chmod775 parent
Personally I'm hoping for advancements that will eventually allow us to build vehicles capable of reaching the moon, but do keep me posted on those tree growing endeavors.
Tree growing?
And I don't follow, we've had vehicles capable of reaching the moon for over 55 years
It's about the immutability of the network at runtime. But I really don't think this is a big deal. General-purpose computers are immutable after they are manufactured, but can exhibit a variety of useful behaviors when supplied with different data. Human intelligence also doesn't rely on designing and manufacturing revised layouts for the nervous system (within a single human's lifetime, for use by that single human) to adapt to different settings. Is the level of mutability used by humans substantially more expressive than the limits of in-context learning? what about the limits of more unusual in-context learning techniques that are register-like, or that perform steps of gradient descent during inference? I don't know of a good argument that all of these techniques used in ML are fundamentally not expressive enough.
LLMs, considered as a function of input and output, are not immutable at runtime. They create tokens that change the function when it is called again. That breaks most theoretical arguments
Sure. Another view is that an LLM is an immutable function from document-prefixes to next-token distributions.
But that view is wrong, the model outputs multiple tokens.
The right alternative view is that it's an immutable function from prefixes to a distribution over all possible sequences of tokens less than (context_len - prefix_len).
There are no mutable functions that cannot be viewed as immutable in a similar way. Human brains are an immutable function from input sense-data to the combination (brain adaptation, output actions). Here "brain adaptation" doing a lot of work, but so would be "1e18 output tokens". There is much more information contained within the latter