Voices in AI – Episode 71: A Conversation with Paul Daugherty
Today’s leading minds talk AI with host Byron Reese
About this Episode
Episode 71 of Voices in AI features host Byron Reese and Paul Daugherty discuss transfer learning, consciousness and Paul’s book “Human + Machine: Reimagining Work in the Age of AI.” Paul Daugherty holds a degree in computer engineering from the University of Michigan, and is currently the Chief Technology and Innovation Officer at Accenture.
Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.
Byron Reese: This is Voices in AI brought to you by GigaOm. Today my guest is Paul Daugherty. He is the Chief Technology and Innovation Officer at Accenture. He holds a computer engineering degree from the University of Michigan. Welcome to the show Paul.
Paul Daugherty: It’s great to be here, Byron.
Looking at your dates on LinkedIn, it looks like you went to work for Accenture right out of college and that was a quarter of a century or more ago. Having seen the company grow… What has that journey been like?
Thanks for dating me. Yeah it’s actually been 32 years, so I guess I’m going on a third of a century, joined Accenture back in 1986, and the company’s evolved in many ways since then. It’s been an amazing journey because the world has changed so much since then and a lot of what’s fueled the change in the world around us has been what’s happened with technology. I think [in] 1986 the PC was brand new, and we went from that to networking and client server and the Internet, cloud computing mobility, internet of things, artificial intelligence and the things we’re working on today. So it’s been a really amazing journey fueled by the way the world’s changed, enabled by all this amazing technology.
So let’s talk about that, specifically artificial intelligence. I always like to get our bearings by asking you to define either artificial intelligence or if you’re really feeling bold, define intelligence.
I’ll start with artificial intelligence which we define as technology that can sense, think, act and learn, is the way we describe it. And [it’s] systems that can then do that, so sense: like vision in a self-driving car, think: making decisions on what the car does next, acts: in terms of they actually steer the car and then learn: to continuously improve behavior. So that’s the working definition that we use for artificial intelligence, and I describe it more simply to people sometimes, as fundamentally technology that has more human-like capability to approximate the things that we’re used to assuming and thinking that only humans can do: speech, vision, predictive capability and some things like that.
So that’s the way I define artificial intelligence. Intelligence I would define differently. Intelligence I would just define more broadly. I’m not an expert in neuroscience or cognitive science or anything, but I define intelligence generally as the ability to both reason and comprehend and then extrapolate and generalize across many different domains of knowledge. And that’s what differentiates human intelligence from artificial intelligence, which is something we can get a lot more into. Because I think the fact that we call this body of work that we’re doing artificial intelligence, both the word artificial and the word intelligence I think lead to misleading perceptions on what we’re really doing.
So, expand that a little bit. You said that’s the way you think human intelligence is different than artificial, — put a little flesh on those bones, in exactly what way do you think it is?
Well, you know the techniques we’re really using today for artificial intelligence, they’re generally from the branch of AI around machine learning, so machine learning, deep learning, neural nets etc. And it’s a technology that’s very good at using patterns and recognizing patterns in data to learn from observed behavior, so to speak. Not necessarily intelligence in a broad sense, it’s ability to learn from specific inputs. And you can think about that almost as idiot savant-like capability.
So yes, I can use that to develop Alpha Go to beat the world’s Go master, but then that same program wouldn’t know how to generalize and play me in tic-tac-toe. And that ability, the intelligence ability to generalize, extrapolate, rather than interpolate, is what human intelligence is differentiated by, and the thing that would bridge that, would be artificial general intelligence, which we can get into a little bit, but we’re not at that point of having artificial general intelligence, we’re at a point of artificial intelligence, where it could mimic very specific, very specialised, very narrow human capabilities, but it’s not yet anywhere close to human-level intelligence.
Listen to this one-hour episode or read the full transcript at www.VoicesinAI.com
Byron explores issues around artificial intelligence and conscious computers in his new book The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity.