> Based on our understanding of biology and evolution we know that a squirrel understands its world more similarly to the way we do than an LLM.

Bro. Evolution is random walk. That means most of the changes are random and arbitrary based on whatever allows the squirrel to survive.

We know squirrels and humans diverged from a common ancestor but we do not know how much has changed since the common ancestor and we do not know what changed and we do not know the baseline for what this common ancestor is.

Additionally we don’t even understand the current baseline. We have no idea how brains work. if we did we would be able to build a human brain but as of right now LLMs are the closest model we have ever created to something that simulates or is remotely similar to the brain.

So your fuzzy qualitative statement of we understand evolution and biology is baseless. We don’t understand shit.

> We also see that a squirrel, like us, is capable of continuous learning driven by its own goals, all on an energy budget many orders of magnitude lower. That last part is a strong empirical indication that suggests that LLMs are a dead end for AGI.

So an LLM cant continuously learn? You realize that LLMs are deployed agentically all the time now so they both continuously learn and follow goals? Right? You’re aware of this i hope.

The energy efficiency is a byproduct of hardware. The theory of LLMs and machine learning is independent from the flawed silicon technology that is causing the energy efficiencies. Like how a computer can be made mechanical an LLM can be as well. The LLM is independent of the actual implementation and energy inefficiencies. This is not at all a strong empirical indication that LLMs are a dead end. It’s a strong indication that your thinking is illogical and flawed.

> Also remember that Sutton is still of an AI maximalist. He isn't saying that AGI isn't possible, just that LLMs can't get us there.

He can’t say any of this because he doesn’t actually know. None of us know for sure. We literally don’t know why LLMs work. The fact that training transformers on massive amounts of data produced this level of intelligence was a total surprise for all the experts and we still have no idea why this stuff works. His statements are too overarching and glossing over a lot of things we don’t actually know.

Yann lecuun for example called LLMs stochastic parrots. We now know this is largely incorrect. The reason Yan can be so wrong is because nobody actually knows shit.

> Bro. Evolution is random walk. That means most of the changes are random and arbitrary based on whatever allows the squirrel to survive.

For the vast majority of evolutionary history, very similar forces have shaped us and squirrels. The mutations are random, but the selections are not.

If squirrels are a stretch for you, take the closest human relative: chimpanzees. There is a very reasonable hypothesis that their brains work very similarly to ours, far more similarly than ours to an LLM.

> So an LLM cant continuously learn? You realize that LLMs are deployed agentically all the time now so they both continuously learn and follow goals?

That is not continuous learning. The network does not retrain through that process. It's all in the agent's context. The agent has no intrinsic goals nor ability to develop them. It merely samples based on it's prior training and it's current content. It doesn't retrain through this process. Biological intelligence does retrain constantly.

> The energy efficiency is a byproduct of hardware. The theory of LLMs and machine learning is independent from the flawed silicon technology that is causing the energy efficiencies.

There is no evidence to support that a transformer model's inefficiency is hardware based.

There is direct evidence to support that the inefficiency is influenced by the fact that LLM inference and training are both auto-regressive. Auto-regression maps to compute cycles maps to energy consumption. That's a problem with the algorithm, not the hardware.

> The fact that training transformers on massive amounts of data produced this level of intelligence was a total surprise for all the experts

The level of intelligence produced is only impressive compared to the prior state of the art, and at its impressive modeling the narrow band of intelligence represented by encoded language (not all language) produced by humans. In most every other aspect of intelligence - notably continuous learning driven by intrinsic goals - LLMs fail.