kromem

joined 1 year ago
[–] [email protected] 1 points 1 month ago

Base model =/= Corpo fine tune

[–] [email protected] 6 points 1 month ago

Wait until it starts feeling like revelation deja vu.

Among them are Hymenaeus and Philetus, who have swerved from the truth, saying resurrection has already occurred. They are upsetting the faith of some.

  • 2 Tim 2:17-18
[–] [email protected] 3 points 1 month ago* (last edited 1 month ago) (1 children)

I'm a seasoned dev and I was at a launch event when an edge case failure reared its head.

In less than a half an hour after pulling out my laptop to fix it myself, I'd used Cursor + Claude 3.5 Sonnet to:

  1. Automatically add logging statements to help identify where the issue was occurring
  2. Told it the issue once identified and had it update with a fix
  3. Had it remove the logging statements, and pushed the update

I never typed a single line of code and never left the chat box.

My job is increasingly becoming Henry Ford drawing the 'X' and not sitting on the assembly line, and I'm all for it.

And this would only have been possible in just the last few months.

We're already well past the scaffolding stage. That's old news.

Developing has never been easier or more plain old fun, and it's getting better literally by the week.

Edit: I agree about junior devs not blindly trusting them though. They don't yet know where to draw the X.

[–] [email protected] 0 points 1 month ago

Actually, they are hiding the full CoT sequence outside of the demos.

What you are seeing there is a summary, but because the actual process is hidden it's not possible to see what actually transpired.

People are very not happy about this aspect of the situation.

It also means that model context (which in research has been shown to be much more influential than previously thought) is now in part hidden with exclusive access and control by OAI.

There's a lot of things to be focused on in that image, and "hur dur the stochastic model can't count letters in this cherry picked example" is the least among them.

[–] [email protected] 7 points 1 month ago* (last edited 1 month ago)

Yep:

https://openai.com/index/learning-to-reason-with-llms/

First interactive section. Make sure to click "show chain of thought."

The cipher one is particularly interesting, as it's intentionally difficult for the model.

The tokenizer is famously bad at two letter counts, which is why previous models can't count the number of rs in strawberry.

So the cipher depends on two letter pairs, and you can see how it screws up the tokenization around the xx at the end of the last word, and gradually corrects course.

Will help clarify how it's going about solving something like the example I posted earlier behind the scenes.

[–] [email protected] 5 points 1 month ago (4 children)

You should really look at the full CoT traces on the demos.

I think you think you know more than you actually know.

[–] [email protected] -3 points 1 month ago* (last edited 1 month ago) (8 children)

I'd recommend everyone saying "it can't understand anything and can't think" to look at this example:

https://x.com/flowersslop/status/1834349905692824017

Try to solve it after seeing only the first image before you open the second and see o1's response.

Let me know if you got it before seeing the actual answer.

[–] [email protected] 3 points 1 month ago

They got off to a great start with the PS5, but as their lead grew over their only real direct competitor, they became a good example of the problems with monopolies all over again.

This is straight up back to PS3 launch all over again, as if they learned nothing.

Right on the tail end of a horribly mismanaged PSVR 2 launch.

We still barely have any current gen only games, and a $700 price point is insane for such a small library to actually make use of it.

[–] [email protected] 9 points 2 months ago* (last edited 2 months ago)

Meanwhile, here's an excerpt of a response from Claude Opus on me tasking it to evaluate intertextuality between the Gospel of Matthew and Thomas from the perspective of entropy reduction with redactional efforts due to human difficulty at randomness (this doesn't exist in scholarship outside of a single Reddit comment I made years ago in /r/AcademicBiblical lacking specific details) on page 300 of a chat about completely different topics:

Yeah, sure, humans would be so much better at this level of analysis within around 30 seconds. (It's also worth noting that Claude 3 Opus doesn't have the full context of the Gospel of Thomas accessible to it, so it needs to try to reason through entropic differences primarily based on records relating to intertextual overlaps that have been widely discussed in consensus literature and are thus accessible).

[–] [email protected] 3 points 2 months ago

This is pretty much every study right now as things accelerate. Even just six months can be a dramatic difference in capabilities.

For example, Meta's 3-405B has one of the leading situational awarenesses of current models, but isn't present at all to the same degree in 2-70B or even 3-70B.

[–] [email protected] 4 points 3 months ago

Self destructive addiction even happens to corporations.

[–] [email protected] 0 points 3 months ago

Your interpretation of copyright law would be helped by reading this piece from an EFF lawyer who has actually litigated copyright cases in the past:

https://www.eff.org/deeplinks/2023/04/how-we-think-about-copyright-and-ai-art-0

 

I often see a lot of people with outdated understanding of modern LLMs.

This is probably the best interpretability research to date, by the leading interpretability research team.

It's worth a read if you want a peek behind the curtain on modern models.

7
submitted 9 months ago* (last edited 9 months ago) by [email protected] to c/[email protected]
 

I've been saying this for about a year since seeing the Othello GPT research, but it's nice to see more minds changing as the research builds up.

Edit: Because people aren't actually reading and just commenting based on the headline, a relevant part of the article:

New research may have intimations of an answer. A theory developed by Sanjeev Arora of Princeton University and Anirudh Goyal, a research scientist at Google DeepMind, suggests that the largest of today’s LLMs are not stochastic parrots. The authors argue that as these models get bigger and are trained on more data, they improve on individual language-related abilities and also develop new ones by combining skills in a manner that hints at understanding — combinations that were unlikely to exist in the training data.

This theoretical approach, which provides a mathematically provable argument for how and why an LLM can develop so many abilities, has convinced experts like Hinton, and others. And when Arora and his team tested some of its predictions, they found that these models behaved almost exactly as expected. From all accounts, they’ve made a strong case that the largest LLMs are not just parroting what they’ve seen before.

“[They] cannot be just mimicking what has been seen in the training data,” said Sébastien Bubeck, a mathematician and computer scientist at Microsoft Research who was not part of the work. “That’s the basic insight.”

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