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Terence Tao on O1

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Re: Terence Tao on O1

#251

Post by __loam »

a_wild_dandan wrote: post #232
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…

My sheer productivity boost from these models is miraculous. It's like upgrading from a text editor to a powerful IDE. I've saved a mountain of hours just by removing tedious time sinks -- one-off language syntax, remembering patterns for some framework, migrating code, etc. And this boost applies to nearly all of my knowledge work. Then I see contrarians claiming that LLMs are literally never useful for anyone, and…

It's a system that is designed to convince you.

Re: Terence Tao on O1

#252

Post by eigenvalue »

derefr wrote: post #213
eigenvalue wrote: post #205

Earlier quoted context omitted.

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 t…

Yes agreed, I wasn’t trying to say it’s totally useless, but it’s not as helpful as synthesizing all that context intelligently. It’s more of a parlor trick. But that trick can be handy if you need something like that. Really, the main issue with Gemini is that it’s simply not very smart compared to the competition, and the big context doesn’t make up for that in the slightest.

Re: Terence Tao on O1

#253

Post by rtsil »

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…

Here's the thing. 99% of people aren't writing compilers or debuggers, they're writing glorified CRUDs. LLM can save a lot of time for these people, just like 99% of people only use basic arithmetic operations, and MS Excel saves a lot of time for these people. It's not about solving new problems, it's about solving old and known problems very fast.

Re: Terence Tao on O1

#254

Post by dartos »

a_wild_dandan wrote: post #232
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…

My sheer productivity boost from these models is miraculous. It's like upgrading from a text editor to a powerful IDE. I've saved a mountain of hours just by removing tedious time sinks -- one-off language syntax, remembering patterns for some framework, migrating code, etc. And this boost applies to nearly all of my knowledge work. Then I see contrarians claiming that LLMs are literally never useful for anyone, and…

> 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 not identical, syntax. That’s about where the gains end for me.

Maybe at this point I’m just so fast about looking up documentation. Maybe the languages/problems I’m facing aren’t well represented in the training data, but I just don’t see this amazing advancement.

I’d really love to see, live, someone programming who really gets these big productivity gains.

Re: Terence Tao on O1

#255

Post by ants_everywhere »

christofosho wrote: post #241
a_wild_dandan wrote: post #232

Earlier quoted context omitted.

My sheer productivity boost from these models is miraculous. It's like upgrading from a text editor to a powerful IDE. I've saved a mountain of hours just by removing tedious time sinks -- one-off language syntax, remembering patterns for some framework, migrating code, etc. And this boost applies to nearly all of my knowledge work. Then I see contrarians claiming that LLMs are literally never useful for anyone, and…

I think there may be a set of people that have figured out, 1) how to interact with LLMs; and 2) what in their lives is improved when interacting with LLMs. I am in the group that has not found the best use case for my own life, and have never needed it for improving anything I need to get done. Always looking for suggestions, though!

> I think there may be a set of people that have figured out, 1) how to interact with LLMs....

1) is all about experimenting, which is what Tao is doing.

Having a playful and open minded attitude is like 80% of the game

Re: Terence Tao on O1

#256

Post by wokwokwok »

WhatIsDukkha wrote: post #26
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 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…

> I've found that I haven't written a line of code in weeks

Please post a video of your workflow.

It’s incredibly valuable for people to see this in action, otherwise they, quite legitimately, will simply think this is not true.

Re: Terence Tao on O1

#257

Post by talldayo »

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 would better believe that if any superior software was being primarily designed by AI.

Re: Terence Tao on O1

#258

Post by __loam »

darby_nine wrote: post #226

JUST when you thought the chatbot was dead

This seems like it's just feeding the output back into the model and using more compute to try and get better answers. If that's all, I don't see how it fundamentally solves any of the issues currently present in LLMs. Maybe a marginal improvement in accuracy at the cost of making the computation more expensive. And you don't even get to see the so called reasoning tokens.

Re: Terence Tao on O1

#259

Post by farresito »

__loam wrote: post #250
JumpCrisscross wrote: post #246

Earlier quoted context omitted.

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.

It's going to be incredible watching you people write way more code than you can feasibly maintain.

Once we have AI-based language servers, which will, at some point in the future, be able to track entire repositories, I think maintaining projects will actually be far easier than right now.

Re: Terence Tao on O1

#260

Post by throwaway765123 »

apsurd wrote: post #74
vasco wrote: post #69

Earlier quoted context omitted.

ChatGPT voice interface plugged into the audio stream, with the prompt: - I need you to assist me during a programming interview, you will be listening to two people, the interviewer and me. When the interviewer asks a question, I'd like you to feed me lines that seem realistic for an interview where I'm nervous, don't give me a full blown answer right away. Be very succinct. If I think you misunderstood something, I…

This made me laugh. I can't deny it isn't already happening. But wow people work so hard to avoid working hard.

It's not about avoiding hard work - the audience on HN skews wealthy due to heavy representation of skilled devs in their 30s+, but the average person does not earn anything close to FAANG salaries. Even most devs in general don't earn like that. The interview process being fairly well understood in general, any advantage that can possibly get a person from $60k/year to generationally-life-changing $300k/year will be used eventually.
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