Comment by jacquesm

Comment by jacquesm 16 hours ago

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I think I perceive a massive bottleneck. Today's incarnation of AI learns from the web, not from the interaction with the humans it talks to. And for sure there is a lot of value there, it is just pointless to see that interaction lost a few hundred or thousand words of context later. For humans their 'context' is their life and total memory capacity, that's why we learn from the interaction with other, more experienced humans. It is always a two way street. But with AI as it is, it is a one way street, one that means that your interaction and your endless corrections when it gets stuff wrong (again) is lost. Allowing for a personalized massive context would go a long way towards improving the value here, at least like that you - hopefully - only have to make the same correction once.

tim333 15 hours ago

There was stuff on a possible way around that from Google Research out the other day called Nested Learning https://research.google/blog/introducing-nested-learning-a-n...

My understanding is at the moment you train something like ChatGPT on the web, setting weights with backpropagation till it works well, but if you give some more info and do more backprop it can forget other stuff it's learned, called 'catastrophic forgetting'. The nested learning approach is to split things into a number of smaller models so you can retrain one without mucking up the other ones.

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  • imtringued an hour ago

    That's pretty cool that you point it out.

    >We introduce Nested Learning, a new approach to machine learning that views models as a set of smaller, nested optimization problems, each with its own internal workflow, in order to mitigate or even completely avoid the issue of “catastrophic forgetting”, where learning new tasks sacrifices proficiency on old tasks. [0].

    It feels funny to be vindicated by rambling something random a week before someone makes an announcement that they did something incredibly similar with great success:

    >Here is my stupid and simple unproven idea: Nest the reinforcement learning algorithm. Each critic will add one more level of delay, thereby acting as a low pass filter on the supervised reward function. Since you have two critics now, you can essentially implement a hybrid pre-training + continual learning architecture. The most interesting aspect here is that you can continue training the inner-most critic without changing the outer critic, which now acts as a learned loss function. [1]

    [0] https://research.google/blog/introducing-nested-learning-a-n... [1] https://news.ycombinator.com/item?id=45745402

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halfcat 14 hours ago

> For humans their 'context' is their life and total memory capacity

And some number of billions of years of evolutionary progress.

Whatever spacial understanding we have could be thought of as a simulation at a quantum level, the size of the universe, for billions of years.

And what can we simulate completely at a quantum level today? Atoms or single cells?

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  • jacquesm 13 hours ago

    Idealized atoms and very, very simplified single cells.

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