(upbeat music) >> From our studios, in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Hello everyone, welcome to theCUBE studio here in Palo Alto. I'm John Furrier your host of theCUBE. We're here introducing a new format for CUBE panel discussions, it's called Around theCUBE and we have a special segment here called Get Smart: Unpacking AI with some great with some great guests in the industry. Gene Santos, Professor of Engineering in College of Engineering Dartmouth College. Bob Friday, Vice President CTO at Mist at Juniper Company. And Ed Henry, Senior Scientist and Distinguished Member of the Technical Staff for Machine Learning at Dell EMC. Guys this is a format, we're going to keep score and we're going to throw out some interesting conversations around Unpacking AI. Thanks for joining us here, appreciate your time. >> Yeah, glad to be here. >> Okay, first question, as we all know AI is on the rise, we're seeing AI everywhere. You can't go to a show or see marketing literature from any company, whether it's consumer or tech company around, they all have AI, AI something. So AI is on the rise. The question is, is it real AI, is AI relevant from a reality standpoint, what really is going on with AI, Gene, is AI real? >> I think a good chunk of AI is real there. It depends on what you apply it to. If it's making some sort of decisions for you, that is AI that's blowing into play. But there's also a lot of AI left out there potentially is just simply a script. So, you know, one of the challenges that you'll always have is that, if it were scripted, is it scripted because, somebody's already developed the AI and now just pulled out all the answers and just using the answers straight? Or is it active learning and changing on its own? I would tend to say that anything that's learning and changing on its own, that's where you're having the evolving AI and that's where you get the most power from. >> Bob what's your take on this, AI real? >> Yeah, if you look at Google, What you see is AI really became real in 2014. That's when the AI and ML really became a thing in the industry and when you look why did it become a thing in 2014? It's really back when we actually saw TensorFlow, open source technology really become available. It's all that Amazon Compute story. You know, you look what we're doing here at Mist, I really don't have to worry about compute storage, except for the Amazon bill I get every month now. So I think you're really seeing AI become real, because of some key turning points in the industry. >> Ed, your take, AI real? >> Yeah, so it depends on what lens you want to kind of look at it through. The notion of intelligence is something that's kind of ill defined and depending how how you want to interpret that will kind of guide whether or not you think it's real. I tend to all things AI if it has a notion of agency. So if it can navigate its problem space without human intervention. So, really it depends on, again, what lens you kind of want to look at it through? It's a set of moving goalposts, right? If you take your smartphone back to Turing When he was coming up with the Turing test and asked them if this intelligent, or some value intelligent device was AI, would that be AI, to him probably back then. So really it depends on how you kind of want to look at it. >> Is AI the same as it was in 1988? Or has it changed, what's the change point with AI because some are saying, AI's been around for a while but there's more AI now than ever before, Ed we'll start with you, what's different with AI now versus say in the late 80s, early 90s? >> See what's funny is some of the methods that we're using aren't different, I think the big push that happened in the last decade or so has been the ability to store as much data as we can along with the ability to have as much compute readily disposable as we have today. Some of the methodologies I mean there was a great Wired article that was published and somebody referenced called, method called Eigenvector Decomposition they said it was from quantum mechanic, that came out in 1888 right? So it really a lot of the methodologies that we're using aren't much different, it's the amount of data that we have available to us that represents reality and the amount of compute that we have. >> Bob. >> Yeah so for me back in the 80s when I did my masters I actually did a masters on neural networks so yeah it's been around for a while but when I started Mist what really changed was a couple things. One is this modern cloud stack right so if you're going to have to build an AI solution really have to have all the pieces ingest tons of data and process it in real time so that is one big thing that's changed that we didn't have 20 years ago. The other big thing is we had access to all this open source TensorFlow stuff right now. People like Google and Facebook have made it so easy for the average person to actually do an AI project right? You know anyone here, anyone in the audience here could actually train a machine learning model over the weekend right now, you just have to go to Google, you have to find kind of the, you know they have the data sets you want to basically build a model to recognize letters and numbers, those data sets are on the internet right now and you personally yourself could go become a data scientist over the weekend. >> Gene, your take. >> Yeah I think also on top of that because of all that availability on the open software anybody can come in and start playing with AI, it's also building a really large experience base of what works and what doesn't work and because they have that now you can actually better define the problem you're shooting for and when you do that you increase you know what's going to work, what's not going to work and people can also tell you that on the part that's not going to work, how's it going to expand but I think overall though this comes back to the question of when people ask what is AI, and a lot of that is just being focused on machine learning and if it's just machine learning that's kind of a little limited use in terms of what you're classifying or not. Back in the early 80s AI back then is really what people are trying to call artificial general intelligence nowadays but it's that all encompassing piece. All the things that you know us humans can do, us humans can reason about, all the decision sequences that we make and so you know that's the part that we haven't quite gotten to but there is all the things that's why the applications that the AI with machine learning classification has gotten us this far. >> Okay machine learning is certainly relevant, it's been one of the most hottest, the hottest topic I think in computer science and with AI becoming much more democratized you guys mentioned TensorFlow, a variety of other open source initiatives been a great wave of innovation and again motivation, younger generations is easier to code now than ever before but machine learning seems to be at the heart of AI and there's really two schools of thought in the machine learning world, is it just math or is there more of a cognition learning machine kind of a thing going on? This has been a big debate in the industry, I want to get your guys' take on this, Gene is machine learning just math and running algorithms or is there more to it like cognition, where do you guys fall on this, what's real? >> If I look at the applications and look what people are using it for it's mostly just algorithms it's mostly that you know you've managed to do the pattern recognition, you've managed to compute out the things and find something interesting from it but then on the other side of it the folks working in say neurosciences, the first people working in cogno-sciences. You know I have the interest in that when we look at that, that machine learning does it correspond to what we're doing as human beings, now because the reason I fall more on the algorithm side is that a lot of those algorithms they don't match what we're often thinking so if they're not matching that it's like okay something else is coming up but then what do we do with it, you know you can get an answer and work from it but then if we want to build true human intelligence how does that all stack together to get to the human intelligence and I think that's the challenge at this point. >> Bob, machine learning, math, cognition is there more to do there, what's your take? >> Yeah I think right now you look at machine learning, machine learning are the algorithms we use, I mean I think the big thing that happened to machine learning is the neural network and deep learning, that was kind of a mild stepping stone where we got through and actually building kind of these AI behavior things. You know when you look what's really happening out there you look at the self driving car, what we don't realize is like it's kind of scary right now, you go to Vegas you can actually get on a driving bus now, you know so this AI machine learning stuff is starting to happen right before our eyes, you know when you go to the health care now and you get your diagnosis for cancer right, we're starting to see AI in image recognition really start to change how we get our diagnosis. And that's really starting to affect people's lives. So those are cases where we're starting to see this AI machine learning stuff is starting to make a difference. When we think about the AI singularity discussion right when are we finally going to build something that really has human behavior. I mean right now we're building AI that can actually play Jeopardy right, and that was kind of one of the inspirations for my company Mist was hey, if they can build something to play Jeopardy we should be able to build something answer questions on par with network domain experts. So I think we're seeing people build solutions now that do a lot of behaviors that mimic humans. I do think we're probably on the path to building something that is truly going to be on par with human thinking right, you know whether it's 50 years or a thousand years I think it's inevitable on how man is progressing right now if you look at the technologically exponential growth we're seeing in human evolution. >> Well we're going to get to that in the next question so you're jumping ahead, hold that thought. Ed, machine learning just math, pattern recognition or is there more cognition there to be had? Where do fall in this? >> Right now it's, I mean it's all math, so we collect something some data set about the world and then we use algorithms and some representation of mathematics to find some pattern, which is new and interesting, don't get me wrong, when you say cognition though we have to understand that we have a fundamentally flawed perspective on how maybe the one guiding light that we have on what intelligence could be would be ourselves right. Computers don't work like brains, brains are what we determine embody our intelligence right, computers, our brains don't have a clock, there's no state that's actually between different clock cycles that light up in the brain so when you start using words like cognition we end up trying to measure ourselves or use ourselves as a ruler and most of the methodologies that we have today don't necessarily head down that path. So yeah that's kind of how I view it. >> Yeah I mean stateless those are API kind of mindsets, you can't run Kubernetes in the brain. Maybe we will in the future, stateful applications are always harder than stateless as we all know but again when I'm sleeping, I'm still dreaming. So cognition in the question of human replacement. This has been a huge conversation. This is one, the singularity conversation you know the fear of most average people and then some technical people as well on the job front, will AI replace my job will it take over the world is there going to be a Skynet Terminator moment? This is a big conversation point because it just teases out what could be and tech for good tech for bad. Some say tech is neutral but it can be shaped. So the question is will AI replace humans and where does that line come from. We'll start with Ed on this one. What do you see this singularity discussion where humans are going to be replaced with AI? >> So replace is an interesting term, so there I mean we look at the last kind of Industrial Revolution that happened and people I think are most worried about the potential of job loss and when you look at what happened during the Industrial Revolution this concept of creative destruction kind of came about and the idea is that yes technology has taken some jobs out of the market in some way shape or form but more jobs were created because of that technology, that's kind of our one again lighthouse that we have with respect to measuring that singularity in and of itself. Again the ill defined definition, or the ill defined notion of intelligence that we have today, I mean when you go back and you read some of the early papers from psychologists from the early 1900s the experiment specifically who came up with this idea of intelligence he uses the term general intelligence as kind of the first time that all of civilization has tried to assign a definition to what is intelligent right? And it's only been roughly 100 years or so or maybe a little longer since we have had this understanding that's been normalized at least within western culture of what this notion of intelligence is so singularity this idea of the singularity is interesting because we just don't understand enough about the one measure ruler or yardstick that we have that we consider intelligence ourselves to be able to go and then embed that inside of a thing. >> Gene what's your thoughts on this, reasoning is a big part of your research you're doing a lot of research around intent and contextual, all these cool behavioral things you know this is where machines are there to augment or replace, this is the conversation, your view on this? >> I think one of the things with this is that that's where the downs still lie, if we have bad intentions, if we can actually start communicating then we can start getting the general intelligence yeah I mean sort of like what Ed was referring to how people have been trying to define this but I think one of the problems that comes up is that computers and stuff like that don't really capture that at this time, the intentions that they have are still at a low level, but if we start tying it to you know the question of the terminator moment to the singularity, one of the things is that autonomy, you know how much autonomy that we give to the algorithm, how much does the algorithm have access to? Now there could be you know just to be on an extreme there could be a disaster situation where you know we weren't very careful and we provided an API that gives full autonomy to whatever AI we have to run it and so you can start seeing elements of Skynet that can come from that but I also tend to come to analysis that hey even with APIs, while it's not AI, APIs a lot of that also we have the intentions of what you're going to give us to control. Then you have the AI itself where if you've defined the intentions of what it is supposed to do then you can avoid that terminator moment in terms of that's more of an act. So I'm seeing it at this point. And so overall singularity I still think we're a ways off and you know when people worry about job loss probably the closest thing that I think that can match that in recent history is the whole thing on automation, I grew up at the time in Ohio when the steel industry was collapsing and that was a trade off between automation and what the current jobs are and if you have something like that okay that's one thing that we go forward dealing with and I think that this is something that state governments, our national government something we should be considering. If you're going to have that job loss you know what better study, what better form can you do from that and I've heard different proposals from different people like, well if we need to retrain people where do you get the resources from it could be something even like AI job pack. And so there's a lot of things to discuss, we're not there yet but I do believe the lower, repetitive jobs out there, I should say the things where we can easily define, those can be replaceable but that's still close to the automation side. >> Yeah and there's a lot of opportunities there. Bob, you mentioned in the last segment the singularity, cognition learning machines, you mentioned deep learning, as the machines learn this needs more data, data informs. If it's biased data or real data how do you become cognitive, how do you become human if you don't have the data or the algorithms? The data's the-- >> I mean and I think that's one of the big ethical debates going on right now right you know are we basically going to basically take our human biases and train them into our next generation of AI devices right. But I think from my point of view I think it's inevitable that we will build something as complex as the brain eventually, don't know if it's 50 years or 500 years from now but if you look at kind of the evolution of man where we've been over the last hundred thousand years or so, you kind of see this exponential rise in technology right from, you know for thousands of years our technology was relatively flat. So in the last 200 years where we've seen this exponential growth in technology that's taking off and you know what's amazing is when you look at quantum computing what's scary is, I always thought of quantum computing as being a research lab thing but when you start to see VC's and investing in quantum computing startups you know we're going from university research discussions to I guess we're starting to commercialize quantum computing, you know when you look at the complexity of what a brain does it's inevitable that we will build something that has basic complexity of a neuron and I think you know if you look how people neural science looks at the brain, we really don't understand how it encodes, but it's clear that it does encode memories which is very similar to what we're doing right now with our AI machine right? We're building things that takes data and memories and encodes in some certain way. So yeah I'm convinced that we will start to see more AI cognizance and it starts to really happen as we start with the next hundred years going forward. >> Guys, this has been a great conversation, AI is real based upon this around theCUBE conversation. Look at I mean you've seen the evidence there you guys pointed it out and I think cloud computing has been a real accelerant with the combination of machine learning and open source so you guys have illustrated and so that brings up kind of the final question I'd love to get each of you's thought on this because Bob just brought up quantum computing which as the race to quantum supremacy goes on around the world this becomes maybe that next step function, kind of what cloud computing did for revitalizing or creating a renaissance in AI. What does quantum do? So that begs the question, five ten years out if machine learning is the beginning of it and it starts to solve some of these problems as quantum comes in, more compute, unlimited resource applied with software, where does that go, five ten years? We'll go start with Gene, Bob, then Ed. Let's wrap this up. >> Yeah I think if quantum becomes a reality that you know when you have the exponential growth this is going to be exponential and exponential. Quantum is going to address a lot of the harder AI problems that were from complexity you know when you talk about this regular search regular approaches of looking up stuff quantum is the one that allows you now to potentially take something that was exponential and make it quantum. And so that's going to be a big driver. That'll be a big enabler where you know a lot of the problems I look at trying to do intentions is that I have an exponential number of intentions that might be possible if I'm going to choose it as an explanation. But, quantum will allow me to narrow it down to one if that technology can work out and of course the real challenge if I can rephrase it into say a quantum program while doing it. But that's I think the advance is just beyond the step function. >> Beyond a step function you see. Okay Bob your take on this 'cause you brought it up, quantum step function revolution what's your view on this? >> I mean your quantum computing changes the whole paradigm right because it kind of goes from a paradigm of what we know, this binary if this then that type of computing. So I think quantum computing is more than just a step function, I think it's going to take a whole paradigm shift of you know and it's going to be another decade or two before we actually get all the tools we need to actually start leveraging quantum computing but I think that is going to be one of those step functions that basically takes our AI efforts into a whole different realm right? Let us solve another whole set of classic problems and that's why they're doing it right now because it starts to let you be able to crack all the encryption codes right? You know where you have millions of billions of choices and you have to basically find that one needle in the haystack so quantum computing's going to basically open that piece of the puzzle up and when you look at these AI solutions it's really a collection of different things going underneath the hood. It's not this one algorithm that you're doing and trying to mimic human behavior, so quantum computing's going to be yet one more tool in the AI toolbox that's going to move the whole industry forward. >> Ed, you're up, quantum. >> Cool, yeah so I think it'll, like Gene and Bob had alluded to fundamentally change the way we approach these problems and the reason is combinatorial problems that everybody's talking about so if I want to evaluate the state space of anything using modern binary based computers we have to kind of iteratively make that search over that search space where quantum computing allows you to kind of evaluate the entire search space at once. When you talk about games like AlphaGo, you talk about having more moves on a blank 19 by 19 AlphaGo board than you have if you put 1,000 universes on every proton of our universe. So the state space is absolutely massive so searching that is impossible. Using today's binary based computers but quantum computing allows you to evaluate kind of search spaces like that in one big chunk to really simplify the aspect but I think it will kind of change how we approach these problems to Bob and Gene's point with respect to how we approach, the technology once we crack that quantum nut I don't think will look anything like what we have today. >> Okay thank you guys, looks like we have a winner. Bob you're up by one point, we had a tie for second but Ed and Gene of course I'm the arbiter but I've decided Bob you nailed this one so since you're the winner, Gene you guys did a great job coming in second place, Ed good job, Bob you get the last word. Unpacking AI, what's the summary from your perspective as the winner of Around theCUBE. >> Yeah no I think you know from a societal point of view I think AI's going to be on par with kind of the internet. It's going to be one of these next big technology things. I think it'll start to impact our lives and people when you look around it it's kind of sneaking up on us, whether it's the self driving car the healthcare cancer, the self driving bus, so I think it's here, I think we're just at the beginnings of it. I think it's going to be one of these technologies that's going to basically impact our whole lives or our next one or two decades. Next 10, 20 years is just going to be exponentially growing everywhere in all our segments. >> Thanks so much for playing guys really appreciate it we have an inventor entrepreneur, Gene doing great research at Dartmouth check him out, Gene Santos at Dartmouth Computer Science. And Ed, technical genius at Dell, figuring out how to make those machines smarter and with the software abstractions growing you guys are doing some good work over there as well. Gentlemen thank you for joining us on this inaugural Around theCUBE unpacking AI Get Smart series, thanks for joining us. >> Thank you. >> Thank you. >> Okay, that's a wrap everyone this is theCUBE in Palo Alto, I'm John Furrier thanks for watching. (upbeat funk music)