 Live from London, England, it's theCUBE, covering AWS Summit London 2019. Brought to you by Amazon Web Services. We're at the AWS Summit here in London at the Excel Center. There are thousands and thousands of delegates here looking to see the future for their own technologies and what cloud will hold for them. And as well as lots of the other established players here, there are plenty of startups. I'm Shazana Streeter and this is my co-host Dave Vellante and we're going to be talking to a few of the startup founders who are with us here on theCUBE. But it's great to have you here. So first up, Hube Hygen, who is the co-founder of the 3D mapping-based service and this is called Escape Technologies, but also Chandini Jain. And you are the co-founder as well, or founder, I believe, is it founder, co-founder? Co-founder of your organization called Aucoin. Now, let me first of all start talking to you, Chandini, about what you do because you're offering a service to financial services, aren't you? And helping them with machine learning to try and offer the best portfolio managers for wealth investment. How does it work, what you're offering? Yeah, so our platform basically allows traders, portfolio managers, asset managers who want to make smarter investment decisions to build machine learning models to do this. The idea is that data-driven investing should help funds make more profits for themselves and their clients, but there's not enough data scientists, skilled data scientists who can actually build models for them. And we address this lack of talent by using a community of data scientists, people who come from outside of finance to help fund managers crowdsource models using their intelligence, their talent. So the process is really simple. Clients come to us with what we like to call an investment problem or a finance problem. We take that problem and convert it into a pure math and machine learning problem that someone who is not from finance can understand and solve. It's really interesting you say that because I've spoken to other founders of other data companies who say, for example, been looking at the stars for their main bread and butter but then can transfer those skills and astronomy to the financial sector. And those are types of people that you're trying to harness their skills. Yeah, exactly. So our community is made up of people who work at tech companies, at Google, at Amazon, HubSpot, of people who are pursuing graduate program in computer science and math, machine learning, but don't necessarily know finance. And the idea is can we make these problems into problems, can we make finance problems into problems that this community of data scientists or really smart data scientists understand without needing to know finance? It's interesting that it launched because of a lack of data scientists, really. But do you think if you eliminate all the kind of heavy lifting out of what you do in the future there will be a need for fewer data scientists? I don't know that we need for fewer data scientists but there wouldn't be a need for a firm to have in-house teams. They will basically be able to, a data scientist working in any commerce company should be able to solve problems of a finance company. A data scientist working in Uber should be able to solve problems for a hedge fund because we're building this translator that can allow knowledge from anywhere to be used to solve any kinds of problems. Okay, let me talk to you, Hu, because you do 3D mapping services. Why do you think these are essential for technologies large and small going forward? So in every future industry, in the future it's going to have some autonomous aspect to it. So if we think about autonomous vehicles, if we think about delivery drones, these are going to be machines that are going to be acting autonomously in human-like environments and they're going to make decisions based on purely what they're observing but how the human in between. So the only way that this can happen intelligently and safely is if those machines also have a human-like understanding of a human-like environment, just like us humans do. So what we are providing these machines with is that human-like understanding and the first service that we're building towards that is a visual positioning system to provide the machines with the ability to answer the question, where am I? Now the only way that you can provide a visual positioning system is if you also have a visual map of the world. And this map needs to be updated in real time. So for every future industry, having a real-time, up-to-date version of the real world is fundamental. That's the pinnacle around every single decision that an autonomous agent is going to make is going to be based upon this map. So this map is really the value piece, the core piece that we're building. So I've often wondered, people talk about autonomous cars, but we don't have things like autonomous carts. Right now people will say, well, an Amazon warehouse would have that, but they're following beacons or stripes. What you're talking about is potentially taking us to that point where you can break that barrier. Is that fair? Exactly, and for warehouses, I would forever advise to use those beacons because warehouses are pre-massaged environments. You define what the environment looks like. Whereas us humans, we walk around in cities, in nature, and all these places that are not pre-processed. We have to take our cues from the visuals that we observe. So if you go back to your hometown, for example, and you observe a Starbucks logo and you observe a street sign, you might be able to infer your position based on those visual cues, even though the environment itself was not pre-processed to provide those cues. The cues are already in the nature. But we've heard though that there have been, in these trials, there have been accidents. There's a limit, though, isn't there? Oh yeah, totally, so at the moment, there sure are accidents, but you are a human and you can navigate properly within a human environment using your visual sensor, your eyes. Therefore, any machine will, in the future, only need that visual sensor as well, so only a camera to navigate around the world. And we're seeing great, great progress on neural networks, deep learning, as well as on the geometry and visual image processing, like the type of computer vision that we do, that are making so much progress that guaranteed a couple of years from now, devices will have that understanding of the world, like humans do, and we'll be able to make decisions even better than humans do, because they don't get tired, they don't need coffee. So, and they'll be guaranteed more safe than any human nowadays. Shandini, you probably hate the term robo-investing, right? But it sounds like you're doing that form of machine investing for and with hedge funds, is that fair? And is your background finance, data science, or both? Both, actually, I studied engineering, but I started working as a trader in a derivatives trading company in Chicago, and when I started with them, we were very old school discretionary, you know, a couple of very senior guys who were making everything based on their past experience and their intuition about the market. And in my time with them, I started shifting from this manual human process-driven trading to something that was more systematic and consistent. Yeah, and that's where the whole idea for Aucoin came from. I saw firsthand the benefits that making your trading more data-driven, more model and algorithms-driven could have. You're unique, you probably hate this term too, you're a unicorn, but I'm guessing you guys have no IT shop, is that right? Your IT is in the cloud, is that right, or? Correct. Okay. It is in the cloud. The cloud did do it in that startup. I mean, you didn't exist before. Yeah, yeah, we launched trade in the cloud, yeah. Right, and you got a team of developers, they program infrastructure, totally... Yeah, we have a team of four developers and the CTO, so total tech team of five, they're based out of India. We have a DevOps guy who basically runs everything for us, our website, our platform, where the data scientist participate in a competition, where our clients see the models, where our clients run for data to us, and where our machine learning computations run. Right, 3D mapping, you used to buy a box, big Unix box, maybe get a database, and some other software. Yeah, so we're a startup as well, right? So what you need is to process, if you want to create a 3D map of even a city, what we have to do is run 800 GPUs in parallel, blasting through imagery data. Now this is impossible, if we as a startup had to buy a GPU rack right from the bat, we would have been bankrupt even before we started. So like being able to spin up GPU servers in the cloud and also killing them after we're done with them, saves us a lot of money, but also provides so much flexibility for us to do prototyping and to make everything affordable and easy to implement with a very, very small team of a very talented system. So it's a real kind of pick and mix approach, you just think what kind of services do I need, to get them off the shelf and then adapt them to your needs? I think one of the great things that ADS has been able to do, like infrastructure used to be a very dusty and tangled industry. And one of the beauties that ADS was able to do is actually productize infrastructure. So you can now actually pick and choose different products from the ADS library and put them together, connect them, tie them up very, very cleanly with a very small team and create something that just exceeds any expectation from a startup so 20 years ago. So why AWS? A lot of other clouds out there, Google's got a good cloud, Microsoft has a big cloud. Why did you guys migrate or move to AWS, not move to start with AWS? How was that decision made? I mean, we started with AWS because we were on a startup program with AWS, but then we just really liked the support that we got. We had access to someone 24-7. We had a dedicated person who was helping us and we were just starting out. So this was our first time interacting with like cloud infrastructure. The support was great and then the pricing worked out great for a startup. But who said it's just as a startup, you are cost sensitive and the ability to turn on and off services as and when we need them, I think that was fantastic. Does it concern you that we've heard a lot about how the cost of services has come down? Quite a lot, there's a lot of cost cutting going on. But in the future, if you're overly reliant on your own provider, can't that put you into a corner? I mean, yeah, you get into troubles if you're at Spotify scale, but as a startup, the environment that AWS created for startups to flourish is incredible. The amount of, I think you have the same like you receive a huge amount of credits just for starting. So if you raise a seed round of money, which is let's say a one million US dollars, AWS puts 100,000 worth of credits on top of that. That's 10% extra funding for free, provided by AWS. Furthermore, they have this great architects that help you out with all the questions that you might have. If this is the first time that you are actually designing a whole architecture around a data processing pipeline or an API or a web platform, they're very, very supportive. What's the one thing AWS could do to make your life easier? If you're sitting here with Andy Jassy, what would you tell him? I mean, it's already fantastic. It's made our lives so much easier. I really don't think of anything that could have gone better with AWS. Really? It's pretty good. I mean, yeah, it reduced the cost even more. No, you didn't say reduce the price. Okay, well, thank you so much for talking to us about your experiences here on theCUBE. Hube, Hynan, thank you. Co-founder of Scape and also Janine Jane. It's really been fascinating to hear how you've grown your businesses. So I really appreciate you joining us here with me and Dave Vellante here at AWS Summit in London.