In my recent article, The Holy Grail of Robotics, I wrote that I was impressed with Jeff Hawkins' description of goal-oriented behavior in his book On Intelligence. In a recent blog post about the Obama administration's $3 billion science initiative known as the Brain Activity Map Project (BAM), Hawkins wrote the following:
The activity and connection maps envisioned by BAM will be useful, but brain theorists today are not lacking in empirical data. We haven’t come close to understanding the tremendous amount of data we already have. If we want to understand how brains work, then a better direction is to focus on brain theory, not brain mapping. We should set goals for brain theory and goals for machine intelligence tasks based on those theories. That is what we do at Numenta. For example, we set goals to understand how neurons in the neocortex form sparse distributed representations and then how they learn to predict future events. This resulted in Cortical Learning Algorithm (CLA) which is the heart of our Grok streaming prediction engine. The next big theoretical challenge we are working on is how the cortex generates behaviors from predictions, what is sometimes called the sensory-motor integration problem.This is even bigger news than Numenta's recent announcement of Grok, in my opinion. I would not think so had it come from anybody other than Hawkins. As I wrote elsewhere, Hawkins is no dummy. This tells me that Numenta is aiming to solve the entire intelligence problem single-handedly. Why? Because, once you have figured out how to do both perceptual and motor learning, there isn't much more to add other than an appetitive/aversive learning mechanism. This is psychology lingo for a pain and pleasure (reward and punishment) mechanism, a must for adaptation. But this is a rather trivial problem once you've gotten this far down the road.
Creating a viable model for sensorimotor behavior is not an easy task. It starts with designing a working perceptual learning system (both pattern and sequence recognizers) and an attention mechanism. I don't think Numenta has perfected either of those, regardless of the hype emanating from Redwood City. An attention mechanism is a must because there can be no coherent motor behavior without the ability to focus.
There is more to motor learning and behavior than what happens in the neocortex, however. Mammals and birds have an additional sensorimotor control structure known as the cerebellum. Humans use it to help with a bunch of automatic tasks such as walking, standing, maintaining posture, and even driving. The cerebellum works on completely different motor control principles than the neocortex. It is needed because it frees the neocortex from having to handle routine motor behavior so it can focus on other things. But even without a cerebellum, a robot could still learn some sophisticated skills.
Even without a good perceptual learner, one can still build a very impressive learning robot with multiple degrees of freedom. This is assuming one has the motor learning part right. Hawkins can save his company a boatload of money by reading my recent article on goal-oriented motor learning. In it, I gave a biologically plausible definition of goal and explained how the neocortex finds the right motor connections for any given goal. I already did a major part of the homework on motor learning. It took me a while to arrive at my current model but it's easy to explain to others once you know how it works. So, in my opinion, Hawkins would do well to skip to something else. For example, he will have to figure out how to handle motor conflicts but, from my perspective, this is not that hard either.
The AI Race Is On
There is something new in the air. There has been a frenzy of activity in AI and brain research in the last couple of weeks. A lot of money is suddenly being allocated for research by both the government and the private sector. It's strange but there is a sense of desperation in the air, as if time was of the essence. I don't know what but something must have happened to trigger this. What is certain is that the race is on to be the first to understand how the brain works.
My prediction is that these initiatives will fail. Like Hawkins, I don't think that throwing money at the problem is the way to go. At this time, I think Jeff Hawkins has the best chance to unlock the secrets of the brain. He knows a few already. However, unless he can fully grok perceptual learning and attention (he doesn't, even if he thinks he does), his efforts will also fail in the end. He may come up with a useful gadget or some other product but the holy grail of intelligence and robotics will remain out of his grasp.
In the meantime, I continue with my own efforts and I don't need a million dollar budget. All I need is a personal computer and some spare time. May the best model win.
The Holy Grail of Robotics
Goal-Oriented Motor Learning