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Readers should make sure to contextualize this. We're talking about people researching AGI. Current LLM models are amazing, and will have business and societal

Readers should make sure to contextualize this. We're talking about people researching AGI. Current LLM models are amazing, and will have business and societal impact. Previous ML models also had business and societal impact. None of that is contested here. The question is, what path leads to AGI, do LLM scale to AGI? That is the question being asked here, and some researchers think that it won't, it will scale superbly to many things, but something else might be needed for full AGI.

privatelypublic 1y ago
I don't think the debate will end until Hard AI's are common- and even then philosophers will keep going.
qnleigh 1y ago
Although it may be true that LLMs will not achieve AGI in the purest sense, they have at least forced us to move a lot of goalposts. I don't know what Gary Marcus was saying a few years ago, but I think many people would have said that e.g achieving a gold medal at the Mathematics Olympiads would require AGI, not just LLMs.
ants_everywhere 1y ago
The relevant question is whether Humans + LLMs are much more likely to get to AGI than humans without LLMS. And the answer is pretty obviously yes. I don't think anyone was arguing that we would get to AGI by just training on more data with exactly the same models. Practically every advance in the last few years has been building additional functionality on top of LLMs, not just scaling up the same architecture to more data. But zooming out, LLMs are universal approximators, so it's trivially true that they can approximate any function that describes AGI. It's also true that logic (from logos or "word") is about reasoning constrained by language and conversations. So an LLM is the right sort of device you'd expect to achieve general intelligence. There are arguably non-linguistic forms of intelligence, such as visual intelligence. But those also can operate on written symbols (e.g. the stream of bits from an image file). The other relevant question is why does Gary Macus always seem so angry? It's draining reading one of his posts.
- nomel 1y ago
  The concept of mostly static weights holding the bulk of base intuition/knowledge (foundation if you will ;)) seems like a good bet, since it's how the mammalian brain works (with updates of those long term weights mostly happening while you sleep [1]). I very naively assume the "easy" path will be similar: a very different system that's bolted on/references the foundation models, to enable the realtime/novel reasoning (outside the fixed latent space) bit that isn't possible now. [1] https://animalcare.umich.edu/our-impact/our-impact-monitoring-sleeping-rats-may-solve-puzzle-behind-long-term-memory-processing/ https://animalcare.umich.edu/our-impact/our-impact-monitorin...
nomel 1y ago
I think it's pretty rare for someone to use a pure LLM, today, or even a year ago. Function calls, MCP, tricks with thinking models, etc, all make these system "impure", and also much more capable.

privatelypublic 1y ago

I don't think the debate will end until Hard AI's are common- and even then philosophers will keep going.

qnleigh 1y ago

Although it may be true that LLMs will not achieve AGI in the purest sense, they have at least forced us to move a lot of goalposts. I don't know what Gary Marcus was saying a few years ago, but I think many people would have said that e.g achieving a gold medal at the Mathematics Olympiads would require AGI, not just LLMs.

ants_everywhere 1y ago

The relevant question is whether Humans + LLMs are much more likely to get to AGI than humans without LLMS. And the answer is pretty obviously yes. I don't think anyone was arguing that we would get to AGI by just training on more data with exactly the same models. Practically every advance in the last few years has been building additional functionality on top of LLMs, not just scaling up the same architecture to more data. But zooming out, LLMs are universal approximators, so it's trivially true that they can approximate any function that describes AGI. It's also true that logic (from logos or "word") is about reasoning constrained by language and conversations. So an LLM is the right sort of device you'd expect to achieve general intelligence. There are arguably non-linguistic forms of intelligence, such as visual intelligence. But those also can operate on written symbols (e.g. the stream of bits from an image file). The other relevant question is why does Gary Macus always seem so angry? It's draining reading one of his posts.

nomel 1y ago
The concept of mostly static weights holding the bulk of base intuition/knowledge (foundation if you will ;)) seems like a good bet, since it's how the mammalian brain works (with updates of those long term weights mostly happening while you sleep [1]). I very naively assume the "easy" path will be similar: a very different system that's bolted on/references the foundation models, to enable the realtime/novel reasoning (outside the fixed latent space) bit that isn't possible now. [1] https://animalcare.umich.edu/our-impact/our-impact-monitoring-sleeping-rats-may-solve-puzzle-behind-long-term-memory-processing/ https://animalcare.umich.edu/our-impact/our-impact-monitorin...

nomel 1y ago

I think it's pretty rare for someone to use a pure LLM, today, or even a year ago. Function calls, MCP, tricks with thinking models, etc, all make these system "impure", and also much more capable.