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None of the things you mentioned are even close to AI. They’re applied statistics, and they mostly use techniques we’ve known about for decades but have only no

by cirgue 8y ago

None of the things you mentioned are even close to AI. They’re applied statistics, and they mostly use techniques we’ve known about for decades but have only now found a use case because computing and storage is cheap enough to make them viable.

nostrademons 8y ago
The recommendation, translation, & image classification algorithms are all done with deep-learning; that's considered AI now. There was a time, not all that long ago, when SVMs, Bayesian networks, and perceptrons were considered AI. That's behind the spam filters, predictive keyboards, and most of the search signals. There was a time, a bit longer ago, when beam search and A* were considered AI. That's behind the game opponents. As the linked Wikipedia article says, "AI is whatever we don't know how to do yet." There will be a time (rapidly approaching) where deep learning and robotics are common knowledge among skilled software engineers, and we won't consider them AI either. We'll find something else to call AI then, maybe consciousness or creativity or something.
- cirgue 8y ago
  This is my point: the term AI has always been BS. It was BS when beam search was AI, it was BS when expert systems were AI, and it is equally as BS when applied to neural networks. It comes to the same thing: the 'AI' tools we use are increasingly good function approximators. That's it. It's still reaching the moon by building successively taller ladders.
  - hetzeljt 8y ago
    I think Judea Pearl would agree with you in part. From an interview in https://www.theatlantic.com/technology/archive/2018/05/machine-learning-is-stuck-on-asking-why/560675/ https://www.theatlantic.com/technology/archive/2018/05/machi... : As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. From the point of view of the mathematical hierarchy, no matter how skillfully you manipulate the data and what you read into the data when you manipulate it, it’s still a curve-fitting exercise, albeit complex and nontrivial. And I left the arena to pursue a more challenging task: reasoning with cause and effect. Many of my AI colleagues are still occupied with uncertainty. There are circles of research that continue to work on diagnosis without worrying about the causal aspects of the problem.
  - mindcrime 8y ago
    This is my point: the term AI has always been BS I don't see how this follows from this: the 'AI' tools we use are increasingly good function approximators Nothing in the definition of AI says that AI has to work the same way the human brain does... and as far as that goes, we're probably not 100% sure that, in the end, the brain is anything more than a really good function approximator and some applied statistics. I would say the canonical definition of AI, to the extent that there is one, is roughly something like "making computers do things that previously only humans could do". If people think "AI is bullshit" I'd say it's because they're applying their own definition to the term, where there definition imposes much more stringent requirements.
- Nimitz14 8y ago
  > There was a time, a bit longer ago, when beam search and A* were considered AI. Yeah, maybe the marketing people said that..
  - makapuf 8y ago
    Well in game design you still call them AI. Of course this use of AI does not impress anyone per se, it's just a name and not a selling point.
abhgh 8y ago
This is an interesting comment - where would you draw the line between AI and applied statistics? A lot of AI which happens to be ML (not saying there is non-ML AI, just that a significant chunk of AI being practiced today is ML) also happens to be applied statistics. Or have statistical interpretations. Also, because something has been around for decades does not make it not AI. For ex the cheque OCR mentioned probably runs off (or can feasibly run off) of a neural network. I think the parent's comment holds well - not sure about the last line though ...
- ModernMech 8y ago
  The line is clear: everything today branded "AI" is just applied statistics. AI is a buzzword. I don't know what the definition of intelligence is, but I have a feeling it doesn't rest anywhere near concepts like function approximation, and that's all even the most sophisticated "AIs" at Google or Facebook or Apple boil down to.
  - abhgh 8y ago
    What was not clear from your earlier comment, and is now, is that when you say AI you don't mean AI as is practiced by most of academia and the industry but the vision of Artificial General Intelligence (AGI). If so, yes, that's a good point to make. However, it is debatable whether the path of statistical learning wont lead to AGI, or is not how our brains function, or the truth partly does comprise of statistical learning and part of something else. The Norvig-Chomsky debate is an example of the arguments on both sides.
    - ModernMech 8y ago
      I didn't make an earlier comment. You're replying to my one and only comment. > when you say AI you don't mean AI as is practiced by most of academia and the industry but the vision of Artificial General Intelligence (AGI). What I actually mean is people practicing what they call "AI" in academia and the industry have co-opted the name to make what they do sound more interesting. First it was called "statistics". Then it was called "pattern matching". Then it was called "machine learning". Now it's called "AI". But it hasn't changed meaningfully through any iteration of these labels.
abiox 8y ago
> None of the things you mentioned are even close to AI. then what is 'close to AI'?
- ItsMe000001 8y ago
  Something that actually learns on its own and is not completely stumped when it encounters something new but actually learns. When it recognizes failure it should go and start learning by itself, i.e. try to get more data and analyze that and do its own trial and error - so that it actually grows in capabilities (on its own).
  - TheOtherHobbes 8y ago
    This excludes most human intelligence.
    - ItsMe000001 8y ago
      You speak for yourself?
mindcrime 8y ago
techniques we’ve known about for decades FWIW, "AI" as a field has been around since the 1950's. So calling something "AI" in no way implies that the techniques are especially new.

by cirgue 8y ago

nostrademons 8y ago

The recommendation, translation, & image classification algorithms are all done with deep-learning; that's considered AI now. There was a time, not all that long ago, when SVMs, Bayesian networks, and perceptrons were considered AI. That's behind the spam filters, predictive keyboards, and most of the search signals. There was a time, a bit longer ago, when beam search and A* were considered AI. That's behind the game opponents. As the linked Wikipedia article says, "AI is whatever we don't know how to do yet." There will be a time (rapidly approaching) where deep learning and robotics are common knowledge among skilled software engineers, and we won't consider them AI either. We'll find something else to call AI then, maybe consciousness or creativity or something.

cirgue 8y ago
This is my point: the term AI has always been BS. It was BS when beam search was AI, it was BS when expert systems were AI, and it is equally as BS when applied to neural networks. It comes to the same thing: the 'AI' tools we use are increasingly good function approximators. That's it. It's still reaching the moon by building successively taller ladders.
- hetzeljt 8y ago
  I think Judea Pearl would agree with you in part. From an interview in https://www.theatlantic.com/technology/archive/2018/05/machine-learning-is-stuck-on-asking-why/560675/ https://www.theatlantic.com/technology/archive/2018/05/machi... : As much as I look into what’s being done with deep learning, I see they’re all stuck there on the level of associations. Curve fitting. That sounds like sacrilege, to say that all the impressive achievements of deep learning amount to just fitting a curve to data. From the point of view of the mathematical hierarchy, no matter how skillfully you manipulate the data and what you read into the data when you manipulate it, it’s still a curve-fitting exercise, albeit complex and nontrivial. And I left the arena to pursue a more challenging task: reasoning with cause and effect. Many of my AI colleagues are still occupied with uncertainty. There are circles of research that continue to work on diagnosis without worrying about the causal aspects of the problem.
- mindcrime 8y ago
  This is my point: the term AI has always been BS I don't see how this follows from this: the 'AI' tools we use are increasingly good function approximators Nothing in the definition of AI says that AI has to work the same way the human brain does... and as far as that goes, we're probably not 100% sure that, in the end, the brain is anything more than a really good function approximator and some applied statistics. I would say the canonical definition of AI, to the extent that there is one, is roughly something like "making computers do things that previously only humans could do". If people think "AI is bullshit" I'd say it's because they're applying their own definition to the term, where there definition imposes much more stringent requirements.
Nimitz14 8y ago
> There was a time, a bit longer ago, when beam search and A* were considered AI. Yeah, maybe the marketing people said that..
- makapuf 8y ago
  Well in game design you still call them AI. Of course this use of AI does not impress anyone per se, it's just a name and not a selling point.

abhgh 8y ago

This is an interesting comment - where would you draw the line between AI and applied statistics? A lot of AI which happens to be ML (not saying there is non-ML AI, just that a significant chunk of AI being practiced today is ML) also happens to be applied statistics. Or have statistical interpretations. Also, because something has been around for decades does not make it not AI. For ex the cheque OCR mentioned probably runs off (or can feasibly run off) of a neural network. I think the parent's comment holds well - not sure about the last line though ...

ModernMech 8y ago
The line is clear: everything today branded "AI" is just applied statistics. AI is a buzzword. I don't know what the definition of intelligence is, but I have a feeling it doesn't rest anywhere near concepts like function approximation, and that's all even the most sophisticated "AIs" at Google or Facebook or Apple boil down to.
- abhgh 8y ago
  What was not clear from your earlier comment, and is now, is that when you say AI you don't mean AI as is practiced by most of academia and the industry but the vision of Artificial General Intelligence (AGI). If so, yes, that's a good point to make. However, it is debatable whether the path of statistical learning wont lead to AGI, or is not how our brains function, or the truth partly does comprise of statistical learning and part of something else. The Norvig-Chomsky debate is an example of the arguments on both sides.
  - ModernMech 8y ago
    I didn't make an earlier comment. You're replying to my one and only comment. > when you say AI you don't mean AI as is practiced by most of academia and the industry but the vision of Artificial General Intelligence (AGI). What I actually mean is people practicing what they call "AI" in academia and the industry have co-opted the name to make what they do sound more interesting. First it was called "statistics". Then it was called "pattern matching". Then it was called "machine learning". Now it's called "AI". But it hasn't changed meaningfully through any iteration of these labels.

abiox 8y ago

> None of the things you mentioned are even close to AI. then what is 'close to AI'?

ItsMe000001 8y ago
Something that actually learns on its own and is not completely stumped when it encounters something new but actually learns. When it recognizes failure it should go and start learning by itself, i.e. try to get more data and analyze that and do its own trial and error - so that it actually grows in capabilities (on its own).
- TheOtherHobbes 8y ago
  This excludes most human intelligence.
  - ItsMe000001 8y ago
    You speak for yourself?

mindcrime 8y ago

techniques we’ve known about for decades FWIW, "AI" as a field has been around since the 1950's. So calling something "AI" in no way implies that the techniques are especially new.