In the early 1980s Douglas Lenat wrote EURISKO, a program Eliezer called "[maybe] the most sophisticated self-improving AI ever built". The program reportedly had some high-profile successes in various domains, like becoming world champion at a certain wargame or designing good integrated circuits.
Despite requests Lenat never released the source code. You can download an introductory paper: "Why AM and EURISKO appear to work" [PDF]. Honestly, reading it leaves a programmer still mystified about the internal workings of the AI: for example, what does the main loop look like? Researchers supposedly answered such questions in a more detailed publication, "EURISKO: A program that learns new heuristics and domain concepts." Artificial Intelligence (21): pp. 61-98. I couldn't find that paper available for download anywhere, and being in Russia I found it quite tricky to get a paper version. Maybe you Americans will have better luck with your local library? And to the best of my knowledge no one ever succeeded in (or even seriously tried) confirming Lenat's EURISKO results.
Today in 2009 this state of affairs looks laughable. A 30-year-old pivotal breakthrough in a large and important field... that never even got reproduced. What if it was a gigantic case of Clever Hans? How do you know? You're supposed to be a scientist, little one.
So my proposal to the LessWrong community: let's reimplement EURISKO!
We have some competent programmers here, don't we? We have open source tools and languages that weren't around in 1980. We can build an open source implementation available for all to play. In my book this counts as solid progress in the AI field.
Hell, I'd do it on my own if I had the goddamn paper.
Update: RichardKennaway has put Lenat's detailed papers up online, see the comments.
There are a number of DARPA and IARPA projects we pay attention to, but I'd largely agree that their approaches and basic organization makes them much less worrying.
They tend towards large, bureaucratically hamstrung projects, like PAL, which the last time I looked included work and funding for teams at seven different universities, or they suffer from extreme narrow focus, like their intelligent communication initiatives, which went from being about adaptive routing via deep introspection of multimedia communication and intelligent networks, to just being software radios and error correction.
They're worth keeping any eye on mostly because they have the money to fund any number of approaches, and often in long periods. But the biggest danger isn't their funded, stated goals, it's the possibility of someone going off-target, and working on generic AI in the hopes of increasing their funding or scope in the next evaluation, which could be a year or more later.