How to Build a Digital Brain

Palm founder Jeff Hawkins on neurology, big data, and the future of artificial intelligence

AM: So how does this relate to your previous work on the brain?

JH: First of all, we have a very complex brain; it’s got all these different components. But we’re just talking neocortex here. Every mammal, from a human to a mouse to a dolphin, has one. What is the neocortex doing? It’s building a model of the world, of what we call sensory motor contingencies or sensory motor patterns: Why are you wearing glasses and what does that mean? Or: If I turn my head to the right, I have expectations about what I’m going to see. Most of what we learn about the world is how it behaves when we interact with it. The neocortex builds a model of what should happen in a particular context. A bigger neocortex lets you make a more complex model, and it lets you have more sensors. And that’s what intelligence is: it’s learning this model of the world.

AM: And Grok uses a similar principle?

JH: Here’s what we do inside Grok: we build this 60,000-neuron neural network that emulates a very small part of one layer of the neocortex. It’s about a thousandth the size of a mouse brain and a millionth the size of a human brain. So: not super-intelligent, but we’re using the principle by which the brain does all the inference and motor behavior. I’m very confident that this sequence memory we use is the core of how all intelligence works. The brain’s taking in streaming data, they’re noisy, they’re constantly changing, and it has to figure out what the patterns are and make predictions from them.

AM: Is this different from other artificial-intelligence research that’s going on these days?

JH: I’ve been observing the AI and AI-neural-network fields for years, and I’ve always been a bit of a contrarian. My view has been: let’s figure out how the neocortex works, and once we understand those principles, that will be the path of building machine intelligence. Classic AI says: forget the neuroscience; it’s a matter of programming and algorithms.

AM: I have to ask, why would you want to build super-intelligent machines?

JH: We can make the world more efficient, we can save energy, we can save resources, we can help detect diseases. When I ask myself, What’s the purpose of life?, I think a lot of it is figuring out how the world works. These machines will help us do that. Many, many years from now, we’ll be able to build machines that are super-physicists and super-mathematicians, and explore the universe. The idea that we could accelerate our accretion of knowledge is very exciting.

AM: But what are all the people going to do once there are all these super-intelligent machines?

JH: Take these models we’re building with Grok. No human is going to be displaced by these things. No one is doing this—it’s impossible. Take the telephone system, where electronic switching replaced all those operators. If we had to have an operator place every telephone call in the world, there would be a billion telephone operators. Did we lose a billion jobs? Not really. We lost a few jobs, and advanced the quality of life. It’s not some dystopian future where machines do everything and we sit around in lounge chairs.

AM: Actually, that sounds pretty good to me.


Read an extended interview at theatlantic.com/thefuture.

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