Welcome to episode #067 of the SDS Podcast. Here we go!
Today's guest is Vice President of Research at Sentient AI, Risto Miikkulainen
Where is the world of AI going? With developments happening so quickly, it can be hard to keep up. Join industry veteran and practitioner Risto Miikkulainen in today's value-packed episode to learn about the latest developments and the direction in which the field is leading the world.
Risto will share details of the exciting real use cases Sentient AI is carrying out using the latest cutting edge AI technology available. You will also learn the difference between evolutionary algorithms and reinforcement learning algorithms and get some ideas for further resources to stay on top of this fast-moving field.
Tune in now for some fascinating insights and get yourself up to date!
In this episode you will learn:
- Autonomous AI Stock Trading (5:30) – www.sentientim.com
- AI Intelligent Commerce (6:28) – www.sentient.ai/aware
- AI Digital Media Design (7:58) – www.sentient.ai/ascend
- Optimizing Agriculture with AI Technology (11:05) – www.sentient.ai/blog/bringing-artificial-intelligence-agriculture
- 2 Types of AI: Evolutionary Algorithms and Reinforcement Learning Algorithms (15:49)
- Resources for Beginners in AI (18:53)
- Improving Access to AI (21:30)
- How AI is Changing the Employment Landscape (25:53)
Items mentioned in this podcast:
Kirill: This is episode number 67 with Vice President of Research at Sentient AI, Risto Miikkulainen.
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Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today and now let’s make the complex simple.
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Hello and welcome to the SuperDataScience podcast. Today we’ve got a very interesting and exciting guest, Risto Miikkulainen. So there's a lot of things you need to know about Risto and we probably won't have enough time to go through them in this quick intro, but here are a few. So Risto is a professor at the University of Texas in Austin. He's been working in artificial intelligence for the past 40 years. He is also a published author and he's published book titles such as Computational Maps in the Visual Cortex, Sub-Symbolic Natural Language Processing, and others. And also Risto is the Vice President of Research at Sentient Technologies, an Artificial Intelligence startup in the Bay Area.
So as you can see, our guest today is an expert in the space of AI, and that's exactly what we're going to be talking about. And what was interesting today was that we just met with Risto today online and we were going to record a podcast some time later on, but we started getting into such interesting topics already, so we decided to record a podcast already. So this is going to be a quick one because it was quite short notice, Risto didn't have that much time, but hopefully we'll be able to get Risto on a future episode some time down the track.
And another thing that I wanted to point out is Sentient AI. So Risto is working for Sentient AI as their Vice President of Research. This is a great startup in the space of artificial intelligence, it's been around for 10 years, and the whole purpose of this podcast was to help Sentient get their word out there and possibly influence people to get more into AI. It's very cool how passionate they are about artificial intelligence, and we're very grateful that they've "lent", so to speak, one of their top talented people to come on the podcast to share some of the top and most cutting edge things that they're doing in the space of artificial intelligence. So you'll get quite a few cool applications of AI from here.
And also from this podcast, you will understand, hopefully, or see for yourself, why it's so important to slowly start, if not getting straight into the space of AI, but just keeping it on your radar and considering what possibilities and opportunities might exist for you in the future in the space of AI and how quickly it is coming ahead. Because ultimately, this is indeed something to look out for if you're building a career in this space, the space of data, and the space of data science, and in the space of technology.
So there we go, we've got quite an exciting chat ahead. And without further ado, I bring to you Risto Miikkulainen, the Vice President of Research at Sentient Technologies.
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Welcome ladies and gentlemen to the SuperDataScience podcast. Today I've got a special episode with Risto Miikkulainen from Sentient AI. Welcome Risto, and thank you so much for joining us on such short notice.
Risto: Thank you. Happy to be here.
Kirill: So tell us a bit, where are you calling from? It was such a surprise, I was expecting you in San Francisco, and you're completely on the other side of the world!
Risto: Yeah, today I'm in Helsinki, Finland. The weather is just nice here, the sun doesn’t go down so you get a lot of work done.
Kirill: Yeah, that’s fantastic. I heard at this time of the year it’s still sunny at midnight.
Risto: Yeah, it is kind of a little dusk, but it comes up right at 2:00 A.M. again. It’s hard to get any sleep.
Kirill: That’s so cool. It’s interesting as well, at the start we had a quick chat about your background. You come from, or you were a professor at the University of Texas. Is that right?
Risto: Yes I am. Actually, I’m on leave, have been on leave for a while. I’m at UT Austin in the computer science department there.
Kirill: Okay, cool. And give us a bit of a rundown, if you don’t mind, about the work that you do at Sentient and what is Sentient all about.
Risto: Yeah, sure. Sentient is one of the best funded start-ups currently in artificial intelligence. We started with stock trading and have added intelligent commerce and digital media to that. We build products that are based on cutting edge artificial intelligence, in particular deep learning and evolutionary computation, so that is machine learning that not only predicts but also optimizes what you’re doing on the Internet or in the stock market.
Kirill: Okay. And I had a quick chat with Babak who is the CEO, as I understand, of Sentient AI, and he gave me a quick overview of the three products that you have. Could you talk a bit more about them, those current three products that you offer?
Risto: Yeah, sure. So, the training product is what you might expect. It’s stock trading. We evolve, using generic algorithms, stock traders that are completely autonomous. I mean, it takes a long time to evolve them. We’ve actually built a whole grid computer-like system where we evolve these traders for several years. 40 trillion of them were tested in this process over one year, and some good ones were found. And these traders are sets of rules that tell the system how to trade stocks. They observe the time series and then they make trading decisions on their own. In this way, they are completely autonomous. We of course monitor them and make sure they are not getting out of their league, but that very rarely happens. They actually have been trained and tested so well that when we actually deploy them, they do quite well. So that’s stock trading.
The second product is intelligent commerce. The idea there is to build an interface to retail, for instance, some kind of e-commerce site where there’s lots of items in the catalogue and the users, the customers, would like to see them. They would like to be able to navigate that space of all possible products conveniently. Not by using keywords, having to guess what keywords are relevant, but by looking at the product and saying, “Oh, I like something like this,” “I like something like that,” and AI will figure out what it is that you want.
For instance, if you are buying sunglasses or shoes or other apparel, you get a first window that shows you what kind of shoes there might be and then you can click on those that are more to your liking and you get another sampling. And this way, after a couple of clicks you usually find what you are looking for. The beauty of that is that in a normal keyword kind of search, the customers might see 15% of the catalogue, typically in a week 15% of the catalogue is shown to the users. Using this kind of search that’s based on users’ input and feedback, 70% of the catalogue is actually shown, which is much better for the vendor, it’s better for the user, for the manufacturer of shoes, they get more visibility. So this is an intelligent interface. It is based on user’s feedback. In a loop, AI figures out what it is that you’re looking for. That’s the second product, that’s Sentient Aware.
And the third one is Sentient Ascend, which is in the digital media space. It means that, for instance, we are optimizing web interfaces. So, again, in an e-commerce site, it’s a very important concept for the landing page, how good a landing page – is it going to convert the user to a paying customer or somebody who signs up for the membership or something like that? There might be a goal for this website to convert a user into something. And it turns out it’s actually quite hard and quite difficult and it’s a whole art of it and so on, how to design a webpage so that this will happen.
And there are general principles of design, of course, like you have colours that are consistent, you should be clear where the action is. But also things that are harder to understand, like how do we relate to the user, what kind of images to show, what is a good call to action and so on. And there’s a whole area, discipline, called conversion science that has emerged that tries to undercover these principles. And it turns out humans can do a pretty good job, but if we let AI do the design, we can do quite a bit more. Human design – you can only think of a couple of designs and test them and evaluate them. But if you let AI do the designing, particularly evolutionary algorithms, you can test a lot more. And you can test combinations and you can make decisions quicker and find pages that convert that humans would not come up with very easily. For instance, one of the sites that we’ve built, we only optimized the widget, which was the call to action and a button, and they called it “the ugly widget” because the colours were very flashy, but it turned out that was what the users wanted. They wanted to see where the conversion was and they clicked 43% more often using that widget than the human-designed widget. And this can be extended to many other things, but that’s a great example of Sentient Ascend, this kind of digital media optimization using evolutionary algorithms. Evolution is doing the design, not humans. So those are the current three products. Yes.
Kirill: Very interesting. It reminds me of that situation they had with—remember AlphaGo beat the world champion of Go last year in South Korea? And it had a few moves where it was putting the pieces not on the first, second or third line, but on the fourth line, and nobody has ever done that in the past 3,000 years of the history of Go and now this has revolutionized the whole—everybody is now thinking and analysing that because it led the computer to win. So everybody is now “Whoa, maybe we should do that as humans.” That’s really very different.
Risto: Yeah, that’s exactly right. The computer, the AI, does not care. It does not have the same prejudices as humans do. It plays very cool Go and it makes very cool optimization. I’ve got to tell you another story. In addition to those three products, we of course have various proof of concept projects going on. And some of them are not even immediately products, they are something that’s just cool to do. So one of them is cyber-agriculture, which we are doing with MIT Media Lab, Caleb Harper’s lab and Phenom, their for profit arm.
This is a very interesting project. The idea is that they can build containers where you can grow plants – all kinds of plants, food, maybe fibres, maybe medicines, and you completely control what’s in them: temperature, water, light, nutrients, everything is controlled. Now, vertical farming existed for a while, but what if you can completely control everything? It turns out nobody knows what to do. There are some rules of thumb, you know, you should have sufficient water, for instance, sufficient light, but how do you really optimize the growth? So we let the same kind of evolutionary algorithm figure this out and we ran a loop so they planted what our algorithm suggested and three weeks later we get basil that’s growing according to that recipe. And it turned out that we discovered some things that we already know, AI discovered it.
For instance, if you try to optimize the size of the basil plant, you kind of lose on flavour. But if you try to optimize the flavour, you get smaller plants. That’s kind of what you expect – strawberries, tomatoes are like that – but the more interesting thing was that we were optimizing the light, the kinds of light, how much light, how long the light period was during the day. And exactly like you suggested, nobody thought the plants would thrive if there was full daylight all the time. Everybody expected that 8 hours of night is necessary, turn the light off for 8 hours. Well, the algorithm very quickly discovered, “No, you turn on the light and it’s on 24 hours and the basil grows much better.”
So this is something the people did not anticipate, but the AI discovered it again very quickly. So, yes, these kinds of opportunities exist now, especially for the algorithms that discover solutions, don’t just imitate and mimic and predict data as it exists, but they have an opportunity to be in an exploratory mode. They try out things and then we can use predictor to see what actually might come up and they can optimize and discover things that we don’t already know.
Kirill: That’s very cool. I just actually yesterday read a quote by Mark Cuban. You know, Mark Cuban is on “Shark Tank” in the U.S., and he said the world’s first trillionaires are going to be AI entrepreneurs and AI start-ups. It sounds like you guys are right on track for that, right?
Risto: Well, that’s the plan, definitely. AI is everywhere, and it will be everywhere, so currently a lot of the excitement is really in the algorithms and methods, but it’s time now to build real products, things that people use. The techniques are powerful enough. For instance, this website optimization that I mentioned, we have now consistently applied it to many, many customer sites and consistently they beat human design, routinely. And this is now in a real world application, where there are real users who are not AI experts, but are actual users who want to buy something or want to sign up for something. This is what’s really exciting to me.
They’ve had in the evolutionary algorithm conference, GECCO—for a long time there’s a competition called “human competitive results” competition. And for many, many years people have shown that evolutionary algorithms can discover these things that we don’t know how to do as well. But this is the first time to my knowledge that it’s actually out there beating human design over and over again every time it’s applied. That’s really exciting.
And yes, the sky is the limit. That’s kind of cool. And it still requires human ingenuity in identifying the problems and giving evolution enough space to work with, to explore. So we have to think about that, but that's the kind of jobs that will be created, is to work with the AI systems that create new things. You give them the problems, give them the parameters and give them tools and the AI will then discover, given that space, something really good.
Kirill: Okay. That’s fantastic. And you mentioned evolutionary algorithms a couple of times, whereas something that we have already talked about in the courses that I teach with Hadelin de Ponteves is reinforcement learning algorithms which are also AI. As I understand, those are two different types of artificial intelligence. Could you comment on that for our listeners to just give it a quick overview of what’s the difference between the two?
Risto: Yes, absolutely. AI is a very broad area, there are many methods. And even machine learning has many methods in it. Most of the machine learning that you see currently is based on supervised learning, which means that you have a dataset, “This was the situation and this is what happened.” It allows you to predict, given the ground truth. Like meta-prediction, for instance. You know what happened the next day and you can learn to predict that.
Now, reinforcement learning and evolutionary algorithms are a little different in that you don’t know what the right action is. You have to explore and find that action yourself, or really, the algorithm has to do that. So in that sense, reinforcement learning algorithms are in the same category. Not in the supervised learning, but in the reinforcement learning discovery exploration category. So what you require is some kind of a system where the algorithm can exploit, can try out things and see how well they turn out. You don’t know what they should do, what move they should play in Go, or what trades to make in stock trading, but you can tell after a while how well they are doing, how often you win in Go, how much money you make in stock market. That’s the feedback. It’s not the optimal action, it just tells you how good your action is where you may try it. And then you explore many different actions.
Now, in reinforcement learning, the approach is to evaluate values for the actions in different states and then try to interpolate between those actions that you see. In evolutionary algorithms, you are evolving the entire system at once. You are testing it at once. You are not assigning values to individual actions. And in that sense, it’s still different. And what happens then is that reinforcement learning is perhaps best suited for learning over the lifetime of the agent, when you’re learning while you’re performing and it counts, what you’re doing.
In evolutionary algorithms, they are better in discovering good final designs when you can explore very broadly and spend a lot of time and effort into exploring avenues that might be more wild, perhaps, and there’s not big cost for it. So, for instance, if you have a robot that has to learn in real life on your physical system, you might want to use reinforcement learning. If you have a simulator for that robot, you can learn in simulation and you can try things that you would not try in a real robot. And in the end, evolution can then discover in the simulation something that can be done on the robot and works really well.
So they answer a little bit different questions, but they are both examples of this kind of learning, where exploration is the key.
Kirill: Okay, gotcha. Very interesting. And if somebody was new to AI and wanted to get started into the space and completely new to AI, even new to machine learning, where would you recommend for that person to get started?
Risto: Okay, so this is very interesting. It used to be that you could pick up a textbook that was written a couple of years ago and be pretty much up-to-date, maybe look at a few journal papers. And that has changed. Now the field moves very, very fast. Three months is a long time now in AI. And it’s a bit of a problem, it’s very hard to keep up with that, but there’s no question you have to know the fundamentals first and there are certainly textbooks and books that do that. Right now, perhaps the most productive, hottest area is machine learning.
So you might want to pick up a book that’s about machine learning as opposed to more broadly in AI. But there’s much that the rest of AI has also to offer, including various reasoning systems and logic representation. And I believe strongly that this machine learning that has been very powerful in the last couple of years is going to stay powerful. But eventually we need to connect what is being learned with these reasoning systems. So that will also be part of it. And if you want to know where the future is going, yeah, learn about machine learning, but also learn more broadly in AI. There are excellent textbooks in both those areas. Now, if you really want to do the latest, then you have to go to the web. You have to look at archive, you have to look at blog posts. And there are some really good ones. It turns out that sometimes the latest papers are very technical, kind of hard to follow, hard to see the picture, but it turns out there’s also a whole other community of people who write these blogs that describe what these inventions in these papers actually are about and put in perspective.
Sometimes you can find those, they are mostly tutorials, perhaps. A tutorial on generative adversarial networks, for instance; a tutorial on probabilistic soft logic, various aspects; tutorial on evolutionary computation or neuroevolution. After reading the textbooks, pick up some of those tutorials and you can pretty much catch up relatively easy and new. But the field is very broad. So don’t assume that you can catch up with everything immediately. That takes years. But certainly catch up so that you can read the literature and understand what the excitement is about.
Kirill: Okay, gotcha. I totally understand. And I actually agree with that. It’s incredible what we’ve seen. Like, when we’re doing the AI course which we’re creating now, there’s been so many changes even while we’re creating the videos. You just have to keep up with things.
I recently heard an opinion that AI is not only going to get more sophisticated and more powerful in the challenges that it can solve, but it will also get more accessible to people. It will become more kind of drag-and-drop, there will be more tools for people to use AI. Not the most cutting edge AI, but over time things will become more accessible to everybody, even the people who don’t want to pursue the technical side of things. With that in mind, people who are philosophy majors, who have the capacity for critical thinking, who have creativity, we’re going to see an uptake in AI among those people. Would you agree with that, or do you have a different opinion?
Risto: I absolutely agree with that. Tools are very important and this is a big part of why AI is now moving at the speed that it’s moving and so many people are getting into it. Because there are tools that people share, including software packages like TensorFlow, for instance. It’s open source so people can also contribute to that, and people have. So in that sense, you can take TensorFlow and download a model, run it, and see what it’s doing within a couple of hours. And then you can maybe build your own dataset and apply the same model to your dataset and it won’t take that much work to do that and you might already be doing quite well.
If you’re a retailer, if you have some medical data, whatever it is, you can build a model very quickly. You still have to understand what its limitations are. I mean, it’s too easy to attribute a lot of intelligence into what’s happening, and you have to understand what it can and cannot do, and therefore you should read those textbooks and understand it. But then actually running an experiment has become much, much easier.
It used to be that my PhD students would have to take about a year to build the software before they could run a really good experiment, and this is not true at all. Same is true of robotics. Now there are all kinds of robotics kits and hardware that’s available and you can get started and that’s how robotics is also accelerating rapidly.
So yes, I totally agree this is the best time and we need those philosophers, we need the psychologists, and we need the medical people. That’s how we push AI forward. The applications are always exciting and more people get into the field. And when new applications come out, there’s always challenges and then us who develop the algorithms will have to look at it and see how can we improve the methods and actually make this work. So I think it’s exciting.
I’ve never in my career, 30 years I’ve been working in AI, it’s never been like this, that we are so close to application. We are working with people who have the original data, they see the patients, or they collect the statistical data from the customers, and we can talk to them and we are immediately in the thick of things. And I think this is the most exciting thing about this cycle of AI hype, that it feels different because it involves a much broader base of people, both academics, people who develop algorithms, and those who apply them, and those who use them and benefit from them. So, I think if this is going to stay a little longer, we absolutely need the people from outside, who are not AI experts, to use these tools and do what they can.
Kirill: Okay, fantastic. That’s very interesting. And what are your thoughts on the other side of the spectrum? So lots more people are going to have access to AI, and we want more people to use AI, but what about the people whose jobs are threatened by AI, like chartered public accountants seem to be getting slowly edged out by AI, even truck drivers and slowly car drivers and so on? So there’s going to be lots of change in the world. I know this is a very philosophical question and there’s no one correct answer to it, but I just wanted to get your opinion on it, being a person who is driving this change in the world. What are your thoughts on that?
Risto: Yes, that’s a very important question. And there’s no question that it will require change in society and in jobs. This is a discussion that has to go on alongside with these innovations and we cannot really have an immediate answer to any of these issues. Now, one important realization is that we’re not going to have AI replace people completely in all areas. For a long time they’re going to be assistants. They’re going to be making the job of the humans easier and more productive for quite a while. That’s what we develop AI to do. And even if you take, for instance, self-driving cars, they’re going to have to work with people for quite a while and on people’s terms. Same thing in all kinds of medical decision-making. Stock trading – we have autonomous stock traders, but we’re always watching over their shoulder and we decide where to deploy them, what markets and so on.
So, at least for the foreseeable future, they are just simply better tools for people who are working in those areas. But in a longer period, yeah, there will be a change that we don’t need humans to make those low level decisions, the actual decisions anymore, or driving necessarily the actual cars, we need people to plan where the car should go and like I said, what markets to trade. So there will be a little bit of a change. Those jobs that are doing the nitty-gritty actions will be raised a little bit higher so that they can utilize the AI to be more productive.
And there will be entirely new areas of jobs as well. For instance, cyber-agriculture. That means that we will grow plants anywhere. They can be grown underground, on the roof, in the subway. And there will be jobs and markets and maintaining in this kind of [indecipherable] agriculture system. That’s what makes it interesting. It’s kind of hard to imagine what the jobs will be like and it’s very difficult to imagine whether it will happen so quickly that there will be a displacement of people who used to do the old job and it’s very difficult to learn new jobs, or if it will be slow enough that they would gradually be phased out and new people trained for new jobs.
This is a discussion that’s on-going and we continuously have panels and discussions, the government leading some of those, there’s publicity events on this. And I think it’s an important discussion. But my perspective, as in really my personal perspective, is that I believe that there will be a slow change where people are always working with the AI and gradually learning to become more effective because they use AI as tools and in the end we will be better off.
Kirill: Okay, gotcha. I understand. So the future doesn’t look as bad as a lot of people are portraying it. There’s hope, basically, for the human kind.
Risto: Yes, there’s definitely hope. Humans are very resourceful and will figure out how to take advantage of this new tool just like we’ve taken advantage of all the other tools: electricity, steam engines, all those things. They all caused transitional periods that were sometimes more difficult, sometimes easier, but we emerged better off in all those cases.
Kirill: Gotcha. Any thoughts on the Universal Basic Income, the UBI, that’s being pilot-tested?
Risto: That’s getting a little bit outside my expertise. It would be interesting if people could be motivated to do something good with their time which they should be planning out rather than just watch TV – be more creative or travel. I think the Universal Basic Income only works if there’s something to keep people busy and engaged and interested and motivated. And that piece is kind of interesting, how that might happen. It might require cultural change, but it’s certainly something we should research more.
Kirill: Okay, gotcha. Well, thank you very much for the overview. I know we went a bit to the side. I’d like to pull back into Sentient AI. Could you give us a quick overview of the team? How big is the team? I remember Babak told me that you guys have been around for a long time, like 10 years or so, you’re coming up to your 10th anniversary.
Risto: Yes, that’s right, this year. The beginning was the trading system and this is a great first application because it requires nothing. You just have to have money and algorithm and you can do it. You don’t need to have support people, you don’t need to build anything. It’s a very good way to get started. Totally the opposite end is this e-commerce. You have to have customers, users, you have to have web optimization, databases, cloud, everything. So it really takes a village. It’s a huge team and not all of those are AI researchers obviously.
Big computation [indecipherable] so we had to build a grid computer system to just run these algorithms, 40 trillion traders. And now we are running these deep learning networks that we are evolving. They run on 5,000 GPUs simulated in the grid, which makes that computer I think the sixth most powerful computer in the world because those GPUs have so much power when you put them together. So, there’s a dozen people working on the compute and then there’s maybe another dozen doing the core research, underlying algorithms, and then maybe double that, 25 or so who are doing the development based on those algorithms, how you build applications based on that, new features in the products, and then our product development team is maybe about the same size. And on top of that you need to have customer support. You need to have salespeople. That’s something that we are kind of different from many AI companies in that we have real products and we have real salespeople instead of just generating the technology, cool demos. There are actual products and therefore you have to have a very large sales force as well, and marketing and PR and so on.
This is how maybe trillion dollar companies come about. You have to have a well-rounded team. But interestingly enough, the algorithm development is a relatively small team, like I said, 12 people. The algorithms are powerful and you have to have power algorithms, but then to build a product is ten times harder. And this is what I’ve learned. Coming from academia, having great ideas, having published papers, algorithms, demonstrating they work, that’s 1/10 of the job. Then that’s when the real work begins and I’m really thankful to be able to be in a company that has those other facets also covered. And a team that works together to achieve those goals.
Kirill: Very, very cool. And you guys seem to develop products mostly for B2B and then these businesses then apply them to make the world better and maybe serve customers. Have you ever thought of getting into the B2C space and creating products directly for the end consumer?
Risto: Yeah, that’s a good question. Maybe sometime in the future if we find an application to those. Right now I think that’s a little bit different space. What might be an application of that? For instance, you could do optimization for individuals, for their calendar maybe, for their navigation. There are perhaps some such tasks that these techniques could be applied to. But it is easy. Like I said, [indecipherable 33:54] stock trading because you need customers. And here we have B2B, which a major investment can be done that way, and gradually build up and achieve more of those kinds of knowledge and knowhow. But eventually, I think when people get more comfortable with having AI doing discovery for them, I think those applications should also come up. That’s still a couple of years out.
Kirill: Okay, gotcha. Well, I’m a bit cautious of your time. I know you’re a busy man, you have to run somewhere just very soon. I just wanted to ask you quickly, if somebody wants to know more about Sentient AI and get to learn about the company, are you guys open to having those conversations? Are you possibly hiring at the moment? Or what are the best steps that somebody who is interested in AI could take to get more involved with Sentient?
Risto: We have a website like everybody and there’s a blog that describes various things that are going on, especially some of these algorithms and methods. And yeah, we’re always hiring. Everybody always is. But there’s also sentient.ai/careers, it lists the currently opened positions. We are growing and we will grow in the future. So I would suggest first take a look what we have there. We also have papers, they’re really technical. We have technical papers published in GECCO and other conferences and those are the real core algorithms. But also the blog posts give you a general idea what’s going, for instance with the CyberAg. Publication is still a cycle. They always take a long time to come out. The blog posts are a little faster and a lot of times those job announcements are tied to what’s happening right now, so the blog posts might be a better idea. And if there’s no job today there that looks a good match, come back tomorrow. There might be.
Kirill: Yeah, I totally agree. Well, thank you so much for coming on the show. I know this was a quick episode, but it was short notice and I really appreciate you sharing the insights. Hopefully Sentient will cover off some really cool applications in the future and we’ll hear about the first trillion dollar company very soon.
Risto: We’re definitely working on it. Thanks a lot for having me.
Kirill: Okay. Have a good day. Bye.
Risto: You too. Bye.
Kirill: So there you have it. That was Risto Mikkulainen, Vice President of Research at Sentient Technologies. My personal favourite part of this podcast was when Risto talked about the cyber-agriculture. I think that was a really cool application and I’d love to get more information or insights into what comes out of it and we’ll possibly see it in the news some time. This looks like a really cool idea that’s going to reshape how we do agriculture and hopefully produce more food for the world and solve one of the world’s biggest problems of not enough food or not enough food in the right places at the right time.
And apart from that, I hope you enjoyed how Risto spoke about AI and what insights he gave into the different types of AI and how quickly AI is advancing. Hopefully that gave you some ideas to consider for your career for the future. Once again, I’m very grateful to Sentient AI for encouraging or spreading the word about artificial intelligence into the world. So if you’re interested in learning more about this company and their mission and what they’re up to, definitely check out their website. It’s www.sentient.ai. There you can find out more about what they’re doing, also you can find their blog, there’s a link at the top and there you can read some of their latest research and papers, as Risto mentioned, maybe even some research that hasn’t yet been published in papers so be on the forefront of artificial intelligence technology.
And hopefully we’ll hear more from experts from Sentient AI in the future on the podcast. And in the meantime, don’t forget to connect with Risto on LinkedIn and follow his career. You will find the link at the show notes on www.superdatascience.com/67. And on that note, we’re going to wrap up today’s podcast. I really appreciate you taking the time today and I look forward to seeing you next time. Until then, happy analyzing.