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Comment by axg11 | Hacker News Reader

The focus on learning is misguided. Human brains don’t have to use the same mechanism for learning as they do for reasoning. Perhaps they do, but I don’t believe we have enough evidence either way.

The useful part of AI is the inference/output. If we achieve human-level performance AI inference then we have an incredibly powerful tool that can revolutionise society. The learning algorithm, be it gradient descent, evolutionary mechanisms or something entirely new, doesn’t matter. Whatever gets the job done.

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Sure but the article isn't just about the mechanism. A human child doesn't read vast amounts of text to learn how to predict the next word in a sentence. We don't know how to make an AI that learns language like a five-year-old.

I think he's saying something along the lines that intelligence is mutually exclusive to learning.

If we create a neural net that has the equivalent knowledge and intelligence of a human 5 year old but that neural net learns slower and was produced on 10 trillion pieces of data then we STILL have created a sentient AI despite the fact the learning algorithm is shit.

I don’t know if that’s what they meant, but what you’re saying is interesting.

I would soften the idea a little bit by noting that as a little human grows up and learns more things, its brain plasticity goes down over time. AFAIK there’s no evidence that learning “saturates” the ability to learn other things, and the loss of plasticity is probably mainly linked to aging and/or other factors, but functionally the effect is very much the same: humans that know a lot of things typically have less capacity for learning.

Anterograde amnesia is a condition where a human has lost his/her ability form new memories, aka learn. They can't remember anything past 3 minutes*.

https://www.youtube.com/watch?v=ZeiMhUlipTk

These people are clearly still sentient.

There is still some form of memory there but the overall mechanism is largely gone.

I think the point is more that the way the five year old learns isn't similar to assimilate massive quantities of data related to the desired output and perform mathematical transformations on it, even at a subconscious level (and the five year old definitely has a "conscious level" driving it too). The learning speed isn't the issue, it's that the neural net is doing a fundamentally different thing from the five year old brain (and so its "knowledge" is also going to be orthogonal).

Which doesn't stop neural networks being immensely useful (and even significantly more useful than humans in relevant domains, especially five year old humans), but might well stop them from being sentient. Neural nets are probably already better at passing Turing tests than five year olds, but the five year old's love of mummy doesn't come from statistical analysis of a corpus of texts about love and mummies.

We don’t even know what sentience is. We don’t have a good way of defining sentience, nor a common definition. If we were to definitively create sentience, there wouldn’t be a universal consensus on whether we had actually created it.

Yes, thank you for explaining this more clearly :)

AFAIK, we’re not really trying to build neural networks that have a similar structure to the animal/human brain.

I don’t think we need to emulate every connection, every neuron, or every circuit— but I do think the broader neuroanatomy (this region mainly processes inputs from that region, and has these feedback loops, and then mainly has outputs to this other region) is essential to emulating the way brains work.

I may very well be wrong, and I hope people in the field can forgive my insolent impression, but my impression as a biochemist/engineer is that people in the comp sci world and people in the neuroanatomy world don’t really speak the same language: one is usually bad at the other, so there isn’t really a tight transdisciplinary coupling of forces.

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