Slashdot Mirror
The Baby Bootstrap?
An anonymous reader asks: "Slashdot recently covered a story that DARPA
would significantly cut CS research. When I was completing graduate
work in AI, the 'baby bootstrap' was considered the holy grail of military
applications. Simply put, the 'baby bootstrap' would empower a computing device to learn like a child with a very good memory. DARPA poured a small fortune into the research. No sensors, servos or video input - it only needed terminal I/O to be effective. Today the internet could provide a developmental database far beyond any testbed that we imagined, yet there has been no significant progress in over 30 years. MindPixels
and Cycorp seem typical of poorly funded efforts headed in the wrong direction, and all we hear from DARPA is autonomous robots. NIST seems more interested in industrial applications. Even Google
is remarkably void of anything about the 'baby bootstrap'. What went wrong? Has the military really given up on this concept, or has their research moved to other, more classified levels?"
I doubt it would be too difficult to code -- if we knew the mechanism by which it proceded.
Its hard to code a procedure to replicate the working of the mind...if you don't know how the mind does it in the first place.
A human will black out during some types of maneuvers unless the aircraft is prevented from making them (from simple tricks like spring return to center for the stick after a blackout to computers that measure g force and won't let the flight envelope go that far in the first place.)
Pilots use "G-suits" to try and keep blood in their heads by controlling pressure on their legs (for instance) but you can only go so far with that type of thing. And, as it's low tech, the opposition can do it as well.
An AI won't have a problem with a very high G turn. A human is in deep trouble. Airframes can be designed for considerably more than a human can take, if there is no human pilot. If there is, there is little point in such a design -- the aircraft will become pilotless if it enters such a flight regime.
Now, put this up against the fact that most other countries can't afford to put an AI in the pilots seat, and the result is continuous overwhelming air superiority without risk to humans on our side. That's the combination of factors that drives the urge to go in this particular direction.
I've fallen off your lawn, and I can't get up.
I doubt it would be too difficult to code -- if we knew the mechanism by which it proceded.
Its hard to code a procedure to replicate the working of the mind...if you don't know how the mind does it in the first place.
On the other hand, it might be that the reason we don't understand how the mind does certain things is that they're actually extremely complicated, and don't reduce very well to a programmable step-by-step algorithm nor to a simple and general mathematical learning structure. It's hard to tell, although I think it's telling that after decades of work, neither psychologists nor computer scientists can understand or replicate much of what babies do.
Sometimes the best way for a computer to learn something may not be the way a baby does it, anyway; c.f. chess.
There is a need to go to such a low level, unlesss you want to start it off with more data than is available in a strand of DNA.
DNA speaks in the language of proteins. You can't tell what sort of cell a piece of DNA is going to produce or how the cells it produces will be arranged without running the simulation all the way down to the protein level. We have no other cookbook for how to arrange these simulated cells once they exist except a long list that says "produce this protein, then this one, then one of these, then another one, then this...", and we've not any clue how those proteins get turned into a person. We can understand the process at the chemical level, and no higher. The finished product, of course, isn't like that at all. We understand humans on the levels of cells and organs, but DNA isn't so conveniently arranged.
Simulating cells is not sufficient. If it were, we could pour a couple gallons of blood into a bathtub and say "Behold, it is human." The organization of the cells matters just as much as the cells themselves. Simulating a human being to the level of even cellular precision would require that we be able to *scan* a human being at the cellular level to see how he's put together. If we actually knew the weightings of all the neuronal connections in a person's brain, then connectionist AI approaches might be able to produce real intelligence. To quote Levels of Organization in General Intelligence , "The classical hype of early neural networks, that they used 'the same parallel architecture as the human brain', should, at most, have been a claim of using the same parallel architecture as an earthworm's brain." You can't expect high-level organization from low-level simulations unless you want to simulate all the way down to DNA, where the information behind the complexity is really stored.
Or you build the complexity yourself, without relying on the hideously-designed mess that is Homo sapiens. But that's a different kettle of fish.