Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Terence Tao on O1

mathstodon.xyz

Post reply on HN
Print view View original

211–220 of 527 posts

Re: Terence Tao on O1

#211

Post by a_wild_dandan »

minkles wrote: post #148
WhatIsDukkha wrote: post #26

Earlier quoted context omitted.

I entirely agree about their utility. HN, and the internet in general, have become just an ocean of reactionary sandbagging and blather about how "useless" LLMs are. Meanwhile, in the real world, I've found that I haven't written a line of code in weeks. Just paragraphs of text that specify what I want and then guidance through and around pitfalls in a simple iterative loop of useful working code. It's entirely a lea…

That’s fine until your code makes its way to production, an unconsidered side effect occurs and then you have to face me. You are still responsible for what you do regardless of the means you used to do it. And a lot of people use this not because it’s more productive but because it requires less effort and less thought because those are the hard bits. I’m collecting stats at the moment but the general trend in quali…

> You are still responsible for what you do regardless of the means you used to do it. And a lot of people use this not because it’s more productive but because it requires less effort and less thought because those are the hard bits.

Yeah, that's...the whole point of tools. They reduce effort. And they don't shift your responsibility. For many of us, LLMs are overwhelmingly worth the tradeoffs. If your experience differs, then it's unfortunate, and I hate that for you. Don't use 'em!

Re: Terence Tao on O1

#212

Post by kiba »

noch wrote: post #193
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 important point is, I feel, that most people are not even at the level of intelligence of a "a mediocre, but not completely incompetent, graduate student." A mediocre graduate science student, especially of the sort who graduates and doesn't quit, is a very impressive individual compared to the rest of us. For "us", having such a level of intelligence available as an assistant throughout the day is a massive life…

We are more limited by our emotions, and then our skills in learning and acquiring knowledge.

Intelligence is probably a distant third.

Re: Terence Tao on O1

#213

Post by derefr »

eigenvalue wrote: post #205
dyauspitr wrote: post #171

Earlier quoted context omitted.

Can’t Gemini work with a million+ input tokens?

Yes, but its reasoning ability is extremely poor in my experience with real world programming tasks. I’m talking about stuff that Claude3.5 Sonnet handles easily, and GPT4o can also handle if it can fit in its smaller context window, where Gemini 1.5 pro just completely fails. Bigger context is definitely helpful, but not if it comes at the expense of reasoning/analytical ability. I’m always a bit puzzled why people…

> I’m always a bit puzzled why people stress the importance of these “needle in a haystack” tests where the model has to find one specific thing in a huge document. That seems far less relevant to me in terms of usefulness in the real world.

How do you mean?

Half of writing code within a codebase, is knowing what functions already exist in the codebase for you to call in your own code; and/or, what code you'll have to change upstream and downstream of the code you're modifying within the same codebase — or even by forking your dependencies and changing them — to get what you want to happen, to happen.

And half of, say, writing a longform novel, is knowing all the promises you've made to the reader, the active Chekov's guns, and all the other constraints you've placed on yourself by hundreds of pages or even several books ago, that just became relevant again as of this very sentence. Or, moreover, which of those details it's the proper time to make relevant again for maximum impact and proper first-in-last-out narrative bridging structure.

In both cases, these aren't really literal "needle in a haystack" stress-tests; they should properly be tests of the model's ability to perform some kind of "associational priority indexing" on the context, allowing it to build concepts into associational sub-networks and then make long-distance associations where the nodes are entire subnetworks. (Which isn't something we really see yet, in any model.)

Re: Terence Tao on O1

#214

Post by jonahx »

codr7 wrote: post #203
segmondy wrote: post #194

Earlier quoted context omitted.

Then you have a skill issue. 10 million paying are for GPT monthly because a large of them are getting useful value out of it. WolframAlpha has been out for a while and didn't take off for a reason. "GPT couldn't be trusted to flick a light switch on a regular basis" pretty much implies you are not serious or your knowledge about the capabilities of LLM is pretty much dated or derived from things you have read.

FACT: The technology is inherently unreliable in its current form. And the weakness is built in, its not going to go away anytime soon.

The same is true of search engines, yet they are still incredibly useful.

Re: Terence Tao on O1

#215

Post by mfgk »

ahnick wrote: post #210
thewanderer1983 wrote: post #201

Earlier quoted context omitted.

>A mediocre graduate science student, especially of the sort who graduates and doesn't quit, is a very impressive individual compared to the rest of us. Incorrect. University graduates shows a good work ethic, a certain character and a ability to manage time. It's not a measure of being better than the rest of humanity. Also, it's not a good measure of intelligence. If you only want to view the world through credenti…

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…

An excellent example of an LLM (or an imitated LLM output) that fiercely defends the status quo, is overly verbose, does not come to the point, makes incorrect assumptions and lectures from a high horse.

LLMs are good for mediocre poems and presidential speeches that have no shame.

Re: Terence Tao on O1

#216

Post by dpig_ »

ahnick wrote: post #210
thewanderer1983 wrote: post #201

Earlier quoted context omitted.

>A mediocre graduate science student, especially of the sort who graduates and doesn't quit, is a very impressive individual compared to the rest of us. Incorrect. University graduates shows a good work ethic, a certain character and a ability to manage time. It's not a measure of being better than the rest of humanity. Also, it's not a good measure of intelligence. If you only want to view the world through credenti…

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…

If you couldn't be bothered to write this comment, I can't be bothered to read it.

Re: Terence Tao on O1

#217

Post by wenc »

l33t7332273 wrote: post #182
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 an also working in OR and I have had the complete opposite experience with respect to MILP optimization(and the research actually agrees; there was a big survey paper published earlier this year showing LLMs were mostly correct on textbook problems but got more and more useless as complexity and novelty increased.) The results are boiler plate at best, but misleading and insidious at worst, especially when you get…

Can you point me to that paper? What version of the model were they using?

Have you tried again with the latest LLMs? ChatGPT4 actually (correctly) explains what each constraint does in English -- it doesn't just provide the constraint when you ask it for the formulation. Also, not sure if CPLEX should be involved at all -- I usually just ask it for mathematical formulations, not CPLEX calling code (I don't use CPLEX). The OR literature primarily contains math formulations and that's where LLMs can best do pattern matching to problem shape.

Many of the standard formulations are in here:

https://msi-jp.com/xpress/learning/square/10-mipformref.pdf

All the LLM is doing is fitting the problem description to a combination of these formulations (and others).

Re: Terence Tao on O1

#218

Post by macrolime »

delusional wrote: post #53
WhatIsDukkha wrote: post #26

Earlier quoted context omitted.

I entirely agree about their utility. HN, and the internet in general, have become just an ocean of reactionary sandbagging and blather about how "useless" LLMs are. Meanwhile, in the real world, I've found that I haven't written a line of code in weeks. Just paragraphs of text that specify what I want and then guidance through and around pitfalls in a simple iterative loop of useful working code. It's entirely a lea…

What sort of problems do you solve? I tried to use it. I really did. I've been working on a tree edit distance implementation base on a paper from 95. Not novel stuff. I just can't get it to output anything coherent. The code rarely runs, it's written in absolutely terrible style, it doesn't follow any good practices for performant code. I've struggled with getting it to even implement the algorithm correctly, even t…

This kind of problems is really not where LLMs shine.

Where you save loads of time is when you need to write lots of code using unfamiliar APIs. Especially when it's APIs you won't work with a lot and spending loads of time learning then would just be a waste of time. In these cases LLMs call tell you the correct API cells and it's easy to verify. The LLM isn't really solving some difficult technical problem, but saves lots of work.

Re: Terence Tao on O1

#219

Post by schappim »

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…

Showing your work in tests is kind of like “chain of thought” reasoning, but there’s a slight difference. Both force you to break down your process step by step, making sure the logic holds and you aren’t skipping crucial steps. But while showing your work is more about demonstrating the correct procedure, “chain of thought” reasoning pushes you to recall relevant definitions and concepts as you go, ensuring a deeper understanding. In both cases, the goal is to avoid just “winging it,” but “chain of thought” really digs into the recall aspect, which humans tend to avoid because it’s effortful.

Re: Terence Tao on O1

#220

Post by atleastoptimal »

kiba wrote: post #212
noch wrote: post #193

Earlier quoted context omitted.

The important point is, I feel, that most people are not even at the level of intelligence of a "a mediocre, but not completely incompetent, graduate student." A mediocre graduate science student, especially of the sort who graduates and doesn't quit, is a very impressive individual compared to the rest of us. For "us", having such a level of intelligence available as an assistant throughout the day is a massive life…

We are more limited by our emotions, and then our skills in learning and acquiring knowledge. Intelligence is probably a distant third.

Nah. Dogs are far emotionally better than most humans. Their intelligence is their limitation. Also “skills in learning and acquiring knowledge” is basically intelligence
Post reply on HN