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> Virtually everything we call “AI” today is either a theatrical display of essentially scripted behavior (that’s how most game AI works), a massive database (s

by tansey 15y ago

> Virtually everything we call “AI” today is either a theatrical display of essentially scripted behavior (that’s how most game AI works), a massive database (such as Google Suggestions and expert systems) or a vague and decidedly unintelligent jumble of neural networks and genetic algorithms. Okay, I can understand the first two being less attractive, but evolutionary neural networks-- really? I mean, what more do you want than an artificial brain created through simulated evolution? What characteristics would satisfy people like this that something is "AI" [1]? It seems a bit like the old saying that once it's discovered, it's no longer AI. [1] It's worth noting that I'm strongly biased since I work in the Neural Network Research Group at UT Austin.

tel 15y ago
I rather dislike ANNs. I recognize that ANNs perform well and find research to improve their performance interesting, but honestly they're completely opaque black boxes with structure that only plausibly mimics the absolute simplest of biological neural networks. I want a "brain" which was built from principles that allow its workings to be intelligible and efficient based on a technique which reflects the structure of the problem of learning, generalization, and hypothesis search and illuminates it. So I'll buy (and often use) your ANNs, but I can't help but feel a little bit worried about the state of affairs. It's probably a whole lot like that automatically enumerated proof of the 4-color theorem. It's definitely important, but there is something unsatisfying about it. We aren't learning as much as we felt entitled to because the destination was only a small part of the journey.
- Game_Ender 15y ago
  That is the entire machine learning field. Very well designed black boxes which you train to classify or estimate your desired quantities based on you input data. It might not be true "AI" but it gets a lot of practical work done.
  - tel 15y ago
    I don't care a thing about "true AI". I just want ML to learn more from statistics.
- tansey 15y ago
  > they're completely opaque black boxes with structure that only plausibly mimics the absolute simplest of biological neural networks I guess this depends on how advanced your toolkit is. If you're simply using feedforward nets with fixed weights, fixed topology, and sigmoid activation, then yes that's probably right. However, there are a lot of advanced techniques in ANNs that most people never use. Lots of more biologically-inspired approaches have been explored, such as leaky integrator neurons, Hebbian learning, and indirect encodings. > I want a "brain" which was built from principles See, but the problem is that we don't even have the knowledge of how real brains work to start forming such principals. In fact, we're starting to see computational models used to form principals of biological brains (for example, [1]). > that allow its workings to be intelligible And what does that mean? You want to be able to discern some rule base from the system that you can understand? Even biological brains do not have that property. > based on a technique which reflects the structure of the problem of learning, generalization, and hypothesis search and illuminates it. This is very vague. Happy to dive into it if you are willing to expound a bit. > It's probably a whole lot like that automatically enumerated proof of the 4-color theorem. It's definitely important, but there is something unsatisfying about it. We aren't learning as much as we felt entitled to because the destination was only a small part of the journey. Again, very vague. Do you think having children is unsatisfying because you can't understand their actions most of the time? Maybe I'm misunderstanding the issue here. Seriously though, I want to know what will impress people here. It's not going to be a fully general, "human-level" AI, obviously, as that requires huge amounts of semantic knowledge about the world that was encoded through billions of years of evolution, but that to me is not really a necessary condition for AI. [1] http://nn.cs.utexas.edu/?hoffman:biopsych11 http://nn.cs.utexas.edu/?hoffman:biopsych11
  - RobertKohr 15y ago
    Thank you for your solid reply. I think ANN is the real thing, and most of the other "AI" solutions that we have give the impression of intelligence, but are simply algorithms that solve problems we have in very well defined ways. ANN is a general intelligence that gets shaped by experience and adapts figure out its own solutions. This is the path that will lead to our concept of AI presented in sci-fi and what the general public actually thinks of. It isn't some coder banging on the keyboard trying to replicate what a human would do under a specific situation, it is something that truly learns. We have a long way to go before this becomes a reality, but it will happen.
georgecmu 15y ago
a vague and decidedly unintelligent jumble of neural networks and genetic algorithms. Not at all true. More like an unintelligible jumble of Bayesian learning, Markov fields, structured regression, support vector machines, and other statistical models over large volumes of data.
sampo 15y ago
I don't think the writer was criticizing neural nets and genetic algorithms as an approach, just observing that what they have produced so far is "decidedly unintelligent".
- suivix 15y ago
  If something can beat you at chess and jeopardy, it isn't unintelligent.
  - enemieslist 15y ago
    I don't know, I find these the least appealing of any kinds of test for intelligence. On the fly problem solving? Adaptation to new situations? To me, ravens using simple tools to pick up shiny things seem more intelligent than the kind of machine displayed on Jeopardy or at chess matches.
  - alokm 15y ago
    But "Watson" does not excel at chess, nor "deep blue" in jeopardy
    - robertk 15y ago
      That's why IBM is now working on Deep Watson.
robertk 15y ago
> What characteristics would satisfy people like this that something is "AI"? It seems Mr. Turing answered that question a while ago.

by tansey 15y ago

tel 15y ago

I rather dislike ANNs. I recognize that ANNs perform well and find research to improve their performance interesting, but honestly they're completely opaque black boxes with structure that only plausibly mimics the absolute simplest of biological neural networks. I want a "brain" which was built from principles that allow its workings to be intelligible and efficient based on a technique which reflects the structure of the problem of learning, generalization, and hypothesis search and illuminates it. So I'll buy (and often use) your ANNs, but I can't help but feel a little bit worried about the state of affairs. It's probably a whole lot like that automatically enumerated proof of the 4-color theorem. It's definitely important, but there is something unsatisfying about it. We aren't learning as much as we felt entitled to because the destination was only a small part of the journey.

Game_Ender 15y ago
That is the entire machine learning field. Very well designed black boxes which you train to classify or estimate your desired quantities based on you input data. It might not be true "AI" but it gets a lot of practical work done.
- tel 15y ago
  I don't care a thing about "true AI". I just want ML to learn more from statistics.
tansey 15y ago
> they're completely opaque black boxes with structure that only plausibly mimics the absolute simplest of biological neural networks I guess this depends on how advanced your toolkit is. If you're simply using feedforward nets with fixed weights, fixed topology, and sigmoid activation, then yes that's probably right. However, there are a lot of advanced techniques in ANNs that most people never use. Lots of more biologically-inspired approaches have been explored, such as leaky integrator neurons, Hebbian learning, and indirect encodings. > I want a "brain" which was built from principles See, but the problem is that we don't even have the knowledge of how real brains work to start forming such principals. In fact, we're starting to see computational models used to form principals of biological brains (for example, [1]). > that allow its workings to be intelligible And what does that mean? You want to be able to discern some rule base from the system that you can understand? Even biological brains do not have that property. > based on a technique which reflects the structure of the problem of learning, generalization, and hypothesis search and illuminates it. This is very vague. Happy to dive into it if you are willing to expound a bit. > It's probably a whole lot like that automatically enumerated proof of the 4-color theorem. It's definitely important, but there is something unsatisfying about it. We aren't learning as much as we felt entitled to because the destination was only a small part of the journey. Again, very vague. Do you think having children is unsatisfying because you can't understand their actions most of the time? Maybe I'm misunderstanding the issue here. Seriously though, I want to know what will impress people here. It's not going to be a fully general, "human-level" AI, obviously, as that requires huge amounts of semantic knowledge about the world that was encoded through billions of years of evolution, but that to me is not really a necessary condition for AI. [1] http://nn.cs.utexas.edu/?hoffman:biopsych11 http://nn.cs.utexas.edu/?hoffman:biopsych11
- RobertKohr 15y ago
  Thank you for your solid reply. I think ANN is the real thing, and most of the other "AI" solutions that we have give the impression of intelligence, but are simply algorithms that solve problems we have in very well defined ways. ANN is a general intelligence that gets shaped by experience and adapts figure out its own solutions. This is the path that will lead to our concept of AI presented in sci-fi and what the general public actually thinks of. It isn't some coder banging on the keyboard trying to replicate what a human would do under a specific situation, it is something that truly learns. We have a long way to go before this becomes a reality, but it will happen.

georgecmu 15y ago

a vague and decidedly unintelligent jumble of neural networks and genetic algorithms. Not at all true. More like an unintelligible jumble of Bayesian learning, Markov fields, structured regression, support vector machines, and other statistical models over large volumes of data.

sampo 15y ago

I don't think the writer was criticizing neural nets and genetic algorithms as an approach, just observing that what they have produced so far is "decidedly unintelligent".

suivix 15y ago
If something can beat you at chess and jeopardy, it isn't unintelligent.
- enemieslist 15y ago
  I don't know, I find these the least appealing of any kinds of test for intelligence. On the fly problem solving? Adaptation to new situations? To me, ravens using simple tools to pick up shiny things seem more intelligent than the kind of machine displayed on Jeopardy or at chess matches.
- alokm 15y ago
  But "Watson" does not excel at chess, nor "deep blue" in jeopardy
  - robertk 15y ago
    That's why IBM is now working on Deep Watson.

robertk 15y ago

> What characteristics would satisfy people like this that something is "AI"? It seems Mr. Turing answered that question a while ago.