Hacker News Reader

Comment by olalonde | Hacker News Reader

While I agree that ANNs are probably a poor model for how the brain really works, your comparison isn't necessarily a fair one. While ANNs start their learning with an empty/random state, the human brain starts with a bunch of connections that are pre-encoded in DNA.

With that said, my guess is that we'll probably have to take a few more hints from the biology of the brain before we are able to achieve "human level intelligence".

View on HN

You seem to have completely missed that NNs run on physical hardware.

A contemporary CPU and/or GPU is 'a bunch of connections that are pre-encoded' - just not in DNA, but in silicon.

> A contemporary CPU and/or GPU is 'a bunch of connections that are pre-encoded' - just not in DNA, but in silicon.

No, CPUs/GPUs are not relevant if we are interested in the speed of learning in relation to the quantity of stimuli processed. You could even compute an ANN's learning algorithm with a pen and paper and it wouldn't change its "learning speed" within that definition. Pre-trained weights would.

Regardless, this head start is probably not sufficient to explain the disparity between the brain's capacity to learn and modern ANNs. ANNs are probably just not a very good approximation of how the brain works, for now at least.

I read this four times now, but I don't understand what you are saying.

Can you be more specific? What I meant is this: if we postulate that the brain really is just like an ANN, it would be more like an ANN with pre-trained weights, thanks to evolution. In contrast, an ANN is typically initialized with random weights. A pre-trained network learns a lot faster than a randomly initialized one.

Reply on news.ycombinator.com