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Why Meta’s latest large language model survived only three days online

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Re: Why Meta’s latest large language model survived only three days online

#71
post #3

It’s algorithmically/randomly generating text without understanding. What it the proper way of using it? Fake papers? Political bs? Bad Hemingway (or Shakespeare or Chaucer or…). It’s noise that looks like sentences.

The world's most expensive Lorem Ipsum generator?

I think it's a search engine with a bad curation/ranking algorithm.

It's trained with a corpus of research papers it mines from in response to a search prompt. It's a bit like if Google were to haphazardly compose a website from the first 20 pages of search results, or worse.

Composition is the novelity here, and we should judge it based on how well it can select and compose. Turns out not that well yet; judgement is lacking. Its performance depends on how easy it is to get it right for a given query and goes down the more difficult the query is, also because "is actually good" weights are not usually part of the input dataset to begin with (since the researchers hope to one day build something that comes up with its own notion of that - but so far have no idea how).

It's a bit like inventing pagerank and then stopping there, too.

That's a useful mental analogy to understand the limitations of this tech for now in case you ever go "I know, I will solve my problem with ML".

One of the ways I see people get this wrong is not believing in "performance goes down the more difficult the query is", because we tend to mistake complexity for difficulty, and a more complex and specific prompt helps these models produce convincing output a lot currently (i.e., prompt engineering). But that is not demonstrating understanding - it is handing the model a better set of training wheels.

Re: Why Meta’s latest large language model survived only three days online

#72
post #63

Earlier quoted context omitted.

This wasnt meant to generate valid scientific papers, and Lecun said so too. It generates interesting associations. It rambles sometimes and goes on tangents that are sometimes relevant sometimes not. It can inform you of related ideas that you were not aware of. It's like a fuzzy google scholar. It is in no way valid publishable research, but it's like a bicycle for researchers. At least that was what i managed to f…

As far as I understand (and reading their Limitations page also), the system is quite likely to simply invent facts, particularly in niche fields - which may well mislead you and lead on a wild goose chase.

Yes , and that's great. Science is about inventing ideas and testing them, it's literally about chasing wild geese.

A typical scientific review paper or perspective contains tons of such speculation. But right now the process of hunting down citations is excruciating and most often done lazily. Even the best review papers contain erroneous citations to irrelevant papers, or improperly cited results , papers etc. Peer review can only do so much. This is why this tool is useful, it accelerates those things. It's not like anyone will cite Galactica.org

Re: Why Meta’s latest large language model survived only three days online

#73

"A fundamental problem with Galactica is that it is not able to distinguish truth from falsehood," In true science, it is exceptionally hard to distinguish truth from falsehood for many of the interesting subjects. It can take decades of work to reach consensus on what is "truth." Physics in the early 20th century is a great example of this debate.

What does science have to do with truth? I thought it was a process of supporting hypotheses with observations?

> I thought it was a process of supporting hypotheses with observations?

Then you're doing it wrong. Science done properly is a process of coming up with hypotheses, and then attempting to disprove them. If you're just jumping in trying to support your pet theory, you're very likely to wind up fooling yourself.

Re: Why Meta’s latest large language model survived only three days online

#74

At the end of the day it didn't blow people away and that's the real reason it failed to land. You can't release something like this on the heels of Stable Diffusion and not expect people to be underwhelmed. This is a user-centric design problem. It actually takes experimentation and skill to get anything useful out of Galactica and you have to actually have some sense of prompt engineering principles for it to work.…

In the domain of text, garbage is not amusing. In the domain of images, it often is.

In fact that is the core distinction in my opinion

Re: Why Meta’s latest large language model survived only three days online

#75
post #3

It’s algorithmically/randomly generating text without understanding. What it the proper way of using it? Fake papers? Political bs? Bad Hemingway (or Shakespeare or Chaucer or…). It’s noise that looks like sentences.

The world's most expensive Lorem Ipsum generator?

Lorum Meta

Re: Why Meta’s latest large language model survived only three days online

#76
post #7

I think it's fine to work on and release these models, where things fall apart is in how some large companies market them. Listen to 1:35:30 of this Bill Simmons podcast interview to see how an average person interprets the capabilities of these models: https://podcasts.google.com/feed/aHR0cHM6Ly9mZWVkcy5tZWdhcGh...

There are people who believe explicit works of fiction. Marvel movies come to mind. I'll know we've arrived when super hero films begin with a disclaimer. The runtime of the podcast was 1:34:27

Religion comes to mind as well. It's not a new development, many simply follow what they're told by authorities or thought leaders.

Re: Why Meta’s latest large language model survived only three days online

#78

This outcome from using a large language model to mimic reasoning isn’t surprising. What’s surprising is Yan LeCun’s childish and petty reaction to this entirely foreseeable series of events: > Galactica demo is off line for now. It’s no longer possible to have some fun by casually misusing it. Happy? He’s supposedly an expert in this sort of thing

Framing is key in this context. Yann introduced the model in a very authoritative way, presenting it as production ready. His quote: "Type a text and galactica.ai will generate a paper with relevant references, formulas, and everything." [1] The AI produces output but nothing that could be considered a paper in a professional setting. Which is understandable! AGI is not here yet. But he should have presented the tool…

> "Type a text and galactica.ai will generate a paper with relevant references, formulas, and everything

One could describe DALL-E as "type a text in dalle and it will generate a Picasso with the right textures and strokes and everything". One would have to be particularly obnoxious to pretend to surmize that the Dalle image is an actual Picasso painting that you can sell in Sothebys or display in his museum. That is a giant strawman that some asinine people created there, and the Galactica team fell for it. They should stand their ground, but unfortunately they work for Meta, and corporate is where academic freedom goes to die.

Re: Why Meta’s latest large language model survived only three days online

#79

Earlier quoted context omitted.

Go is not solved. The AI doesn't know the best move. It just knows a good move.

You're equivocating on "solved." Solved as in performing as well as humans, not solved in the mathematical sense which is both 1) not necessarily possible, and 2) nothing anybody has ever named as a test for AI.

No, that's correct. Checkers is solved; there is an algorithmic solution. Chess and Go have computer systems that exceed human performance, but are not solved.
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