4 ms·

It is not dissimilar to how many people feel today about ChatGPT et al...

Sharlin 3y ago
I mean, it took a bit more than eight weeks, and a few doublings of hardware capacity, but these days we have obviously made tremendous progress in making machines "use language, form abstractions and concepts, [and] solve kinds of problems now reserved for humans". The self-improvement part is – perhaps luckily – trickier, although what else are generative adversarial networks if not that? AlphaGo rapidly self-improved itself to superhuman levels without any human help after a certain point!
- AshamedCaptain 3y ago
  Stuff like checkers was a "a kind of problem reserved for humans" but was solved decades ago; you can see why the situation is similar to the context of the above quote. Time and time we have been through claims of "X solved, we are just a summer away from thinking machines" ... yet after the smoke cleared we only had a better solver for X, and nothing else. We may have a ridiculously better solver for chess, but that's about it -- it's as far from "abstracting and forming concepts" and "self-improving" as everything from the 60s was (in retrospect). For me the biggest difference is that the current days we are happy with more black boxy methods where it's unclear if they can lead to actual progress or applicability.
  - famouswaffles 3y ago
    LLMs reached super-human ability on their task long ago. Everything else is a nice side quest. https://arxiv.org/abs/2212.11281 https://arxiv.org/abs/2212.11281
  - Sharlin 3y ago
    Or it’s that as computers learn to solve problems previously reserved for humans, people tend to shift the goalposts of what counts as real intelligence accordingly, similar to the so-called "God in the gaps" fallacy. (Incidentally, something very similar has been going on as our understanding of animal intelligence has improved.) That said, I agree that however impressive narrowly intelligent tasks computers have learned to excel at, most of the research involved has not helped us make much (if any) headway towards figuring out how to create a fully general machine intelligence despite the early optimism. But that said, for the past several decades the mainstream AI research community has mostly not even attempted to pursue AGI research, being entirely content with working on different types of narrow AI and indeed trying to dispel any illusions that their work might have anything to do with "science fiction AIs". This, of course, exactly because of those broken early dreams and the AI winter that followed. So, at least within the field itself, I don’t really agree that there has been any tendency to assume that narrow AI breakthroughs imply that AGI is just around the corner. The current hype around GANs and LLMs is an exception, not the rule.

I mean, it took a bit more than eight weeks, and a few doublings of hardware capacity, but these days we have obviously made tremendous progress in making machines "use language, form abstractions and concepts, [and] solve kinds of problems now reserved for humans". The self-improvement part is – perhaps luckily – trickier, although what else are generative adversarial networks if not that? AlphaGo rapidly self-improved itself to superhuman levels without any human help after a certain point!

AshamedCaptain 3y ago
Stuff like checkers was a "a kind of problem reserved for humans" but was solved decades ago; you can see why the situation is similar to the context of the above quote. Time and time we have been through claims of "X solved, we are just a summer away from thinking machines" ... yet after the smoke cleared we only had a better solver for X, and nothing else. We may have a ridiculously better solver for chess, but that's about it -- it's as far from "abstracting and forming concepts" and "self-improving" as everything from the 60s was (in retrospect). For me the biggest difference is that the current days we are happy with more black boxy methods where it's unclear if they can lead to actual progress or applicability.
- famouswaffles 3y ago
  LLMs reached super-human ability on their task long ago. Everything else is a nice side quest. https://arxiv.org/abs/2212.11281 https://arxiv.org/abs/2212.11281
- Sharlin 3y ago
  Or it’s that as computers learn to solve problems previously reserved for humans, people tend to shift the goalposts of what counts as real intelligence accordingly, similar to the so-called "God in the gaps" fallacy. (Incidentally, something very similar has been going on as our understanding of animal intelligence has improved.) That said, I agree that however impressive narrowly intelligent tasks computers have learned to excel at, most of the research involved has not helped us make much (if any) headway towards figuring out how to create a fully general machine intelligence despite the early optimism. But that said, for the past several decades the mainstream AI research community has mostly not even attempted to pursue AGI research, being entirely content with working on different types of narrow AI and indeed trying to dispel any illusions that their work might have anything to do with "science fiction AIs". This, of course, exactly because of those broken early dreams and the AI winter that followed. So, at least within the field itself, I don’t really agree that there has been any tendency to assume that narrow AI breakthroughs imply that AGI is just around the corner. The current hype around GANs and LLMs is an exception, not the rule.