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A non-anthropomorphized view of LLMs

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ants_everywhere ◴[] No.44485225[source]
> I am baffled that the AI discussions seem to never move away from treating a function to generate sequences of words as something that resembles a human.

This is such a bizarre take.

The relation associating each human to the list of all words they will ever say is obviously a function.

> almost magical human-like powers to something that - in my mind - is just MatMul with interspersed nonlinearities.

There's a rich family of universal approximation theorems [0]. Combining layers of linear maps with nonlinear cutoffs can intuitively approximate any nonlinear function in ways that can be made rigorous.

The reason LLMs are big now is that transformers and large amounts of data made it economical to compute a family of reasonably good approximations.

> The following is uncomfortably philosophical, but: In my worldview, humans are dramatically different things than a function . For hundreds of millions of years, nature generated new versions, and only a small number of these versions survived.

This is just a way of generating certain kinds of functions.

Think of it this way: do you believe there's anything about humans that exists outside the mathematical laws of physics? If so that's essentially a religious position (or more literally, a belief in the supernatural). If not, then functions and approximations to functions are what the human experience boils down to.

[0] https://en.wikipedia.org/wiki/Universal_approximation_theore...

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LeifCarrotson ◴[] No.44485574[source]
> I am baffled that the AI discussions seem to never move away from treating a function to generate sequences of words as something that resembles a human.

You appear to be disagreeing with the author and others who suggest that there's some element of human consciousness that's beyond than what's observable from the outside, whether due to religion or philosophy or whatever, and suggesting that they just not do that.

In my experience, that's not a particularly effective tactic.

Rather, we can make progress by assuming their predicate: Sure, it's a room that translates Chinese into English without understanding, yes, it's a function that generates sequences of words that's not a human... but you and I are not "it" and it behaves rather an awful lot like a thing that understands Chinese or like a human using words. If we simply anthropomorphize the thing, acknowledging that this is technically incorrect, we can get a lot closer to predicting the behavior of the system and making effective use of it.

Conversely, when speaking with such a person about the nature of humans, we'll have to agree to dismiss the elements that are different from a function. The author says:

> In my worldview, humans are dramatically different things than a function... In contrast to an LLM, given a human and a sequence of words, I cannot begin putting a probability on "will this human generate this sequence".

Sure you can! If you address an American crowd of a certain age range with "We’ve got to hold on to what we’ve got. It doesn’t make a difference if..." I'd give a very high probability that someone will answer "... we make it or not". Maybe that human has a unique understanding of the nature of that particular piece of pop culture artwork, maybe it makes them feel things that an LLM cannot feel in a part of their consciousness that an LLM does not possess. But for the purposes of the question, we're merely concerned with whether a human or LLM will generate a particular sequence of words.

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1. seadan83 ◴[] No.44487723[source]
>> given a human and a sequence of words, I cannot begin putting a probability on "will this human generate this sequence".

> Sure you can! If you address an American crowd of a certain age range with "We’ve got to hold on to what we’ve got. It doesn’t make a difference if..." I'd give a very high probability that someone will answer "... we make it or not".

I think you may have this flipped compared to what the author intended. I believe the author is not talking about the probability of an output given an input, but the probability of a given output across all inputs.

Note that the paragraph starts with "In my worldview, humans are dramatically different things than a function, (R^n)^c -> (R^n)^c". To compute a probability of a given output, (which is a any given element in "(R^n)^n"), we can count how many mappings there are total and then how many of those mappings yield the given element.

The point I believe is to illustrate the complexity of inputs for humans. Namely for humans the input space is even more complex than "(R^n)^c".

In your example, we can compute how many input phrases into a LLM would produce the output "make it or not". We can than compute that ratio to all possible input phrases. Because "(R^n)^c)" is finite and countable, we can compute this probability.

For a human, how do you even start to assess the probability that a human would ever say "make it or not?" How do you even begin to define the inputs that a human uses, let alone enumerate them? Per the author, "We understand essentially nothing about it." In other words, the way humans create their outputs is (currently) incomparably complex compared to a LLM, hence the critique of the anthropomorphization.