Editor’s note: This is the latest in a series of reports about Artificial Intelligence based on a variety of deep-dive interviews conducted by Alexander Ferguson of YourLocalStudio.

Welcome to UpTech Report’s series on AI. I’m Alexander Ferguson. This video is part of our deep-dive interview series where we share the wealth of knowledge given by one of our panel of experts in the field of artificial intelligence.

For our latest series of deep-dive interviews I sat down with Rett Crocker, CEO and CTO of udu, in Raleigh. Rett has designed and developed over 100 games for mobile devices, personal computers and video game consoles. He’s also invented multiple programming languages, game engines and multi-user content. And he’s created innovative software technologies, in fields ranging from speech synthesis to advergaming, to collaborative education. In the first part of our conversation. I asked Rett about the power of AI, the different facets of artificial intelligence and how it’s evolved over time.

  • I start by asking Rett one simple but important question. What is AI to you?

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A loaded term, that doesn’t mean very much. I think that most people that use the term artificial intelligence are using it, not disingenuously, but certainly it’s marketing more than anything else.

To me, there are sort of certain very specific technologies, some of the deep-learning stuff that Google has done and, you know, some stuff being done here locally that is really some fundamental, fundamentally interesting math and, like, good, core computer science.

But really, I mean, it’s interesting, our particular uses for it are, for example, are almost all just pretty much Stats 101. It’s basic regression analysis with, yeah, we use some neural networks here and there but you know, that’s not, you know, that’s not that hard.

But a lot of people they’re like, oh yeah, AI this, AI that they really are just using it for marketing purposes. The real key, like if I really, truly had to define it and wanted to be not, sort of, making fun of that is trying to use a number of different machine-based techniques to try and predict outcomes. And that’s, if you can do that well it’s very valuable.

But there’s a ton of problems with doing it well. For a while there people were calling it predictive analytics and not using the AI term, which I kind of preferred. Predictive is also a bit of a loaded term. Nobody really uses it anymore because it, well, a variety of reasons but there’s no question though that you can do some pretty interesting, pretty straightforward statistical modeling that gets you interesting results.

  • What are some interesting examples of AI that you’ve seen in business and everyday life?

The examples of that are really the predictive type functionality where you’re saying, I don’t know how much John Smith is gonna spend at my veterinary clinic next year but I can build a model, a statistical model, that makes sense that tells me with some degree of confidence that that’s how much they’re gonna spend next year.

But the real case is probably something that is, that we’re using everyday, Google Maps, when you ask it for the location of the local movie theater is basically doing sort of AI work, particularly since you didn’t write, you didn’t type in the exact the name of that particular movie theater Google’s AI had to say, well, that thing that you wrote is probably, I’m predicting, that it is this particular movie theater and so I’m gonna give you that address.

Think back to the beginning days of email. We had to train our email programs to recognize spam. So we’d see, oh look, there’s another email that mentions Viagra, this is spam. Click that button and you’re basically training a little AI that’s in your email program what you think spam is.

And your version of spam and my version of spam might be different, they’re probably the same, they are now because Google solved that problem for all of us and we all have the same spam filter, but really that little Bayesian algorithm there, that’s AI, straight up. Because it is, I mean it’s just doing statistical analysis.

  • How does artificial intelligence power modern consumer technologies such as Siri, Alexa or Google Assistant?

So, first off, there are sort of two categories of those, right? So category number one is the Alexas of this world. Those are, effectively, the same or slightly advanced versions of the text adventure games from the 80s.

And, you know, you basically say, you know, look at door, you know, go east, pick up, you know, sword, whatever. Those are, that’s basically what Alexa does. It is a programming language, disguised, with a voice interface. And it is, so you have to speak the exact incantation in order to trigger Alexa and have it go do something. Siri, and Google’s Assistant, they call it nowadays, they’re different, and IBM Watson, some of the stuff they’re doing there is different.

They’re single, sort of, state versions of that where they can answer a thing at a time, occasionally they can contain and hold some context and be able to answer more things like you can say, you know, I don’t know, what appointments do I have on Thursday, isn’t there one that takes place in Chapel Hill? And it says, oh, the context was appointments so I’m only gonna look, appointments on Thursday, so I’m only gonna through appointments on Thursday, whereas many of them can say, do versions of, I mean, udu can do this quite easily, give me all the appointments on Thursday that are in Chapel Hill, done.

That’s a multiple variable problem but that’s all one problem. The real challenge is multiple problems connected together, which is part of what we were trying to build and you can do that with udu quite easily but you end up having to do it in this query language that’s effectively JavaScript or a very, very, very light version of JavaScript, but that’s too technical.

  • How do you define the different facets of AI?

I think there’s probably too many too list, because there’s a, I mean, if we just look at the language part of it that’s probably, you know, 10 different sub-disciplines right there. I mean there’s speech synthesis, I mean nowadays Google and Siri, they generate the voice that you hear it’s not some recording of a human and they do that through deep learning.

There’s, underneath it all is the deep learning stuff to begin with which there’s, that’s a broad category too. There’s a bunch of different types of that. There’s natural language generation, figuring out what to say. There’s the understanding piece of it and the, sort of, context piece is completely not fully solved. I thought it would be, as I said earlier, but it’s pretty amazing to me how many different sub-disciplines there are just for that piece.

I was, there was a company I was looking at earlier today, I can’t remember their name off the top of my head but they had a Chief Language Officer. They’re AI-related and they 100% have somebody whose sole, dedicated purpose is language and it’s not surprising.

  • What were your early impressions of artificial intelligence? How have these changed since then?

So game development, inherently, has sort of an AI problem particularly if you’re doing single-player games. So I’ve built a number of AIs over the years with always with the same sort of principles that I have used with udu which is, if you’re building an AI for a game you actually don’t want to make it too smart.

You, there’s a friend of mine, one of the original co-founders of udu long, long ago a guy named Richard Harris, he’s in Scotland, he and I and Frank Boosman, who is the other, sort of, technical co-founder I guess, the three of us had decided we wanted to do something, we started meeting in various cities around the world and brainstorming ideas and stuff like that and there’s a phrase he used to describe udu at one point that I think is just brilliant and it sort of goes back to the way I thought of AI back when I did games which is not artificial intelligence [but] designed stupidity.

You basically allow, in games, what that means is that you make it basic, insanely basic, it just knows how to, I am enemy, I run towards bad guy or the player and I shoot, that’s all I do and then what ends up happening is the human, because we’re sort of narrative beasts, we apply our own narrative to it and we imbue them with thinking even though they don’t actually do anything that intelligent, they’re just running at you and shooting. But because of the fact that they’ve got a few like things where they like, a little bit of randomness, it makes us think oh they’re ducking and dodging and, you know, all that.

There are definitely approaches that people are using now that were basically discarded many many years ago. I mean, neural networks, to a certain degree are an example of that because we’re sort of in the right place, right time because we have processing power to spare, we have a lot of processing power anyway, we have a massive amount of memory structures, we have cloud-based computing, there’s all these technologies that allow us to do the things we do. I used to say when I was first trying to introduce udu to people and, you know, with all the connected APIs and everything when somebody, like a VC in particular, would say, oh yeah, well why now, why this, you know, what you know, all that.

The answer is A, because you couldn’t do it 10 years ago because cloud-based computing didn’t exist, APIs to a large degree didn’t exist, at least in the form they were then, and then the bigger thing is some of the core techniques that we used to build udu didn’t exist.

This was just a taste. Stay tuned as we share the full deep-dive interviews we had with each one of our panel of experts and our upcoming episodes focused on specific topics that will transform the way you think about artificial intelligence. All this, on UpTech Report’s new series on AI.