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My big problem with Kurzweil's singularity is the massive handwaving he does between 'computers are getting exponentially faster' and 'AI will arise'. This dep

My big problem with Kurzweil's singularity is the massive handwaving he does between 'computers are getting exponentially faster' and 'AI will arise'. This depends on the assumption that 'intelligence' (and nobody can really agree on what that means, which is a bad start) is representable in algorithmic form. Maybe it is, maybe it isn't, but the lack of progress in hard AI in the last 30 years isn't a good sign.

ignifero 15y ago
Assuming you dont believe in mind-body duality, why would it not be representable in algorithmic form, since it can be mapped all the way down to deterministic chemical reactions?
arctangent 15y ago
It seems to me that a lot of the problems people have in discussing this field are founded on difficulties in agreeing a common semantic framework.
ekidd 15y ago
There's never any progress in AI, because once we figure out how to do something, we stop calling it "AI". In the last 30 years, computers have won at chess, won at Jeopardy, learned to recognize spam with better than 99.5% accuracy, learned to recognize faces with better than 95% accuracy, achieved semi-readable automatic translation, figured out what movies I should add to my Netflix queue, and started to recognize speech. We've seen huge advances in computer vision and statistical natural language processing, and we're seeing a renaissance in machine learning. Most of this stuff was considered "hard AI" as recently as 1992, but the goalposts have moved. And if intelligence can't be represented in algorithmic form, then what's the brain doing? Even if we have immaterial souls that don't obey the laws of physics, why do some brain lesions cause weirdly specific impairments to our thought process? A huge chunk of our intelligence is clearly subject to the laws of physics, and therefore can be wedged somewhere into the computational complexity hierarchy.
- adrianN 15y ago
  And because we already have proof of concept machines that perfectly exhibit intelligence, we can be sure that the intelligence spot in the complexity hierarchy isn't somewhere in the intractable regions.
- macavity23 15y ago
  Well, maybe you went to different classes than I did, but none of those things you mentioned would ever have been considered hard AI as I was taught it. Hard AI == generalised problem solving (or as wikipedia puts it, 'the intelligence of a machine that can successfully perform any intellectual task that a human being can'), soft/weak AI == problem solving within a specific defined area (such as all of your examples).
  - locopati 15y ago
    Add up enough 'soft AI' and eventually you'll have 'hard(ish) AI'
    - macavity23 15y ago
      Not really. The idea behind hard AI is that you can have one algorithm that can solve general problems, not lots of different algorithms to solve specific problems. Take IBM's research as an example. Deep Blue is very good at solving chess problems, Watson is very good at solving textual analysis problems, but taken together they don't solve any problems that aren't chess or textual analysis. The hard AI problem is a direct parallel of the psychological debate over whether a general intelligence capability (the 'G factor') exists in humans, and whether this can be measured (IQ).
      - locopati 15y ago
        Honest question here - does anyone actually still believe a single algorithm can solve general problems? Certainly the human mind doesn't work that way - we are fairly compartmentalized with a lot of communication between compartments.
rsaarelm 15y ago
This depends on the assumption that 'intelligence' (and nobody can really agree on what that means, which is a bad start) is representable in algorithmic form. We have a working definition and know that it is algorithmically representable nowadays (http://www.hutter1.net/ai/aixigentle.htm http://www.hutter1.net/ai/aixigentle.htm). Now the question is how you can make the algorithm efficiently computable.
- nazgulnarsil 15y ago
  I will bet my entire lifetime wealth that that isn't what humans do except in an extremely abstract sense.
  - rsaarelm 15y ago
    The whole framework is extremely abstract. Unless human brains are doing magic that's beyond what a Turing machine can do, whatever humans are doing probably does boil down to something resembling the Solomonoff induction AIXI is based on. It's also pretty trivial to observe that humans are running some kind of very clever approximation instead of the full abstract AIXI, since we can actually do useful stuff with the amount of sensory information we get in reasonable time. What humans do belongs to the part where you need to figure out how to make this stuff efficiently computable.
    - macavity23 15y ago
      Well, that's the point. Take away the weasel word 'magic', and saying that humans are doing what a Turing machine can do is the same thing as saying they're algorithmically representable. Turing machines are not handed down to us by God; there is no reason to believe they are some kind of Ultimate Representation of Everything.
      - rsaarelm 15y ago
        Doesn't contemporary cognitive science pretty much go with the hypothesis that human brains are Turing-equivalent? Unless we go with magic, hypercomputing brains would require hypercomputing physics, and so far we know pretty much about physics and all of it seems to be Turing-computable (although slowly with quantum stuff), outside pathologies like time travel. We don't know if Turing-equivalent formalisms are the Ultimate Representation of Everything, but they seem to be by far the best Representation of Everything anyone has found so far.

ignifero 15y ago

Assuming you dont believe in mind-body duality, why would it not be representable in algorithmic form, since it can be mapped all the way down to deterministic chemical reactions?

arctangent 15y ago

It seems to me that a lot of the problems people have in discussing this field are founded on difficulties in agreeing a common semantic framework.

ekidd 15y ago

There's never any progress in AI, because once we figure out how to do something, we stop calling it "AI". In the last 30 years, computers have won at chess, won at Jeopardy, learned to recognize spam with better than 99.5% accuracy, learned to recognize faces with better than 95% accuracy, achieved semi-readable automatic translation, figured out what movies I should add to my Netflix queue, and started to recognize speech. We've seen huge advances in computer vision and statistical natural language processing, and we're seeing a renaissance in machine learning. Most of this stuff was considered "hard AI" as recently as 1992, but the goalposts have moved. And if intelligence can't be represented in algorithmic form, then what's the brain doing? Even if we have immaterial souls that don't obey the laws of physics, why do some brain lesions cause weirdly specific impairments to our thought process? A huge chunk of our intelligence is clearly subject to the laws of physics, and therefore can be wedged somewhere into the computational complexity hierarchy.

adrianN 15y ago
And because we already have proof of concept machines that perfectly exhibit intelligence, we can be sure that the intelligence spot in the complexity hierarchy isn't somewhere in the intractable regions.
macavity23 15y ago
Well, maybe you went to different classes than I did, but none of those things you mentioned would ever have been considered hard AI as I was taught it. Hard AI == generalised problem solving (or as wikipedia puts it, 'the intelligence of a machine that can successfully perform any intellectual task that a human being can'), soft/weak AI == problem solving within a specific defined area (such as all of your examples).
- locopati 15y ago
  Add up enough 'soft AI' and eventually you'll have 'hard(ish) AI'
  - macavity23 15y ago
    Not really. The idea behind hard AI is that you can have one algorithm that can solve general problems, not lots of different algorithms to solve specific problems. Take IBM's research as an example. Deep Blue is very good at solving chess problems, Watson is very good at solving textual analysis problems, but taken together they don't solve any problems that aren't chess or textual analysis. The hard AI problem is a direct parallel of the psychological debate over whether a general intelligence capability (the 'G factor') exists in humans, and whether this can be measured (IQ).
    - locopati 15y ago
      Honest question here - does anyone actually still believe a single algorithm can solve general problems? Certainly the human mind doesn't work that way - we are fairly compartmentalized with a lot of communication between compartments.

rsaarelm 15y ago

This depends on the assumption that 'intelligence' (and nobody can really agree on what that means, which is a bad start) is representable in algorithmic form. We have a working definition and know that it is algorithmically representable nowadays (http://www.hutter1.net/ai/aixigentle.htm http://www.hutter1.net/ai/aixigentle.htm). Now the question is how you can make the algorithm efficiently computable.

nazgulnarsil 15y ago
I will bet my entire lifetime wealth that that isn't what humans do except in an extremely abstract sense.
- rsaarelm 15y ago
  The whole framework is extremely abstract. Unless human brains are doing magic that's beyond what a Turing machine can do, whatever humans are doing probably does boil down to something resembling the Solomonoff induction AIXI is based on. It's also pretty trivial to observe that humans are running some kind of very clever approximation instead of the full abstract AIXI, since we can actually do useful stuff with the amount of sensory information we get in reasonable time. What humans do belongs to the part where you need to figure out how to make this stuff efficiently computable.
  - macavity23 15y ago
    Well, that's the point. Take away the weasel word 'magic', and saying that humans are doing what a Turing machine can do is the same thing as saying they're algorithmically representable. Turing machines are not handed down to us by God; there is no reason to believe they are some kind of Ultimate Representation of Everything.
    - rsaarelm 15y ago
      Doesn't contemporary cognitive science pretty much go with the hypothesis that human brains are Turing-equivalent? Unless we go with magic, hypercomputing brains would require hypercomputing physics, and so far we know pretty much about physics and all of it seems to be Turing-computable (although slowly with quantum stuff), outside pathologies like time travel. We don't know if Turing-equivalent formalisms are the Ultimate Representation of Everything, but they seem to be by far the best Representation of Everything anyone has found so far.