Voices in AI – Episode 3: A Conversation with Mark Rolston


In this episode, Byron and Mark talk about computer versus human creativity, connectivity with digital systems, AGI, and the workforce.


Byron Reese: This is Voices in AI, brought to you by Gigaom. I’m Byron Reese. Today, our guest is Mark Rolston. He’s the co-founder and Chief Creative Officer of argodesign. He’s a renowned designer with a twenty-five year career of creating for the world’s largest and most innovative companies.

An early pioneer of software user experience, Mark helped forge the disciplines around user interface design and mobile platforms. A veteran design leader, innovator, and patent holder, he is one of Fast Company’s Most Creative People, and he was nominated for Fast Company’s World’s Greatest Designer in 2014.

Welcome to the show, Mark!

Mark Rolston: Yeah, welcome, thanks!

I want to start off with my question that I ask everybody. So far, no two answers have been the same. What is artificial intelligence?

Oh, god, what is AI? Big question, okay.

I think it’s probably easy to start with what AI isn’t, especially given all the attention that it gets right now. Certainly, every time the topic of AI comes up for me, especially with, let’s say, my family around me, the expectation is that it’s somehow on the level of another fully-living, breathing person—that level of cognition. I think that, every time we want to talk about AI, when we go immediately to that, the idea of a fully-competent mind, we really lose sight of what AI is and what it’s valuable at.

I also think in terms of so much marketing that’s going on, where everyone wants to place AI at the front of their product, say that it’s powered by AI, I think that while the world’s software has gotten a lot better in terms of applying rich data like historical behavior data—you know, you continue to rent movies of this type, then maybe you would like this next movie—or rich algorithms—understanding how to optimize a path home—those things have made software a lot more “intelligent,” but those things are not AI.

For me, I think of it as a spectrum of capabilities that transcends that basic sort of rich data and algorithmic intelligence that software has. To where AI can take a cognitively-complex situation that involves context, that involves ongoing computational value—meaning it’s not simply answering algorithmically or data-based queries immediately, but it can understand something over the course of time. Let’s say, like a habit that somebody has of doing something, or a large set of medical records—to be able to resolve that against immediate context and come up with a conclusion.

I think one of the things, anecdotally, that I tend to do to help people get away from the idea of the sort of Terminator notion of AI, or the 2001 HAL notion of AI, is to ask them to liken it to a two-year-old in intelligence, or maybe even a one-year-old. Except that this one-year-old has, let’s say, every medical record in a tristate area available to it, and can sift through it and find consistent cases and conditions and give you back an answer. Or it can understand every stock fluctuation for a particular stock or industry instantaneously, and give you some thoughtful ideas about that. It still, on other bases, may be a complete idiot. It can’t tie its own shoes. It still wets the bed. It’s still a very simple system.

And so, I think that helps me, helps others, sort of get away from the idea of talking about AI in general terms. Certainly, one day, we’ll get to general AI. I expect we will, but right now to talk about that is incredibly distracting from some of the real practical things that are happening in AI.

Well, help me understand a distinction you’re making. You explicitly said the program that guides my car to where I’m going—routing—isn’t artificial intelligence. Even though it knows the context of where I am, it might have a real-time traffic feed and all of that. And yet, presumably, you think something like IBM Watson – which is able to go through a bunch of cancer journals and recommend treatments for different types of cancer, is a form of artificial intelligence.

Assuming that that is the case, what’s the essential difference between what’s going on in those two cases?

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