[mjburgess]

I confess to the same mirror image issue. I cannot understand why people insist that regressing in a latent space, derived from the mere associative structure of a dataset, ought be given some Noble status.

It is not a model of our intelligence. It's a stupid thing. You can go and learn about animal intelligence -- and merging template cases of what's gone before, as recorded by human social detritus -- doesn't even bare mentioning.

The latent space of all the text tokens on the internet is not a model of the world; and finding a midpoint is just a trick. It's a merging between "stuff we find meaningful over here", and "stuff we find meaningful over there" to produce "stuff we find meaningful" -- without ever having to know what any of it meant.

The trick is that we're the audience, so we'll find the output meaningful regardless. Image generators don't "struggle with hands" they "struggle" with everything -- is we, the observer, who care more about the fidelity of hands. The process of generating pixels is uniformly dumb.

I don't see anything more here than "this is the thing that I know!" therefore "this is a model of intelligence!11.11!01!!" .

It's a very very bad model of intelligence. The datasets involved are egregious proxy measures of the world whose distribution has little to do with it: novels, books, pdfs, etc.

This is very far away from the toddler who learns to walk, learns to write, and writes what they are thinking about. They write about their day, say -- not because they "interpolate" between all books ever written... but because they have an interior representational life which is directly caused by their environment and can be communicated.

Patterns in our communication are not models of this process. They're a dumb light show.

[mordymoop]

I feel like our positions are probably both buried in webs of mutually-difficult-to-communicate worldview assumptions, but for what it’s worth, I care more at this point about the models being useful than being meaningful. I use GPT-4 to do complex coding and copy editing tasks. In both cases, the model understands what I’m going for. As in, I had some specific, complex, nuanced, concept or idea that I want to express, either in text or in code, and it does that. This can’t be me “projecting meaning” onto the completions because the code works and does what I said I wanted. You can call this a light show, but you can’t make it not useful.

[mjburgess]

> because the code works

The output of these systems can have arbitrary properties.

Consider an actor in a film, their speech has the apparent property, say, of "being abusive to their wife" -- but the actor isnt abusive, and has no wife.

Consider a young child reading from a chemistry textbook, their speech has apparent property "being true about chemistry".

But a professor of chemistry who tells you something about a reaction they've just performed, explains how it works, etc. -- this person might say identical words to the child, or the AI.

But the reason they say those words is radically different.

AI is a "light show" in the same way a film is: the projected image-and-sound appears to have all sorts of properties to an audience. Just as the child appears an expert in chemistry.

But these aren't actual properties of the system: the child, the machine, the actors.

This doesnt matter if all you want is an audiobook of a chemistry textbook, to watch a film, or to run some generated code.

But it does matter in a wide variety of other cases. You cannot rely on apparent properties when, for example, you need the system to be responsive to the world as-it-exists unrepresented in its training data. Responsive to your reasons, and those of other people. Responsive to the ways the world might be.

At this point the light show will keep appearing to work in some well-trodden cases, but will fail catastrophically in others -- for no apparent reason a fooled-audience will be able to predict.

But predicting it is easy -- as you'll see, over the next year or two, ChatGPT's flaws will become more widely know. There are many papers on this already.

[YeGoblynQueenne]

>> I feel like our positions are probably both buried in webs of mutually-difficult-to-communicate worldview assumptions, but for what it’s worth, I care more at this point about the models being useful than being meaningful.

The question is how useful they are. With LLMs it seems they can be useful as long as you ask them to do something that a human, or another machine (like a compiler) can verify, like your example of synthesising a program that satisfies your specification and compiles.

Where LLMs will be useless is in taks where we can't verify their output. For example, I don't hear anyone trying to get GPT-4 to decode Linear A. That would be a task of significant scientific value, and one that a human cannot perform -unlike generating text or code, which humans can already do pretty damn well on their own.