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Marvin Minsky On AI
An anonymous reader writes "In a three-part Dr. Dobbs podcast, AI pioneer and MIT professor Marvin Minsky examines the failures of AI research and lays out directions for future developments in the field. In part 1, 'It's 2001. Where's HAL?' he looks at the unfulfilled promises of artificial intelligence. In part 2 and in part 3 he offers hope that real progress is in the offing. With this talk from Minsky, Congressional testimony on the digital future from Tim Berners-Lee, life-extension evangelization from Ray Kurzweil, and Stephen Hawking planning to go into space, it seems like we may be on the verge of another AI or future-science bubble."
HA!....you should read Hubert Dreyfus, "What Computers Still Can't Do"....it chronicles a 20 year debate with Minsky that A.I., as Minsky professes it, will never work on philisophical grounds. A very compelling argument...can't wait to hear his story now.
Well, to get to the heart of your point...
"Just a gut feeling but I don't think that we will develop real general purpose AIs without some type of hardware breakthrough like quantum computers."
Do you think that we humans use some sort of Quantum Coherence to maintain very short decision chains? If so, where in a cell would be stable for such temporary coherence be maintained? Theories suggest that microtubules MIGHT be able to hold containment, but most experts say 'probably not'.
However, to hold that theory, a recent study found that water does really weird things in carbon nanotubules with 4 gigapascals @ 250 K. H2O helixes are quite interesting, and do show promise to any sort of quantum processing in cells.
The site appears to be very slow. In cases this helps anyone else, here are direct download links for the mp3's. Part 1, part 2, part 3.
Mod parent up. The podcast is from 2007, but the talk was "given by Minsky in 2001" (quote from the podcast).
Expert systems are another example of something that was once considered "AI" and is now just another app. Your auto mechanic probably uses an expert system in his diagnostics. In medicine, it sees limited use, mostly just to sanity-check a physician's diagnosis (for example, spasmodic coughing probably isn't symptomatic of glaucoma). The pharmacological expert systems would also have been considered AI 30 years ago, but now it's just a bunch of rules.
I think the biggest problem with AI is lack of integration between different intelligence techniques. Humans generally use multiple skills and combine the results to correct and hone in on the right answer. These include:
* Physical modeling
* Analogy application
* Formal logic
* Pattern recognition
* Language parsing
* Memory
* Others that I forgot
It takes connectivity and cordination between just about all of these. Lab AI has done pretty well at each of these alone, but has *not* found way to make them help each other.
Table-ized A.I.
i've got the kurzweil reader and it's pretty interesting. i think i found it on either mininova or piratebay if anyone else is interested.
In the 1980s I believed that "strong AI" was forthcoming...
In the 1980s, I was going through Stanford CS, where some of the AI faculty were indeed saying that. Read Feigenbaum's "The Fifth Generation", to see how bad it got. It was embarrassing, because very little actually worked.. Expert systems really were awfully dumb. They're just another way to program, as is generally recognized today. But back then, there were people claiming that if you could only write enough rules, intelligence would somehow emerge. I knew it was bogus at the time, and so did some other people, but, unlike most grad students, I was working for an big outside company, not a professor, and could say so. At one point I noted that it was possible to graduate in CS, in AI, at the MSCS level, without ever actually seeing an expert system work. This embarrassed some faculty members.
There was a massive amount of self-delusion in Stanford CS back then. When the whole AI boom collapsed, CS at Stanford was moved from the School of Arts and Sciences to Engineering, to give the place some adult supervision. Eventually, the Stanford AI Lab was dissolved. It's been brought back in the last few years, but with new people.
We're making real progress today, finally. Mainly because of a shift to statistical methods with sound mathematical underpinnings, plus enough compute power to make them go. Trying to hammer the real world into predicate calculus was a dead end. But number crunching is working. Computer vision actually sort of works now. Robots are starting to work. Automatic driving works. Language translation works marginally. Voice recognition works marginally. There are real products now.
But the AI field really was stuck for over a decade. The phrase "AI Winter" has been used.
You can download it here.
The AACS key is NOT 0xF606EEFD628B1CA427BEA93A9CA9773F
No no, you have it the wrong way around: it's YOUR definition of AI that is boring! ;-)
;-), which probably includes philosophic discussions about what being intelligent actually means, and a way to recreate parts of that system.
What do most of us care about computer visions and computational linguistics, it's all just statistics ans formulas, it doesn't teach us enough about ourselves.
That's not to say it isn't interesting work but IMHO it has nothing to do with "Intelligence" (artificial or not, human vision is heavily based on pre-defined brain structures that take care of most of the filtering and pre-processing and has very little to do with being intelligent or not either). The big mistake is that somebody chose to apply the term AI to those fields of investigation anyway even though it's a complete misnomer.
Personally I think AI should be used to refer to the investigation of what makes us "Intelligent" (well, at least some of us