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Meet the man building an AI that mimics our neocortex – and could kill off neural networks

Palm founder Jeff Hawkins on his life's work

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But what are the drawbacks?

So if Hawkins thinks he has the theory and is on the way to building the technology, and other companies are implementing it, then why are we even calling what he is doing a bet? The answer comes down to credibility.

Hawkins' idiosyncratic nature and decision to synthesize insights from two different fields – neuroscience and computer science – are his strengths, but also his drawbacks.

"No one knows how the cortex works, so there is no way to know if Jeff is on the right track or not," Dr Terry Sejnowski, the laboratory head of the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies in San Diego, California, told The Register. "To the extent that [Hawkins] incorporates new data into his models he may have a shot, and there will be a flood of data coming from the BRAIN Initiative that was announced by Obama last April."

Hawkins said that this response is typical of the academic community, and that there is enough data available to learn about the brain. You just have to look for it.

"We're not going to replicate the neocortex, we're not going to simulate the neocortex, we just need to understand how it works in sufficient detail so we can say 'A-ha!' and build things like it," Hawkins said. "There is an incredible amount of unassimilated data that exists. Fifty years of papers. Thousands of papers a year. It's unbelievable, and it's always the next set of papers that people think is going to do it. It's not true that you have to wait for that stuff."

The root of the problems Hawkins faces may be his approach, which stems more from biology than from mathematics. His old colleague and cofounder of Numenta, Dileep George, confirms this.

"I think Jeff is largely right in what he wrote in On Intelligence," George told us. "There are different approaches on how to bring those ideas. Jeff has an angle on it; we have a different angle on it; the rest of the community have another perspective on it."

These ideas are echoed by Google's Norvig. "Hawkins, at least in his general-public-facing-persona, seems to be more driven by duplicating what the brain does, while the deep learning researchers take some concepts from the brain, but then mostly are trying to optimize mathematical equations," Norvig said via email.

"I live in the middle," Hawkins told us. "Where I know the neuroscience details very, very well, and I have a theoretical framework, and I bounce back and forth between these over and over again."

The future

Hawkins reckons that what he is doing today "is maybe five per cent of how humans learn." He believes that during the coming year he will begin work on the next major area of development for his technology: action.

For Hawkins' machines to gain independence – the ability, say, to not only recognize and classify patterns, but actively tune themselves to hunt for specific bits of information – the motor component needs to be integrated, he said.

"What we've proven so far – I say built and tested and put into a product – is pure sensor. It's like an ear listening to sounds that doesn't have a chance to move," he told us.

If you can add in the motor component, "an entire world opens up," he said.

"For example, I could have something like a web bot – an internet crawler. Today's web crawlers are really stupid, they're like wall-following rats. They just go up and down the length up and down the length," he aid.

"If I wanted to look and understand the web, I could have a virtual system that is basically moving through cyberspace thinking about 'what is the structure here? How do I model this?' And so that's an example of a behavioral system that has no physical presence. It basically says, 'OK, I'm looking at this data, now where do I go next to look? Oh, I'm going to follow this link and do that in an intelligent way'."

By creating this technology, Hawkins hopes to dramatically accelerate the speed with which generally applicable artificial intelligence is developed and integrated into our world.

It's taken a lot to get here, and the older Hawkins gets and the more rival companies spend, the bigger the stakes get. As of 2014, he is still betting his life on the fact that he is right and they are wrong. ®

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