4 ms·

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, w

by 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.
      - randallsquared 15y ago
        AIXI is a single algorithm that can solve every problem, given enough time, which is enough to prove that they do exist, however uselessly long it would take AIXI to solve any actual problem.
      - jackpirate 15y ago
        The book "On Intelligence" does a decent job explaining for non-neuoroscientists a theory where all parts of the brain (imaging, hearing, speach, cognition, etc) use the same pattern matching algorithm. It has a pretty decent looking website too here: http://www.onintelligence.org/ http://www.onintelligence.org/
cdavid 15y ago
Half of your examples are, in the grand scheme of things, trivial applications of decades-old techniques. Recommendation, for example, is based on techniques which are almost 100 years old (SVD). Winning chess has been rendered possible through increased machine power, the algorithms necessary for it are hardly ground breaking. If this is AI, then many things are AI: almost any quantitative usage of statistics is AI, for example. There is no agreement on this, but some leading AI researchers consider that most AI problems should be solvable with decades old computers (i.e. we need some completely new paradigms that nobody has yet thought about). See Mc Carthy for example: http://www-formal.stanford.edu/jmc/whatisai/node1.html http://www-formal.stanford.edu/jmc/whatisai/node1.html
- nhaehnle 15y ago
  That was exactly his point, wasn't it? His examples used to be considered problems in AI. Now that the problems are understood, they are simply considered to be algorithmic problems. Lots of output of the MIT AI labs in the early days is now simply taught in algorithms courses, yet back then, those researchers considered themselves to be doing AI research. That is the moving of the goalposts.
- ilamont 15y ago
  As I recall, Kurzweil referred to such applications as "narrow A.I." Human-level AI capable of passing the Turing Test is called "strong A.I." in his lexicon.
rimantas 15y ago
> computers have won at chess But still suck at go (board game, not programming language).
- RodgerTheGreat 15y ago
  Go and Chess are fundamentally in the same class- perfect information and deterministic. Go simply has a much higher branching factor than Chess, limiting the utility of pure brute-force techniques and increasing the importance of good pruning and heuristics. It is no more surprising that Go takes more effort from programmers than Chess than that Chess is trickier than checkers.
  - wnewman 15y ago
    There's probably more to it than that. Humans do typically play Go on 19x19 boards, but Go is still relatively hard for computers (compared to Chess) even on smaller boards like 9x9, where the branching factor is reasonably comparable to Chess. And the branching factor in the kind of Go tactics problems collected as exercises in books for human players isn't that much larger than the branching factor in chess, either. Decades ago chess programmers could remark that their programs solved typical chess tactics problems faster than humans could turn the pages in the book. Even today solving typical Go tactics problems is somewhere between a very serious programming challenge and an open research question. Broadly speaking the difficulties in Go tactics seem to be similar to the difficulties that computers have historically had in Chess endgame tactics, which clearly wasn't a problem of branching factor: branching goes down in the Chess endgame. (However, today there is an important dissimilarity: the space of Chess endgames is small enough that modern computers can tabulate the solution for a significant fraction of the problem space beforehand.)

by ekidd 15y ago

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.
    - randallsquared 15y ago
      AIXI is a single algorithm that can solve every problem, given enough time, which is enough to prove that they do exist, however uselessly long it would take AIXI to solve any actual problem.
    - jackpirate 15y ago
      The book "On Intelligence" does a decent job explaining for non-neuoroscientists a theory where all parts of the brain (imaging, hearing, speach, cognition, etc) use the same pattern matching algorithm. It has a pretty decent looking website too here: http://www.onintelligence.org/ http://www.onintelligence.org/

cdavid 15y ago

Half of your examples are, in the grand scheme of things, trivial applications of decades-old techniques. Recommendation, for example, is based on techniques which are almost 100 years old (SVD). Winning chess has been rendered possible through increased machine power, the algorithms necessary for it are hardly ground breaking. If this is AI, then many things are AI: almost any quantitative usage of statistics is AI, for example. There is no agreement on this, but some leading AI researchers consider that most AI problems should be solvable with decades old computers (i.e. we need some completely new paradigms that nobody has yet thought about). See Mc Carthy for example: http://www-formal.stanford.edu/jmc/whatisai/node1.html http://www-formal.stanford.edu/jmc/whatisai/node1.html

nhaehnle 15y ago
That was exactly his point, wasn't it? His examples used to be considered problems in AI. Now that the problems are understood, they are simply considered to be algorithmic problems. Lots of output of the MIT AI labs in the early days is now simply taught in algorithms courses, yet back then, those researchers considered themselves to be doing AI research. That is the moving of the goalposts.
ilamont 15y ago
As I recall, Kurzweil referred to such applications as "narrow A.I." Human-level AI capable of passing the Turing Test is called "strong A.I." in his lexicon.

rimantas 15y ago

> computers have won at chess But still suck at go (board game, not programming language).

RodgerTheGreat 15y ago
Go and Chess are fundamentally in the same class- perfect information and deterministic. Go simply has a much higher branching factor than Chess, limiting the utility of pure brute-force techniques and increasing the importance of good pruning and heuristics. It is no more surprising that Go takes more effort from programmers than Chess than that Chess is trickier than checkers.
- wnewman 15y ago
  There's probably more to it than that. Humans do typically play Go on 19x19 boards, but Go is still relatively hard for computers (compared to Chess) even on smaller boards like 9x9, where the branching factor is reasonably comparable to Chess. And the branching factor in the kind of Go tactics problems collected as exercises in books for human players isn't that much larger than the branching factor in chess, either. Decades ago chess programmers could remark that their programs solved typical chess tactics problems faster than humans could turn the pages in the book. Even today solving typical Go tactics problems is somewhere between a very serious programming challenge and an open research question. Broadly speaking the difficulties in Go tactics seem to be similar to the difficulties that computers have historically had in Chess endgame tactics, which clearly wasn't a problem of branching factor: branching goes down in the Chess endgame. (However, today there is an important dissimilarity: the space of Chess endgames is small enough that modern computers can tabulate the solution for a significant fraction of the problem space beforehand.)