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This is a pretty common perspective that was introduced to me as “shifting the goalposts” in school. I have always found it a disingenuous argument because it’

This is a pretty common perspective that was introduced to me as “shifting the goalposts” in school. I have always found it a disingenuous argument because it’s applied so narrowly. Humans are intelligent + humans play go => playing go is intelligent Humans are intelligent + humans do algebra => doing algebra is intelligent Meanwhile, humans in general are pretty terrible at exact, instantaneous arithmetic. But we aren’t claiming that computers are intelligent because they’re great at it. Building a machine that does a narrowly defined task better than a human is an achievement, but it’s not intelligence. Although, in the case of LLMs, in context learning is the closest thing I’ve seen to breaking free from the single-purpose nature of traditional ML/AI systems. It’s been interesting to watch for the past couple years because I still don’t think they’re “intelligent”, but it’s not just because they’re one trick ponies anymore. (So maybe the goalposts really are shifting?) I can’t quite articulate yet what I think is missing from current AI to bridge the gap.

Scarblac 2y ago
> Meanwhile, humans in general are pretty terrible at exact, instantaneous arithmetic. But we aren’t claiming that computers are intelligent because they’re great at it. "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." - Edsger Dijkstra
fsmv 2y ago
Is it really in context learning though? It's just recalling and interpolating things from the internet which there's quite a lot of.
- janalsncm 2y ago
  Someday if we have computers that are capable of doing 100% of the cognitive tasks humans do, better than any human can, we might still say it’s “just” doing X or Y. It might even be disappointing that there isn't a “special sauce” to intelligence. But at the end of the day, the mechanism isn’t important. We are already playing with some incredible ingredients. Machines that can instantly recall information, (in principle) connect to any electronic device, and calculate millions of times faster than brains, and perfectly self-replicate. Just using these abilities in a simple way is already pretty darn powerful.
  - onemoresoop 2y ago
    That special sauce is creating new knowledge.
insane_dreamer 2y ago
> breaking free from the single-purpose nature of traditional ML/AI systems it is really breaking free? so far LLMs in action seem to have a fairly limited scope -- there are a variety of purposes to which they can be applied but it's all essentially the same underlying task
- cgearhart 2y ago
  It _is_ all the same task (generating text completions) but that pretraining on that task has seemed to be suitably abstract for the model to work decently well on more narrow problems—certainly they work dramatically better at a collection of tasks like “sentiment analysis of movie ratings” and “spam classifier” than if I took purposes built models for either of those tasks and tried using them like I could an LLM.

Scarblac 2y ago

> Meanwhile, humans in general are pretty terrible at exact, instantaneous arithmetic. But we aren’t claiming that computers are intelligent because they’re great at it. "The question of whether a computer can think is no more interesting than the question of whether a submarine can swim." - Edsger Dijkstra

fsmv 2y ago

Is it really in context learning though? It's just recalling and interpolating things from the internet which there's quite a lot of.

janalsncm 2y ago
Someday if we have computers that are capable of doing 100% of the cognitive tasks humans do, better than any human can, we might still say it’s “just” doing X or Y. It might even be disappointing that there isn't a “special sauce” to intelligence. But at the end of the day, the mechanism isn’t important. We are already playing with some incredible ingredients. Machines that can instantly recall information, (in principle) connect to any electronic device, and calculate millions of times faster than brains, and perfectly self-replicate. Just using these abilities in a simple way is already pretty darn powerful.
- onemoresoop 2y ago
  That special sauce is creating new knowledge.

insane_dreamer 2y ago

> breaking free from the single-purpose nature of traditional ML/AI systems it is really breaking free? so far LLMs in action seem to have a fairly limited scope -- there are a variety of purposes to which they can be applied but it's all essentially the same underlying task

cgearhart 2y ago
It _is_ all the same task (generating text completions) but that pretraining on that task has seemed to be suitably abstract for the model to work decently well on more narrow problems—certainly they work dramatically better at a collection of tasks like “sentiment analysis of movie ratings” and “spam classifier” than if I took purposes built models for either of those tasks and tried using them like I could an LLM.