How to Increase the Reliability of Your IT Infrastructure Using Predictive Analytics REGISTER >
Palm Computing founder Jeff Hawkins has developed a controversial theory of how the brain works, and he's using it to build a new race of computers.
What's the value of such machines?
That's kind of like asking what's the value of building a computer. They asked that question 50 years ago, and someone said, "Well, we can do military ballistic tables" —that's what they first did with computers—or "We might be able to tabulate the accounts of a business." We have a lot of ideas as to how HTMs will be used. The obvious ones are things that humans do. Take vision. There's a crying need for machines that can look at things and know what they're looking at. Right now, when you search on Google for images, someone has written in the data about what those images are. Speech recognition is another problem people have been trying to solve for a long time, and they haven't done a very good job at it. You don't want to do speech recognition; you want to do speech understanding, which is closer to what we're doing.
Automakers are building cars with lots of sensors. They want to know if a dangerous situation is occurring on the road around the car, or if the driver is getting drowsy, and whether the car should warn the driver, or slow down. It sounds easy, but it's actually very difficult to take this data and interpret it. Humans can do it, but in general, there are no machines that go about this the way humans do, no machines that have the insight of a human being. It's sort of the difference between playing chess with a human and playing chess with a computer. The computer can beat the human by being fast and using brute force, but it doesn't really understand what it's doing. Humans have deep intuition and understanding; they have a deeper model of what's going on in front of them.
Meanwhile, some of our customers are looking at complex manufacturing processes. There's all this data, and people sit there and sort through that data to figure out patterns and try to understand what causes the yield to rise and fall. This machine can do that. It can look at disparate data and build a model of how manufacturing lines work and how the yield is affected by various things.
Why has it taken this long to get to a point where we can start talking about actually making these intelligent predictions?
When people ask me about the success of the Palm Pilot, I always point out that there was nothing new in it. There wasn't a single piece of new anything in that product. What was new was the understanding of how to put the ingredients together. That's what we're doing here. We have a deeper understanding of how the brain works, and we can take a little bit of this and a little bit of that, and model it.
But it's a hard problem. First, we had to collect lots of information about the brain so people could sit down and figure out a theory about how the brain works. Before that, I don't think you could have figured this out. Also, our platform requires lots of memory, and a lot of CPU horsepower. So ten years ago we probably couldn't have done it. But today, we can.
One of the reasons I started Numenta is because I want to bring the urgency of economic markets to this scientific problem. From that point of view, it's a bit like the human genome project. The sequencing of the human genome began purely as an academic thing, and it was going to take a decade to complete until someone turned it into a business. Then it ended up taking about 18 months, because suddenly there was profit involved. I'm consciously trying to promote this understanding of the brain, and I'm going to make it happen faster by providing economic incentives for people to work on it.
It's a daunting task, but I think the hardest part is behind us. That's when I was pursuing this without a name, without any money; I was just some guy just trying to do this stuff on his own. Now, after years of working in this field,
I have a lot of experience, and it's starting to come together. We understand it well enough that I can speak confidently that this stuff will happen. And I am certain that over the years we will need to create a whole new set of programming tools and hardware. The earliest Numenta could release our first tool set is by the end of 2006. We know exactly what we need to do; it's just a matter of turning the crank.
Your theory presupposes that consciousness plays no part in the decision-making process, a notion to which many people object. What if your theory is wrong?
It's true that we haven't actually proven any of this stuff. We built a small model that shows it can work, and we understand the theory quite well, but we haven't actually built a system that does any of the things we're talking about. But I would be really, really surprised if the brain doesn't work like this. It's clear to me that what we are building will work. I am as certain about this as I can be about anything.