they were more more than they were people kept investing, and then lo and behold, big failures. In Canadian recent times there was a big fur around the lights of nortel networks. And even more recently in a few other parts of the world in Germany with wire card, or in the United Kingdom, or in England with Thomas Cook travel, which actually affects all of us. Want to be back on a plane or at least having the ability to go and travel. Thankfully, most restrictions and most provinces are moving on, but Thomas Cook travel is a great example, had a clean bill of health and audit performed by a very large public accounting firm. Six months later filed for receivership and bankruptcy. So it affects you, it affects you and how you invest, it affects you and how you bank and how you, how you, you know, transform your ability to have wealth. And so as it relates to my specific world and this is not a plug for, you know, for for my bridge being this great company, although I love it, and I love being there. The ability for us to transition to have folks like auditors and financial professionals be able to use artificial intelligence to spot those errors and spot those challenges as quickly as possible is going to be a requirement for a more efficient financial ecosystem. And as you can see here, Klaus Schwab articulated that by 2025, the respondents expect to have seen that almost all the corporate audits will have had some form of AI performed in them by 2025, which is fantastic to see. I'm sure it will actually take longer everyone that puts a stake in the ground they don't think of all the other factors that go into this. They don't have where where we are. AI is all around you. You are definitely working with it or are accepting it as different parts of your livelihood, and parts of your day to day life. And what I'm hopeful for is that people will start thinking about okay how can I use that, or how can I find products that are going to use that to make sure that my financial stability is there in the future. Last thing I'm going to say before we drop into question and answers right is AI augments human capacity. It doesn't replace humans right. There may be a time for singularity. I'm not here to opine on that, but definitely part of the job of AI is to make it easier for humans to do more things either individually or for their business or for the hopefully the world itself. So that was my little bit of opening up the curiosity Michelle. Maybe we can we can talk about where we go from here. Great. Thank you so so much john for sharing some of the promises that are coming out of this, this new kind of branch of technology, but also some of the challenges and some of the pitfalls and places where we could trip up. So I'm going to, I'm going to invite our audience there's already two questions that have come in but I'm going to invite our audience to find the Q&A button at the bottom and type them in and we'll try and get through as many as possible. But john I figured we threw you an easy question first because the questions coming in are kind of deep. So yes or no and then you can explain further but do you think we'll ever get to the point where finances bookkeeping, auditing etc is ever going to be fully automated by AI. Um, yes. And I say that because we're already starting to see some of this happen and there's three technologies well for. So there's three sub components of artificial intelligence, plus in this in silvery technology that will support this so one is optical character recognition so this is the ability for computers to essentially trans transfer a picture into text right so think about all of the statements the invoices, you know we get in the mail. Imagine that actually all just being fully digital all the time, we're not even there yet right. But if you could have that that's a stepping stone to you know having it fully automated with an AI system. There's already tons of technologies out there from companies like ui path Microsoft blue prism that do something called robotics process automation in this space. And so what they do is they actually use robotics process automation so essentially taking those, those OCR elements, and actually going through and posting entries in a business. So, I went and I bought janitorial supplies or I have an invoice coming in from my marketing agency. It literally comes in to think of it like a big file folder electronically. The RPA will look at it it'll say Oh, this one goes to janitorial expense this one goes to marketing and advertising expense, post it, and then a bot will pick it up and say, Oh, it was net 30 on the janitorial and it was net 45 on the, on the marketing, and I will now pay it and it will go in and create the, the banking to go in and submit those funds to those vendors, and then it will reconcile that at the end of the day that what I came in whatever that amount was went out of my bank balance job done. So that's a really interesting place for us to be. If apologies, that was not meant to hopefully you didn't hear too much of that ringing that was like I didn't even know it was coming from the joys of being at home. That's that's the second piece is our PA the third piece is is actually the the ecosystem of full end to end AI players right where like there's bought keeper which is actually a process where you can submit it anything in customer orders invoices etc. It'll plug it in the last piece of the puzzle for me though is actually blockchain. So totally different technology, we probably need to do a curiosity on stage on that at some stage, but blockchain is where you will have a level of transparency and a level of acceptance by all the parties that will allow us to use AI to do the full spectrum. The thing is, even if we get there. It's not going to replace a level of oversight that we need, whether that's in regulatory bodies whether that's in human bodies at those individual enterprises and organizations, and for yourself. Right. I don't think you, you know, we already get direct deposit payments we already get all these things. I think there's a level of human that always assists, right. It's, but how much can we actually push down I think it's the vast majority in terms of bookkeeping and presenting financial statements. So you're saying the AI is going to do a really good job of finding fraud in my credit card statement but I'm still going to have to skim through and make sure that I've got all those things. Exactly. And there's a there's a great Newfoundland based company Verifin which is protecting all of us. Most of at least most of the people who are banking here in Canada they will they're one of their customers. And they're already doing some of that for us. But yes, you should always eyeball your bill. And maybe eventually you start looking at other types of alerts right and that will all be AI based and this is what TV is trying to do with their. I think it's called my spend report, where every frequency that you set up it'll actually go and look at the types of spends you have in the types of categories and say hey there's a flip over here. Right so that you're you're sort of drawn to it and I think that's that's what we're trying to get to we're trying to get you to the thing that matters not the yeah every week I have a you know payment for this and every month I got my mortgage payment and you know it's not that it's cool that's a spike that doesn't make any sense or wow you're spending way more on shopping than you ever had. I mean obviously for credit card fraud that's that's the place to look at look at those retail look at those. Oil and gas is a is a really big proponent of that but retail travel and things like your, your, your gas for your car places for sure you should be looking at every every statement. Great thanks. So I think it was funny that you just had a little technological issue here because one of the questions in our chat is the, I mean there's going to be a crash, you know at some point something's going to go down. I'm assuming that a AI crash at some point is kind of an inevitability. Could it fail what would be the repercussions are there fail safes. You know, that's a. It's. It's hard to assume that everyone's going to do the right things. So, in a perfect world. Yes, when it fails, it fails gracefully. Right. It will. There's a lot of redundancy and a lot of systems that exist today, although you know we see service outages all the time right with with products that we use. So the question is, when you're building that AI system, what is the level of of trends, really transparency in that element of failing gracefully right what did happen, what do I need to look for. So when it happens, what I'm hopeful for is not like a. I was going to use a TV reference but that's probably it won't translate to everyone who hasn't seen it but you know we don't want to have this situation where the world goes dark. Right. All of a sudden just everything's turned off because the AI system failed. And we have to work really hard to make sure that we don't have that situation happen. And I think that's one of the reasons why as much as the technology exists today to go way further and I'll use the Tesla example that I mentioned a Tesla today could literally drive in the city of Ottawa. Right, with the person sleeping from point to point, and with a high degree of confidence in the 90s, it would get there without incident without any issue. We're not ready for it as humans though, and therefore that sort of give and take of how much we're willing to adopt is going to slow down getting from 90s to 95s to in the computer industry. We look at not, you know, five nines or seven nines, 99 point, and then, you know, either three nines or five nines as being the level of stability we can provide. Right, most SAS vendors or subscription vendors will be looking for that for their uptime. And that's what we need AI to be and we're not there. This is the reality, can we get there yes but it, there's got to be this almost two way dialogue between, well three way if you include the government's governing and regulators governing, you know, whether it gets used but there has to be this, this interaction between the AI provider whatever that looks like, and the consumer to get to a point where we're happy at the end state. Yeah, definitely. Great, thank you. There's a couple of questions coming in here about ethics or we're going to kind of parts this out a little bit. I'm a bit of a sci-fi nerd, I like my sci-fi movies, and we often see AI kind of portrayed as these villains, if we want to think like Terminator, Space Odyssey, Prometheus, Westworld, Blade Runner, like the plots all very, very similar. So, as we're building these AI programs, how do we go about building ethics into them. So you talked about some of the ethical choices that need to be made but how do you actually put ethics into an AI. Program. So, the best way I could describe it or the best way that I would think about it is, we need to have AI systems that have built in checks and balances. Usually we talk about it in terms of resiliency. So, resiliency in an AI context is that there are fail safes that are constantly checking the things that we want them to do. I'm not going to disclose the party that this happened to, but there's a very large technology firm who was using an AI bot to sift through resumes and decide who got selected for things like interviewing. And so this is a very ethical challenge, right. We are all striving for a level of diversity and equality. For sure. Most tech firms, this is one of the things they think of all the time. There's massive pushes into it with programs dedicated towards STEM or science tech engineering and math to increase the level of diversity. There's a large firm that was using their historical data profiles of existing employees to then infer who they should give the time to in an interview situation. And so in a, again, gender DNA perspective male dominated environment in tech for the last 40 years, you can imagine that this bot did something that was not very good. So I have scrapped right so they have to scrap it. And so, so the way to build these, you know, these things in is, is give it more obfuscation of that PII that that sort of sensitive personal information, things like gender and look at core elements in this example right strip away the name strip away anything that could relate it back to a gender age, right because age is a is a thing. And so one of the news of one of my former employers that that, you know, talking about, I'll just say dino babies and you can go and search what that that looks like. It's, we have to strip away elements of this and so when we're building AI we need to be thinking of these things and it needs to be resilient and not single fault tolerant, right it needs to be multiple, multiple fault and so using a big monolithic system is not going to be good for, for pretty much anything and I would, I would urge anyone thinking of AI and building AI to go as wide as you can with, if you will dimensionality of what you're looking for, in order in hopes that you will remove some of those ethical challenges, because it will look at the dimensions as their native, their natural state versus looking at it in terms of what could be ethically compromising today. I today is a tool, right and so the other problem we've got to solve is do people using the tool, right subscribe to a level of ethics so it's a bit of, there's no easy answer to that one, really, I think, going to a resilient multifaceted approach is going to be way better than trying to build a single system that looks at, you know, every information and trees every information as, as sort of. Yeah, I think you kind of get the point. Yeah, so following up from that then obviously the AI is using all this, this data this personal data from ours. Are there regulations either in existence or that you think should be put into existence by say government safety and government about personal data gathering and how it's used. There are there are quite a few. Everything from the Canadian anti spam legislation, which starts to safeguard what information you collect to areas within the technology itself where they have to identify the technology that the data they have on you. And if you request it, you can delete it there's there's some things that have already gone down the path of stronger regulation stronger awareness and transparency. I think what was amazing about the Canadian government's foray into this is they actually built a fairly rigorous program to think about how we should. We should look at AI in in businesses and specifically for the work they do. This is the AI impact assessment report, which essentially is a big piece of trying to get there. Another piece is that the Montreal Declaration so it's, it's an idea that even without regulation firms will sign up and do good right so based on on it's a commitment and right now it's a very much a what do you call that sort of like an honor system. Right. But I think it will have to progress further and it will the reality is it will we will get more and more regulation companies will have to reduce the amount of personal data they capture without, you know, direct related consent and you see this mostly. I'm not picking on a specific industry but you do see it in what's happening with with our smartphones and our smart devices. Right. How much information is shared when you open up from one app to the other app I don't know if you've noticed this behavior but if you're shopping on Amazon, and then you go to Facebook you get some really interesting ads right typically directly related. It's all around around tracking you individually you can set certain things in your Google profile you can set certain things in your Amazon profile you can set things on your smartphone to limit that level of of sort of advertising tracking it's it's, it actually is on an iPhone anyways it's and that will help separate it, but the reality is they're going to find a different way to get to a similar programmatic answer which is geo fencing right so where is the device, you know where does that device normally go does it go to so I live in auto right. Are we at Bayshore mall, or are we at Rideau Center, or are we at Saint Laurent shopping center, right, very different profiles of stores and each one of those. Right, did that device stop at X. And so now when it sees that device and it has a particular home. They're going to try to figure out, can I can I get there, there's going to be no easy answer, other than I would. So I applaud what the federal government's done I actually applaud what the provinces are doing right now as well. But I would say there's more to come and we need to be very laser focused on, you know, investing in businesses that are willing to make the step of being open transparent and and building ethical and responsible AI, and not defunding any of these other ones but making it more of a reporting regulation issue, where people are aware of what they're buying how they're buying it, based on, you know how that company performs. I want to follow up to this. Do you think the onus should be on the companies and the government like consumers I mean you talked about these terms and conditions which I'm guilty of definitely not spending the couple hours required to get through all that information. Yeah. But you know people should kind of be aware of what they're sharing and how it's going to be used is you think there's a way we can better help people understand what they're sharing and how it's going to be used. I've got a few friends in the legal community so I'm going to offend them right now. Maybe I apologize if anyone is online and in the legal profession. I find that the most infuriating thing at the moment is how long it does take you to read those terms and conditions and how long it takes you to find the button or the key that says no thank you. So I think there's, and I get it right that and why I call it the legal profession is they are writing it in a way, right, which covers the basis and limits the liability right we don't want to become a litigious society where anyone and everyone's suing every company for everything. I get why they're so long, I get why they're so involved, but I think simplifying the language would be go a very long way to people being more comfortable and confident, making the choice to say yes or no in an opt in or opt out. Then I'd follow that up with, you know, we need a better way to get at the information so that's on the company, but definitely government has to play so it's it's really, it's not a single industry and it's not a single group. The company has to come from educating consumers and individuals, having a layer that that abstracts the legalese and the, the minutiae detail of protection of liability, right and IP protection into a more simplified state that people can understand that companies signing up to do good things, and then obviously government supporting that and enforcing, especially in, I mean, we've already got massive regulation around banking insurance companies, anyone that's, that's, you know, that's touching your finances, as well as, you know, even just things like CRTC and what's happening in communications right Rogers and tell us and Sean and bell, they have other levels of, you know, what they're allowed to keep in store and capture based on on your consumption of content but I think it's, it's all three parties right it's the businesses for sure, right, I think, you know, if we gave transparency of whether you are using ethical and you have, you know, you signed up for it and you get an ethical, you know, audit non bias audit whatever it might look like that would be a step for the old businesses, then individuals need to get more in tune with it, and that means we actually have to force the businesses to simplify, right so that it can be for everyone, right, versus just folks that have gone through and understood the legal ease and then government has to have the right and appropriate influence from a regulatory perspective, or from a from a direct sort of consequence perspective of of whether these businesses should be able to do the things that they're doing. Right. We're coming close to the end so I want to do a little bit of a speed around with you. A couple of these questions. So the rule is you have 30 seconds to a minute to answer the next couple questions. Alright. Okay, question one. Do you think AI tech will reduce financial inequality or exacerbate it. Do you think exacerbating got desire to have it reduce right. I think that it should, it should get to the point where it reduces that inequality, people like wall simple and a few others, giving better access to trading tools and information, and smartly investing is huge for people of all walks of life, but right now it is cited the other way. It needs to become more more equal. So do humans use IQ to measure intelligence and we know there's issues associated with that but is there such a scale for a rating system for AI. Not yet. The algorithmic and impact assessment tries to get there. The Canadian government believes to be rating it in terms of how much reliance you can, you can have on the AI system. And so I think they've done a good job to start creating that level of awareness. There's nothing concrete. It, and this is a bit of a dilemma. It needs to be a concerted effort but from the firms and from from the public coming together, trying to get to something that they can, they can agree on. And that's going to take a while. So there is no scoring system today but the more diverse and the more resilient someone's built something, they will want to tell you about it, because it's, it's the way to go. I do imagine it be tricky to come up with a system when all the AI programs are programmed to do such different things and use different types of intelligences. Are there any AI technologies that you think are a little scarier that we should be wary of? I wasn't expecting that one. I don't, I don't think anyone should jump into an area that they're not willing to, it's a risk reward system when dealing with AI, I guess is what I'm going to try to say. I don't think everyone, it's not a one size fits all. So my tolerance for risk on whether AI is good or bad, because I happen to be in the space, I probably am a higher degree risk profile, right, I'm willing to take more risk because I understand elements of those consequences. But there's no technology out there today that's really being used that I think is doing something nefarious. Yes, you get the phishing scams with the Prince of XYZ country asking for money or you won some lottery, right. I think that type of AI trying to target you that way. Yeah, we got it, we got to stay protected. But I think for general mainstream use in things that you're probably touching today. I don't think there's anything that's that's super scary yet. Yes. All right. Last question. Do you think we will achieve singularity a AI that is self aware intelligence on all of the different levels that we consider intelligence which passes for a living being. I think there will be enough experimentation that based on our current definition of what it takes to be a human. The answer is yes. I think there is a point in time where we will do enough experimentation with robotics and all the accoutrements to get to that point with all the coding systems. And that is that is an all likelihood, based on what we qualify it as today. I think we should maybe update that a little bit, because I think of I think of just, you know, just simple tasks right driving the car. I think it's going to absolutely be better at driving a car than I will 100% and it should be there and it should but is it sentient enough to know while it's driving. Oh hey, I forgot about this other thing that I was supposed to do so I'm going to make a left here. Could it happen. Yeah, for sure. Should it happen. Not sure yet. But I think definitely based on our current, you know, definition, you know, Webster's Miriam dictionary whoever we want to use of what intelligence is. We will absolutely get to that point at some stage with with AI systems for sure. The question is is whether we move the needle or not on what we feel it is to be intelligent. Well thank you so we're at four o'clock so it is unfortunately time for us to wrap this party up. So I'd like to say a huge thank you john to you for speaking with us this afternoon thank you for your time and your passion, and kind of showing us, giving us a bit of an insight into AI that we might not have, might not have had before and and and showing this might not have noticed it's being used before. I'd also like to thank our audience here for joining us and for participating and for giving us some questions I know we didn't get to all of them so I'm going to put john on the spot and hopefully ask if maybe he can answer some of them in a written format great super that we will publish to the engine channel afterwards. We would like to hear your thoughts we're very interested in continuing to develop these presentations and make them better so if there's any feedback you've got for us there is a survey link which should be appearing in the chat shortly. And there will also be one coming into your inbox in the next little bit as well. So my final plug for the evening is if you did enjoy what you heard tonight, I would encourage you to register for our next e, our next curiosity on stage. Which is the final one in the series of beyond injections 100 years of diabetes are 100 years of insulin I'm sorry and the future of diabetes. And that's going to be presented by Lisa Hefner on May 12. And she's going to be telling us about the human trial which is the story of a biotech startup on the verge of a major medical breakthrough cure for type one diabetes. Thank you so much for coming to our museum's website, subscribe to our membership if you want to hear these updates as they're coming out live. And on behalf of myself, and john and the Canada Science and Technology Museum, thank you so much for coming and we hope to see you in the future.