>> Narrator: From Denver, Colorado, it's theCUBE. Covering Super Computing '17, brought to you by Intel. (electronic music) >> Hey, welcome back everybody. Jeff Frick here with theCUBE. We're at Denver, Colorado at Super Computing 2017. 12,000 people, our first trip to the show. We've been trying to come for awhile, it's pretty amazing. A lot of heavy science in terms of the keynotes. All about space and looking into brain mapping and it's heavy lifting, academics all around. We're excited to have our next guest, who's an expert, all about speed and that's John Lockwood. He's the CEO of Algo-Logic. First off, John, great to see you. >> Yeah, thanks Jeff, glad to be here. >> Absolutely, so for folks that aren't familiar with the company, give them kind of the quick overview of Algo. >> Yes, Algo-Logic puts algorithms into logic. So our main focus is taking things are typically done in software and putting them into FPGAs and by doing that we make them go faster. >> So it's a pretty interesting phenomenon. We've heard a lot from some of the Intel execs about kind of the software overlay that now, kind of I guess, a broader ecosystem of programmers into hardware, but then still leveraging the speed that you get in hardware. So it's a pretty interesting combination to get those latencies down, down, down. >> Right, right, I mean Intel certainly made a shift to go on into heterogeneous compute. And so in this heterogeneous world, we've got software running on Xeons, Xeon Phis. And we've also got the need though, to use new compute in more than just the traditional microprocessor. And so with the acquisition of Altera, is that now Intel customers can use FPGAs in order to get the benefit in speed. And so Algo-Logic, we typically provide applications with software APIs, so it makes it really easy for end customers to deploy FPGAs into their data center, into their hosts, into their network and start using them right away. >> And you said one of your big customer sets is financial services and trading desk. So low latency there is critical as millions and millions and millions if not billions of dollars. >> Right, so Algo-Logic we have a whole product line of high-frequency trading systems. And so our Tick-To-Trade system is unique in the fact that it has a sub-microsecond trading latency and this means going from market data that comes in, for example on CME for options and futures trading, to time that we can place a fix order back out to the market. All of that happens in an FPGA. That happens in under a microsecond. So under a millionth of second and that beats every other software system that's being used. >> Right, which is a game change, right? Wins or losses can be made on those time frames. >> It's become a must have is that if you're trading on Wall Street or trading in Chicago and you're not trading with an FPGA, you're trading at a severe disadvantage. And so we make a product that enables all the trading firms to be playing on a fair, level playing field against the big firms. >> Right, so it's interesting because the adoption of Flash and some of these other kind of speed accelerator technologies that have been happening over the last several years, people are kind of getting accustomed to the fact that speed is better, but often it was kind of put aside in this kind of high-value applications like financial services and not really proliferating to a broader use of applications. I wonder if you're seeing that kind of change a little bit, where people are seeing the benefits of real time and speed beyond kind of the classic high-value applications? >> Well, I think the big change that's happened is that it's become machine-to-machine now. And so humans, for example in trading, are not part of the loop anymore and so it's not a matter of am I faster than another person? It's am I faster than the other person's machine? And so this notion of having compute that goes fast has become suddenly dramatically much more important because everything now is going to machine versus machine. And so if you're an ad tech advertiser, is that how quickly you can do an auction to place an ad matters and if you can get a higher value ad placed because you're able to do a couple rounds of an auction, that's worth a lot. And so, again, with Algo-Logic we make things go faster and that time benefit means, that all thing else being the same, you're the first to come to a decision. >> Right, right and then of course the machine-to-machine obviously brings up the hottest topic that everybody loves to talk about is autonomous vehicles and networked autonomous vehicles and just the whole IOT space with the compute moving out to the edge. So this machine-to-machine systems are only growing in importance and really percentage of the total compute consumption by far. >> That's right, yeah. So last year at Super Computing, we demonstrated a drone, bringing in realtime data from a drone. So doing realtime data collection and doing processing with our Key Value Store. So this year, we have a machine learning application, a Markov Decision Process where we show that we can scale-out a machine learning process and teach cars how to drive in a few minutes. >> Teach them how to drive in a few minutes? >> Right. >> So that's their learning. That's not somebody programming the commands. They're actually going through a process of learning? >> Right, well so the Key Value Store is just a part of this. We're just the part of the system that makes the scale-outs that runs well in a data center. And so we're still running the Markov Decision Process in simulations in software. So we have a couple Xeon servers that we brought with us to do the machine learning and a data center would scale-out to be dozens of racks, but even with a few machines though, for simple highway driving, what we can show is we start off with, the system's untrained and that in the Markov Decision Process, we reward the final state of not having accidents. And so at first, the cars drive and they're bouncing into each other. It's like bumper cars, but within a few minutes and after about 15 million simulations, which can be run that quickly, is that the cars start driving better than humans. And so I think that's a really phenomenal step, is the fact that you're able to get to a point where you can train a system how to drive and give them 15 man years of experience in a matter of minutes by the scale-out compute systems. >> Right, 'cause then you can put in new variables, right? You can change that training and modify it over time as conditions change, throw in snow or throw in urban environments and other things. >> Absolutely, right. And so we're not pretending that our machine learning, that application we're showing here is an end-all solution. But as you bring in other factors like pedestrians, deer, other cars running different algorithms or crazy drivers, is that you want to expose the system to those conditions as well. And so one of the questions that came up to us was, "What machine learning application are you running?" So we're showing all 25 cars running one machine learned application and that's incrementally getting better as they learn to drive, but we could also have every car running a different machine learning application and see how different AIs interact with each other. And I think that's what you're going to see on the highway as we have more self-driving cars running different algorithms, we have to make sure they all place nice with each other. >> Right, but it's really a different way of looking at the world, right, using machine learning, machine-to-machine versus single person or a team of people writing a piece of software to instruct something to do something and then you got to go back and change it. This is a much more dynamic realtime environment that we're entering into with IOT. >> Right, I mean the machine-to-human, which was kind of last year and years before, were, "How do you make interactions "between the computers better than humans?" But now it's about machine-to-machine and it's,"How do you make machines interact better "with other machines?" And that's where it gets really competitive. I mean, you can imagine with drones for example, for applications where you have drones against drones, the drones that are faster are going to be the ones that win. >> Right, right, it's funny, we were just here last week at the commercial drone show and it's pretty interesting how they're designing the drones now into a three-part platform. So there's the platform that flies around. There's the payload, which can be different sensors or whatever it's carrying, could be herbicide if it's an agricultural drone. And then they've opened up the STKs, both on the control side as well as the mobile side, in terms of the controls. So it's a very interesting way that all these things now, via software could tie together, but as you say, using machine learning you can train them to work together even better, quicker, faster. >> Right, I mean having a swarm or a cluster of these machines that work with each other, you could really do interesting things. >> Yeah, that's the whole next thing, right? Instead of one-to-one it's many-to-many. >> And then when swarms interact with other swarms, then I think that's really fascinating. >> So alright, is that what we're going to be talking about? So if we connect in 2018, what are we going to be talking about? The year's almost over. What are your top priorities for next year? >> Our top priorities are to see. We think that FPGA is just playing this important part. A GPU for example, became a very big part of the super computing systems here at this conference. But the other side of heterogeneous is the FPGA and the FPGA has seen almost, just very minimal adoption so far. But the FPGA has the capability of providing, especially when it comes to doing network IO transactions, it's speeding up realtime interactions, it has an ability to change the world again for HPC. And so I'm expecting that in a couple years, at this HPC conference, that what we'll be talking about, is the biggest top 500 super computers, is that how big of FPGAs do they have. Not how big of GPUs do they have. >> All right, time will tell. Well, John, thanks for taking a few minutes out of your day and stopping by. >> Okay, thanks Jeff, great to talk to you. >> All right, he's John Lockwood, I'm Jeff Frick. You're watching theCUBE from Super Computing 2017. Thanks for watching. >> Bye. (electronic music)

>>from >>Around the globe. It's the cube with digital coverage of IBM think 2021 brought to you by IBM >>Hello and welcome back to the cubes ongoing coverage of IBM Think 2021. The virtual edition, my name is David and I'm excited to introduce our next segment. We're going to dig into the intersection of machines and humans and the changing nature of work, worker productivity and the potential of humans with me is Glenn Finch, who's the global managing partner for data and ai at IBM Glenn great to see you again. Thanks for coming on. >>Dave good to be with you. Always a lot of fun to chat. >>So I'm interested in this concept that you've been working on about amplifying worker potential. You've got humans, you've got digital workers coming together and maybe you could talk a little bit about what you're seeing at that intersection. >>You know, it's um it's interesting for most of my career, I've always thought about um amplifying human worker potential. And you know I would say over the last five years we start to think about this concept of digital workers and amplifying their potential so that human potential can extend even further. What's cool is when we get them both to work together, amplifying digital worker potential. Amplifying human worker potential to radically change how services experienced by an end consumer. I mean that's really the winner is when you start seeing the end consumer, the end user fundamentally feeling the difference in the experience. >>I mean a lot of the you see a lot of the trade press and the journalists they like to focus on the sort of the negative of automation. But when you talk to people who have implemented things they take it, for example R. P. A. They're so happy that they're not have to do these menial tasks anymore. And then it sort of the interesting discussion is, okay well what are you what are you doing with your free time? What are you doing with your weekend? So how should we be thinking about that? What you what you called? Amplifying human worker potential? What has to occur for that outcome? >>You know? Um The all my life I've spent time making money for people, right? And this uh last year I was involved in a project where it fundamentally changed. It's tied to answer that exact question. You know the servicemen and women in America who are willing to risk their lives. Um you know for our country um they file claims for medical benefits. And on average it would take 15 days to get a response Actually for about 70 or 80 of them. We've taken that down to like 15 minutes and to do that. You can't just drop in a R. P. A. You can't just drop in a. I. It's not one thing right? It's this it's this seamless interaction between digital workers and human workers right? So that a lot of the more routine mundane tasks can be done by ai and robotics. But all of the really hard complex cases that only a human being can adjudicate. That's what the folks that were doing, the more monday work can can go focus on. So I mean God that's what makes me come to work every day is if I can change the life of a serviceman or woman that was willing to risk their lives for our country. So that's that's the concept now. The critical piece of what I said, it's not about implementing Ai and robotics anymore because a lot of that started to get very wrote but picking up on okay we've liberated this block of human capability. How do we reposition it? How do we re skillet? How do we get them to focus on new things? That's just as important. The human change aspect incredibly important. >>Yeah, I mean that's interesting because you're right. I mean the downside, you mentioned our P. A. A lot of it is paving the cow path and you know the human in the loop piece has been it's been missing and that's obviously changing. But what about the what about the flip side of that equation? Where you know you ask the question okay what can humans do that machines can't do that equation continues to evolve. But maybe you could talk about where you have amplified the digital worker potential. >>Yeah. So you know um one of our clients is anthem and you know we've you know they've been on a variety of programs with us to talk about this. But you know, we just recorded, um, you know, another session with them for think where, um, the chief technology officer came and talked about how they wanted to radically change their member experience. And when you think about the last year, I mean, I don't know. Dave, I know you travel a lot because I see you in all the places that I'm in, right? But I don't remember like 15 months ago, if you had to wait on the phone for two minutes, you thought it was an eternity, right? You're like, what's the matter with me? I'm a frequent flyer. I deserve a better service on this. Then as Covid started to roll around those wait times or two hours and then 30 days into Covid. If you got a call back within two days or two weeks, it was a blessing. Right? So all of our expectations changed in an instant. Right? So I have to say, over the last 12-15 months, that's where we've been spending a lot of our time in all of those human contact human touch places to radically transition the ability to be responsive, touch people with With the same experience that we had 15 months ago to get an answer back in two minutes. You can't get enough people right now to do that. And so we're forced to make sure that the digital experience is what that needs to be. So the digital worker has to be up and on and extending the brand. Experience the same way that the human worker was back when everybody could be at a call center. That makes sense. Yeah. I >>mean, I think I like about this conversation, Glenn is it's not an either or. It's not a zero sum game, which is kind of, it's sort of used to be. I mean, we've talked about this before. Humans have machines have always replaced humans at certain tasks, but never really a cognitive task. And that's why I think there's a lot of fear out there. But what you're talking about is is the potential to amplify both human and digital capabilities. And I think people might look at that and say, well wait a minute, is it isn't a zero sum game, but it's not explain why. >>Yes, So we're never finding the zero sum game because there is um there is always something for people to do, right? And so, you know, I talked about the one an amplification of digital worker at anthem, let me let me switch to an amplification of a human worker, right? So state of Rhode Island, Um you know, we had the great honor to work with their governor and their Department of Health and Human Services, around again, around the whole covid thing. We started out just answering basic questions and helping with contact tracing. And then from there we moved into helping them with their data and ai being able to answer questions. Why are there are hotspots? Why should I shut this person of the city down? Should I shut fires down? Should I do this? And the Governor and Health and human Services Director were constantly saying on press briefings in the morning. Well, you know, we learned from our partners, IBM, that we want to consider this, right? And we we did pinpoint vaccinations and and other things like that. To me, that's that whole continuum. So, you know, we liberated some people from one spot. They went to work in another spot. All human beings guided by ai so, you know, I think this is all about, you know, for the first time in our lives being able to realize sort of the vaulted member experience or client experience that everybody has already talked about using a blend of digital workers and human workers. It's just it's all about the experience. I think >>you're laying out some really good outcomes. You mentioned some of the folks in the military, the healthcare examples. Um and I'm struck because if you think about look at the numbers, I mean the productivity gains over the last 20 years, particularly in the US. and Europe, doesn't it's not the case for China the productivity exploding, but but it's gone down. And so when you think about the big problems that we face in society, um climate change, income inequality, I mean, these are big, chewy problems that, you know, what kind of humans, you just can't throw humans at the problem that's, that's been proven. Um, and I'm curious as to if you know how you see it in terms of some of those other outcomes of the potential that is there and, and, and can you give us a glimpse as to what tech is involved underneath all this? Sure. >>So, you know, um, the first of all on outcomes, you know, that whole picture changes with the business cycle, right? I'd love to tell you that it's always these three outcomes, but you know, during downturns in business cycles, costs based outcomes are, you know, are paramount because people are thinking about survival right? In upticks, people are worried about, you know, converting new business growth, they're worried about net promoter score, they're worried about experience score. And then Over the last 12 to 18 months, you know, we've seen this whole concept of carbon footprint and sustainability All tied into the outcomes. So hey, did you realize that shifting these 22 legacy applications from here to the cloud would reduce your carbon footprint by 3%? No. Right. And so, so you know, the big hitters are always, you know, the cost metric, the sort of time to value or the whole cycle time of the process and net promoter score. Those are generally in all of the, you know, all the plays, obviously the book ends, you know, around, um, what's happening with, you know, the, the economy, what's happening with carbon, what's happening with sustainability are always in there. Now, the technology side boy, that's the cool part about working for IBM, right, is that there is a new thing that shows up on my door every two weeks from either the math and science labs or from a new ecosystem partner. Right. And that's one of the things that I will say about over the last 12 to 15 months, you've seen this massive shift from IBM to to go away from pure blue to embrace the whole ecosystem. So you know, Dave the stuff I work with every day is, you know, ai computer vision, Blockchain automation, quantum uh connected operations. Uh not just software robots, but now human robots, Digital Twin, all these things where we are digitally rendering um what used to be a very paper based legacy. Right. So boy, I couldn't be more excited to be a part of that. And then now with the opening up to all the hyper scholars, the Microsoft, the google the amazon, the, you know, uh salesforce adobe, all those folks. It's like a candy store. And quite honestly, my single greatest challenge is to kind of bring all of that together and point it at a series of three or 4 buyers at a chief marketing officer experience officer for the whole customer piece, at a chief human resource officer around the town peace and at a CFO or a chief procurement officer for finance and supply chain. I'm sorry to answer. So, you know, long winded, but it's it's awesome out there. >>It was a great answer. And I think, you know, I joke the other day, glenn that Milton Friedman must be turned over his grave because he said, you know, the only job of a company has to make profits for shareholders and increase shareholder value. But but you're but but ironically, you know, things like E. S. G. Sustainability, his climate change, he said they actually make business sense. So it's really not antithetical to Friedman economics necessarily but it's good business. And I think I think the other thing that I'm excited about is that there is some like deep tech we're seeing an explosion of of something as fundamental as processing power like we've never seen before but he talks about Moore's law being dead. Well okay with the doubling of of of of processor performance every 24 months. We're now at a quadrupling when you include GPU S. And N. P. U. S. And accelerators and all. I mean that is gonna power the next wave of machine intelligence and that really is exciting. >>Yeah I am. You know it's I feel blessed every day to come to work that you know I can you know a mass all these technologies and change how human beings experience service. I mean that's man, that whole service experience. that's what I've lived for for, you know, 2.5 decades in my career is to not just just to make and deploy stuff. That's cool, technically, but to change people's lives. I mean, that's it for me. That's you know, that's that's the way that I want to ride. So I couldn't be more excited to do that stuff. Well, glad >>Thanks so much for coming on. Is your your passion shows right through the camera and hopefully we'll face to face, you know, sometime soon, maybe, maybe later on this year. But for sure Lockwood 2022. All right. Hey, great to see you. Thank you so much. >>Dave same to you. Thanks have a great rest of the day. >>All right. Thank you. And thanks for following along with our continuing broadcast of IBM think 2021 you're watching the cube the leader in digital tech coverage right back. Mhm. Yeah.