Practical A.I. applications for business | Deep-dive with Richard Boyd (Part 3)

What are practical applications of A.I. in business? How are you using Machine Learning? What kind of success have you found with using Artificial Intelligence? These are some of the questions answered by A.I. expert Richard Boyd in Part 3 of this deep-dive interview series on Artificial Intelligence.

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DISCLAIMER: Below is an AI generated transcript. There could be a few typos but it should be at least 90% accurate. Watch video or listen to the podcast for the full experience!

Alexander Ferguson 0:01
Welcome to UpTech Report series on AI. I’m Alexander Ferguson. In this episode, we continue our conversation with Richard Boyd, founder of Tanjo in Carrboro. Richard is a successful entrepreneur, author and speaker. So we wanted to know, how has his thinking on technology and AI evolved? How does he use it in his own business? And how could other business leaders use it in theirs?

Richard Boyd 0:31
Early on our our team was working in computer gaming it started with when I met David Smith, here in North Carolina. When I met him he was beginning he had just done a game called the colony, which was the first real time 3d adventure game that attracted a lot of attention from people like Tom Clancy and guys like James Cameron, who at the time was working on a movie called The Abyss down in South Carolina. And so just this idea of taking technology and applying it to problems to solve them, like what we saw, we helped James Cameron solve some visualization problems around the movie The Abyss early on, and that was fascinating process. But I guess today, the so I guess it was a natural evolution, right, like, applying technologies to problems. We ended up getting really interested in artificial intelligence was a way to build deeper meaning into the virtual worlds we were building. And like I said, with computer environments, building more convincing characters that you can believe in more convincing environments, and it sort of just evolved from there. I mentioned 2009 is the time when we got religion, so to speak on machine learning. And that’s when David and I went out to Microsoft research labs where Alex Kipman was working on the Microsoft Kinect, if you remember that it was called natal at the time, they were just trying to teach that. So they were claiming this this. I understand. They don’t sell it anymore. But there was a piece of hardware you could attach to your Microsoft Xbox that would watch you in your living room. And you could use your body as the controller. That was the central idea. But in order for that to work, well, the sensors had to be amazing. So they called Lockheed Martin. And Lockheed bought my last company. So David and I were there, we went out to Microsoft research labs. And it happened to be during the Game Developers Conference, we were out there anyway, walked in and saw Alex and there’s a guy named Jaron Lanier, there who I’ve known for a long time, he’s the guy who came up with the term virtual reality. He’s the kind of dreadlock guy you see some pictures of me online with. And, and initially, we were looking at how can we help you with the sensors is should it be time of flight, should it be structured light, whatever. But we found out pretty quickly they had that nailed, what they built was like the optimal solution to that problem in the form factor that they had to fit it in. But the other thing that they showed us was, we’re trying to teach this system what a living room is. And that is a difficult computational problem. And so, again, there were two approaches, the approach at that time still could have been, let me just program in and tell them what a chair is, what a table is, what a plant is, or whatever. Or the other way, which they thankfully used was machine learning, which is, let me just have examples of living rooms from all over the world, Asian, European, South American, us, rural versus urban, whatever, everything you might encounter, and that and give them all the give the system all those examples, millions and millions of examples. And whatever they did had to fit within about less than 100 megabytes of space, the whole brain for the system, and they were able to achieve that. So that just blew us away. And I that changed our thinking completely.

Alexander Ferguson 3:53
How did that revelation change the course in direction of your business?

Richard Boyd 3:58
Secretary of Education at the time, Arne Duncan and his deputy Jim Shelton came to Lockheed and said, Hey, we in the government have lots and lots of information at the Smithsonian and all over the place in the Library of Congress. How do we make it available to teachers in an easy way, like we’re trying to scan and digitize all this stuff in? But how do I make it discoverable? by tagging it right now, we’ve got armies of human beings in there trying to put tags on stuff. And I usually use a picture of that last scene and the Raiders of the Lost Ark where you got a clerk with this crate, and it says Ark thingy on it. It’s the Ark of the Covenant that can destroy or save the planet, you know, and it’s inside this box is putting in this massive warehouse with a tag that says Ark. Like, that’s undiscoverable it’s a potent thing that’s valuable. But undiscoverable most of the information we have is what we call dark, dark data, right? It’s squirreled away somewhere inaccessible and undiscoverable because it’s not digitized or not tagged, even if it’s digitize, it’s not tagged? Well, what’s amazing with what we did for the what’s called the learning registry for the Department of Education was built a system that could go and look at that stuff. Read, you could read the Declaration of Independence, or the Magna Carta or any other documents, or look at images of things. And if it had something similar to it in its massive, multi dimensional lookup table, it would go ahead and tag it, right. And if it didn’t recognize the thing, then it would say I need a human expert, they would call for help, right phone a friend, in this case, it’s a human to come in and say, Oh, that’s actually an ancient cluniac drinking vessel from 100 BC, you know, and, and go ahead and tag it. But of course, once it’s been tagged once, the great thing about machines is they never forget, how can

Alexander Ferguson 5:47
businesses and organizations implement practical AI applications.

Richard Boyd 5:51
So whether you’re practicing law, or you’re practicing architecture, or whatever, what you want now is a machine learning brain like I’ve just described, that goes through everything that you have all the assets that you have, and reads everything every document created every ideally, to be honest, every email written by all of your people. And it understands like what, you know, what do people know, what is our organizational knowledge, maps it all and by the way, locates where everything is, which is something that’s incredibly important for digital transformation. And then, you know, once it’s mapped, now you can track things like how did it How does new information enter organization? Who’s championing it? Who’s challenging it? How do decisions get made? Why do we choose this vendor over that vendor? Why do we choose this strategy over that strategy, and would be able to tell you forensically, you know, what decisions made and why and maybe help you make better decisions in the future? What made Red Hat really successful? Here, again, here in the triangle was this idea that, you know, implementing Unix based servers, you know, you know, within your organization is a complex activity, it’s also a very intimate activity, because that’s where all your people are connecting, right? So do you want to just outsource that to someone else? Or do you want to buy a turnkey, on premise tested solution that works extremely well, that you can Shepherd and manage going forward? And that was that decision that led to how much did they just get bought for 34 billion, or whatever it was, right from, from free software. So I think that same principle applies here is that, again, whether you’re a government organization, or you’re a company, or an architecture, firm, whatever, get your own machine learning system, inside your firewall, under your control, and, and make sure you know, where your data is, and where it’s going. If you want to get people fluent and comfortable with this idea that, hey, I want this asset. Here, I want this machine learning companion, that’s going to help me do my job better. But also, it becomes an enduring sort of map of how decisions and how work is done within the organization. So we’re doing that for the all North Carolina community colleges in North Carolina. So there’s 58 of them, right. And when this was fully implemented, you know, one of the things I because I’m on the Board of Trustees of white tech, right, so I understand how turnover happens and the sort of complexity of some of these organizations at Wake Tech, we have like 70,000 students a year. You know, it’s a $250 million enterprise that’s underway that does a lot of good in the community. But you know, we just our president, just event like presidents do they retire. So now we got to put a new person in place. And there’s lots of other turnover that happens at various levels throughout the organization. What if when that new person steps in, they could see right away like they have a little companion AI, that assistant we talked about earlier, that says, well, it looks like you’re approaching your first board meeting? Well, the last person, here’s when they approach this kind of problem, here’s how they here’s the resources they went to, here’s the people they went to. And here’s here’s how they did that job. It’s also really good for the organization to have that have a map of that intelligence within the organization. When people leave your company, you’ve invested a lot of money in those people. And time you’d like to have some model of of of that, that stays behind after they leave.

Alexander Ferguson 9:24
What kinds of success have you found with using AI,

Richard Boyd 9:27
our entire solution set is around something you can implement in less than six months, that will have a 10x return on investment. And that that’s our kind of guiding algorithm for everything that we do. So that means we’re looking at the low hanging fruit like things like accounting. So we work with a local accounting firm, found out that there were some new rules around revenue recognition and lease recognition, and we’re coming out. We looked at that and said, that’s perfect because all I need to do is get a bunch of sales contracts, feed it to a system and don’t don’t Let’s see what Kanye West or anybody else is doing. Nothing else on the internet just focus on this very tightly bound realm of like, how does what kind of language will you encounter in a sales agreement, whether you’re selling software, hardware, you know, services, consumer goods, whatever it happens to be, and become familiar with all of the terms and everything, not just like 10,000, not 100,000. But millions of contracts, let it read all that create its own sort of inferred understanding of how to how to process that and then just do the basic job of like been sorting like, Yeah, this is a really standard contract. This one, these have a few like non standard elements that a human being needs to look at. These are on fire, like this is all non standard, whoever’s doing this is probably trying to cheat you. And this needs a lot of human attention, or you probably should not do do business with that person or whatever, whatever the rules are, right? And so applying it to things that are easy to digest and get those get that 10x return in a short amount of time. That’s where we find our success.

Alexander Ferguson 11:07
That concludes the audio version of this episode. To see the original and more visit our UpTech Report YouTube channel. If you know a tech company, we should interview you can nominate them at UpTech Or if you just prefer to listen, make sure you’re subscribed to this series on Apple podcasts, Spotify or your favorite podcasting app.


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How has A.I. evolved? Deep-dive with Richard Boyd (Part 2)

Machine Learning Explained (what business leaders need to know)