 Live from Las Vegas, it's theCUBE covering HPE Discover 2017 brought to you by Hewlett Packard Enterprise. Welcome back everyone, we're live here in Las Vegas for SiliconANGLE's CUBE coverage of HPE Discover 2017. This is our seventh year covering HPE Discover, now HPE Discover, and it's second year. I'm John Furrier with my co-host Dave Vellante. We've got two great guests, two doctors, PhDs in the house here. So, England Go, VP and SGI CTO, PhD. And at Dumas and Holm, Professor at Carnegie Mellon University of Computer Science and also runs the Marketplace Lab there. Welcome to theCUBE guys, doctors. Thank you. Thank you. So the patient is on the table, it's called Machine Learning AI Cloud Computing. We are living in a really amazing place. I call it open bar and open source. There's so many new things being contributed to open source, so much new hardware coming on with HPE, that there's a lot of innovation happening. So I want to get your thoughts first on, you know, how you guys are looking at this big trend where all this new software is coming in and there's new capabilities, what's the vibe? How do you look at this? I mean, you must be at Carnegie Mellon going, wow, this is an amazing time, thoughts. Yeah, it is an amazing time. And I'm seeing it both in the academic side and the startup side that, you know, you don't have to invest into your own custom hardware. We are like, we are using HPE through the Pittsburgh Supercomputing Center in academia, using cloud in the startups. So it really makes the entry for academic research and startups much easier. And also the high end on the academic research, you know, you don't have to worry about maintaining and staying up to speed with all of the latest hardware or networking and all that. You know, it kind of, yeah. Focus on your research, get your coding. Focus on the algorithms, focus on the AI and you can trust that the rest is taken care of. Hey, talk about the supercomputer world that's now there. I mean, if you look at the abundant compute, you have the intelligent edge. I mean, we're seeing genome sequencing done in minutes, the prices are dropping. I mean, high performance computing used to be this magical special thing that you'd have to get a lot of money to pay for access to, the democratization is pretty amazing. Can you just hear your thoughts on what you see happening? Yes, democratization. In the traditional HPC approach, the goal is to do prediction and forecast. Whether the engine will stay productive. Let's use all financial forecast, whether you should buy or sell or hold. Let's use the weather as an example. In traditional HPC, for the last 30 years, what we do is to predict tomorrow's weather, what we do first is to write all the equations that models the weather, measure today's weather and feed that in. And then we apply supercomputing power in the hopes that you will predict tomorrow's weather faster than tomorrow's coming. So that has been the traditional approach, but things have changed. Two big things change in the last few years that got these scientists to think, perhaps there is a new way of doing it. Instead of calculating your prediction, can you not use data intensive method to do an educated guess at your prediction? And this is what they do. Instead of feeding today's weather information into the machine learning system, they feed 30 years every day, 10,000 days. Every day they feed the data in, the machine learning system guess at whether it will rain tomorrow. If it gets it wrong, it's okay. It just goes back to the weights that control the inputs and adjust them. Then you take the next day and feed it in again. After 10,000 tries, what started out as a wild guess becomes an educated guess. And this is how the new way of doing data intensive computing is starting to emerge, using machine learning. A democratization is a theme. I threw that out because I think it truly is happening, but let's get specific now. I mean, a lot of science has been, whoa, is climate change real? I mean, this is something that's in the news. We see that in today's news cycle about climate change, things of that, and you mentioned weather. So there's other things, there's other financial models, there's other in healthcare, in disease, and there's new ways to get at things that were kind of hocus pocus, maybe some science, some modeling, forecasting. What are you seeing that's right, low hanging fruit right now that's going to impact lives? What key things that will HPC impact besides weather, is healthcare there? I mean, where is everyone getting excited? I think health and safety, immediately, right? Health and safety, you mentioned gene sequencing, drug designs, and you also mentioned in gene sequencing, drug design, there is also safety in designing of automobiles and aircrafts. These methods have been traditionally using simulation, but more and more now they are thinking, while these engines, for example, are flying, can you collect more data so that you can predict when this engine would fail? And also predict, say, when the aircraft lands, what sort of maintenance you should be applying on the engine without having to spend some time on the ground, which is unproductive time, some time on the ground diagnosing the problems. So you start to see an application of data intensive methods increased in order to improve safety and health. I think that's good and I agree with that. You can also kind of look at from a technology perspective as to what kind of AI is going to be next. And if you look back over the last five to seven years, deep learning has become a very hot part of machine learning and machine learning is part of AI. So that's really lifted that up. But what's next there is not just classification or prediction, but decision making on top of that. So we'll see AI move up the chain to actual decision making on top of just the basic machine learning. So optimization, things like that. And another category is what we call strategic reasoning. Traditionally, in games like chess or checkers and now go, people have fallen to AI. And now we did this in January in poker as well after 14 years of research. So now we can actually take real strategic reasoning under imperfect information settings and apply it to various settings like business strategy optimization, automated negotiation, or certain areas of finance, cybersecurity, and so forth. So is it? Go ahead. If I could interject, yeah. So we were very honored and impressed, right? If you look back years ago, IBM beat the world top chess player, right? And that was an expert system. And then more recently, Google Alpha Go beat even a more complex game goal, right? Beat humans and that. But what the professor has done recently is develop an even more complex game, right? In the sense that it is incomplete information, it is poker, right? You don't know the other party's cards unlike in the board game you would know, right? And this is very much real life in business negotiation, in auctions. You don't quite know what the other party's thinking. So I believe now you're looking at ways, I hope, right? Applying that poker playing AI software that can handle incomplete information, not knowing the other party's, but still be able to play expertly and apply that in business. And that's a great interest for us. I want to double down on that. I know Dave's got a question, but I want to just follow this thread through. So the AI, in this case augmented intelligence, it's not so much artificial because you're augmenting without the imperfect information. It's interesting because one of the debates in the big data world has been, well, the streaming of all this data is so high velocity and so high volume that we have to, we don't know what we're missing. So everyone's been trying to get at the perfect information in the streaming of the data. Really on point, yes. And this is where the machine learned, if I get your point here, you can do this meta reasoning or this reasoning on top of it. Let's try to use that and say, hey, let's not try to solve the world's problems and boil the ocean over and understand it all. Let's use that as a variable for AI. Does it get that right? Kind of, I would say, in that it's not just a technical barrier to getting the big data. It's also kind of a strategic barrier that companies, even if I could tell you all of my strategic information, I wouldn't want to. So you have to worry not just about not having all the information, but the other guys explicitly hiding information, misrepresenting, and vice versa, you doing strategic action as well. And unlike in games like Go or chess where it's perfect information, you need totally different kinds of algorithms to deal with these imperfect information games, like negotiation or strategic pricing where you have to think about the opponent's responses in advance. Knowing what you don't know. And your point about huge amounts of data, we are talking about looking for a needle in a haystack. But when the data gets so big and the needles get so many, you end up with a haystack of needles. So you need some augmentation to help you to deal with it because the humans would be in on data with the needles themselves. So is HP sort of enabling AI or is AI driving HPC? Both. Yeah, that's right. Yeah, so we answered together. In fact, yes, AI is driving HPC because it is a new way of using that supercomputing power, right? From not just doing it compute intensive calculation, but also doing it data intensive AI, machine learning. Then we are also driving AI because our customers are now asking that same questions, how do I transition? From just only a compute intensive approach to a data intensive one also. And this is where we come in. What are your thoughts on how this affects society, individuals, particularly students coming in. You mentioned the Gary Kasparov losing to the IBM supercomputer. But he didn't stop there. He said, I'm going to go beat the supercomputer and he got supercomputers and humans together and now holds a contest every year. So everybody talks about the impact of machines replacing humans. And that's always happened. But what do you guys see? Where's the future of work, of creativity for young people and the future of the economy? What does this all mean? Do you want to take it? We can both, yeah. Okay, do you want to go first or second? You go ahead first. Okay. I love the fight. It's a big topic. I know. This is a fun topic, yeah. So there's a lot of worry about AI, of course. But I think of AI as a tool, much like a hammer or a saw. So it's going to make human lives better and it's already making human lives better. And a lot of people won't even understand all of the things that already have AI that are helping them out. And, you know, there's this worry that, okay, maybe there's going to be a super species of AI that's going to take over humans. I don't think so. I don't think there's any demand for a super species of AI. Like a hammer and a saw. A hammer and a saw is better than a hammer saw. So I actually think of AI as better being separate tools for separate applications and that is very important for mankind and also nations and the world in the future. One example is our work on kidney exchange. So we run the nationwide kidney exchange for the United Network for Organ Sharing which saves hundreds of lives. And this is an example, not only that saves lives and makes better decisions than humans can, but it's also interesting for me. In terms of kidney candidates, timing, is all this, is that what you're... It's a long story. But basically, when you have willing but incompatible life donors, incompatible with their patients, they can swap their donors. So pair A gives to pair B, gives to pair C, gives to pair A, for example. And we also co-invented this idea of chains where an altruist donor triggers a whole chain through a network. And but then the question of which combination of cycles and chains is the best solution. Is there no manual involvement? Do machines take over the heavy lifting? It's hard because the number of possible solutions is bigger than the number of atoms in the universe. So you have to have optimization AI to actually make the decisions. And our AI makes twice a week these decisions for the country or 66% of the transplant centers in the country twice a week. Dr. Go, would you add anything to the societal impact? Yes, absolutely. On the prof's point about the sword and hammer, that's why these AI systems today are very specific. That's why some call them artificial specific intelligence, not general intelligence. Now, whether a hundred years from now you take a hundred of these specific intelligence and combine them, whether you get an emergent property of a general, that's something else, right? But for now, what they do is to help the analysts, the human, the decision maker. And more and more, you will see that as they train, as you train these models, they start to make a lot of correct decisions. But ultimately, there's a difference between a correct decision and I believe a right decision, you see? So therefore, there is needs always to be a human supervisor there to ultimately make the right decision. Of course, you will listen to the machine learning algorithm suggesting the correct answer. But ultimately, the human values have to be applied to decide whether society accepts this decision. All models are wrong, some are useful. Yes. So, on these things, so there's kind of two benefits of AI. One is that it's just saves time, saves effort, kind of logistical labor savings, automation. The other is better decision making. So we're seeing the better decision making now become more of the important part instead of just labor savings or what have you. We're seeing that in the kidney exchange and now with strategic reasoning, now for the first time we can do better strategic reasoning than the best humans in imperfect information settings. Now it becomes almost a competitive need. You have to have what I call strategic augmentation as a business to be competitive. I want to get your final thoughts before we end the segment. This is more of a sharing component. A lot of young folks are coming in to computer science and or related sciences and then they don't have to be a computer science major per se, but they have all the benefits of this goodness we're talking about here. Your advice, both of you could share your opinion and thoughts and reaction to the trend where the question we get all the time is what should young people be thinking about if they're going to be modeling and simulating? A lot of new data science are coming in. Some of them are more practitioner oriented, some are more hardcore. As this evolution of simulations and modeling that we're talking about at scale here changes, what should they know? What should the best practice be for learning, applying thoughts? For me, the key thing is to be comfortable about using tools. And for that, I think the young chaps of the world as they come out of school, they are very comfortable with that. So I think I'm actually less worried. It will be a new set of tools, these intelligent tools, leverage them. If you look at the entire world as a single system, what we need to do is to move our leveraging of tools up to a level where we become an even more productive society rather than worrying, well, of course, we must be worried and then adapt to it about jobs going to AI. Rather, we should move ourself up to leverage AI to be an even more productive world. And then hopefully better distribute that world so the entire human race becomes more comfortable given AI. Thomas, your thoughts? Yeah, I think that people should be ready to actually, for the unknown, so you've got to be flexible when in your education, get the basics right, because those basics don't change. You know, STEM, math, science, get that stuff solid. And then be ready to, instead of thinking about, I'm going to be this in my career, you should think about I'm going to be this first and then maybe something else. I don't even know even. Don't memorize the test, you don't know you're going to take yet. So be more adaptive. Yeah, and creativity is very important and adaptability. And people should start thinking about that at a young age. Doctors, thanks so much for sharing your input. What a great world we live in right now. A lot of opportunities, a lot of challenges that are opportunities to solve with high-performance computing, AI and whatnot. Thanks so much for sharing. This is theCUBE bringing you all the best coverage from HPE Discover. I'm John Furrier with Dave Vellante. We'll be back with more live coverage after the short break of three days of wall-to-wall live coverage. We'll be right back. Thanks for having us. Thank you.