>You could actually wonder that one possible explanation for the human sample efficiency that needs to be considered is evolution. Evolution has given us a small amount of the most useful information possible.

It's definitely not small. Evolution performed a humongous amount of learning, with modern homo sapiens, an insanely complex molecular machine, as a result. We are able to learn quickly by leveraging this "pretrained" evolutionary knowledge/architecture. Same reason as why ICL has great sample efficiency.

Moreover, the community of humans created a mountain of knowledge as well, communicating, passing it over the generations, and iteratively compressing it. Everything that you can do beyond your very basic functions, from counting to quantum physics, is learned from the 100% synthetic data optimized for faster learning by that collective, massively parallel, process.

It's pretty obvious that artificially created models don't have synthetic datasets of the quality even remotely comparable to what we're able to use.

I think it’s a bit different. Evolution did not give us the dataset. It helped us to establish the most efficient training path, and the data, the enormous volume of it starts coming immediately after birth. Humans learn continuously through our senses and use sleep to compress the context. The amount of data that LLMs receive only appears big. In our first 20 years of life we consume by at least one order of magnitude more information compared to training datasets. If we count raw data, maybe 4-5 orders of magnitude more. It’s also different kind of information and probably much more complex processing pipeline (since our brain consciously processes only a tiny fraction of input bandwidth with compression happening along the delivery channels), which is probably the key to understanding why LLMs do not perform better.

If we think of every generation as a compression step of some form of information into our DNA and early humans existed for ~1.000.000 years and a generation is happening ~20years on average, then we have only ~50.000 compression steps to today. Of course, we have genes from both parents so they is some overlap from others, but especially in the early days the pool of other humans was small. So that still does not look like it is on the order of magnitude anywhere close to modern machine learning. Sure, early humans had already a lot of information in their DNA but still

Aren't you agreeing with his point?

The process of evolution distilled down all that "humongous" amount to what is most useful. He's basically saying our current ML methods to compress data into intelligence can't compare to billions of years of evolution. Nature is better at compression than ML researchers, by a long shot.

Please stop comparing these things to biological systems. They have very little in common.