5 ms·

I thought this article seemed like well articulated criticism of the hype cycle - can you be more specific what you mean? Are the results in the Apple paper inc

I thought this article seemed like well articulated criticism of the hype cycle - can you be more specific what you mean? Are the results in the Apple paper incorrect?

astrange 1y ago
Gary Marcus always, always says AI doesn't actually work - it's his whole thing. If he's posted a correct argument it's a coincidence. I remember seeing him claim real long-time AI researchers like David Chapman (who's a critic himself) were wrong anytime they say anything positive. (em-dash avoided to look less AI) Of course, the main issue with the field is the critics /should/ be correct. Like, LLMs shouldn't work and nobody knows why they work. But they do anyway. So you end up with critics complaining it's "just a parrot" and then patting themselves on the back, as if inventing a parrot isn't supposed to be impressive somehow.
- foldr 1y ago
  I don’t read GM as saying that LLMs “don’t work” in a practical sense. He acknowledges that they have useful applications. Indeed, if they didn’t work at all, why would he be advocating for regulating their use? He just doesn’t think they’re close to AGI.
  - kadushka 1y ago
    The funny thing is, if you asked “what is AGI” 5 years ago, most people would describe something like o3.
    - deleted 1y ago
      [deleted]
    - foldr 1y ago
      Even Sam Altman thinks we’re not at AGI yet (although of course it’s coming “soon”).
      - kadushka 1y ago
        Markus has been consistently wrong over the many years predicting the (lack of) progress of the current deep learning methods. Altman has been correct so far.
        deleted 1y ago
        [deleted]
        foldr 1y ago
        Marcus has made some good predictions and some bad ones. That’s usually the way with people who make specific predictions — there are no prophets. Not sure I’d agree that SA has been any more consistently right. You can easily find examples of overconfidence from him (though he rarely says anything specific enough to count as a prediction).
barrkel 1y ago
You need to read everything that Gary writes with the particular axe to grind he has in mind: neurosymbolic AI. That's his specialism, and he essentially has a chip in his shoulder about the attention probabilistic approaches like LLMs are getting, and their relative success. You can see this in this article too. The real question you should be asking is if there is a practical limitation in LLMs and LRMs revealed by the Hanoi Towers problem or not, given that any SOTA model can write code to solve the problem and thereby solve it with tool use. Gary frames this as neurosymbolic, but I think it's a bit of a fudge.
- krackers 1y ago
  Hasn't the symbolic vs statistical split in AI existed for a long time? With things like Cyc growing out of the former. I'm not too familiar with linguistics but maybe this extends there too, since I think Chomsky was heavy on formal grammars over probabilistic models [1]. Must be some sort of cognitive sunk cost fallacy, after dedicating your life to one sect, it must be emotionally hard to see the other "keep winning". Of course you'd root for them to fall. [1] https://norvig.com/chomsky.html https://norvig.com/chomsky.html
- charcircuit 1y ago
  >with tool use A LLM with tool use can solve anything. It is interesting to try and measure its capabilities without tools.
  - barrkel 1y ago
    I don't think the first is true at all, unless you imagine some powerful oracle tools. I think the second is interesting for comparing models, but not interesting for determining the limits of what models can automate in practice. It's the prospect of automating labour which makes AI exciting and revolutionary, not their ability when arbitrarily restricted.
    - charcircuit 1y ago
      Search is already a pretty powerful oracle to defer an answer to a human and is a common tool most AI use today. What current models can automate is not what the paper was trying to answer.

I thought this article seemed like well articulated criticism of the hype cycle - can you be more specific what you mean? Are the results in the Apple paper incorrect?

astrange 1y ago

Gary Marcus always, always says AI doesn't actually work - it's his whole thing. If he's posted a correct argument it's a coincidence. I remember seeing him claim real long-time AI researchers like David Chapman (who's a critic himself) were wrong anytime they say anything positive. (em-dash avoided to look less AI) Of course, the main issue with the field is the critics /should/ be correct. Like, LLMs shouldn't work and nobody knows why they work. But they do anyway. So you end up with critics complaining it's "just a parrot" and then patting themselves on the back, as if inventing a parrot isn't supposed to be impressive somehow.

foldr 1y ago
I don’t read GM as saying that LLMs “don’t work” in a practical sense. He acknowledges that they have useful applications. Indeed, if they didn’t work at all, why would he be advocating for regulating their use? He just doesn’t think they’re close to AGI.
- kadushka 1y ago
  The funny thing is, if you asked “what is AGI” 5 years ago, most people would describe something like o3.
  - deleted 1y ago
    [deleted]
  - foldr 1y ago
    Even Sam Altman thinks we’re not at AGI yet (although of course it’s coming “soon”).
    - kadushka 1y ago
      Markus has been consistently wrong over the many years predicting the (lack of) progress of the current deep learning methods. Altman has been correct so far.
      - deleted 1y ago
        [deleted]
      - foldr 1y ago
        Marcus has made some good predictions and some bad ones. That’s usually the way with people who make specific predictions — there are no prophets. Not sure I’d agree that SA has been any more consistently right. You can easily find examples of overconfidence from him (though he rarely says anything specific enough to count as a prediction).

barrkel 1y ago

You need to read everything that Gary writes with the particular axe to grind he has in mind: neurosymbolic AI. That's his specialism, and he essentially has a chip in his shoulder about the attention probabilistic approaches like LLMs are getting, and their relative success. You can see this in this article too. The real question you should be asking is if there is a practical limitation in LLMs and LRMs revealed by the Hanoi Towers problem or not, given that any SOTA model can write code to solve the problem and thereby solve it with tool use. Gary frames this as neurosymbolic, but I think it's a bit of a fudge.

krackers 1y ago
Hasn't the symbolic vs statistical split in AI existed for a long time? With things like Cyc growing out of the former. I'm not too familiar with linguistics but maybe this extends there too, since I think Chomsky was heavy on formal grammars over probabilistic models [1]. Must be some sort of cognitive sunk cost fallacy, after dedicating your life to one sect, it must be emotionally hard to see the other "keep winning". Of course you'd root for them to fall. [1] https://norvig.com/chomsky.html https://norvig.com/chomsky.html
charcircuit 1y ago
>with tool use A LLM with tool use can solve anything. It is interesting to try and measure its capabilities without tools.
- barrkel 1y ago
  I don't think the first is true at all, unless you imagine some powerful oracle tools. I think the second is interesting for comparing models, but not interesting for determining the limits of what models can automate in practice. It's the prospect of automating labour which makes AI exciting and revolutionary, not their ability when arbitrarily restricted.
  - charcircuit 1y ago
    Search is already a pretty powerful oracle to defer an answer to a human and is a common tool most AI use today. What current models can automate is not what the paper was trying to answer.