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?"

Comments

Funny you should mention that...

[Anonymous Coward]

I just got back from a workshop on this very subject, but nobody uses the term "baby bootstrap". It is now called "Developmental Robotics", and encompasses embodied agents, machine learning, and other biologically-inspired metaphors.

There is now a website dedicated to the idea. See http://DevelopmentalRobotics.org/ and http://cs.brynmawr.edu/DevRob05/ for a collection of papers on the subject.

Re:baby bootstrap

[bluephone]

"Sometimes the best way for a computer to learn something may not be the way a baby does it, anyway; c.f. chess."

Except computers never learned chess; humans programmed complex move analysis routines along with the rules, and many times a database of strategies with statistical weighting. There's a limited capacity to "learn: against opponents, but that's usually just more preprogrammed analysis and pattern matching than actualy spontaneous data linking. And like a poster higher up said, ther ewas a time we thought that was all one needed. It's not. We already have rudimentary AIs in labs that can "learn" in the sense they can create accurate spontaneous data links. The human brain (or the brain of any semi complex organism, really) is a black box with such unimaginable gears inside we're fumbling in the dark. It's hard to reverse engineer a mind becuase unlike reverse engineering a BIOS or widget, we don't really understand how a mind works, is put together, or even what it's really comprised of.