I wanted to give another parallel - we understand what DNA does from a structural level of the building blocks up, but... We're not close to being able to bio-

I wanted to give another parallel - we understand what DNA does from a structural level of the building blocks up, but... We're not close to being able to bio-engineer a 20 point IQ improvement, or the ability to regrow a missing limb, or the ability to grow muscle on demand, or the ability to forgo lipogenesis beyond some amount or or or... To me the epistemological question on this is not just understanding the parts but being able to reason with some high degree of certainty about cause and effect and be able to simulate things, etc. I do not think it is totally correct to say that since we initiated the training runs on the LLMs and hence made them we understand them. There is a large difference between computers as physical engineered devices and X terabyte matrix weights that are a result of an emergent iterative process wherein we train it on a vast amount of data. The latest estimation for GPT3 training set is 500B tokens. Unpredictable behavior manifests itself in the size of models demonstrated in that paper, which are quite small. Even with interpretability tools we have a long way to understanding relatively smaller models, as shown in said paper. The state of the art for the size for current models is vastly larger, and the interpretability challenges are larger too. We're not talking about word2vec here but we're talking about neural nets. Can a large enough neural net with enough layers reproduce the effects of the human brain? Due to speed of neurons firing a lot of actions humans take are akin a few dozens to low hundred layers of a neural net. GPT3 has 96 layers. Yes, there are possibly temporal signals and more complexity in the way the human brain works, but we can solve that with scale. Imagine a model, running on a computer, that is enough to almost perfectly simulate the thinking process a human brain does. Do we know how it works? I don't think we can say we know how human brains work from an information processing capacity. I think we're defining the problem differently. Understanding the underlying hardware (though frankly, I for example don't know what is running on Intel's Management Engine) and principles of software still do not necessarily scale to understanding, especially when in a relatively short time we will have multi trillion parameter multi terabyte scale LLMs or other NN powered models. Now, let's scale it a few more orders of magnitude... Thank you for linking the paper on The Surprising Creativity of Digital Evolution.