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  • (bright music)

  • >> Narrator: Live from Austin, Texas.

  • It's theCUBE, covering South by Southwest 2017.

  • Brought to you by Intel.

  • Now here's John Furrier.

  • >> We're here live in South by Southwest Austin, Texas.

  • Silicon Angle, theCUBE, our broadcast,

  • we go out and extract the signal from noise.

  • I'm John Furrier, I'm here with Naveene Rao,

  • the vice president general manager of

  • the artificial intelligence solutions group at Intel.

  • Welcome to theCUBE.

  • >> Thank you, yeah.

  • >> So we're here, big crowd here at Intel, Intel AI lounge.

  • Okay, so that's your wheelhouse.

  • You're the general manager of AI solutions.

  • >> Naveene: That's right.

  • >> What is AI? (laughs)

  • I mean--

  • >> AI has been redefined through time a few times.

  • Today AI means generally applied machine learning.

  • Basically ways to find useful structure

  • in data to do something with.

  • It's a tool, really, more than anything else.

  • >> So obviously AI is a mental model,

  • people can understand kind of what's going on with software.

  • Machine learning and IoT gets kind of in the industry,

  • it's a hot area, but this really is

  • points to a future world where you're seeing software

  • tackling new problems at scale.

  • So cloud computing, what you guys are doing with the chips

  • and software has now created a scale dynamic.

  • Similar to Moore's, but Moore's Law is done for devices.

  • You're starting to see software impact society.

  • So what are some of those game changing impacts

  • that you see and that you're looking at at Intel?

  • >> There are many different thought labors

  • that many of us will characterize as drudgery.

  • For instance, if I'm an insurance company,

  • and I want to assess the risk of 10 million pages of text,

  • I can't do that very easily.

  • I have to have a team of analysts run through,

  • write summaries.

  • These are the kind of problems we can start to attack.

  • So the way I always look at it is

  • what a bulldozer was to physical labor, AI is to data.

  • To thought labor, we can really get through

  • much more of it and use more data

  • to make our decisions better.

  • >> So what are the big game changing things

  • that are going on that people can relate to?

  • Obviously, autonomous vehicles

  • is one that we can all look at and say,

  • "Wow, that's mind blowing."

  • Smart cities is one that you say,

  • "Oh my god, I'm a resident of a community.

  • "Do they have to re-change the roads?

  • "Who writes the software, is there a budget for that?"

  • Smart home, you see Alexa with Amazon,

  • you see Google with their home product.

  • Voice bots, voice interfaces.

  • So the user interface is certainly changing.

  • How is that impacting some of the things

  • that you guys are working on?

  • >> Well, to the user interface changing,

  • I think that has an entire dynamic on how people use tools.

  • Easier something is, the more people use,

  • the more pervasive it becomes,

  • and we start discovering these emergent dynamics.

  • Like an iPod, for instance.

  • Storing music in a digital form,

  • small devices around before the iPod.

  • But when it made it easy to use,

  • that sort of gave rise to the smartphone.

  • So I think we're going to start seeing

  • some really interesting dynamics like that.

  • >> One of the things that I liked

  • about this past week in San Francisco,

  • Google had their big event, their cloud event,

  • and they talked a lot about, and by the way,

  • Intel was on stage with the new Xeon processor,

  • up to 72 cores, amazing compute capabilities,

  • but cloud computing does bring that scale together.

  • But you start thinking about data science

  • has moved into using data, and now you have

  • a tsunami of data, whether it's taking

  • an analog view of the world

  • and having now multiple datasets available.

  • If you can connect the dots, okay, a lot of data,

  • now you have a lot of data plus a lot of datasets,

  • and you have almost unlimited compute capability.

  • That starts to draw in some of the picture a little bit.

  • >> It does, but actually there's one thing missing

  • from what you just described, is that our ability

  • to scale data storage and data collection

  • has outpaced our ability to compute on it.

  • Computing on it typically is some sort

  • of quadratic function, something faster

  • than when your growth on amount of data.

  • And our compute has really not caught up with that,

  • and a lot of that has been more about focus.

  • Computers were really built to automate streams of tasks,

  • and this sort of idea of going highly parallel

  • and distributed, it's something somewhat new.

  • It's been around a lot in academic circles,

  • but the real use case to drive it home

  • and build technologies around it is relatively new.

  • And so we're right now in the midst of

  • transforming computer architecture,

  • and it's something that becomes a data inference machine,

  • not just a way to automate compute tasks,

  • but to actually do data inference

  • and find useful inferences in data.

  • >> And so machine learning is the hottest trend right now

  • that kind of powers AI, but also there's some talk

  • in the leader circles around learning machines.

  • Data learning from engaged data, or however

  • you want to call it, also brings out another question.

  • How do you see that evolving, because do we need to

  • have algorithms to police the algorithms?

  • Who teaches the algorithms?

  • So you bring in this human aspect of it.

  • So how does the machine become a learning machine?

  • Who teaches the machine, is it...

  • (laughs) I mean, it's crazy.

  • >> Let me answer that a little bit with a question.

  • Do you have kids?

  • >> Yes, four.

  • >> Does anyone police you on raising your kids?

  • >> (laughs) Kind of, a little bit, but not much.

  • They complain a lot.

  • >> I would argue that it's not so dissimilar.

  • As a parent, your job is to expose them to

  • the right kind of biases or not biased data

  • as much as possible, like experiences, they're exactly that.

  • I think this idea of shepherding data

  • is extremely important.

  • And we've seen it in solutions that Google has brought out.

  • There are these little unexpected biases,

  • and a lot of those come from just what we have in the data.

  • And AI is no different than a regular intelligence

  • in that way, it's presented with certain data,

  • it learns from that data and its biases are formed that way.

  • There's nothing inherent about the algorithm itself

  • that causes that bias other than the data.

  • >> So you're saying to me that exposing more data

  • is actually probably a good thing?

  • >> It is.

  • Exposing different kinds of data, diverse data.

  • To give you an example from the biological world,

  • children who have never seen people of different races

  • tend to be more, it's something new and unique

  • and they'll tease it out.

  • It's like, oh, that's something different.

  • Whereas children who are raised

  • with people of many diverse face types or whatever

  • are perfectly okay seeing new diverse face types.

  • So it's the same kind of thing in AI, right?

  • It's going to hone in on the trends that are coming,

  • and things that are outliers, we're going to call as such.

  • So having good, balanced datasets, the way we collect

  • that data, the way we sift through it

  • and actually present it to an AI is extremely important.

  • >> So one of the most exciting things

  • that I like, obviously autonomous vehicles,

  • I geek out on because, not that I'm a car head,

  • gear head or car buff, but it just,

  • you look at what it encapsulates technically.

  • 5G overlay, essentially sensors all over the car,

  • you have software powering it,

  • you now have augmented reality, mixed reality

  • coming into it, and you have an interface to consumers

  • and their real world in a car.

  • Some say it's a moving data center,

  • some say it's also a human interface

  • to the world, as they move around in transportation.

  • So it kind of brings out the AI question,

  • and I want to ask you specifically.

  • Intel talks about this a lot in their super demos.

  • What actually is Intel doing with the compute

  • and what are you guys doing to make that accelerate faster

  • and create a good safe environment?

  • Is it just more chips, is it software?

  • Can you explain, take a minute to explain

  • what Intel's doing specifically?

  • >> Intel is uniquely positioned in this space,

  • 'cause it's a great example of a full end to end problem.

  • We have in-car compute, we have software,

  • we have interfaces, we have actuators.

  • That's maybe not Intel's suite.

  • Then we have connectivity, and then we have cloud.

  • Intel is every one of those things,

  • and so we're extremely well positioned

  • to drive this field forward.

  • Now you ask what are we doing in terms of hardware

  • and software, yes, it's all of it.

  • This is a big focus area for Intel now.

  • We see autonomous vehicles as being

  • one of the major ways that people interact

  • with the world, like locality between cars

  • and interaction through social networks

  • and these kinds of things.

  • This is a big focus area, we are working

  • on the in-car compute actively,

  • we're going to lead that, 5G is a huge focus for Intel,

  • as you might've seen in other, Mobile World Congress,

  • other places.

  • And then the data center.

  • And so we own the data center today,

  • and we're going to continue to do that

  • with new technologies and actually enable

  • these solutions, not just from

  • a pure hardware primitives perspective,

  • but from the software-hardware interaction in full stack.

  • >> So for those people who think of Intel

  • as a chip company, obviously you guys

  • abstract away complexities and put it into silicon,

  • I obviously get that.

  • Google Next this week, one thing I was really impressed by

  • was the TensorFlow machine learning algorithms

  • in open source, you guys are optimizing the Xeon processor

  • to offload, not offload, but kind of take on...

  • Is this kind of the paradigm that Intel looks at,

  • that you guys will optimize the highest performance

  • in the chip where possible, and then to let the software

  • be more functional?

  • Is that a guiding principle, is that a one off?

  • >> I would say that Intel is not just a chip company.

  • We make chips, but we're a platform solutions company.

  • So we sell primitives to various levels,

  • and so, in certain cases, yes, we do optimize for software

  • that's out there because that drives adoption

  • of our solutions, of course.

  • But in new areas, like the car for instance,

  • we are driving the whole stack, it's not just the chip,

  • it's the entire package end to end.

  • And so with TensorFlow, definitely.

  • Google is a very strong partner of ours,

  • and we continue to team up on activities like that.

  • >> We are talking with Naveene Rao,

  • vice president general manager Intel's AI solutions.

  • Breaking it down for us.

  • This end to end thing is really interesting to me.

  • So I want to get just double click on that a little bit.

  • It requires a community to do that, right?

  • So it's not just Intel, right?

  • Intel's always had a great rising tide

  • floats all boats kind of concept

  • over the life of the company, but now, more than ever,

  • it's an API world, you see integration points

  • between companies.

  • This becomes an interesting part.

  • Can you talk up to that point about

  • how you guys are enabling partners to work with,

  • and if people want to work with Intel,

  • how do they work, from a developer to whoever?

  • How do you guys view this community aspect?

  • I mean, sure you'd agree with that, right?

  • >> Yeah, absolutely.

  • Working with Intel can take on many different forms.

  • We're very active in the open source community.

  • The Intel Nervana AI solutions are completely open source.

  • We're very happy to enable people in the open source,

  • help them develop their solutions on our hardware, but also,

  • the open source is there to form that community

  • and actually give us feedback on what to build.

  • The next piece is kind of one quick down,

  • if you're actually trying to build an end to end solution,

  • like you're saying, you got a camera.

  • We're not building cameras.

  • But these interfaces are pretty well defined.

  • Generally what we'll do is, we like to select some partners

  • that we think are high value add.

  • And we work with them very closely,

  • and we build stuff that our customers can rely on.

  • Intel stands for quality.

  • We're not going to put Intel branding on something,

  • unless it sort of conforms to some really high standard.

  • And so that's I think a big power here.

  • It doesn't mean we're not going to enable the people

  • that aren't our channel partners or whatever,

  • they're going to have to be enabled

  • through a more of a standard set of interfaces,

  • software or hardware.

  • >> Naveene, I'll ask you, in the final couple minutes

  • we have left, to kind of zoom out and look at the coolness

  • of the industry right now.

  • So you're exposed, your background, we got your PhD,

  • and then you topic wise now heading up the AI solutions.

  • You probably see a lot of stuff.

  • Go down the what's cool to you scene,

  • share with the audience some of the cool things

  • that you can point to that we should pay attention to

  • or even things that are cool that we should be aware

  • that we might not be aware of.

  • What are some of the coolest things

  • that are out there that you could share?

  • >> To share new things, we'll get to that in a second.

  • Things I think are one of my favorites, AlphaGo,

  • I know this is like, maybe it's hackneyed.

  • But as an engineering student in CS in the mid-90s,

  • studying artificial intelligence back then

  • or what we called artificial intelligence,

  • Go was just off the table.

  • That was less than 20 years ago.

  • In that time, it looked like such an insurmountable problem,

  • the brain is doing something so special

  • that we're just not going to figure it out in my lifetime,

  • to actually doing it is incredible.

  • So to me, that represents a lot.

  • So that's a big one.

  • Interesting things that you may not be aware of

  • are other use cases of AI, like we see it in farming.

  • This is something we take for granted.

  • We go to the grocery store, we pick up our food

  • and we're happy, but the reality is,

  • that's a whole economy in and of itself,

  • and scaling it as our population scales

  • is an extremely difficult thing to do.

  • And we're actually interacting with companies

  • that are doing this at multiple levels.

  • One is at the farming level itself, automating things,

  • using AI to determine the state of different props

  • and actually taking action in the field automatically.

  • That's huge, this is back-breaking work.

  • Humans don't necessarily--

  • >> And it's important too, because people are worried about

  • the farming industry in general.

  • >> Absolutely.

  • And what I love about that use case of like

  • applying AI to farming techniques is that,

  • by doing that, we actually get more consistency

  • and you get better yields.

  • And you're doing it without any additional chemicals,

  • no genetic engineering, nothing like that,

  • you're just applying the same principles we know better.

  • And so I think that's where we see

  • a lot of wonderful things happening.

  • It's a solved problem, but just not at scale.

  • How do I scale this problem up?

  • I can't do that in many instances,

  • like I talked about with the legal documents

  • and trying to come up with a summary.

  • You just can't scale it today.

  • But with these techniques, we can.

  • And so that's what I think is extremely exciting,

  • any interaction there, where we start to see scale--

  • >> And new stuff, and new stuff?

  • >> New stuff.

  • Well, some of it I can't necessarily talk about.

  • In the robot space, there's a lot happening there.

  • I'm seeing a lot in the startup world right now.

  • We have a convergence of the mechanical part of it

  • becoming cheaper and easier to build

  • with 3D printing, the Maker revolution,

  • all these kind of things happening,

  • which our CEO is really big on.

  • So that, combined with these techniques becoming mature,

  • is going to come up with some really cool stuff.

  • We're going to start seeing The Jetsons kind of thing.

  • It's kind of neat to think about, really.

  • I don't want to clean my room, hey robot, go clean my room.

  • >> John: I'd love that.

  • >> I'd love that too.

  • Make me dinner, maybe like a gourmet dinner,

  • that'd be really awesome.

  • So we're actually getting to a point

  • where there's a line of sight.

  • We're not there yet, I can see it in the next 10 years.

  • >> So the fog is lifting.

  • All right, final question, just more of a personal note.

  • Obviously, you have a neuroscience background,

  • you mentioned that Go is cool.

  • But the humanization factor's coming in.

  • And we mentioned ethics, came up, we don't have time

  • to talk about the ethics role, but as societal changes

  • are happening, with these new impacts of technologies,

  • there's real impact.

  • Whether it's solving diseases and farming,

  • or finding missing children, there's some serious stuff

  • that's really being done.

  • But the human aspects of converging with algorithms

  • and software and scale.

  • Your thoughts on that, how do you see that

  • and how would you, a lot of people are trying

  • to really put this in a framework to try to advance more

  • either sociology thinking, how do I bring sociology

  • into computer science in a way that's relevant.

  • What are some of your thought here?

  • Can you share any color commentary?

  • >> I think it's a very difficult thing to comment on,

  • especially because there are these emergent dynamics.

  • But I think what we'll see is,

  • just as like social network have interfered in some ways

  • and actually helped our interaction with each other,

  • we're going to start seeing that more and more.

  • We can have AIs that are filtering interactions for us.

  • A positive of that is that we can actually

  • understand more about what's going on around in our world,

  • and we're more tightly interconnected.

  • You can sort of think of it as

  • a higher bandwidth communication between all of us.

  • When we're in hunter-gatherer societies,

  • we can only talk to so many people in a day.

  • Now we can actually do more, and so

  • we can gather more information.

  • Bad things are maybe that things become more impersonal,

  • or people have to start doing weird things

  • to stand out in other people's view.

  • There's all these weird interactions--

  • >> It's kind of like Twitter. (laughs)

  • >> A little bit like Twitter.

  • You can say ridiculous things sometimes to get noticed.

  • We're going to continue to see that,

  • we're already starting to see that at this point.

  • And so I think that's really

  • where the social dynamic happened.

  • It's just how it impacts our day to day communication.

  • >> Talk to Naveene Rao, great conversation here

  • inside the Intel AI lounge.

  • These are the kind of conversations

  • that are going to be on more and more kitchen tables

  • across the world, I'm John Furrier with theCUBE.

  • Be right back with more after this short break.

  • >> Thanks, John.

  • (bright music)

(bright music)

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インテルのNaveen Rao博士 - SXSW 2017 - #IntelAI - #theCUBE (Dr. Naveen Rao, Intel - SXSW 2017 - #IntelAI - #theCUBE)

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