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Terence Tao on O1

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Re: Terence Tao on O1

#391

Post by jvvw »

kzz102 wrote: post #12

It's interesting that humans would also benefit from the "chain of thought" type reasoning. In fact, I would argue all students studying math will greatly increase their competence if they are required to recall all relevant definition and information before using it. We don't do this in practice (including teachers and mathematicians!) because recall is effortful, and we don't like to spent more effort than necessar…

I assumed that everybody did this when trying to solve a maths problem they are stuck on (thinking university type level maths rather than school maths) and when I was teaching I would always get people to go back to the definitions.

I wasn't amazing at maths research (did a PhD and post-doc and then gave up) but my experience was that it was partly thinking hard about things and grappling with what was going on and trying to break it down somehow, but also scanning everything you know related to the problem, trying to find other problems that resemble it in some way that you can steal ideas from etc.

Re: Terence Tao on O1

#392

Post by hcks »

sweeter wrote: post #207
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)…

Or people say "I've been pumping out thousands of lines of perfectly good code by writing paragraphs and paragraphs of text explaining what I want!" its like what are you programming dog? and they will never tell you, and then you look at their github and its like a dead simple starter project. I recently built a Brainfuck compiler and TUI debugger and I tested out a few LLM's just to see if I could get some useful o…

> I recently built a Brainfuck compiler and TUI debugger

Highly representative of what devs make all day indeed

Re: Terence Tao on O1

#393

Post by [deleted] »

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…

[deleted]

Re: Terence Tao on O1

#394

Post by meroes »

fnordpiglet wrote: post #386
meroes wrote: post #240

Earlier quoted context omitted.

I mean paying several hundred to thousands of grad students to RLHF for several years and you get a corpus of grad-student text. I'm not surprised at all. AI companies hire grad students to RLHF in every subject matter (chemistry, physics, math, etc). The grad-students write the prompts, correct the model, and all of that is fed into a "more advanced" model. It's corpi of text. Repeat this for every grade level and s…

Except you’re talking about a general purpose foundation model that’s doing all these subjects at once. It’s not like you choose the subject specific model with Claude or gpt-01. The key isn’t whether these things are smart or not. The key is that they put something that can answer basic grad level questions on almost any subject. For people that don’t have a graduate level education in any subject this is a remarkab…

Well ya once each project, e.g. “grad level math”, “k—12 math”, “undergrad math”, “k-12 chemistry”, etc is sufficient they are all fed into a larger more powerful model.

Maybe my RLHF work does make it harder for me to dream, but I teach models math which means a lot of prompt writing, and yet I have not found a way to have the model teach me math I don’t know yet (and there’s a lot I don’t know). It’s fun to play around with, but I still gravitate toward the isolated texts, not the aggregation as too much is lost or averaged in my opinion/experience. But hey maybe I’m overtrained on the traditional learning methods.

Re: Terence Tao on O1

#395

Post by ben_w »

rvnx wrote: post #151
skydhash wrote: post #121

Earlier quoted context omitted.

I'm not arguing that you can't get result with LLMs, I'm just asking is it worth the actual effort especially when there's better way to get that result you're seeking (or if the result is really something that you want). An LLM is a word (token?) generator which can be amazingly consistent according to its model. But rarely is my end goal to generate text. It's either to do something, to understand something, or to…

It works quite nicely if you consider LLMs as a translator (and that’s actually why Transformers were created). Enter technical specifications in English as input language, get code as destination language.

It's absolutely a translator, but they're similar good/bad/weird/hallucinaty at natural translation translations, too.

Consider this round-trip in Google Translate:

"དེ་ནི་སྐད་སྒྱུར་པ་ཞིག་ཡིན། འོན་ཀྱང་ཁོང་ཚོ་རང་བྱུང་སྐད་སྒྱུར་གྱི་སྐད་སྒྱུར་ནང་ལ་ཡག་པོ/ངན་པ/ཁྱད་མཚར་པོ/མགོ་སྐོར་གཏོང་བ་འདྲ་པོ་ཡོད།"

"It's a translator. But they seem to be good/bad/weird/delusional in natural translations. I have a"

(Google translate stopped suddenly, there).

I've tried using ChatGPT to translate two Wikipedia pages from German to English, as it can keep citations and formatting correct when it does so; it was fine for the first 2/3rds, then it made up mostly-plausible statements that were not translated from the original for the rest. (Which I spotted and fixed before saving, because I was expecting some failure).

Don't get me wrong, I find them impressive, but I think the problem here is the Peter Principle: the models are often being promoted beyond their competence. People listen to that promotion and expect them to do far more than they actually can, and are therefore naturally disappointed by the reality.

People like me who remember being thrilled to receive a text adventure casette tape for the Commodore 64 as a birthday or christmas gift when we were kids…

…compared to that, even the Davinci model (that really was autocomplete) was borderline miraculous, and ChatGPT-3.5 was basically the TNG-era Star Trek computer.

But anyone who reads me saying that last part without considering my context, will likely imagine I mean more capabilities than I actually mean.

Re: Terence Tao on O1

#396

Post by ben_w »

lolc wrote: post #228
rvnx wrote: post #151

Earlier quoted context omitted.

It works quite nicely if you consider LLMs as a translator (and that’s actually why Transformers were created). Enter technical specifications in English as input language, get code as destination language.

English as input language works in simple scenarios but breaks down very very quickly. I have to get extremely specific and deliberate. At some point I have to write pseudocode to get the machine to get say double checked locking right. Because I have enough experiences where varying the prompting didn't work, I revert to just writing the code when I see the generator struggling. When I encounter somebody who says th…

I definitely still write code. But I also prefer to break down problems into chunks which are small enough that an LLM could probably do them natively, if only you can convince it to use the real API instead of inventing a new API each time — concrete example from ChatGPT-3.5, I tried getting it to make and then use a Vector2D class — in one place it had sub(), mul() etc., the other place it had subtract(), multiply() etc.

It can write unit tests, but makes similar mistakes, so I have to rewrite them… but it nevertheless still makes it easier to write those tests.

It writes good first-drafts for documentation, too. I have to change it, delete some stuff that's excess verbiage, but it's better than the default of "nobody has time for documentation".

Re: Terence Tao on O1

#397

Post by iudqnolq »

mewpmewp2 wrote: post #261
dartos wrote: post #254

Earlier quoted context omitted.

> At this point, such sentiments feel either willfully ignorant, or said in bad faith. I feel exactly the same, but in the opposite direction. As someone who’s been programming for 17 years and working professionally for 10, I’m unable to get any huge productivity boosts from AI tools. They’re better than Google+stack overflow for asking random questions, but in a specific context and they’re good for repetitive, but…

Most gains are from using Copilot, do you use that?

I tried it for a while and thought it was helping a lot. Then I happened to use an IDE without it and realized it was increasing my rate of syntax tokens per hour but reducing the rate of features implemented per hour. In particular I was constantly rewriting boilerplate instead of ever writing helper functions.

Re: Terence Tao on O1

#398

Post by andrepd »

wenc wrote: post #5

Once GPT is tuned more heavily on Lean (proof assistant) -- the way it is on Python -- I expect its usefulness for research level math to increase. I work in a field related to operations research (OR), and ChatGPT 4o has ingested enough of the OR literature that it's able to spit out very useful Mixed Integer Programming (MIP) formulations for many "problem shapes". For instance, I can give it a logic problem like "…

I take cynicism over unbridled optimism. People speak as if we were on the cusp of technological singularity, but I've seen nothing to indicate we're not already past the inflection point of the logistic curve, and well into diminishing returns territory.

Re: Terence Tao on O1

#399

Post by IshKebab »

JumpCrisscross wrote: post #246
fnordpiglet wrote: post #149

Rewind your mind to 2019 and imagine reading a post that said “The experience seemed roughly on par with trying to advise a mediocre, but not completely incompetent, graduate student.” With regard to interacting with the equivalent of Alexa. That’s a remarkable difference in 5 years.

The first profession AI seems on track to decimate is programming. In particular, the brilliant but remote and individual contributor. There is an obvious conflict of interest in this forum.

I doubt it. It can do some impressive stuff for sure, but I very rarely get a perfectly working answer out of ChatGPT. Don't get me wrong, it's often extremely useful as a starting point and time saver, but it clearly isn't close to replacing anyone vaguely competent.

Re: Terence Tao on O1

#400

Post by andrepd »

lolc wrote: post #228
rvnx wrote: post #151

Earlier quoted context omitted.

It works quite nicely if you consider LLMs as a translator (and that’s actually why Transformers were created). Enter technical specifications in English as input language, get code as destination language.

English as input language works in simple scenarios but breaks down very very quickly. I have to get extremely specific and deliberate. At some point I have to write pseudocode to get the machine to get say double checked locking right. Because I have enough experiences where varying the prompting didn't work, I revert to just writing the code when I see the generator struggling. When I encounter somebody who says th…

Exactly! What is this job that you can get where you don't code and just copy-paste from ChatGPT? I want it!

My experience is just as you describe it: I ask a question whose answer is in stackoverflow or fucking geeks4geeks? Then it produces a good answer. Anything more is an exercise in frustration as it tries to sneak nonsense code past me with the same confident spiel with which it produces correct code.

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