Jeff Hawkins: Where open source and machine learning meet big data

At OSCON in Portland, Ore., last month, I had the chance to meet Jeff Hawkins, the inventor of the Palm Pilot and arguably the father of the smartphone. I learned that he is now pioneering the analysis of huge streams of real-time data using insights gained as a neuroscientist. His company offers a product that can learn the characteristics of data streams, predict their future actions, and identifiy anomalies.

He has just recently taken the core of that product and released it as a GPLv3-licensed open source project on GitHub so that anyone can build machine intelligence into their systems. Below is a video of our discussion, followed by an edited version of the interview.

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You don’t do Palm Pilots any more. Tell me about your true passion.

My true passion, and it has been for over 30 years, is neuroscience. Understanding how the human brain works and building machines that work on those same principles. The whole period of Palm and Handspring [a company Hawkins co-founded to create Palm-compatible devices] was like a sideshow. I couldn’t get the gig I wanted in neuroscience, so I was building mobile computing. I loved it, I enjoyed it, I was totally excited about it. But my real passion was brains, how they work, and building machines that work on the same principles.

So now you’ve been working most recently on software that can predict the future?

That’s right, we’ve actually been working on modeling, figuring out how parts of the neocortex works — which is the big region on top of your head — and we figured out first what it does and then how it does it. But we’ve been applying it to problems where you can take streams of data, model the data, and then predict the future. We have a product called Grok that takes data from windmills, energy meters, and other machines and can predict future values. It can detect when anomalies are occurring and things like that.

We have a business side to what we’re doing, but we’re here at OSCON to talk about the new open source project where we take these learning algorithms, which are essentially models of the neocortex, a slice of the neocortex, and put them in an open source project.

It’s been up for a couple of months now. It’s called NuPIC, the Numenta Platform for Intelligent Computing. It’s the same code tree we use in our products, so you can go and see what we’re releasing every day. We have an active community already — we’ve had our first hackathon. It’s only been going for a couple of months, but it’s been going pretty well so far, we’re pretty excited about it.

What would I use that for as an open source hacker?

There are a number of reasons why you might be interested in this. People are interested in applying it to new problems. We’re applying it to machine-generated data, but there’s lots of other applications where people might apply it to.

In the same vein as we’re using Grok, you might apply it to financial data, or you might apply it to new sources of data that we don’t look at. There’s a lot of people interested in taking it and building more complex systems — robotics, vision, music, things like that — and this requires extending the algorithms. It’s a memory system, so making them bigger, putting them in a hierarchy, and so on.

There’re people who want to apply it to existing products — “I want to predict something” — people who want to build new types of products, people who are interested in language, semantics, and so on. Then there’re people who are interested in doing pure research. They’re doing mathematical analysis of these algorithms, so it’s kind of broad.

We’re talking about the beginning of building brains in software and hardware, and that’s a big, big field. It’s going to be huge, and it’s just getting started.

And I should mention we have a number of people interested in doing hardware implementations of these algorithms. There are some big companies — IBM, Seagate, some others that actually have programs on the way because they’re pretty excited about this stuff.

I’d sum it up as “using brain science to work out how to handle big data.”

That’s one way of looking at it. I’m a neuroscientist, so I always want to talk about the neuroscience, but from a hacker or coding point of view, yes. Today, what you can do with it is stream fast data to it, and it builds models of the data in an online fashion, meaning every record that comes in its updating the model, it makes predictions, and it can detect anomalies.

I’ll give you an example, a simple example we actually did. People are interested in predicting energy usage. A building consumes energy throughout the day — it’s up and down depending on what the building is doing or what’s going on. If you can predict what the energy consumption will be four hours from now or 24 hours from now, it’s sometimes advantageous. You can pre-cool the building, you can do a thing called demand response where you basically buy energy at different prices. That’s the kind of thing we do with our product Grok today. It works very well at that. Constantly learning, and if the patterns in the world change, it adapts to it automatically.

What was it that made you give up Palm Pilots and get into neuroscience instead?

I think that brain science — understanding the brain and how it works and building intelligent machines — is actually a bigger societal impact long-term than mobile computing. Much, much larger. You know, absolutely everyone in the world is going to have a computer in their pocket; it’s a process for democratization and education, so thats all great. But people don’t realize yet how big intelligent machines are going to be. It’s sort of like starting the whole computing industry all over again.

In my OSCON talk, I mentioned that we’re like the 1950s in computing. The 1950s in computing was when they were just starting to build computers, they were just starting to be useful, but we had decades of advances still to go. Today, we’re starting to build intelligent machines that work on the principles of the brain, we’re just getting started, and it’s going to be decades. But where it’s going, it’s just going to be unbelievable. We’re going to be able to make machines that are a million times faster at thinking than we are. We’re going to be able to make machines that have much more memory than we do. We’re going to be able to make machines that can sense things that we can’t sense.

It’s hard to know where its going to go, just as in the 1950s it was hard to know where the computer was going to go. But intelligent machines, machines that learn in the way that brains do, are just going to have an amazing impact on society, the Earth, and humanity.

How do you think being an open source project is going to contribute toward achieving that vision for you?

First of all, my goal has always been to make this happen sooner, to be a catalyst for this, so anything I can do to spread ideas is a good thing. I’m not in this at this point trying to make a lot of money. I’m in this because I think it’s cool, it’s fun, it’s good to do, it’s important.

Even though we made this technical, scientific discovery four years ago and we published it, I waited until we had real demand before we made an open source project. I wanted people to come to me, and they did! People came to us and said, “Can you give us the source code to this, it’s really cool, we want to work on this,” “I want to use my PhD thesis on this,” “We want to embed it.” So when people asked us, we said, “Great!” — and of course this was my goal from the beginning.

You can’t help but put these ideas out there — putting the code out there, showing how this stuff works. Some number of people are going to pick it up, they’re going to go, “This is great, I get it,” they’re going to invest in it. It’s not something anyone can own, it’s not one thing. it’s like saying, “Could the computing industry be closed?” No, it couldn’t, it had to have lots of competitors, lots of ideas, and this is like that.

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