Mark Torrance.com

Steve Jurvetson took issue with a claim in On Intelligence that modular components of these artificial intelligences would be interchangeable or reusable, allowing researchers to build on each others' work. We agreed that an evolved AI could be copied and reused by others, but Steve was skeptical that a module from one AI would be compatible to be included within another. I however proposed that just as a person who loses one sense learns to compensate by reusing those parts + pathways of their brain for advanced processing of other senses, so it's possible that a module of an AI "Al" which knows how to speak another language could be used + leveraged if it were "grafted in" to a second AI "Ben".

I find the book to be quite credible because it makes, and identifies, several falsifiable claims about the way the brain works. I imagine over the next 5-10 years some neuroscientists will be in a position to support or refute these claims.

Some have objected to Hawkins' approach of publishing these ideas in the popular press without trying to get them accepted first peer-reviewed journals. Personally, I think it's great that he was able to capitalize on his fame to achieve this publication -- that wouldn't be true for just anyone. I also think the ideas are big and interesting enough to deserve the treatment he gave them in a popular book.

My only real issue with the book is the way he puts down the whole field of AI which came before him. There are large subsets of the researchers in that field who share his lofty goals of understanding human intelligence by modelling the human brain. Likewise, as he states, there are big subfields of AI which long ago gave up any pretense of trying to do human modelling; instead they are concerned with achieving usable performance in a variety of areas such as automated vision systems, planning, robot control, etc. I don't view those practitioners as harming or taking anything away from the field of AI; at worst they contributed to the field having a poor reputation due to overpromising and underdelivering.

Patrick Winston, at the time the director of the MIT AI Lab, told me in 1995 that his planning software company wrote algorithms which were used for logistics planning in Operations Desert Shield and Desert Storm. He was told by the head of DARPA that the money saved by the military in that one use-case alone was more than the total amount spent to date, cumulative, by DARPA on all AI research. I take this as a great example of the practical consequences which can fall out of a research agenda which is nominally aimed at grand goals.

But I think Hawkins is right to ask us to refocus our energy on a brain-centered model of intelligence, and I think he's got some very appealing theories about how simple, repeated functional units in the brain, with huge amounts of interconnect and feedback connections, can achieve the amazing feats we observe.

As for me, I feel I'm getting closer to understanding intelligence each time I look for connections or similarities in the way I do things in different fields. How do "mental leaps" work? How do I get to learn a piece of music through repetition, and how is it that the first few notes cue me to sing through the rest of the song? Why is my memory for music and certain other patterns very "linear", while following a "train of thought" or "word associations" feels more undirected and associational?

I was also wondering about a model for neural processing which would leverage existing commodity hardware such as AltiVec vector processing units, or graphics processors. It seems like a densely connected neural model, with strengths on each connection and activity levels at each neuron, can be modelled as a large array math problem. This is probably old hat for the Neural Network guys, but I wonder what could be accomplished by running huge nets on desktop hardware...

I noticed recently that Jeff Hawkins' company Numeta posted a job opening for VP of Engineering. Now that's a job I would love to have.