Hi and welcome to data futurology the podcast where you get to hear the stories and the lessons learned from Top industry leaders out there in the field today with them. We discussed things like data analytic strategy applications of machine learning and AI team building stakeholder management all the skills that you need to take your career to the next level. My name is Felipe Flores. I am your host. Thank you so much for tuning in. I hope that you're having a a wonderful week today. We have a trait of an episode. We are speaking with Alex and more live. Alex is based in San Francisco and California. He has a wealth of experience. He started with a very technical background, which is has been able to keep throughout his career and then ventured into running a finance department doing product management being in business development starting an advising. Startups he worked in Nvidia. And now he's the director of a I had change health care, which is a big Health Care technology company in the US. Alex is a wealth of knowledge and experience. I had an absolute blast speaking with him hearing about his journey. I hope that you enjoyed the episode just as much and if you do, please share it with a friend that would mean the world to me. Thank you so much for listening. Here's the episode. With Alex. Hi, this is Felipe today. I'm speaking with Alex Alex. Thank you so much for making the time. I'm so excited to be speaking with you. How are you doing today? I'm doing great. Thank you very much. How are you? Excellent, very excited at the beginning of the interview. I wanted to ask you how did you get involved in the world of data in the first place? What was it that Drew you in? What was that first experience for you? Actually, my first experience was pretty long time ago. My original decree was In the economic cybernetics, and I also got an MBA later early in my career spend a lot of time working with data which would be called data scientist now, but now it's used to be called operations analyst and then during the my career I guess chance to work on different projects. Some are more data oriented. A lot of it is software re-entered. So I kind of know the area from both sides interesting. Yes. And what was your first project or application that you really enjoyed in the space? It depends how we Define the space specifically in AI I think the first project I contributed for was expert systems when I was just right out of college expert systems for an interesting technology, which worked actually quite well in very few scenarios. And I think this is kind of informative the history of AI and history of data science is that expert systems were able to throw up to 70% You receive when implemented correctly. It did require certain amount of data, but not as much as the data requirements we have today, but as well as several companies, which did a good job and implemented and got very massive benefit from it. And there are a lot of other cases where people spend time allocated big-budget big teams and were unable to show the results then and I think it's kind of one of the early cases of AI and I think is demonstrate that it's very important to find the right project. For the technology you have and don't make an assumption that technology can do something that's not able to do yet, but it's coming or is about to be there something it's almost best to make sure you choose fewer projects and you invest sufficiently and you choose the projects very carefully so you can get to the results rather than spending a lot of money and time and achieving laughing. So in my case, I walked that experience was that develops and we had an expert system, which basically we're able to Solve the problem with silicone equipment. So every night our system would call into the switches Tilikum switches all over the country into the private switches and styled Company locations and to run Diagnostics and then I tried to fix the problem. So if the Diagnostics produce an alarm our expert system was able to resolve the salaam in 70% of the cases. So it's one of the foods and one of the very successful implantation of the expert system. Not the only one IBM did a great job implementing expert system for For tracking defects and hard drives. It's another well-known example, but overall in an expert system world. The number of success stories is not as big as we would hope but when the things for that well, it's it work. Yes. That's right interesting to be involved in this type of technology so early on and could you give us an overview of your career since then until now so I got my degrees in economics hibernate 6 7 and 6 is Artificial intelligence today. I got my degree in Moscow at the end of the studies. I came to United States for internship at the Bell labs and basically State here in the country after the lapse. I get my MBA I worked for Microsoft for seven years and different roles some technical and business. I did product management at your business development was head of one of these apartments in finance of all places, then I did start up for seven or eight years. I built a couple started. Oops advice about 2,000 overtime during the my startup. Here's I did get a chance to work on several are projects. Mostly contributor. I always keep looking for those two projects and they always show up in a strange places. Like it's usually one of idea which might not work but there were always some small number of AI projects in the background some people try to use AI to let's say ensures that the TV advertisement is shown Property types, for example, so they take a picture of TV stream take one frame and check against what they were supposed to show some people used it for different optical character recognition. Obviously, some people use it for different brand logo type stuff. So there were always some number for your projects, but they always been small and always kind of in the background and about five years ago. I started getting a lot of attention again, and that happened after Alex kraszewski Ilya sutskever 0 tape Paper the University of Toronto where they were able to show very good results for image recognition. The paper was think written 2012. I think publish 2013. And since that time what this paper has demonstrated is it is possible to achieve much higher level accuracy with AI systems, then we were able to see before and that high-level life accuracy opens a bunch of new scenarios, which were basically impossible or an addressable prior to that point. That paper published in 2015, but it's just K and Ilya sutskever actually changed the whole course of development. Suddenly. You have a massive number of companies interested in the eye. All of the big tech companies are all carrying and buying scientists and investing heavily in AI a lot of other companies doing now, so it's much much much broader now, so as the a I start becoming popular again, I decided that I'm going to dedicate all of my time to your projects and a few Nails. I spend a couple years working for and vtm invidious a cheap manufacture. It reduced graphical processing units. It's a parallel processing microprocessor. So what we did at the video we were able to repurpose the microprocessor which was traditionally used for graphics applications into the cheap for a i compute. So it's kind of very well time but within a couple of years in which he became a dominant parallel processing Computing platform for AI and the process will It's like 1 billion dollar business within a period of two years, right? Well then stick right after in vitia. I spent some time working on several small projects for the past three months someone and self-driving some in tools developer tools for you. And after that I joined change Healthcare change Healthcare is one of the largest technology company for the healthcare industry and it's not very well known company. The name recognition isn't very high because because it was formed through a lot of Acquisitions, but do they change Healthcare manage about 60% of Mexico claims and United States about 20% of Medical Imaging 20% of electronic health records. So it's a company which has massive amount of data. It's like the largest data management company in healthcare industry. The idea was since this is the company that has all the data and Healthcare space and the data is what's the biggest limitation at the moment for AI algorithms there for this company should Be the one that's best position to take advantage of AI Revolution and apply it to healthcare. So the company decided to build a large team High a lot of good people and in order to pursue this opportunity very aggressively. So we have a team based in San Francisco about 60 people now trying to apply a i to all of the different data sets. We have across the company and some of it is medical claims, which is document which several Pages loan which describes what happens. And what was done we do it for medical text understanding looking at a person's complete medical history and charts and looking what happened doing some of the patient identification which is more of a traditional like recommender system or marketing type AI applications and finally a lot of workflow. I have several examples, but I can probably give you those examples during the interview that sounds so exciting. That's fantastic. Thank you for that. It seems like You always been quite Technical and obviously now doing so much work in AI it seems that you are very strong in that space. How did you find it when you were moving into leading Finance function in companies or doing business development for some time? And how did you find that transition between the technical side and the business side? How did you manage that those moves and what to do learn? Yeah, I think in my case, it's kind of an interesting Journey when I I joined Microsoft Edge owner finance team. So I was a worldwide licensing pricing controller for Microsoft for a couple years, which is basically like a finance director which manage particular part of the organization and case in this case. I was Finance director for the licensing Microsoft licensing which was like 14 billion dollars business and we did a lot of work with business models, but this is kind of a job that you have right after MBA. I enjoyed it but a lot of data obviously the tools With Microsoft a pretty much the same tools that people only now getting elsewhere that was like fifteen years ago. But in most of the cases, I am actually kind of hybrid because in places where I'm the most valuable is when building the bridge between Business and Technology because I can understand technology and I can talk business look at the code and tell people what's working what's not but I'm also able to talk in a business language and communicate this business people. Understand help them understand what's going on. So in most of my jobs the optimal place for me is something that requires both the knowledge of technology and understanding of the business. So that's kind of my role and how did you develop the skills on the business side? Do you feel that? I was primarily through the MBA or did you have a particular professional experiences that help you on that side MBE? Yes, but then I think the best way to develop knowledge in any areas just move into that area, so it wasn't Finance for three years and they say whatever you need 10,000 hours of experience not to be good at something right. So basically you move to an area you spend long hours learning the craft and that's kind of how you learn and then you move to another and my resume a little bit strange for most people because it's almost looks like rotational experience that I spent three years in finance than three years in product management Centre as a business development, but this is kind of works well for me, so moving around And learning new skills and this and this is not traditional but this is certainly worked. Well for me personally keep me on my toes. Keep me interested. Exactly and I think that their skills that are so important and so relevant in data science today and it's something that I see a lot of people in the space that we need to improve our business skills. Their communication skills are product development. How have you seen those additional skills benefit your career, so it's a benefit you greatly I think in general you enter a new job on your own you company as more of a technical specialist. So if you join your company, you generally looked at for your technical skills, but in order to move up or move into different roles or just continue to progress within your career you tend to rely more on your general social skills ability to manage people ability to communicate get things done influence others. All those kind of things knowing the business understanding the business. - I think in general and data science. We definitely have a shortage of those skills at the moment. A lot of data scientists come from our technical background and don't necessarily spend as much time a lot of younger data scientist at least tend to come from a technical background because the tools are still kind of hard to use you need to have a lot of background knowledge, but I think over time is this particular role becomes a little bit more mature. I think everybody is picked up subject matter expertise and the areas are working. Salon more about the business are helping to optimize. So I think overall overtime overall Improvement that space. Yes, that's correct. And how was your move to business development? Is that something that you seek doubt or an opportunity that came to you you decided to take it? How did that happen? It's actually was kind of little bit more of an accident. So I was doing product management for the products that Microsoft was developing for business application market and then the Opportunity become at some point to work with other people doing something that Microsoft was doing but in a different segment or in a complimentary way, so basically I had some subject matter knowledge and I was able to bring it with me. They understand if the actual space and work with other people doing something very similar to what we were doing internally. So it feels a bit of an accident I guess. Yeah. And what do you think of that experience looking back? Oh, it was great experience. I think business development is very All you learn many skills, which you don't learn in other areas because it's easier to sit in the office and assume that the world Works certain way and once you get out there, you will quickly realize that it doesn't work the way you expect it to work and that's kind of the most essential part of the business development trying to kind of find a way to get the things done. Even though the world is not what one would hope it is and it was actually fun experience. That was the last few years my I time with Microsoft the products we had was very International. We did quite a bit of travel internationally to all the big cities big markets. So it was also fun experience to see the world. That's very good. And I know it was a while back but do you remember something that you had one perspective coming into that role? And then in that role you had to learn that the world worse worked in a different way or the world had a different perspective and then that you had to adjust during Our time doing that role. Yeah, I think maybe the way I can describe the fallen way. Like let's say I go for a business trip together for a week and let's say I'm in the office and I kind of know what's going on and I go for a week. I see so many new things. I got so much new information. I meet so many new people and come back to the office and in the office there is nothing changed. So I think from that point of view that's kind of one example, where just you realize that the company operates in certain way, which can Be successful at the time right if you focus on one thing and you do it very well. You can be very successful but there are many cases where the perspectives that you have. And the way the company operates may not be necessarily what the world wants and sometimes the company see roles like business development is externally kind of oriented. So it's more of a company communicating with the world, but I think the best way of using those four walls actually the opposite learn how the world wants to operate and bring this information back to the company and be able to Change the company to accomplish those goals. I think that's kind of what more productive way of thinking about it really great. And what led you to going to start ups after that Microsoft was maturing as a company and I felt that I quite sufficient amount of information to start my own business. I worked on several startups. That was a lot of fun. I learned a lot because in startups certain things are easier, right? You don't have to do anything. You don't want to do you only two essential things you don't have Have to worry about all the massive amount of administration and the communication and collaboration and coordination, which is not really necessary. It is not our fault you only focus on solving a specific problem and all of the referral staff. You just drop it, right? You just don't do it. So that was fun. But also within the startup fault the amount of resources you have suddenly very limited. It's not like you don't have enough resources. You just sometimes have not right because having less resources actually sometimes useful because it helps people to be active but when you have no resources at all or like next to nothing, right that's quite a bit of a challenge and then startups and massive amount of time figuring out how to get those resources built in the decks pitching investors and doing a lot of other things which works but it's also a very significant amount of time and so some of the things that you wanted to not do while working for the ride share companies some of the things like managing expectations does kind of thanks once you become a start-up founder you often find yourself doing Doing the same things managing expectations acquiring allies and doing all this other things very true. Good lessons from the corporate world that translate into the startup World quite nicely. Then what type of applications or problems where you tackling with your startup's overtime. So some of the early applications with it for it was a rough start up. So I'm just going to give you a couple examples. Some of them were in the developer tools where we were building different tools to Of course Technologies, which are no longer that popular anymore. But at the time they were hot like flash and Silverlight late. I had a bigger start off with I spent a large amount of time which was focused on real-time communication. So we were building something kind of in a go-to meeting space, but we were trying to go off the different Market with different scenario, but still a real time with video communication over the Internet. So in that respect, it's kind of similar to go to meet Union WebEx, but we were trying to do it. It for different scenarios like real-time sales. So you go to the internet. You see something online you click the button and you talk to somebody like real-time voice video. We will basically little too early for that particular Market, but I am still believes that there will be some point at which this bicycle Market will work very well for everybody. Yes. I agree. How do you feel about that being too early or possibly being too early with an idea? How do you feel about that now? And what did you learn from pursuing it? Less yeah, I think as a start-up you want to be a couple years early because before you put something together, it takes a couple years so you do want to be early but the question is how to be couple years earlier but not for and not zero right as a start-up. If you're trying to be ahead of the market. You're always taking risks the risks and not eliminate the fully you take a risk and you just accept the fact that there is a risk in doing something. You the this cannot be eliminated, right? The risk is just a part of the game and if you like doing something new and if you're willing to take risk and if you are in a position to do so because if you have a kids to feeds and don't do this, so I think it's very important for people to understand the interest in doing something Innovative and their own risk tolerance. And if you have a reasonably high risk tolerance and you're strong leads Innovation, that's a good path for you if you want to see something predictable or Or something currant seed just don't do startups. It's all going to work from that point of view every type of person with your own risk profile and your interest should choose a path optimal for you. Don't try to be in startups because it's called try to find a path which is meets your needs very good advice. And when you went into startups, was it in line with your risk tolerance? Yes to that risky world. Yeah for me. It was a good match. Obviously. I wish we were not that early. Lee but we were a bit early but that was a great experience and I'm sure I would love to do it again. There is a good quote from I think it was Max levchin who said first time I did start up we got nothing the second time we do the start time forget next to nothing the third time we do the startup I end up with enough money to buy Starbucks for a year, and he's a temple and of he become billionaire on PayPal. So yeah, I didn't my attempt some before but we'll see. Maybe I'll There were they great that would be fantastic and from startups you moved into Nvidia. Is that right? Yes after being in startups for several years having income from time to time, but it was a little bit unpredictable environment right? I wanted to figure out who is making money on AI and I looked around in 2015 say okay who can make money in Ai, and of course when the industries so early and a lot of things don't work as expected. Add the best way to make money is to sell shovels right for the gold rush and Nvidia was a company. That's very well Shinto sell the travel for the AI gold rush because the chips that they made was suitable for the type of AI applications which were getting a lot of traction at the moment for deep learning. So for deep learning and VT is a very good match. So 2015 as I was looking for who is going to be like some of the early beneficiary of the AI. Of the new era of Illusion and Vijay was an attractive and they were like not reasonable proximity from a house and Company with certain amount of security but also an opportunity to enter this particular market and for me, I am little bit less interested in other parts of Indonesia and not really had Wagga. I'm not really Graphics guy. But AI in particular was very interesting to me because of my prior experience and my personal interest. So I joined Nvidia and I was the head of a I develop relations. So where we we built very powerful business within a couple of years. That was a pretty wild right. Wow. And what is the role of developer relations? It's actually kind of not the same for every company, but my role had a couple things some of it is working with developers who are trying to build a applications on a media platform and part of the role is working with the large companies some of the largest software companies which were trying to adopt and video technology and the set Question slightly different for the startups for the smaller companies as they try to make something work pretty fast for larger companies. They want to understand how to take and video technology and integrate into their own stock how to make it work. So it's a visual logic companies a lot of kind of ongoing meetings when you have calls every couple of weeks or every month where you discuss the next set of issues that needs to be resolved for working with startups. It's a lot of broader Outreach speaking of the conference's helping the community to understand what's possible. Bill and help them develop those algorithms. So those Technologies it's a need to develop for their specific use case. But overall as a result of my work there are hundreds of people who are dedicated to writing the platforms on top of invitees stack and there are thousands of people doing AI now, it's become pretty big Market pretty fast exactly right Your Role there sounds amazing actually because you'd be helping organizations across the board adopting the Tools of AI for their particular applications some ensuring that week you'd see a number of completely different applications of AI and get to work and interact with the people trying to solve those problems. Is that right? Oh, yeah. Absolutely. I saw hundreds of different use cases and I saw a lot of people succeeding. I saw a lot of people failing the use cases for all over the place. Some was Peach some I'm Imaging some of the text some of the more traditional like Cyber, or fraud a lot of Enterprise AI so a lot of use cases all over the place. The number of use cases suitable for AI is actually on one hand is kind of tremendous. It's a thousand different use cases where I can actually help solve the problem right now today at the same time not every use case a manageable can be addressed using e i so on one hand you have thousands of use cases and other hand you have a lot of situations where people try to solve the specific problems that they have and what Fine is AI is not what they need. They need to solve some other problem which can be data problem or as a technology problem organizational problem. So while the number of use cases for your eye is actually kind of huge at the same time. Once you look a specific role or specific problems that people have not every problem can prepare just using AI very true. What were some of the commonalities that use so in applications that succeeded and versus the applications that didn't so I usually talk about pattern problem and data those three components. The first one is a pattern. So AI today as we know narrow AI the technology we have today including tip learning artificial narrow intelligence. This technology is basically pattern recognition technology, either my chain, so you find the pattern and data and then you use this pattern that you found to is a pretty big toe detect something as you look through the data the Thing you need to make sure that is that pattern there. The pattern has to be strong and persistent. There are many cases where patterns don't exist or they're not strong for example in stock markets the patterns tend to be weak because most of the useful information is actually outside stock market at self not invalid and stock price, but it's an outside stock market and corporate announcement. So in Market sentiments or purchase orders or whatever it is the buttons wooden stock market itself tend to be weak. So it's one area where where a lot of people try to apply a eye and set some progress but it's hard work and it's true metal improvements even though even with in stock market. There are some areas where yeah, I can be applicable most successfully like Foreign Exchange in some kind of specific areas, which works well, but overall, you know in general just looking at the stock market and predicting that the price next day is not very good example of a i usage way. It works very well is where the patterns are strong and persistent but they're slightly different. So for example viruses new viruses, we have computer viruses. There are several millions of new viruses couple million viruses generated every day couple million new viruses, but all of them explore the same exploits with in operating systems. So the number of exploits is like five per year and number of viruses is like 2 million per day. So a lot of the computer viruses are basically variation on the same thing right there slightly different, but they do pretty much the same thing and This is great for AI because for AI pattern recognition is the most important. So you want something that kind of the same but slightly different and therefore what you learned by looking at all viruses can be applied to find any new ones. So I think computer virus is very good example of the pattern recognition technology. We have today fraud fraud is another example where AI works very well because protons to be done in certain patterns with it. Silicon fraud or e-commerce fraud or financial fraud. It's usually follows particular pattern, but people do certain things and certain order in order to scam the bank. And once you learn what people do and how they do it you can detect when they do the next time. So if you detect the pattern and then you apply to Future cases to prevent additional fraud those kind of scenarios are very good A lot of the good scenarios again patterns that are repetitive for example looking for the facts in a production line. Ends in the industrial scenario when the fabric comes off the machine, for example, you can train a i to detect defects in fabric. You don't need to use human for that. So it's a pattern the pattern is it still takes a lot of efforts to program the computer to do it, but the pattern is there the pattern is pretty clear and you can trainee a to understand recognize patterns and take actions when something goes wrong parameter. Did you have much involvement with people that had to create new sense? For that we're doing work, I guess and the iot space in order to capture the data to be fed to the AI that they were building. Oh, yes. Oh, yeah, usually the kind of Downstream from that some of the examples we have is like every time the plane takes flight. It lands in the new airport. It's collects massive amount of data like five terabytes of data because it has tons of different sensors and so recorded and you download this amount of data. From the different it systems and then you have that massive amount of data where people basically don't know what to do with it because you have everything but what do you do with such a huge amount of data? And that's where AI comes in it comes first capturing everything everything everything and then once you have the data, you can apply a i to detect very specific. Thanks, but based on the amount of data you have let's say after each plane flight you can pretty much determined pretty accurately how soon the engine is going to fail, right? For example, so you can predict agent failure based on the vibration of something something right. The data is already there. The algorithms have to be improved and developed specifically for this application. But once you have this type of information, if you can predict the engine failure pretty accurately then you actually saving tons of money because you don't need to run technicians with every potential alert because you kind of have an idea you can basically predict much more accurately when the engine needs to be replaced when the crew needs to to be dispatched and when the Lord can be ignored because it's not a problem like this particular application. You can save billions of dollars just but having this prediction of the engine itself exactly right and tell me what type of things you're working on at the moment. So right now we work in healthcare space a change Healthcare is one of the largest Health Care technology company which manage myself amount of data in Health Care system in the United States as there are a lot of inefficiencies the government call it fraud waste and abuse. Plus a bunch of arrows on necessary Services. A lot of this inefficiency can be captured and eliminated people want to do so, let me give you one example the recently launched claim life cycle AI capabilities. It's going to be a whole bunch of different prediction capabilities. The first one is just predicting where the claim is going to be tonight or if it's going to be paid very kind of simple question, but in the United States alone, we have 262 billion dollars. Hours worth of claim getting stuck in process the computer will submit a claim to insurance company which describe what was done exactly six weeks late is they get an answer if I got an attachment and then you have to resubmit and the hospital says I already did that a judgment but you lost it or whatever right? Sometimes it's ridiculous. Sometimes it's much more meaningful conversation where the question is of Eligibility pre-approval all of other things but overall in the United States alone. It's a 262 billion dollars worth of claims get stuck in the Administrative process sometimes it's necessary most of the time it's just something that wasn't completed. Right? I wasn't submitted right or the insurance companies might have additional expectations which hospital did know that they supposed to have I mean, there's all kinds of different ways of doing things on Hospital side as well as on the insurance side, but basically there is my different amount of inefficiency and if we can improve this process we can save huge amount of money for the system of a something that is very, Very straightforward. So this is one humble way. You can improve the system very significantly and have a massive massive impact with pretty doable AI project. What is the project and look like we have a team who put together team the team has as usual data scientist date Engineers software engineers and subject matter experts a lot of the data. We use is the data were to have an hour systems historical data of medical claims since we manage such a huge. Amount of data we manage about 14 billion transactions per year since we manage such a huge amount of data. We can train the algorithms to find a pattern for example in a while insurance company my more likely to decline a claim which has something done suddenly and the as insurance company might do it for different reasons, right people submitting claim obviously have no idea how it's done. But if you look at the records insurance companies are not doing things randomly. There is a pattern why they find something eligible for investment and other things are not and you can try in the algorithms to detect those patterns on the overall data. The accuracy can be very if we train the algorithm on the data specific to hospital. Obviously, the accuracy will be even higher so because there is difference between hospitals there is different with insurance companies for human just to remember all that stuff is pretty much impossible. But for you I is very doable. We don't even need to do like medical text understanding for this just the data. We have a claim System just to structured data all the codes and the rules and procedures. It's sufficient to generate pretty massive savings for that. It's really great with a huge amount of data that you guys have. How do you pick which applications of AI to tackle? Yeah, actually I mentioned earlier interviews that will look at three things patterns problem and data. I kind of talked about patterns, but I didn't talk about other things. So a lot of the time was spent is evaluating the problem space. Face because what we want to have is a problem that's big expensive and the core to what we do claims is obviously very expensive problem for the industry overall a very expensive very big problem. And what we try to do is we don't actually do any development any data science work until we have a complete revelation of the opportunity and the complete evaluation includes Financial estimation how much money we can save by doing something and how much money? And make as a company and in general like in case of claim if we save billions of dollars and capture one percent of that as a revenue, that's good enough, but don't really need to capture a lot just capturing one percent of the savings is good enough because the numbers are so huge. Yeah, but the full Financial relations like what the business model like how we're going to get a Investments back. I've also do very extensive legal review because in the United States Healthcare is highly regulated industry and there is actually three different things compliance legal and contractual. So we do very in-depth analysis. Sometimes the legal review takes longer than AI development but that's kind of part of the situation. But in basic terms if we using our client data to improve the products offered to that specific line. So we improving the products for your friend to them. There is not that much restrictions on improving the products already providing. So there are some cases where the legal review is pretty straightforward. There are other cases where it's a little bit not in some cases we need to do the contract. That's where we're just the contract and resign it for the customer to make sure everything is done correctly. So it just depends on the situation. So we always start with visibility which has to be Financial which has to be feasible from are algorithms point of view because not all the problems were able to solve using technology we have today and the third part is the legal review so has to be financially feasible. It has to be visible technically so we can actually do it and it has to be legally feasible. So we meet all the legal and compliance requirements we have Right and at that point then you choose to Embark or take on the project. Yeah, after that. Once we have this analysis done will basically go after the biggest opportunities. We have some of the biggest opportunity we have can pay for the cost of AI development within the first year most of the easiest project to do if you can generate the money right away then obviously much easier to get approval for the management for doing the work some of the projects don't generate as much return and in my need to several years to pay back. So those projects usually put lower in a stack so we can get to them live it laters and projects which are generating immediate returns. Yes, that makes sense. And what point in the process of bringing one of these projects to life. Do you incorporate the end user? Well in our case in most cases the end user is the one of the business units within the company and for the stem projects, so they there from the very beginning Financial Visibility is done based on basically we get all the numbers we get from them. We don't have any other sources of numbers. So Financial visibility done based on what they think they can generate in revenue or reduce in terms of costs. So it's the numbers Financial feasibility is their number the data that we use is their data so they providing data as well and then they using the results of our modeling the models we develop or apis we built to put into their own process so there Basically always involved because we can't really do much without them. What makes sense are gorgeous good we good, and I was also thinking that in the case of healthcare, sometimes the customer that pays for something is very different to the user. That would be using the new product. So that's why I wanted to ask about that but it sounds like in your case is closer together, sir. Yes. So in our case a lot of the projects were doing initially the projects for internal business units, so we basically enhance The existing products using Ai and that's much faster. Right select. There is existing products with company rate is offering. We make those products better or more efficient. I can be products or services and we make them cheaper better more efficient and more effective between rate leaving you this way. So this allows us to cut the development time significantly. Now, we do have a plans to build brand new products from scratch. So the products the company offer, but we plan to develop so we have You think that in the works for that but this would obviously a lot longer process that might take two or three years to do. So our first priority is to enhance the existing products generate some returns show The credibility throw we can do good job and then start doing brand-new products from scratch great. And is there a particular application or project that you have work done in this role or are working on that? You're particularly excited about I think some of the more exciting things. Things that can be used in many different ways and can improve situation like across the board. For example, I think like medical text understanding is very good example in the United States when patient become a regular customer of the Healthcare System people who have a chronic condition of things like that we end up generating very substantial amount of information on that person. There will be results lab results for many years down every few weeks and there's complete medical history with As Physicians entering the information and we get to 400 pages on the medical chart. This type of information is extremely valuable. But this step of intelligence is actually been underutilized in the Health Care System the doctors obviously know their patients personal and in all the conditions a lot of the things can be done better. If we simply use the data were already generating, for example, you can use the data generated within the system to do prediction for different medical conditions ahead of time. And this is where the way I can help and it can be done on the time series data like lab results where you can track minor changes in the results obviously doctors do it for the main parameters right there looking for the main Trends, but you can use AI to generate much more nuanced understanding whether the person gets in better was so developing particular site conditions, you can use and this is from a Time series from lab results. You can do a lot of bad Diagnostics. Using Imaging which are part of the current health records. You can use a lot of things even on sext. For example, somebody wrote a paper where they basically just detected the look that HIV population people with HIV is and look for how many of them have depression. So if the person has depression, it's actually in the medical records. It's in the chart. So it's already in the system. They don't often receive an additional treatment and if you can simply provide additional treatment for depression the outcome can be improved. Significantly. I don't know if I'm making it clear but for many of The Chronic there are multiple conditions and patients often treated for the main condition and the secondary or the third. Thanks secondary or third of the things don't get captured or don't get treated for and by simply providing more comprehensive care across all specials. You can significantly improve the outcomes. And then this is the best depression is one of the things that's very easy to detect because people say they don't feel good, right so whatever they say, It's in the records you but unless you treat it your probability of survival or you outcomes are not going to be very good. So interesting that is an amazing application that can impact the lives of so many people what would be roll out or the execution of a project like that look like once a the Pai is developed that is able to pick the people that need treatment for conditions that are not being treated for what would happen with our recommendation after that. Yeah. It's actually very big Question within Healthcare System, you are trying to introduce piece of technology into very human-like very human process. Right and there are those type of tension. So like difficulties both on a data capture as well as the incorporation the results into the workflow because in order to have the data that it has to be in the system, but let's say suppose the data is in there. Then what happens who is getting notified who is going to get paged and whether the results are Short enough. So the healthcare professionals don't ignore them, but actually act on them and change the way they treat particular patients. So in healthcare AI is often part of the problem, but not the only part the big problem is if something is detected we have to detect it with very high degree of accuracy or otherwise the healthcare professionals will not they are and then if something is detected and the accuracy is high enough who is going to do what how different right who is going to run somewhere something and Etc. So you actually have enough oil to be effective in many cases of healthcare organization. You actually have to change the human processes to react to those are recommendations. Yes. And is that a challenge that you had to tackle so far or is that upcoming in this particular case? We haven't got fine enough to deal with this problem. There are people who experience challenges working with Healthcare System on the regular basis many startups having a lot of problems getting the So I can be knighted states. There is a lot of startups will fund that doing Ai and Healthcare that's like several thousand more than a thousand startups get funded. Well and the process is usually takes let's say six months to get funded over there were something like that. Right? So you got the finding then takes another six months to hire a team of Engineers did scientists. Sometimes you get my journey peoples and you'd like but let's say it takes six months to get the team and then it's often takes 18 months for start-up to actually get the data that's usable and then the Develop the algorithm and then they try to implement algorithms and Healthcare System. And the question is like every Dimension. So who is going to do something different based on whatever I recommend Because unless somebody does something differently. Your ear is not being used in doesn't matter that your prediction accurate or not. It has to be human process where somebody will do something differently which will result in human out in better outcomes. And as I say I going to be very effective even though it might be perfectly wonderful, okay? Exactly, and that might be a tougher question who is going to change their behavior. And how are they going to change? It might be a tougher question then they will be Dai sometimes sometimes it is but that's kind of happens with a new technology. So every time we have a new technology coming like big innovation waves, it's usually comes in different stages. Like let's say the couple of the recent waves of big loan Innovation waves were driven by internal combustion engine. And the semiconductor circuits so internal combustion agent early 20th century we started from just figuring out how to build the engine then let's move to the mass production of cars. And then what happens is we get actually most of the economic benefit by restructuring society around that Innovation not from the navigation help but from the destruction of society, so building out suburbs shopping malls move address that's kind of the side effect of internal combustion engine and for the internet the core. Sorry, not for the Anyway, if the coroner vision is semiconductor circuit and then mass production of computing but then a lot of the benefit we get from telecommute change from talking to people overseas, like we're doing right now from internet, which is old like by-product. So side effects Westsides restructure the way it does things around the new technology. So I think with AI is going to be kind of the same way the first thing we need to figure out just how to make it work. Then we'll start producing AI algorithms in Mass cheaply quickly just crank the I'm out on a conveyor belt like we do computers today, but the biggest benefit will come later when we actually structuring the side society around us AI algorithms in healthcare from that point is very exciting. For example, many of the basic applications of AI in healthcare detect something or predict something let's say there are a couple of good papers recently one demonstrated. Then you can predict Alzheimer five years earlier than before or six years before and another one. Yes, you can project breast cancer five years earlier than before the AI itself just give you the prediction. The projection itself is what AI does but the biggest values actually not a prediction solved. But what are you going to do about it? Right? So like if you can detect Alzheimer's six years earlier or breast cancer five years earlier. We probably don't even have a therapist to involve that early because it's not going to be camera therapy or some going to be radiation because the cancer is not quite cancer yet. It's kind of Of the prayer concern so we need a new technologies or new procedures to treat it. But once we do once we figure out how to structure the things around our new protective capabilities, maybe we're not going to have a breast cancer is a problem in the world. Who knows right? Wow, that is incredible. I'm so glad that we have people like you and your team working on these type of challenges. It is exactly what we need what we need in the world. I wanted to ask you two questions before we wrap up. So curious because you've had such and varied and amazing background and trajectory new career. So I wanted to ask you what are you most proud of that you've done in your career. I kind of have several things that are kind of interesting. I had several experiences where I had to build a billion-dollar business and my last job with Microsoft to build a billion-dollar division for Microsoft a lot with through Acquisitions at NVC will build a billion-dollar business also then a couple of years By finding a new market for existing semiconductors. So finding or developing a market for the GPU circuits. I believe within Healthcare System. We have at least as much opportunity to make compact believe we have a at least billion dollar opportunity to do what we're doing just focus on Innovation. I think this is something that I personally enjoy and sometimes is difficult to quantify the results because Innovation is obviously not as predictable as running a big business. So sometimes you succeed. Times you feel about this is something that I personally enjoyed greatly and that's something that I think will be left after I'm gone definitely and I think that might be the answer to the last question that I wanted to ask you which is a piece of advice that you would like to give the listeners. You might have just given the answer. But what would you say to them? I still probably say that we're currently in your list ages of AI adoption some of the projects that I mentioned very doable to today and some projects are little bit longer term and I think my advice is be patient invest in your skill set and your knowledge and choose your projects wisely because there are some things you can do with AI today, which is a bit more pedestrian like solving the claims problems where we can make very in be compact today without waiting your without spending a lot of money or spending many years working on that as AI problems little bit further. Out some of the other things I mentioned like solving the breast cancer problem or Alzheimer that will probably take a few years. So sometimes you work for an organization which has a long-term vision and able to invest in it for several years, and that's great. And as the cases you don't have this ability to work on a single projects on 10 years and still produce results. So choose your projects wisely and kind of don't assume that moonshot solve cancer problem is necessarily the best one because the fact is Early in is a adoption some of the tools we have and not perfect. Some of the knowledge we have is actually liking so but if you continue working on data science and pushing the boundaries, I think we have a tremendous opportunity and we can make huge progress for the society. That is a fantastic note to end on Alex. Thank you so much for sharing all your insights your wisdom your perspectives your journey all the Lessons Learned. I really really appreciate it. It's been a Absolute blast. Thank you so much. Thank you very much for having me. I wanted to tell you on the rmit online masters of data science strategy and Leadership. I was one of the industry advisors for this program. It's an online Master's program and it covers both a design strategy and Leadership and it has also a technical component highly highly recommended for people wanting to get ahead with the program. You can gain this Advanced strategic leadership and data science capabilities required to influence executive leadership teams and deliver. Nationwide solutions for more information visit online at rmit dot edu dot a you that brings this episode to conclusion. Thank you so much for listening. Please find us on data futurology.com or on Facebook Twitter LinkedIn or Instagram as that of utility. Also go to data futurology.com forward slash podcast to find the show notes for this and any other episodes. If you liked this episode, it would mean a lot to us. If you could leave us a review wherever you listen to our podcast. I hope you enjoyed this episode and that it was helpful and valuable for you. Thanks again and you next time.
Alex Ermolaev has been involved in the software industry for 20 years, including AI-specific experience at Bell Labs, Microsoft, several startups and now Nvidia. He is currently a leading AI software developer and works with groundbreaking companies that are implementing incredible AI solutions across several domains. In this episode, Alex describes how he started in the data space. Early in his career, he got a chance to work on a lot of data and software products. Enjoy the show! We speak about:   [01:50] How Alex started in the data space   [04:55] Alex’s professional background    [10:30] Working for the finance team at Microsoft    [14:55] Business development skills    [18:50] Challenges working with startups    [22:10] Working at Nvidia    [26:20] Successful and unsuccessful AI patterns    [30:00] AI and collecting data    [35:15] How to tackle data problems using AI    [40:30] Exciting uses for AI    [43:15] The execution of new AI programs    [49:00] What Alex is most proud of    [50:20] Be patient and invest in your knowledge    Resources: Alex’s LinkedIn: https://www.linkedin.com/in/alexermolaev  Quotes:    “The best way to develop knowledge in any area is to experience it.”    “It is easier to sit in an office and assume the world works in a certain way.”    “Don’t be in startups because it’s cool, try and find a path that meets your own needs.”    “Working with startups is a lot of broader outreach and helping the community understand what is possible.”   Thank you to our sponsors: Fyrebox - Make Your Own Quiz! RMIT Online Master of Data Science Strategy and Leadership Gain the advanced strategic, leadership and data science capabilities required to influence executive leadership teams and deliver organisation-wide solutions. Visit online.rmit.edu.au for more information  And as always, we appreciate your Reviews, Follows, Likes, Shares and Ratings. Enjoy the show! 