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GeekWire: Microsoft CTO Kevin Scott sees ‘mind-blowing’ potential for AI, the cloud, 5G and devices

Microsoft CTO Kevin Scott at the GeekWire Cloud Summit in Bellevue, Wash. (GeekWire Photo / Kevin Lisota)

Kevin Scott is the chief technology officer for Microsoft, a company that built its business on software, but he also appreciates hardware and devices. In fact, he has been building his own machine shop and hardware lab as a personal hobby in his spare time, looking to demonstrate the potential of combining hardware with the latest software and cloud technologies.

One of his upcoming projects, for example, is a vacuum-siphon coffee machine with a vision and speech interface.

“No buttons,” he said. “The user interface will be all AI.”

Sorry, coffee lovers, but it’s not an upcoming Microsoft product.

“For the love of God, don’t tell Satya I’m doing this,” Scott joked, referring to Microsoft CEO Satya Nadella.

However, the project does reflect a broader trend that Scott, Nadella and Microsoft believe will drive new waves of innovation in the years ahead: the confluence of machine learning, artificial intelligence, 5G mobile broadband, cloud technologies and a growing number of sensors and IoT devices around the world.

“You’ve got all of this opportunity for places where you can interface with the physical world,” Scott said. “It’s just where business opportunity is going to live.”

That was one of the takeaways from Scott’s talk at the GeekWire Cloud Summit. The Microsoft CTO was previously vice president of engineering and operations at LinkedIn, led engineering and operations at mobile advertising company AdMob, and worked two stints at Google, starting as a senior engineering manager in 2005. He’s also the host of the podcast Behind the Tech and is involved in diversity initiatives both inside and outside the company.

Listen to our conversation with Microsoft CTO Kevin Scott in the podcast above, watch the video below, or subscribe in your favorite podcast app. Continue reading for edited highlights.

The confluence of trends that he’s watching closely today: There are a bunch of things that are really important right now where you have a convergence of a bunch of technological trends. We have the end of Moore’s Law rapidly approaching. The important thing that’s already gone is Dennard scaling, which says that you can put more transistors onto a chip and actually power them without burning your dyes up. We’re already at the point where the density improvements we get in silicon aren’t as usable as they were when Dennard scaling was actually working.

That’s coming at the same time that we have these AI workloads — even what’s happened over the past six to nine months has just been mind-boggling. We’re in this mode where the quality of the results that you can compute with the most ambitious flavors of these machine learning systems, whether it’s simulation-based reinforcement learning or pure unsupervised learning, is entirely a proportion of how much compute you can throw at them. You have this new workload that is hungry for compute in ways that nothing ever has been before. That is a really interesting set of factors. It has implications for how we build next generation compute and networks and software stacks. What you’re going to be able to do with all of this AI is just mind-blowing in ways that are hard to predict.

Then you also have this other really interesting thing where IoT and sensing are accelerating because actually Moore’s Law is still working there. Most of these low cost micro-controllers and microprocessors are still on older process technologies. We’ve got another couple or three doublings of price-performance coming, and so more and more of the world is going to get instrumented and you’re going to be able to take these data-enriched AI powered feedback loops that have built the consumer internet, for instance, and that’s going to migrate into all sorts of things like industrial controls and home automation.

Unsupervised learning and reinforcement learning probably sounds far off in the distance and some of the stuff about IoT probably sounds like it’s far off in the distance. The insight that I have is, don’t think of it as far off in the distance. Especially on the machine learning front, that needs to be a part of every company and every developer’s toolkit. Progress is happening so quickly that if you wait too long, you’re going to be in this position where you’re scrambling to catch up.

The state of machine learning and artificial intelligence: The thing that’s really changed across the entire AI community over the past six months is there have been a couple of astounding breakthroughs in unsupervised learning for natural language processing, natural language understanding. We have figured out a new way to build models that build some sort of conceptual notion of what natural language looks like, and from that, you can build a bunch of very specific applications that are higher performing and it’s much lower cost to adapt the general model to a use case and the use cases are all over the place, from question answering to document summarization, to search, to literally there are a dozen or more things that folks are looking at right now and so we are just starting to see these things sort of trickle out into the product portfolio.

Practical applications of artificial intelligence: AI is not a product. It is a technology that you use to build products. We have a different and better way now to build the features of a whole bunch of products and so the way it will show up is not in some sort of Big Bang thing. It’s going to be that a bunch of the features of a whole lot of products are going to get better in material ways and some of it is, you’re just not going to be aware what’s going on. Content moderation on the Xbox platform, for instance, has got a whole bunch of this tech in it and it’s being used to try to prevent bullying and bad behavior on the platform and to protect kids, for instance. It’s just much easier to scale those systems when they’re powered by AI and enhanced by human beings.

The intersection of software and hardware: The interface between software and the physical world is extremely interesting. The real world is not governed by rules that are as logical and straightforward as the rules that we have for software systems. That’s not to diminish how complex software actually is, but machine learning gives us a bunch of powerful new tools and techniques to deal with that complicated, messy, interface. You can’t expect an average tinkerer to sit around and understand these nonlinear partial differential equations that characterize a physical system, whereas you may be able to give them a set of tools like a machine learning system that can give them enough control over that physical thing that they can do something interesting with it. You’ve got all of this opportunity for places where you can interface with the physical world. I think it’s just where business opportunity is going to live.

A visualization of Kevin Scott’s GeekWire Cloud Summit talk by Guillaume Wiatr of MetaHelm.

The potential of mobile broadband: What 5G does is it makes the edge platforms even more interesting. You get lower latency and higher throughput. You still will have to have different solutions for rural areas because the 5G technologies require higher densities of towers than we even have right now with 4G, but there are solutions. 5G presents this opportunity for us to really think about the architecture of these edge applications in different ways. For instance, one of the things that we see already emerging is these computer vision models. On a constrained compute edge device, you’ll be able to run one level of inference. You can do generic event detection, for instance, on the edge and then refer those interesting events back up to the cloud to do a heavyweight inference to actually classify an object or whatnot. 5G just lets you draw those lines differently, and you can build much more interesting applications.

The state of privacy in tech: I was lucky enough to be an engineer and I think many engineers are very aware of what’s going on inside of all of these systems and have very strong opinions about privacy and security in the first place. I’ve always thought that it’s extraordinarily important at AdMob and I was an early Google employee. We took all of these things deadly, deadly seriously, just in terms of access controls and whatnot. But if anything, it gets more and more important every day. AdMob was 2007-2010. Nine years later, the world of data that is residing in clouds is many orders of magnitude larger, and presents this very interesting challenge for folks. The digital world has become more complex. The footprint you’re leaving as you use digital services has grown much larger as we spend more time online, and so I think the onus is on all technology companies, large and small, that are handling people’s data. You’ve got some regulatory things that you have to comply with, like GDPR. I think more of those regulations are coming and I think that’s a good thing for consumers.We as an industry need to go above and beyond because we have early line of sight on what the trends are with privacy and security as well.

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Fortune: Microsoft’s tech chief Kevin Scott talks artificial intelligence, mixed reality and sod farming

As Microsoft’s chief technology officer, Kevin Scott has the challenging job of keeping his company atop all of the tech trends.

Scott became Microsoft’s CTO two years ago after six years directing software engineering at LinkedIn, which Microsoft bought in 2016 for $26 billion. One of his first jobs at Microsoft was to identify all the technologies used by the company’s sprawling business units—to gauge their usefulness—and then make sure that the popular ones were available to every division.

The exercise reflects the philosophy of Microsoft CEO Satya Nadella, Scott says. Nadella not only spends a lot of time thinking about what to do, but also “what are we not doing that we’re going to regret.”

Microsoft missed out on a few tech revolutions, in particular the rise of smartphones, which rivals Apple and Google ended up capitalizing on. Nadella wants Scott to make sure that nothing similar happens again.

In this edited interview with Fortune, Scott talks about artificial intelligence, Microsoft’s continued push into mixed reality [Microsoft lingo for both virtual and augmented reality tech], and the challenges of deep learning.

Fortune: How do you distinguish Microsoft’s AI from other companies?

Kevin Scott: We’re a platform company by DNA. If you listen to how Bill Gates has always defined what a platform company is, it’s one that builds technology that creates all of this opportunity in which you don’t have all of the economic value concentrated in one company. We’re increasing the overall size of the pie. The PC, for instance, created an enormous economic opportunity. We see AI as essentially the same thing.

When I think of platforms, I think of things like Windows, which other companies can build apps on top of. Is this how you see AI?

The thing that we’re pushing hard on is that a lot of AI right now is still unnecessarily difficult for many people to get up to speed on. There are maybe in the high tens of thousands of developers out there who are hardcore machine learning/data science folks. Almost every customer we interact with is thinking about using AI to help its business run better. And you can’t expect each and every one of them to hire a bunch of Ph.Ds and machine learning engineers. At the rate AI is unfolding, not enough of those people exist.

One of our challenges is to build technologies that lower the barriers to entry so a much larger pool of developers can use machine learning in their products and services. Microsoft itself is a microcosm for this because we have about 55,000 developers in the company and not all of them are machine learning/data science experts.

I imagine it’s a challenge for companies exploring the AI technique of deep learning to get used to the idea that a lot of their experiments will fail.

It’s incumbent upon us as platform providers to give people better tools—to guide you in better ways toward paths that get you to success.

I think you do have to expect some of this stuff not to work. You have to get into it with this experimental mindset. It’s not like you’re proving a theorem and you walk through the steps and it’s done and predictable. It’s more like lab science.

The most technologically-savvy companies are used to this trial-and-error process. We just know through our own efforts that the first thing is not going to work, and you have to push and push. When you get the win, it totally covers all of the costs of the experimentation.

What’s your background in AI?

I’m writing a book on AI right now. It’s about why we should be optimistic about a future that includes AI. The contrarian thing is that I think it’s net beneficial even to people in rural parts of the country.

I was a poor kid from rural central Virginia—Campbell County, a little town called Gladys. I went back there a year ago for the book. All the industry there evaporated years ago. Tobacco, textiles, furniture manufacturing all went poof. But some interesting things are emerging there now, some of which is powered by AI and advanced automation.

What’s going on in Gladys?

I went to school with people whose families’ have been tobacco farmers for five generations. Their business basically went sideways when the tobacco markets collapsed, and they had to figure out what to do. They were fairly entrepreneurial and they knew technology would play a role in what they were doing.

All the land that they used to plant tobacco on is sod now, and the unit economics is about as good as tobacco. Part of the reason is that they use a bunch of advanced automation—tractors, and fairly sophisticated technology to let them grow sod on these very large tracks of land. It’s more labor intensive than tobacco was, but with the technology they have about the same number of employees. So technology hasn’t reduced jobs.

On the horizon are things like drones that can fly over crops to do aerial inspections. It’s not that you don’t need a human being, but you can fly over it more frequently and get more data about what’s going on in your field so you can better adjust fertilizers and water.

Because of the technology, you don’t need a giant factory with thousands of people in order to just get your unit economics right. You can start a business and have 30 people working in this place and have that 30-person business in Campbell County, Va. be a global business. Some people believe you won’t have jobs coming back where there are 100 companies with 10,000 jobs a piece, but you’ll have 100,000 companies with 100 higher-skilled jobs each.

Will those jobs pay more?

Yeah. I know for sure.

Some people are concerned that while automation will make companies more efficient, only management will benefit and not the workers.

I think both can happen and I think we should be cautious. What I’ve seen working on this book and talking with customers the size of Walmart all the way down to small and medium sized businesses is that there’s lots of things to be hopeful about.

Virtual reality and augmented reality seemed really big three years ago, and now many venture capital investors aren’t as focused on it because they couldn’t get returns fast enough. How do you plan for and adjust when a technology hasn’t caught on as fast as hoped?

Part of my job is making sure that we maintain our focus and our commitment to some of these investments over long periods. The thing I can say is we have not reduced our investments in mixed reality [Microsoft makes the HoloLens augmented reality headset]. If anything, we increased things—not dramatically up, but it’s growing.

If you’re thinking of yourself as a platform company, you have to be thinking about what the future platforms are going to be. We have three things that we believe are going to be important platforms that are in different stages of development.

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One is quantum computing, which at some point is going to be very important. There’s mixed reality, which we think is probably in a shorter time horizon is going to be a very important platform. And on a shorter time horizon than that, this notion of an intelligent edge, which you can think of as a mashup of IOT [Internet-connected devices], sensors, and AI.

We believe all three of those will be extremely important platforms in the future. And to make a global scale platform work, you have to invest and believe it’s real. It’s a question of when and not if.

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Podcast: Microsoft CTO Kevin Scott’s insights on the history and future of computing

Kevin Scott

Chief Technology Officer Kevin Scott

Episode 36, August 8, 2018

Kevin Scott has embraced many roles over the course of his illustrious career in technology: software developer, engineering executive, researcher, angel investor, philanthropist, and now, Chief Technology Officer of Microsoft. But perhaps no role suits him so well – or has so fundamentally shaped all the others – as his self-described role of “all-around geek.”

Today, in a wide-ranging interview, Kevin shares his insights on both the history and the future of computing, talks about how his impulse to celebrate the extraordinary people “behind the tech” led to an eponymous non-profit organization and a podcast, and… reveals the superpower he got when he was in grad school.

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Episode Transcript

Kevin Scott: It’s a super exciting time. And it’s certainly something that we are investing very heavily in right now at Microsoft, in the particular sense of like, how do we take the best of our development tools, the best of our platform technology, the best of our AI, and the best of our cloud, to let people build these solutions where it’s not as hard as it is right now?

Host: You’re listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. I’m your host, Gretchen Huizinga.

Kevin Scott has embraced many roles over the course of his illustrious career in technology: software developer, engineering executive, researcher, angel investor, philanthropist, and now, Chief Technology Officer of Microsoft. But perhaps no role suits him so well – or has so fundamentally shaped all the others – as his self-described role of “all-around geek.”

Today, in a wide-ranging interview, Kevin shares his insights on both the history and the future of computing, talks about how his impulse to celebrate the extraordinary people “behind the tech” led to an eponymous non-profit organization and a podcast, and… reveals the superpower he got when he was in grad school. That and much more on this episode of the Microsoft Research Podcast.

Host: Kevin Scott, welcome to the podcast today.

Kevin Scott: Well thank you so much for having me.

Host: So, you sit in a bit chair. I think our listeners would like to know what it’s like to be the Chief Technical Officer of Microsoft. How do you envision your role here, and what do you hope to accomplish in your time? I.E., what are the big questions you’re asking, the big problems you’re working on? What gets you up in the morning?

Kevin Scott: Well, there are tons of big problems. I guess the biggest, and the one that excites me the most and that prompted me to take the job in the first place, is I think technology is playing an increasingly important role in how the future of the world unfolds. And, you know, has an enormous impact in our day-to-day lives from the mundane to the profound. And I think having a responsible philosophy about how you build technology is like a very, very important thing for the technology industry to do. So, in addition to solving all of these, sort of, complicated problems of the “how” – what technology do we build and how do we build it? – there’s also sort of an “if” and a “why” that we need to be addressing as well.

Host: Drill in a little there. The “if” and the “why.” Those are two questions I love. Talk to me about how you envision that.

Kevin Scott: You know, I think one of the more furious debates that we all are increasingly having, and I think the debate itself and the intensity of the debate are good things, is sort of around AI and what impact is AI going to have on our future, and what’s the right way to build it, and what are a set of wrong ways to build it? And I think this is sort of a very important dialogue for us to be having, because, in general, I think AI will have a huge impact on our collective futures. I actually am a super optimistic person by nature, and I think the impact that it’s going to have is going to be absolutely, astoundingly positive and beneficial for humanity. But there’s also this other side of the debate, where…

Host: Well, I’m going to go there later. I’m going to ask you about that. So, we’ll talk a little bit about the dark side. But also, you know, I love the framework. I hear that over and over from researchers here at Microsoft Research that are optimistic and saying, and if there are issues, we want to get on the front end of them and start to drive and influence how those things can play out. So…

Kevin Scott: Yeah, absolutely. There’s a way to think about AI where it’s mostly about building a set of automation technologies that are a direct substitute for human labor, and you can use those tools and technologies to cause disruption. But AI probably is going to be more like the steam engine in the sense that the steam engine was also a direct substitute for human labor. And the people that benefited from it, initially were those who had the capital to build them, because they were incredibly expensive, and who had the expertise to design them and to operate and maintain them. And, eventually, the access to this technology fully democratized. And AI will eventually become that. Our role, as a technology company that is building things that empower individual and businesses, is to democratize access to the technology as quickly as possible and to do that in a safe, thoughtful, ethical way.

Host: Let’s talk about you for a second. You’ve described yourself as an engineering executive, an angel investor, and an all-around geek. Tell us how you came by each of those meta tags.

Kevin Scott: Yeah… The geek was the one that was sort of unavoidable. It felt to me, all my life, like I was a geek. I was this precociously curious child. Not in the sense of you know like playing Liszt piano concertos when I’m 5 years old or anything. No, I was the irritating flavor of precocious where I’m sticking metal objects into electric sockets and taking apart everything that could be taken apart in my mom’s house to try to figure out how things worked. And I’ve had just sort of weird, geeky, obsessive tastes in things my entire life. And I think a lot of everything else just sort of flows from me, at some point, fully embracing that geekiness, and wanting – I mean, so like angel investing for instance is me wanting to give back. It’s like I have benefited so much over the course of my career from folks investing in me when it wasn’t a sure bet at all that that was going to be a good return on their time. But like I’ve had mentors and people who just sort of looked at me, and, for reasons I don’t fully understand, have just been super generous with their time and their wisdom. And angel investing is less about an investment strategy and more about me wanting to encourage that next generation of entrepreneurs to go out and make something, and then trying to help them in whatever way that I can be successful and find the joy that there is in bringing completely new things into the world that are you know sort of non-obvious and -complicated.

Host: Mmmm. Speaking of complicated. One common theme I hear from tech researchers here on this podcast, at least the ones who have been around a while, is that things aren’t as easy as they used to be. They’re much more complex. And in fact, a person you just talked to, Anders Hejlsberg, recently said, “Code is getting bigger and bigger, but our brains are not getting bigger, and this is largely a brain exercise.”

Kevin Scott: Yes.

Host: So, you’ve been around a while. Talk about the increased complexity you’ve seen and how that’s impacted the lives and work of computer scientists and researchers all around.

Kevin Scott: I think interestingly enough, on the one hand, it is far more complicated now than it was, say, 25 years ago. But there’s a flipside to that where we also have a situation where individual engineers or small teams have unprecedented amounts of power in the sense that, through open-source software and cloud computing and the sophistication of the tools that they now use and the very high level of the abstractions that they have access to that they use to build systems and products, they can just do incredible things with far fewer resources and in far shorter spans of time than has ever been possible. It’s almost this balancing act. Like, on the other hand, it’s like, oh my god, the technology ecosystem, the amount of stuff that you have to understand if you are pushing on the state-of-the-art on one particular dimension, which is what we’re calling upon researchers to do all the time, it’s really just sort of a staggering amount of stuff. I think about how much reading I had to do when I was a PhD student, which seemed like a lot at the time. And I just sort of look at the volume of research that’s being produced in each individual field right now. The reading burden for PhD students right now must be unbelievable. And it’s sort of similar, you know, like, if you’re a beginning software engineer, like it’s a lot of stuff. So, it’s this weird dichotomy. I think it’s, perhaps if anything, the right trade off. Because if you want to go make something and you’re comfortable navigating this complexity, the tools that you have are just incredibly good. I could have done the engineering work at my first startup with far, far, far fewer resources, with less money, in a shorter amount of time, if I were building it now versus 2007. But I think that that tension that you have as a researcher or an engineer, like this dissatisfaction that you have with complexity and this impulse to simplicity, it’s exactly the right thing, because if you look at any scientific field, this is just how you make progress.

Host: Listen, I was just thinking, when I was in my master’s degree, I had to take a statistics class. And the guy who taught it was ancient. And he was mad that we didn’t have to do the math because computer programs could already do it. And he’s not wrong. It’s like, what if your computer breaks? Can you do this?

Kevin Scott: That is fascinating, because we have this… old fart computer scientist engineers like me, have this… like we bemoan a similar sort of thing all the time, which is, ahhh, these kids these days, they don’t know what it was like to load their computer program into a machine from a punch paper tape.

Host: Right?

Kevin Scott: And they don’t know what ferrite core memories are, and what misery that we had to endure to… It was fascinating and fun to, you know, learn all of that stuff, and I think you did get something out of it. Like it gave you this certain resilience and sort of fearlessness against these abstraction boundaries. Like you know, if something breaks, like you feel like you can go all the way down to the very lowest level and solve the problem. But it’s not like you want to do that stuff. Like all of that’s a pain in the ass. You can do so much more now than you could then because, to use your statistic professor’s phrase, because you don’t have to do all of the math.

(music plays)

Host: Your career in technology spans the spectrum including both academic research and engineering and leadership in industry. So, talk about the value of having experience in both spheres as it relates to your role now.

Kevin Scott: You know, the interesting thing about the research that I did is, I don’t know that it ever had a huge impact. The biggest thing that I ever did was this work on dynamic binary translation and the thing I’m proudest of is like I wrote a bunch of software that people still use, you know, to this day, to do research in this very arcane, dark alley of computer science. But what I do use all the time that is almost like a superpower that I think you get from being a researcher is being able to very quickly read and synthesize a bunch of super-complicated technical information. I believe it’s less about IQ and it’s more of the skill that you learn when you’re a graduate student trying to get yourself ramped up to mastery in a particular area. It’s just like, read, read, read, read, read. You know, I grew up in this relatively economically depressed part of rural, central Virginia, town of 250 people, neither of my parents went to college. We were poor when I grew up and no one around me was into computers. And like somehow or another, I got into this science and technology high school when I was a senior. And like I decided that I really, really, really wanted to be a computer science professor after that first year. And so, I went into my undergraduate program with this goal in mind. And so, I would sit down with things like the Journal of the ACM at the library, and convince, oh, like obviously computer science professors need to be able to read and understand this. And I would stare at papers in JACM, and I’m like, oh my god, I’m never, ever going to be good enough. This is impossible. But I just kept at it. And you know it got easier by the time that I was finishing my undergraduate degree. And by the time I was in my PhD program, I was very comfortably blasting through stacks of papers on a weekly basis. And then, you know, towards the end of my PhD program, you’re on the program committees for these things, and like not only are you blasting through stacks of papers, but you’re able to blast through things and understand them well enough that you can provide useful feedback for people who have submitted these things for publication. That is an awesome, awesome, like, super-valuable skill to have when you’re an engineering manager, or if you’re a CTO, or you’re anybody who’s like trying to think about where the future of technology is going. So, like every person who is working on their PhD or their master’s degree right now and like this is part of their training, don’t bemoan that you’re having to do it. You’re doing the computer science equivalent of learning how to play that Liszt piano concerto. You’re getting your 10,000 hours in, and like it’s going to be a great thing to have in your arsenal.

Host: Anymore, especially in a digitally-distracted age, being able to pay attention to dense academic papers and/or, you know, anything for a long period of time is a superpower!

Kevin Scott: It is. It really is. You aren’t going to accomplish anything great by you know integrating information in these little 2-minute chunks. I think pushing against the state-of-the-art, like you know creating something new, making something really valuable, requires an intense amount of concentration over long periods of time.

Host: So, you came to Microsoft after working at a few other companies, AdMob, Google, LinkedIn. Given your line of sight into the work that both Microsoft and other tech giants are doing, what kind of perspective do you have on Microsoft’s direction, both on the product and research side, and specifically in terms of strategy and the big bets that this company is making?

Kevin Scott: I think the big tech companies, in particular, are in this really interesting position, because you have both the opportunity and the responsibility to really push the frontier forward. The opportunity, in the sense that you already have a huge amount of scale to build on top of, and the responsibility that knowing that some of the new technologies are just going to require large amounts of resources and sort of patience. You know like one example that we’re working on here at Microsoft is we, the industry, have been worried about the end of Moore’s Law for a very long time now. And it looks like for sort of general purpose flavors of compute, we are pretty close to the wall right now. And so, there are two things that we’re doing at Microsoft right now that are trying to mitigate part of that. So, like one is quantum computing, which is a completely new away to try to build a computer and to write software. And we’ve made a ton of progress over the past several years. And our particular approach to building a quantum computer is really exciting, and it’s like this beautiful collaboration between mathematicians and physicists and quantum information theory folks and systems and programming language folks trained in computer science. But when, exactly, this is going to be like a commercially viable technology? I don’t know. But another thing that we’re you know pushing on, related to this Moore’s wall barrier, is doing machine learning where you’ve got large data sets that you’re fitting models to where you know sort of the underlying optimization algorithms that you’re using for DNNs or like all the way back to more prosaic things like logistic regression, boil down to like a bunch of sort of linear algebra. We are increasingly finding ways to solve these optimization problems in these embarrassingly parallel ways where you can use like special flavors of compute. And so like there’s just a bunch of super interesting work that everybody’s doing with this stuff right now, like, from Doug Burger’s Project Brainwave stuff here at Microsoft to… uh, so it’s a super exciting time I think to be a computer architect again where the magnitude and the potential payoffs of some of these problems are just like astronomically high, and like it takes me back to like the 80s and 90s, you know which were sort of the, maybe the halcyon days of high-performance computing and these like big monolithic supercomputers that we were building at the time. It feels a lot like that right now, where there’s just this palpable excitement about the progress that we’re making. Funny enough, I was having breakfast this morning with a friend of mine, and you know like both of us were saying, man, this is just a fantastic time in computing. You know, like on almost weekly basis, I encounter something where I’m like, man, this would be so fun to go do a PhD on.

Host: Yeah. And that’s a funny sentence right there.

Kevin Scott: Yeah, it’s a funny sentence. Yeah.

(music plays)

Host: Aside from your day job, you’re doing some interesting work in the non-profit space, particularly with an organization called Behind the Tech. Tell our listeners about that. What do you want to accomplish? What inspired you to go that direction?

Kevin Scott: Yeah, a couple of years ago, I was just looking around at all of the people that I work with who were doing truly amazing things, and I started thinking about how important role models are for both kids, who were trying to imagine a future for themselves, as well as professionals, like people who are already in the discipline who are trying to imagine what their next step ought to be. And it’s always nice to be able to put yourself in the shoes of someone you admire, and say, like, “Oh, I can imagine doing this. I can see myself in this you know in this career.” And I was like we just do a poorer job I think than we should on showing the faces and telling the stories of the people who have made these major contributions to the technology that powers our lives. And so that was sort of the impetus with behindthetech.org. So, I’m an amateur photographer. I started doing these portrait sessions with the people I know in computing who I knew had done impressive things. And then I hired someone to help you know sort of interview them and write a slice of their story so that you know if you wanted to go somewhere and get inspired about you know people who were making tech, you know, behindthetech.org is the place for you.

Host: So, you also have a brand-new podcast, yourself, called Behind the Tech. And you say that you look at the tech heroes who’ve made our modern world possible. I’ve only heard one, and I was super impressed. It’s really good. I encourage our listeners to go find Behind the Tech podcast. Tell us why a podcast on these tech heroes that are unsung, perhaps.

Kevin Scott: I have this impulse in general to try to celebrate the engineer. I’m just so fascinated with the work that people are doing or have done. Like, the first episode is with Anders Hejlsberg, who is a tech fellow at Microsoft, and who’s been building programing languages and development tools for his entire 35-year career. Earlier in his career, like, he wrote this programming language and compiler called Turbo Pascal. You know like I wrote my first real programs using the tools that Anders built. And like he’s gone on from Turbo Pascal to building Delphi, which was one of the first really nice integrated development environments for graphical user interfaces, and then at Microsoft, he was like the chief architect of the C# programming language. And like now, he’s building this programming language based on JavaScript called TypeScript that tries to solve some of the development-at-scale problems that JavaScript has. And that, to me, is like just fascinating. How did he start on this journey? Like, how has he been able to build these tools that so many people love? What drives him? Like I’m just intensely curious about that. And I just want to help share their story with the rest of the world.

Host: Do you have other guests that you’ve already recorded with or other guests lined up?

Kevin Scott: Yeah, we’ve got Alice Steinglass, who is the president of Code.org, who is doing really brilliantly things trying to help K-12 students learn computer science. And we’re going to talk with Andrew Ng in a few weeks, who is one of the titans of deep neural networks, machine learning and AI. We’re going to talk with Judy Estrin, who is former CTO of Cisco, a serial entrepreneur, board director at Disney and FedEx for a long time. And just you know one of the OGs of Silicon Valley. Yeah, so it’s you know like, it’s going to be a really good mix of folks.

Host: Yeah, well, it’s impressive.

Kevin Scott: All with fascinating stories.

Host: Yeah, and just having listened to the first one, I was – I mean, it was pretty geeky. I will be honest. There’s a lot of – it was like listening to the mechanics talking about car engines, and I know nothing, but it was…

Kevin Scott: Yeah, right?

Host: But it was fun.

Kevin Scott: That’s great. And like you know I hadn’t even thought about it before. But like if could be like the sort of computer science and engineering version of Car Talk, that would be awesome.

Host: You won first place at the William Campbell High School Talent Show in 1982 by appearing as a hologram downloaded from the future. Okay, maybe not for real. But an animated version of you did explain the idea of the Intelligent Edge to a group of animated high school hecklers. Assuming you won’t get heckled by our podcast audience, tell us how you feel like AI and machine learning research are informing and enabling the development of edge computing.

Kevin Scott: You know I think this is one of the more interesting emergent trends right now in computing. So, there are basically three things that are coming together at the same time. You know one thing is the growth of IoT, and just embedded computing in general. You can look at any number of estimates of where we’re likely to be, but we’re going to go from about 11 or 12 billion devices connected to the internet to about 20 billion over the next year and a half. But you think about these connected devices – and this is sort of the second trend – like they all are becoming much, much more capable. So, like, they’re coming online and like the silicon and compute power available in all of these devices is just growing at a very fast clip. And going back to this whole Moore’s Law thing that we were talking about, if you look at $2 and $3 microprocessor and microcontrollers, most of those things right now are built on two or three generations older process technologies. So, they are going to increase in power significantly over the coming years, like particularly this flavor of power that you need to run AI models, which is sort of the third trend. So, like you’ve got a huge number of devices being connected with more and more computer power and like the compute power is going to enable more and more intelligent software to be written using the sensor data that these devices are processing. And so like those three things together we’re calling the intelligent edge. And we’re entering this world where you’ll step into a room and like there are going to be dozens and dozens of computing devices in the room, and you’ll interface with them by voice and gesture and like a bunch of other sort of intangible factors where you won’t even be aware of them anymore. And so that implies a huge set of changes in the way that we write software. Like how do you build a user experience for these things? How do you deal with information security and data privacy in these environments? Just even programming these things is going to be fundamentally different. It’s a super exciting time. And it’s certainly something that we are investing very heavily in right now at Microsoft, in the particular sense of like, how do we take the best of our development tools, the best of our platform technology, the best of our AI, and the best of our cloud, to let people build these solutions where it’s not as hard as it is right now?

Host: Well, you know, everything you’ve said leads me into the question that I wanted to circle back on from the beginning of the interview, which is that the current focus on AI, machine learning, cloud computing, all of the things that are just like the hot core of Microsoft Research’s center – they have amazing potential to both benefit our society and also change the way we interact with things. Is there anything about what you’re seeing and what you’ve been describing that keeps you up at night? I mean, without putting too dark a cloud on it, what are your thoughts on that?

Kevin Scott: The number one thing is, I’m worried that we are actually underappreciating the positive benefit that some of these technologies can have, and are not investing as much as we could be, holistically, to make sure that they get into the hands of consumers in a way that benefits society more quickly. And so like just to give you an example of what I mean, we have healthcare costs right now that are growing faster than our gross domestic product. And I think the only way, in the limit, that you bend the shape of that healthcare cost growth curve, is through the intervention of some sort of technology. And like, week after week over the past 18 months, I’ve seen one technology after another that is AI-based where you sort of combine medical data or personal sensor data with this new regime of deep neural networks, and you’re able to solve these medical diagnostic problems at unbelievably low costs that are able to very early detect fairly serious conditions that people have when the conditions are cheaper and easier to treat and where you know the benefit to the patient, like they’re healthier in the limit. And so, I sort of see technology after technology in this vein that is really going to bring higher-quality medical care to everyone for cheaper and help us get ahead of these, you know sort of, significant diseases that folks have. And you know, there’s a similar trend in precision agriculture where, in terms of crop yields and minimizing environmental impacts, particularly in the developing world where you still have large portions of the world’s population sort of trapped in this you know sort of agricultural subsistence dynamic, AI could fundamentally change you know the way that we’re all living our lives, all the way from you know like all of us getting like you know sort of cheaper, better, locally-grown organic produce with smaller environmental impact, to you know like how does a subsistence farmer in India dramatically increase their crop yield so that they can elevate the economic status of their entire family and community?

Host: So, as we wrap up, Kevin, what advice would you give to emerging researchers or budding technologists in our audience, as many of them are contemplating what they’re going to do next?

Kevin Scott: Well, I think congratulations is in order to most folks, because this is like just about as good a time I think as has ever been for someone to pursue a career in computer science research, or to become an engineer. I mean, the advice that I would give to folks is like, just look for ways to maximize the impact of what you’re doing and so like I think with research, it’s sort of the same advice that I would give to folks starting a company, or engineers thinking about the next thing that they should go off and build in the context of a company: find a trend that is really a fast growth driver, like the amount of available AI training compute, or the amount of data being produced by the world in general, or by some particular you know subcomponent of our digital world. Just pick a growth driver like that and try to you know attempt something that is either buoyed by that growth driver or that is directly in the growth loop. Because I think those are the opportunities that tend to have both the most head room in terms of you know like if there are lots of people working on a particular problem, it’s great if the space that you’re working in, the problem itself, has a gigantic potential upside. Those things will usually like accommodate lots and lots and lots of sort of simultaneously activity on them and not be a winner-takes-all or a winner-takes-most dynamic. You know and there are also sort of the interesting problems as well. You know it’s sort of thrilling to be on a rocket ship in general.

Host: Kevin Scott. Thanks for taking time out of your super busy life to chat with us.

Kevin Scott: You are very welcome. Thank you so much for having me on. It was a pleasure.

Host: To learn more about Kevin Scott, and Microsoft’s vision for the future of computing, visit microsoft.com/research.