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And yet no serious research is being put into hard AI. Just like five years ago. Or ten. Or twenty. My childhood passed in reading circa-computer books of 60s,

And yet no serious research is being put into hard AI. Just like five years ago. Or ten. Or twenty. My childhood passed in reading circa-computer books of 60s, 70s, 80s, where full, general AI seemed to be just around the corner. Obviously this problem is far harder than it seemed to be. But the (almost universal) lack of trying is extremely disheartening.

jacquesm 12y ago
It's definitely not for the lack of trying. It mostly is because it really is a hard problem.
- jimmaswell 12y ago
  No, hard problem means aligning elements with CSS or using a database. Anyone on Hacker News can tell you that.
  - jimmaswell 12y ago
    Are people not getting the sarcasm? HN posters abuse the term "hard problem" daily.
- eli_gottlieb 12y ago
  It's not that hard a problem. It's a problem that looks hard because we were looking in the wrong directions based on "philosophy of mind" equating intelligence with consciousness.
  - jacquesm 12y ago
    Any problem can be solved by creative redefinition.
    - eli_gottlieb 12y ago
      My whole point is that the AI problem has always been different from the consciousness problem. Why does AI need to be conscious to do any job I might give it?
  - tim333 12y ago
    Making something that does a good job on the turing test is hard regardless which direction you look philosophically.
    - eli_gottlieb 12y ago
      It's difficult, but it's not a Hard Problem in the sense of requiring a complete paradigm shift. It's a mere research problem.
Epenthesis 12y ago
Hypothesis: Because the solution to "hard AI" isn't actually "general". Ie, what humans view as "intelligence" isn't actually an emergent property of the right set of rules. Rather, it's a massive and hopelessly self-intersecting set of ad-hoc solutions and special casing all jumbled together over millions of years of evolution, producing something that looks general, but only because we're looking at it from within itself. An easy example: Having a computer identify all images of a thing called a "cup". That's an arbitrary category that has no definition reducible to actual physical properties. What makes something a "cup"? A human saying it is one. For something to look like "full, general AI" to humans, we'd need to either: Build something that replicates all that specialized hyper-meta-spaghetti-code mental processing of ours to a level of fidelity that's beyond both our current understanding of our brain's structure and the level of complexity tenable by human computer science. Or leave out those evolutionarily-discovered "optimization circuits" and require far, far more processing power than we'll have access to for a good, long time.
- jimmaswell 12y ago
  "What makes something a "cup"? A human saying it is one" And the human got their definition of cup by seeing a lot of things others called a cup and extrapolating rules, something that's already obtainable for some problems with solutions like neural networks.
  - Epenthesis 12y ago
    True, but that doesn't really negate the hypothesis. What are those rules? Are they the same as the ones generated by a neural network? Or are they qualitatively different? (Diagnostic experiment: Is there a significant subset of "cups" that a naive neural network would always fail to include or only include when also including a significant subset of non-"cups"?)
  - fit2rule 12y ago
    So you're saying we can't have proper AI until we've had some semi-decent AI? That explains everything: what constitutes decent AI, versus good AI, versus superlative AI? The problem is: nobody knows. Its a bit like two cavemen banging rocks together until one says "hey, lets use these rocks to kill something" and the other one goes "eh? How?" and the first one says "I don't know, keep bashing until something dies.."
deleted 12y ago
[deleted]
lotsofmangos 12y ago
We already have hard AI. Sort of. It is wetware and it exists as networks of real neurons on silicon. It is sort of cheating, but I think it is probably going to generate a self-aware machine before pure silicon does. It arguably has already produced an aware one. http://en.wikipedia.org/wiki/Hybrot http://en.wikipedia.org/wiki/Hybrot edit - as it gets more sophisticated it is going to generate some fairly strange debates. What rights do you give to a bathtub of human neurons that you are trying to use to predict the stock market?
- deleted 12y ago
  [deleted]
ThomPete 12y ago
Hard AI is a result of a lot of soft AI approaches just like we are a result of many sub-processes.
olalonde 12y ago
From what I understand (layman here), general AI research is roughly divided in two school of thoughts. One school believes GAI will be achieved by incrementally working on the different components (vision, language, learning, etc.) separately and eventually gluing those components together. The other school believes we should try to understand the human brain at a biological level and simulate it. The latter school believes general intelligence will emerge from a relatively simple algorithm and we should concentrate our efforts into discovering this algorithm. The former school seems to be a lot more popular these days although research is being done on both fronts.
- eli_gottlieb 12y ago
  And the third school believes that general intelligence is normatively-correct reasoning in some sufficiently rich (where Turing-complete is a lower bound on sufficiency) domain/class of possible-words/hypotheses.
  - deleted 12y ago
    [deleted]
- Matumio 12y ago
  I (another layman) don't think AI research is divided that way. The most visible area of research is "machine learning", where the goal is not AGI but to find better algorithms to solve well-defined statistical engineering problems. In contrast to that, there is a wide area of research that doesn't focus on well-defined problems. On the philosophical side, it's about understanding what intelligence even is. Or about understanding what tasks the brain is solving, or why we make some mistakes but not others. On the more technical side are the embodied people who focus a lot more on grounding of an agent's perception and on interaction with the environment, rather than thinking about intelligence as a passive task that only involves the brain.
MacsHeadroom 12y ago
Creating a GAI which can learn how to act human, on its own, in virtual worlds and in robots with realistic bodies isn't serious research? http://youtu.be/EfGD2qveGdQ?t=2m30s http://youtu.be/EfGD2qveGdQ?t=2m30s
tim333 12y ago
>And yet no serious research is being put into hard AI Google is doing some research. One quote: "One of my favourite things is artificial intelligence, but it has gotten a very bad rap, but my prediction is that when AI happens it's going to be a lot of computation and not so much clever algorithms but just a lot of computation. My theory is that if you look at your programming, your DNA, it's about 600 megabytes compressed, so it's smaller than any modern operating system, smaller than Linux or Windows or anything like that, your whole operating system, that includes booting up your brain. So your program algorithms probably aren't that complicated, it's probably more about the overall computation. We have some people at Google who are trying to build artificial intelligence and to do it on a large scale to make search better. Very few [other] people are working on this, and I don't think it's as far off as people think." - Larry Page, February 2007 http://www.abc.net.au/radionational/programs/scienceshow/changing-the-world---larry-page/3394966#transcript http://www.abc.net.au/radionational/programs/scienceshow/cha...

jacquesm 12y ago

It's definitely not for the lack of trying. It mostly is because it really is a hard problem.

jimmaswell 12y ago
No, hard problem means aligning elements with CSS or using a database. Anyone on Hacker News can tell you that.
- jimmaswell 12y ago
  Are people not getting the sarcasm? HN posters abuse the term "hard problem" daily.
eli_gottlieb 12y ago
It's not that hard a problem. It's a problem that looks hard because we were looking in the wrong directions based on "philosophy of mind" equating intelligence with consciousness.
- jacquesm 12y ago
  Any problem can be solved by creative redefinition.
  - eli_gottlieb 12y ago
    My whole point is that the AI problem has always been different from the consciousness problem. Why does AI need to be conscious to do any job I might give it?
- tim333 12y ago
  Making something that does a good job on the turing test is hard regardless which direction you look philosophically.
  - eli_gottlieb 12y ago
    It's difficult, but it's not a Hard Problem in the sense of requiring a complete paradigm shift. It's a mere research problem.

Epenthesis 12y ago

Hypothesis: Because the solution to "hard AI" isn't actually "general". Ie, what humans view as "intelligence" isn't actually an emergent property of the right set of rules. Rather, it's a massive and hopelessly self-intersecting set of ad-hoc solutions and special casing all jumbled together over millions of years of evolution, producing something that looks general, but only because we're looking at it from within itself. An easy example: Having a computer identify all images of a thing called a "cup". That's an arbitrary category that has no definition reducible to actual physical properties. What makes something a "cup"? A human saying it is one. For something to look like "full, general AI" to humans, we'd need to either: Build something that replicates all that specialized hyper-meta-spaghetti-code mental processing of ours to a level of fidelity that's beyond both our current understanding of our brain's structure and the level of complexity tenable by human computer science. Or leave out those evolutionarily-discovered "optimization circuits" and require far, far more processing power than we'll have access to for a good, long time.

jimmaswell 12y ago
"What makes something a "cup"? A human saying it is one" And the human got their definition of cup by seeing a lot of things others called a cup and extrapolating rules, something that's already obtainable for some problems with solutions like neural networks.
- Epenthesis 12y ago
  True, but that doesn't really negate the hypothesis. What are those rules? Are they the same as the ones generated by a neural network? Or are they qualitatively different? (Diagnostic experiment: Is there a significant subset of "cups" that a naive neural network would always fail to include or only include when also including a significant subset of non-"cups"?)
- fit2rule 12y ago
  So you're saying we can't have proper AI until we've had some semi-decent AI? That explains everything: what constitutes decent AI, versus good AI, versus superlative AI? The problem is: nobody knows. Its a bit like two cavemen banging rocks together until one says "hey, lets use these rocks to kill something" and the other one goes "eh? How?" and the first one says "I don't know, keep bashing until something dies.."

deleted 12y ago

[deleted]

lotsofmangos 12y ago

We already have hard AI. Sort of. It is wetware and it exists as networks of real neurons on silicon. It is sort of cheating, but I think it is probably going to generate a self-aware machine before pure silicon does. It arguably has already produced an aware one. http://en.wikipedia.org/wiki/Hybrot http://en.wikipedia.org/wiki/Hybrot edit - as it gets more sophisticated it is going to generate some fairly strange debates. What rights do you give to a bathtub of human neurons that you are trying to use to predict the stock market?

deleted 12y ago
[deleted]

ThomPete 12y ago

Hard AI is a result of a lot of soft AI approaches just like we are a result of many sub-processes.

olalonde 12y ago

From what I understand (layman here), general AI research is roughly divided in two school of thoughts. One school believes GAI will be achieved by incrementally working on the different components (vision, language, learning, etc.) separately and eventually gluing those components together. The other school believes we should try to understand the human brain at a biological level and simulate it. The latter school believes general intelligence will emerge from a relatively simple algorithm and we should concentrate our efforts into discovering this algorithm. The former school seems to be a lot more popular these days although research is being done on both fronts.

eli_gottlieb 12y ago
And the third school believes that general intelligence is normatively-correct reasoning in some sufficiently rich (where Turing-complete is a lower bound on sufficiency) domain/class of possible-words/hypotheses.
- deleted 12y ago
  [deleted]
Matumio 12y ago
I (another layman) don't think AI research is divided that way. The most visible area of research is "machine learning", where the goal is not AGI but to find better algorithms to solve well-defined statistical engineering problems. In contrast to that, there is a wide area of research that doesn't focus on well-defined problems. On the philosophical side, it's about understanding what intelligence even is. Or about understanding what tasks the brain is solving, or why we make some mistakes but not others. On the more technical side are the embodied people who focus a lot more on grounding of an agent's perception and on interaction with the environment, rather than thinking about intelligence as a passive task that only involves the brain.

MacsHeadroom 12y ago

Creating a GAI which can learn how to act human, on its own, in virtual worlds and in robots with realistic bodies isn't serious research? http://youtu.be/EfGD2qveGdQ?t=2m30s http://youtu.be/EfGD2qveGdQ?t=2m30s

tim333 12y ago

>And yet no serious research is being put into hard AI Google is doing some research. One quote: "One of my favourite things is artificial intelligence, but it has gotten a very bad rap, but my prediction is that when AI happens it's going to be a lot of computation and not so much clever algorithms but just a lot of computation. My theory is that if you look at your programming, your DNA, it's about 600 megabytes compressed, so it's smaller than any modern operating system, smaller than Linux or Windows or anything like that, your whole operating system, that includes booting up your brain. So your program algorithms probably aren't that complicated, it's probably more about the overall computation. We have some people at Google who are trying to build artificial intelligence and to do it on a large scale to make search better. Very few [other] people are working on this, and I don't think it's as far off as people think." - Larry Page, February 2007 http://www.abc.net.au/radionational/programs/scienceshow/changing-the-world---larry-page/3394966#transcript http://www.abc.net.au/radionational/programs/scienceshow/cha...