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To be clear, this is research priorities for "given that we have proto-AI technologies, how do we manage the impact on society and make sure the full-AI tech do

To be clear, this is research priorities for "given that we have proto-AI technologies, how do we manage the impact on society and make sure the full-AI tech doesn't do something vastly unintended." Which is somewhat worthwhile, but presupposes the existence of general AI techniques, which is an incredibly low-investment area. Seriously, the amount of research being pointed at anything that's plausibly "full AI" in nature is miniscule. If anything it's lower than in the 80s (and maybe appropriately, given the lack of payoff for the more general work). (I could go on in detail about how there is approximately zero research being devoted to, eg, automated refactoring of large codebases and other stuff that you'd expect to be absolutely essential to general AI. But I'll just stop with the naked contention.) And it's rather silly to conjecture effects when you don't even know the order of magnitude of the timelines, the resources required, or the bootstrapability of the technology. There is a huge difference between "a Top100 cluster can emulate a human brain on a 1/100 timeframe" and "a cell phone possesses general-purpose optimization algorithms that are better at learning arbitrary tasks than your average human".

wmf 12y ago
The PDF mentions some short-term topics relating to automation of jobs, self-driving cars, and autonomous weapons.
zeendo 12y ago
If you read the linked research priorities document you can see that this isn't really about "general AI techniques"...it's about steady progression along the lines of things that exist but aren't necessarily to market yet (e.g. self-driving cars).
deleted 12y ago
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munin 12y ago
> I could go on in detail about how there is approximately zero research being devoted to, eg, automated refactoring of large codebases and other stuff that you'd expect to be absolutely essential to general AI. the programming languages community has a fairly significant amount of effort devoted to automated re-factoring of large codebases, there is even a DARPA program for this: http://www.darpa.mil/Our_Work/I2O/Programs/Mining_and_Understanding_Software_Enclaves_%28MUSE%29.aspx http://www.darpa.mil/Our_Work/I2O/Programs/Mining_and_Unders... this isn't seen as "AI" work though...
- fidotron 12y ago
  That's the classic AI success story problem in a nutshell: anything successful from the AI field gets rebranded as something else.
  - ScottBurson 12y ago
    In this case the situation is different. AI researchers didn't succeed at program analysis; they gave up. @fiatmoney is right: it's no longer considered an AI problem. But computer scientists haven't solved it either, because they keep bumping their heads against the computability ceiling. It's very clear to me that AI is actually required for general, accurate program analysis.
    - munin 12y ago
      why? we get general and accurate program analysis all the time using just unification and simple inference rules...
      - ScottBurson 12y ago
        No, you don't. You may be able to infer some properties of some programs, but you can't take an arbitrary program of real-world size and answer an arbitrary question of a kind that a human maintainer of that program would want to know; for example, under what circumstances, if any, it ever performs an invalid operation -- such as an array bounds violation, throwing a runtime exception, whatever is considered invalid in whatever language it's written in. That's what I mean by "general and accurate".
        munin 12y ago
        yes you can, in many dimensions. one of those we call "type checking". other examples are the myriad examples of using model checking and symbolic execution to identify errors in programs automatically. can you identify all errors? no, but that has nothing to do with weak or strong AI. but you can definitely learn general properties about general programs using today's technology.

wmf 12y ago

The PDF mentions some short-term topics relating to automation of jobs, self-driving cars, and autonomous weapons.

zeendo 12y ago

If you read the linked research priorities document you can see that this isn't really about "general AI techniques"...it's about steady progression along the lines of things that exist but aren't necessarily to market yet (e.g. self-driving cars).

deleted 12y ago

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munin 12y ago

> I could go on in detail about how there is approximately zero research being devoted to, eg, automated refactoring of large codebases and other stuff that you'd expect to be absolutely essential to general AI. the programming languages community has a fairly significant amount of effort devoted to automated re-factoring of large codebases, there is even a DARPA program for this: http://www.darpa.mil/Our_Work/I2O/Programs/Mining_and_Understanding_Software_Enclaves_%28MUSE%29.aspx http://www.darpa.mil/Our_Work/I2O/Programs/Mining_and_Unders... this isn't seen as "AI" work though...

fidotron 12y ago
That's the classic AI success story problem in a nutshell: anything successful from the AI field gets rebranded as something else.
- ScottBurson 12y ago
  In this case the situation is different. AI researchers didn't succeed at program analysis; they gave up. @fiatmoney is right: it's no longer considered an AI problem. But computer scientists haven't solved it either, because they keep bumping their heads against the computability ceiling. It's very clear to me that AI is actually required for general, accurate program analysis.
  - munin 12y ago
    why? we get general and accurate program analysis all the time using just unification and simple inference rules...
    - ScottBurson 12y ago
      No, you don't. You may be able to infer some properties of some programs, but you can't take an arbitrary program of real-world size and answer an arbitrary question of a kind that a human maintainer of that program would want to know; for example, under what circumstances, if any, it ever performs an invalid operation -- such as an array bounds violation, throwing a runtime exception, whatever is considered invalid in whatever language it's written in. That's what I mean by "general and accurate".
      - munin 12y ago
        yes you can, in many dimensions. one of those we call "type checking". other examples are the myriad examples of using model checking and symbolic execution to identify errors in programs automatically. can you identify all errors? no, but that has nothing to do with weak or strong AI. but you can definitely learn general properties about general programs using today's technology.