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Terence Tao on O1

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Re: Terence Tao on O1

#521

Post by noch »

thewanderer1983 wrote: post #520
ahnick wrote: post #210

Earlier quoted context omitted.

Here are the key points outlining why thewanderer1983's response misinterprets noch's comment and contains inaccuracies: Misinterpretation of the Original Point: Intelligence vs. Moral Superiority: Noch discusses the intelligence level of a mediocre graduate science student compared to the general population. Thewanderer1983 misreads this as a claim of moral or inherent superiority over "the rest of humanity," which…

I can play this silly game also. Let’s evaluate the correctness of Thewanderer’s argument in detail: Distinction Between Credentials and Intelligence: Correctness: Thewanderer is correct in stating that a university degree is not a definitive measure of intelligence. Intelligence is a complex trait that encompasses various cognitive abilities, problem-solving skills, creativity, and emotional intelligence. Academic c…

> I can play this silly game also.

Please could you share your prompt or a link to the conversation?

I'm genuinely puzzled that you're more interested in doubling down and justifying yourself and making new points (different from what I initially presented) than understanding the other person's point of view.

If you share your prompt, I'll have a better understanding of your motivations and whether you are arguing in good faith.

As far as silly games go: if you honestly believe a game is silly, you shouldn't play it, unless you want to win silly prizes.

Re: Terence Tao on O1

#522

Post by atlex2 »

Nadya wrote: post #83
skydhash wrote: post #68

Earlier quoted context omitted.

> Much Much more productive world by just knuckling down and learning how to do the work. The fact everyone that say they've become more productive with LLMs won't say how exactly. I can talk about how VIM have make it more enjoyable to edit code (keybinding and motions), how Emacs is a good environment around text tooling (lisp machine), how I use technical books to further my learning (so many great books out here)…

Ever seen someone try and search something on Google and they are just AWFUL at it? They can never find what they're looking for and then you try and can pull it up in a single search? That's what it is like watching some people try to use LLM's. Learning how to prompt an LLM is as much a learned skill as much as learning how to phrase internet searches is a learned skill. And as much as people decried that "searchin…

Could you reference any youtube videos, blog posts, etc of people you would personally consider to be _really good_ at prompting? Curious what this looks like.

While I can compare good journalists to extremely great and intuitive journalists, I don't have really any references for this in the prompting realm (except for when the Dall-e Cookbook was circulating around).

Re: Terence Tao on O1

#523

Post by kevinventullo »

lupire wrote: post #443
kevinventullo wrote: post #339

Earlier quoted context omitted.

I believe this is the farthest anyone has gotten: https://deepmind.google/discover/blog/ai-solves-imo-problems... No FLT yet, but as someone who was initially quite skeptical, I’m starting to be convinced!

Those are not serious mathematical problems. Those are toy math problems, crafted backwards from known facts, designed to be solved in under 1hr, that are hard for most humans because they lack the memorization and recall and search speed that the computer has.

Sure. But even many high-caliber research mathematicians can’t do Putnam problems in a heartbeat. If we get to the point where an LLM can solve any homework problem that appears in a textbook, including graduate textbooks, that would already be something like a “lemma prover” if not a full-blown “theorem prover”.

Anyway, I think five years ago I was skeptical that ML would even get to the point of being able to solve competition problems, and I was proven wrong, so my priors have been updated.

Re: Terence Tao on O1

#524

Post by elbear »

imiric wrote: post #374
elbear wrote: post #362

Earlier quoted context omitted.

I would say all software is chaining APIs together.

Well, that depends on how you look at it. All software calls APIs, but some rely on literally "just chaining" these calls together more than writing custom behavior from scratch. After all, someone needs to write the APIs to begin with. That's not to say that these projects aren't useful or valuable, but there's a clear difference in the skill required for either. You could argue that it's all APIs down to the hardwa…

| You could argue that it's all APIs down to the hardware level, but that's not a helpful perspective in this discussion.

Yes, that's what I'm arguing. Why isn't useful? I think it's useful, because it demystifies things. You know that in order to do something, you need to know how to use the particular API.

Re: Terence Tao on O1

#525

Post by knotthebest »

nybsjytm wrote: post #109

Daniel Litt, an algebraic geometer on twitter, said "Pretty impressed by o1-preview! Still not having much luck asking it to do any interesting math but it seems much more reliable with simple things; I can actually imagine it being a net time-saver at this point with some non-mathematical tasks." Any other takes by mathematicians out there?

Do note that Terry has access to the full o1. o1-preview is, well, a preview.

Re: Terence Tao on O1

#526

Post by jackleeb »

ocular-rockular wrote: post #490
cwillu wrote: post #488

Earlier quoted context omitted.

Someone at the top of their field discussing the capability of models in their field is much more interesting than someone mediocre in their field making trite observations about capabilities outside their field.

There is any number of people discussing o1 from the context of their field. So again, why are we valuing one set of discussions above another? Terence Tao may be great, but he's not the end all, be all of commentary. There's plenty of other PhDs talking about this very same thing.

there are levels to this shit.

the best competitive programmer in the world (gennady korotkevich, aka tourist) recently crossed the 4000 ELO barrier in Codeforces. o1 is about 1807 ELO.

the best ai model is compared against the best human in the context of competition programming, to set a clear standard of comparison.

similarly, terence tao represents the highest levels of math in analysis. his input is valuable in regards to math. his summary of the current capabilities of o1 is important because we can then understand the level of competence the best ai models have right now, and set a standard of comparison just like with coding.

site note: any number of phds = not the same expertise. there are thousands of phds who graduate every year, let alone thousands of unemployable phds who fail to get a professorship.

there are only 2-4 fields medalists chosen every 4 years.

Re: Terence Tao on O1

#527

Post by Nadya »

atlex2 wrote: post #522
Nadya wrote: post #83

Earlier quoted context omitted.

Ever seen someone try and search something on Google and they are just AWFUL at it? They can never find what they're looking for and then you try and can pull it up in a single search? That's what it is like watching some people try to use LLM's. Learning how to prompt an LLM is as much a learned skill as much as learning how to phrase internet searches is a learned skill. And as much as people decried that "searchin…

Could you reference any youtube videos, blog posts, etc of people you would personally consider to be _really good_ at prompting? Curious what this looks like. While I can compare good journalists to extremely great and intuitive journalists, I don't have really any references for this in the prompting realm (except for when the Dall-e Cookbook was circulating around).

Sorry for the late response - but I can't. I don't really follow content creators at a level where I can recall names or even what they are working on. If you browse AI-dominated spaces you'll eventually find people who include AI as part of their workflows and have gotten quite proficient at prompting them to get the results they desire very consistently. Most AI stuff enters into my realm of knowledge via AI Twitter, /r/singularity, /r/stablediffusion, and Github's trending tab. I don't go particularly out of my way to find it otherwise.

/r/stablediffusion used to (less so now) have a lot of workflow posts where people would share how they prompt and adjust the knobs/dials of certain settings and models to make what they make. It's not so different from knowing which knobs/dials to adjust in Apophysis to create interesting fractals and renders. They know what the knobs/dials adjust for their AI tools and so are quite proficient at creating amazing things using them.

People who write "jailbreak" prompts are a kind of example. There is some effort put into preventing people from prompting the models and removing the safeguards - and yet there are always people capable of prompting the model into removing its safeguards. It can be surprisingly difficult to do yourself for recent models and the jailbreak prompts themselves are becoming more complex each time.

For art in particular - knowing a wide range of artist names, names of various styles, how certain mediums will look, as well as mix & matching with various weights for the tokens can get you very interesting results. A site like https://generrated.com/ can be good for that as it gives you a quick baseline of how including certain names will change the style of what you generate. If you're trying to hit a certain aesthetic style it can really help. But even that is a tiny drop in a tiny bucket of what is possible. Sometimes it is less about writing an overly detailed prompt but rather knowing the exact keywords to get the style you're aiming for. Being knowledgeable about art history and famous artists throughout the years will help tremendously over someone with little knowledge. If you can't tell a Picasso from a Monet painting you're going to find generating paintings in a specific style much harder than an art buff.

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