Author: Byron Reese / Source: Gigaom
Today’s leading minds talk AI with host Byron Reese
About this Episode
Episode 79 of Voices in AI features host Byron Reese and Naveen Rao discussing intelligence, the mind, consciousness, AI, and what the day to day looks like at Intel. Byron and Naveen also delve into the implications of an AI future.
Visit www.VoicesinAI.com to listen to this one-hour podcast or read the full transcript.
Transcript Excerpt
Byron Reese: This is Voices in AI brought to you by GigaOm, and I’m Byron Reese. Today I’m excited that our guest is Naveen Rao. He is the Corporate VP and General Manager of Artificial Intelligence Products Group at Intel. He holds a Bachelor of Science in Electrical Engineering from Duke and a Ph.D. in Neuroscience from Brown University. Welcome to the show, Naveen.
Naveen Rao: Thank you. Glad to be here.
You’re going to give me a great answer to my standard opening question, which is: What is intelligence?
That is a great question. It really doesn’t have an agreed-upon answer. My version of this is about potential and capability. What I see as an intelligent system is a system that is capable of decomposing structure within data. By my definition, I would call a newborn human baby intelligent, because the potential is there, but the system is not yet trained with real experience. I think that’s different than other definitions, where we talk about the phenomenology of intelligence, where you can categorize things, and all of this. I think that’s where the outcropping of having actually learned the inherent structure of the world.
So, in what sense by that definition is artificial intelligence actually artificial? Is it artificial because we built it, or is it artificial because it’s not real intelligence? It’s like artificial turf; it just looks like intelligence.
No. I think it’s artificial because we built it. That’s all. There’s nothing artificial about it. The term intelligence doesn’t have to be on biological mush, it can be implemented on any kind of substrate. In fact, there’s even research on how slime mold, actually…
Right. It can work mazes…
… can solve computational problems, Yeah.
How does it do that, by the way? That’s really a pretty staggering thing.
There’s a concept that we call gradients. Gradients are just how information gets more crystalized. If I feel like I’m going to learn something by going one direction, that direction is the gradient. It’s sort of a pointer in the way I should go. That can exist in the chemical world as well, and things like slime mold actually use chemical gradients that translate into information processing and actually learn dynamics of a system. Our neurons do that. Deep neural networks do that in a computer system. They’re all based on something similar at one level.
So, let’s talk about the nematode worm for a minute.
Okay.
You’ve got this worm, the most successful creature on the planet. Seventy percent of all animals are nematode worms. He’s got 302 neurons and exhibits certain kinds of complex behavior. There have been a bunch of people in the OpenWorm Project, who spent 20 years trying to model those 302 neurons in a computer, just to get it to duplicate what the nematode does. Even among them, they say: “We’re not even sure if this is possible.” So, why are we having such a hard time with such a simple thing as a nematode worm?
Well, I think this is a bit of a fallacy of reductive thinking here, that, “Hey, if I can understand the 302 neurons, then I can understand the 86 billion neurons in the human brain.” I think that fallacy falls apart because there are different emergent properties that happen when we go from one size system to another. It’s like running a company of 50 people is not the same as running a company of 50,000. It’s very different.
But, to jump in there… my question wasn’t, “Why doesn’t the nematode worm tell us something about human intelligence?” My question was simply, “Why don’t we understand how a nematode worm works?”
Right. I was going to get to that. I think there are a few reasons for that. One is, interaction of any complex system – hundreds of elements – is extremely complicated. There’s a concept in physics called the three-body problem, where if I have two pool balls on a pool table, I can actually 100 percent predict where the balls will end up if I know the initial state and I know how much energy I’m injecting when I hit one of the balls in one direction with a certain force. If you make that three, I cannot do that in a closed form system. I have to simulate steps along the way….
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