Here’s a rant, for your delectation, or distaste:
This month (Jan. 2010) MIT (Massachusetts Institute of Technology, Cambridge, Mass. USA) kicks off a five year, five-million dollar (at least) research orgy called the Mind Machine Project (MMP). Machine Mind = Artificial intelligence (AI). Now there’s a concept. Oh wait. We tried that, what? Fifty years ago? The nineties were big on AI. Last year? Still trying, obviously. It’s like the old joke about a doctor’s practice – “Oh, he still hasn’t got it right!” I’m not sure if the Mind Machine Project is a resurrection of AI, an exhumation, or a back-to-the-laboratory Dr. Frankenstein operation. Probably all of that and more.
MIT has assembled a lot of famous natural intelligence to participate in the project: Newton Howard, Marvin Minsky, David Patrick, Eliot Gershenfeld, and Ed Boyden (all pioneers or well-known practitioners of AI). This could be wonderful – a lot of creativity, big thinking, and very smart people. It could be awful – huge egos stepping all over each other. The betting line should be on ‘messy’ – good things will come out of it, but perhaps as inceptions of ideas that will find more fruitful ground elsewhere.
I can’t help feeling some of this project is atonement for sins, well, perhaps not atonement but possibly secular restoration to good repute despite past sins of the intellect and methodology. Among the many fields of technology, seldom have so many predicted so much and produced so little. I am not saying that AI research produced nothing. Speech recognition and endless telephone menus came out of AI research. Clever use of people’s expertise came out of Expert Systems (a dead branch of AI). There are thousands of useful algorithms and subroutines floating around the world of software that owe their heritage to AI research. However, I am saying that compared to the predictions, “…machines will be capable, within twenty years, of doing any work a man can do.” (Herbert Simon, 1965), the actual products of AI were nowhere nearly so spectacular.
So here we go, again. Time for a rethink. Time for a reinvention of AI. Why not? Any concept that’s been kicking around for half a century with questionable success is a candidate for a rethink. Ah, but this is science and technology. AI was conceived in a state of almost pure ignorance (the 1950’s). Since then the fields of study such as cognitive psychology, neurology, and supercomputing have made great strides. We’ve come a long way, baby. Nobody is going to make the kind of silly predictions that were made back then…
One of the projects being developed by the group is a form of assistive technology they call a brain co-processor. This system, also referred to as a cognitive assistive system, would initially be aimed at people suffering from cognitive disorders such as Alzheimer’s disease. The concept is that it would monitor people’s activities and brain functions, determine when they needed help, and provide exactly the right bit of helpful information — for example, the name of a person who just entered the room, and information about when the patient last saw that person — at just the right time.
[Source: Dr. Dobb’s Journal]
Oh. It’s a mind reading project, among other things. Of course, this is just what was reflected through the media. I’m assuming the people behind the ideas have much more sophisticated knowledge of the challenges involved. Most of them should have, they’ve been through this kind of hype before, a couple of times. Trouble is, as they say, the line between thinking big and blowing it out the ass is right down the spine.
The Mind Machine Project confronts the same issue that dogged AI from the beginning – we don’t know how the brain works, which means we don’t really know what intelligence is. We know some things at the gross physical level about which areas of the brain are involved with some kinds of mental activity. We know much less about how the brain works at the molecular level. Consequently, whatever we think we know about ‘the mind,’ which is all the things the brain does taken together, is mostly based on informed speculation – though some would say based on a lot of imagination, or worse, psychoanalysis and philosophy.
Any project that tries to synthesize or create intelligence with computers based at all on how the brain works (much less ‘the mind’) is also working on partial information, a lot of speculation, and perhaps no small amount of fantasy. That’s why some argue, at least for now, that it’s futile to attempt analogous imitation of the brain. What we should be doing is simply creating ‘intelligence’ of various kinds, however we define it, with whatever techniques our current level of technology can permit.
Others say, ‘Let the experimentation begin.” It’s the process that counts. By addressing the issues, both current and past, confronted by Artificial Intelligence, we should at least be able to hammer out some sort of workable framework toward computational intelligence. This will inevitably be composed of several approaches. As it is, the project recognizes these areas of research: The nature of mind (What is it?), memory (how does it work?), and body (combining computer science with physical science). By patching together what we do know about the brain with what we know can be done with computers – throw in some interesting new paradigms such as quantum computing and parallel processing – and who knows what insights about reproducing intelligence through computing may pop out? Of course, the project also predicts usable hardware and software.
It will be worthwhile to follow the stories out of the Mind Machine Project. Look for answers to questions like these: How much of the project seems to be focused on imitating the brain? How much of the project is taking what computer technology can do and looking for pathways to some kind of intelligence? There’s a dynamic here, which could be focusing, if it is recognized; or fractionating, if it is not.
In a way, the Mind Machine Project is a sumptuous feast of intellectual endeavor, the table well set with funding, intelligence, and purpose. However, in the ensuing Bacchanal, the diners may forget what they’re consuming. Other researchers, leaner of means but keen of eye, should be like wise old ravens watching the table for choice scraps that fall to the ground or go unguarded on the table top. Some of the tid-bits could be more productive than the main course.
The key to the value of the Mind Machine Project may be its transparency. The more fully described – including failures, dumb stuff, wild ideas, and disagreements – the more those watching from the outside may learn. To get the details like a fly on the wall (or its equivalent micro surveillance device) would be useful. It’s easy to suspect that with the MMP some of the liveliest ideas, including the pro and con discussion, will take place impromptu in the halls, at local food and watering holes, and even the small meetings. The technical details will also, for some, be critical. If details are hidden because it is hoped there will be future patents and commercial benefit – the MMP will not do well for the broader AI community.