 From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE conversation. Hey, welcome back, everybody. Jeff Frick here with theCUBE. We are in our Palo Alto studios today. We're still getting through COVID. Thankfully, media was a necessary industry, so we've been able to come in and keep a small COVID crew, but we can still reach out to the community and through the magic of the internet and cameras on laptops, we can reach out and touch base with our friends. So we're excited to have somebody who's talking about working on kind of the next big edge, the next big cutting thing going on in technology, and that's the Internet of Things you've heard about at the Industrial Internet of Things. There's a lot of different words for it, but at the foundation of it is this company, it's Intel. We're happy to have joined us, Bill Pearson. He is the vice president of Internet of Things, often said IOT for Intel. Bill, great to see you. Same, Jeff. Nice to be here. Yeah, absolutely. So I just was teasing, getting ready for this interview doing a little homework, and I saw you talking about Internet of Things in a 2015 interview, actually referencing a 2014 interview. So you've been at this for a while. So before we jump into where we are today, I wonder if you can share kind of a little bit of a perspective of what's happened over the last five or six years. I mean, I think data has really grown at a tremendous pace, which has changed the perception of what IOT is going to do for us. And the other thing that's been really interesting is the rise of AI. And of course we need it to be able to make sense of all that data. So one thing that's different is today we're really focused on how do we take that data that is being produced at this rapid rate and really make sense of it so that people can get better business outcomes from that. Right, right. But the thing that's so interesting on the things part of the Internet of Things, and even though people are things too, is that the scale and the pace of data that's coming off kind of machine generated activity versus people generated is orders of magnitude higher in terms of the frequency, the variety and all kind of your classic big data meme. So that's a very different challenge than kind of the growth of data that we had before and the types of data because it's really kind of exponential across every single vector. Absolutely, it has. I mean, we've seen estimates that data is going to increase by about five times as much as it is today over the next just a couple of years. So it's exponential as you said. Right. The other thing that's happened is cloud. And so kind of breaking the mold of the old mold where all the compute was either in your mini computer or data center or mainframe or on your laptop. Now, with cloud and instant connectivity, it opens up a lot of different opportunities. So now we're coming to the edge and Internet of Things. So when you look at kind of edge and Internet of Things kind of now folding into this ecosystem, what are some of the tremendous benefits that we can get by leveraging those things that we couldn't with kind of the old infrastructure and our old way of kind of gathering and storing and acting on data? Yeah, so one of the things we're doing today with the edge is really bringing the compute much closer to where all the data is being generated. So these sensors and devices are generating tons and tons of data. And for a variety of reasons, we can't send it somewhere else to get processed. There may be latency requirements for that control loop that you're running in your factory or there's bandwidth constraints that you have or there's just security or privacy reasons to keep it on site. And so you've got to process a lot of this data on site. Some estimates or maybe half of the data is going to remain on site here. And when you look at that, that's where you need compute. And so the edge is all about taking compute, bringing it to where the data is, and then being able to use the intelligence, the AI and analytics to make sense of that data and take actions in real time. Right, right. But it's a complicated situation, right? Because depending on where that edge is, what the device is, does it have power? Does it not have power? Does it have good connectivity? Does it not have good connectivity? Does it have even the ability to run those types of algorithms or does it have to send it to some interim step even if it doesn't have kind of the ability to send it all the way back to the cloud or all the way back to the data center for latency. So as you kind of slice and dice all these pieces of the chain, where do you see the great opportunity for Intel? Where's a good kind of sweet spot where you can start to bring in some compute horsepower and you can start to bring in some algorithmic processing and actually do things between just the itty-bitty sensor at the itty-bitty end of the chain versus the data center that's way upstream and far, far away. Yeah, our business is really high-performance compute and it's this idea of taking all of these workloads and bringing them in to this high-performance compute to be able to run multiple software-defined workloads on single boxes to be able to then process and analyze and store all that data that's being created at the edge. Do it in a high-performance way and whether that's a retail smart shelf, for example, that we can do real-time inventory on that shelf as things are coming and going or whether it's a factory and somebody doing real-time defect detection of something moving across their textile line. So all of that comes down to being able to have the compute horsepower to make sense of the data and do something with it. Right, right. So you wouldn't necessarily, like in your shelf example, that the compute might be done there at the local store or some aggregation point beyond just that actual kind of sensor that's underneath that one box of Tide, if you will. Absolutely. Yeah, you could have that on-prem, a big box that does multiple shelves, for example. Okay, great. So there's a great example and you guys have the software development kit. You have a lot of resources for developers and one of the case studies that I just wanted to highlight before we jump into the dev side was I think Audi was the customer and it really illustrates a point that we talked about a lot in kind of the big data meme which is people used to take action on a sample of data after the fact. And I think this case, they were talking about running 1,000 cars a day through this factory. They're doing so many welds, 5 million welds a day and they would pull one at the end of the day sample a couple welds and do we have a good day or not? Versus what they're doing now with your technology is actually testing each and every weld as it's being welded based on data that's coming off the welding machine and they're inspecting every single weld. So I just love you've been at this for a long time when you talk to customers about what is possible from a business point of view when you go from after the fact with a sample of data to in real time with all the data how that completely changes your view and ability to react to your business. Yeah, I mean, it makes people be able to make better decisions in real time as you've got cameras on things like textile manufacturers or footwear manufacturers or even these real time inventory examples we mentioned people are going to be able to make and can make decisions in real time about how to stock that shelf, what to order about what to pull off the line am I getting a good product or not? And this has really changed, as you said we don't have to go back and sample anymore you can tell right now as that part is passing through your manufacturing line or as that item is sitting on your shelf what's happening to it. It's really incredible. So let's talk about developers. So you've got a lot of resources available for developers and everyone knows Intel obviously historically and PCs and data centers and you would do what they call design wins back when I was there many moons ago you try to get a design win and then they're going to put your microprocessors and a bunch of other components in a device. When you're trying to work with kind of cutting edge developers in kind of new fields and new areas this feels like a much more direct touch to the actual people building the applications than the people that are really just designing the systems of which Intel becomes a core part of. I wonder if you could talk about the role developers and really Intel's outreach to developers and how you're trying to help them kind of move forward in this new crazy world. Yeah developers are essential to our business they're essential to IOT. Developers as you said create the applications that are going to really make the business possible. And so we know the value of developers and want to make sure that they have the tools and resources that they need to use our products most effectively. We've done some things around OpenVINO Toolkit as an example to really try and simplify democratize AI applications so that more developers can take advantage of this and take the ambitions that they have to do something really interesting for their business and then go put it into action. And the whole, you know, our whole purpose is making sure we can actually accomplish that. Right, so let's talk about OpenVINO it's an interesting topic. So I actually found out what OpenVINO means open visual inference and neural optimization toolkit. So it's a lot about computer vision. So I will, you know, and computer vision is an interesting early AI application that I think a lot of people are familiar with through Google Photos or other things where, you know, suddenly they're putting together a little highlight movies for you or they're pulling together all the photos of a particular person or a particular place. So the computer vision is pretty interesting. Inference is a special subset of AI. So I wonder, you know, you guys are way behind OpenVINO where do you see the opportunities and visualization? What are some of the instance that you're seeing with the developers out there doing innovative things around computer vision? Yeah, there's a whole variety of use cases with computer vision. You know, the one that we talked about earlier here was looking at defect detection. There's a company that we work with that has a 360 degree view. They use cameras all around their manufacturing line and from there, they know what a good part looks like and using inference and OpenVINO they can tell when a bad part goes through or there's a defect in their line and they can go and pull that and make corrections as needed. We've also seen, you know, use cases like smart shopping where there's a point of sale fraud detection, we call it. You know, is the item being scanned the same as the item that, you know, is actually going through the line? And so we can be much smarter about understanding retail. One example that I saw was a customer who was trying to detect if it was vodka or potatoes that was being, you know, scanned in an automated checkout system. And again, using cameras in OpenVINO, they can tell the difference. We haven't talked about computer tasting yet. We're still sticking with computer vision and the natural processing. I know one of the areas you're interested in and it's going to only increase in importance is education, especially what's going on. I keep waiting for someone to start rolling out some national, you know, best practice education courses for kindergarten and third graders and sixth graders and, you know, all these poor teachers that are learning to teach on the fly from home. You guys are doing a lot of work in education. I wonder if you can share, I think you're doing some work with Udacity. Udacity, what are you doing? Where do you see the opportunity to apply some of this AI and IoT in education? Yeah, we launched a nano degree with Udacity and it's all about OpenVINO and Edge AI. The idea is, again, get more developers educated on this technology, take a leader like Udacity, partner with them to make the coursework available and get more developers understanding, using and building things using Edge AI. And so we partnered with them as part of their million developer goal. We're trying to get as many developers as possible through that. Okay. And I would be remiss if we talked about IoT and I didn't throw 5G into the conversation. So 5G is a really big deal. I know Intel's put a ton of resources behind it and I've been talking about it for a long, long time. I think the huge value in 5G is a lot around IoT as opposed to my handset going faster, which is funny that they're actually releasing 5G handsets out there. But when you look at 5G combined with the other capabilities in IoT, again, how do you see 5G being this kind of step function in ability to do real-time analysis and make real-time business decisions? I think it brings more connectivity, certainly, and bandwidth and reduces latency. But the cool thing about it is when you look at the applications of it, we talked about factories. A lot of those factors may want to have private 5G networks that are running inside that factory, running all the machines or robots or things in there. And so it brings capabilities that actually make a difference in the world of IoT and the things that developers are trying to build. That's great. So before I let you go, you've been at this for a while. You've been at Intel for a while. You've seen a lot of big sweeping changes kind of come through the industry. As you sit back with a little bit of perspective, and it's funny, even IoT, like I said, you've been talking about it for five years and 5G we've been waiting for, but the waves keep coming, right? That's kind of the fun of being in this business. As you sit there where you are today, kind of looking forward the next couple of years, couple, four, five years, what has just surprised you beyond compare and what are you still kind of surprised it's still a little bit lagging that you would have expected to see a little bit more progress at this point? You know, to me, the incredible thing about the computing industry is just the insatiable demand that the world has for compute. It seems like we always come up with, our customers always come up with more and more uses for this compute power. As we've talked about data and the exponential growth of data, now we need to process and analyze and store that data. It's impressive to see developers just constantly thinking about new ways to apply their craft and new ways to use all that available computing power. And you know, I'm delighted because I've been at this for a while, as you said, and I just see this continuing to go for as far as the eye can see. Yeah, yeah, I think you're right. There's no shortage of opportunity. I mean, the data explosion is kind of funny. The data's always been there. We just weren't keeping track of it before. And, you know, and the other thing that, as I look at your internet of things kind of toolkit, you guys have such a broad portfolio now where a lot of times people think of Intel pretty much as a CPU company. But as you mentioned, you've got FBGAs and VPUs and Vision Solution Stretch applications. So Intel has really done a good job in terms of broadening the portfolio to go after, you know, kind of this disparate or kind of sharding, if you will, of all these different types of computer applications have very different demands in terms of power and bandwidth and crunching utilization to technical. Yeah, absolutely. The various computer architectures really just, they help our customers with the needs, whether it's, you know, high power, low performance, a mixture of both. Being able to use all of those heterogeneous architectures with a tool like OpenVINO so you can program once, right once, and then run your application across any of those architectures, helps simplify the life of our developers, but also gives them the compute performance, the way that they need it. All right, Bill. We'll keep at it. Thank you for all your hard work and hopefully it won't be five years before we're checking in to see how far this IoT thing is going. Hopefully not. Thanks, Jeff. All right, Bill. Thanks a lot. He's Bill. I'm Jeff. You're watching theCUBE. We'll see you next time.