Hacker News Reader

> isn't sufficiently well understood to tell us how close the software neural network is to being practically equivalent

I’d argue that we do know enough to say conclusively that they’re not mathematically equivalent.

Where is potentiation? Plasticity? You can’t apply the universal approximation theorem against something that’s changing all the time.

View on HN

Great questions. We are incredibly far off in understanding the brain of humans beyond what will I believe we retrospectively be seen as basic and will likely be seen as quite flawed. A few more well known examples of where knowledge already falls short is traumatic brain injuries that are diagnosed in post-mortem, or chronic fatigue symptoms (with Long Covid related triggered onset and numerous others) that have diagnostic challenges, many mechanisms of action still to be discoverd, and little in terms of treatments that provide known cures without experimentation. Another commonly known one is the personal patient response and triggered side effects of SSRIs and SNRIs. If one attempts to dig deeper into where we are at in the understanding of the human brain operation in real-time, we already have a lot of knowns unknowns and discoveries left that will reshape how we model human intelligence.

I mean you can, but uat is way weaker than what people want it to be. I think it should be fairly obvious that it does not (because it obviously cannot be true) say that you can approximate any function by doing sgd on a finite set of samples of that function.

Reply on news.ycombinator.com