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Terence Tao on O1

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Re: Terence Tao on O1

#511

Post by fnordpiglet »

j_timberlake wrote: post #504
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.

Terry is a genius that can get that value out of an LLM. Average Joe can't do anything like that yet, both because he won't be as good at prompting the model, and because his problems in life aren't text-based anyway.

I think this is where multi modal LLMs are so powerful. The ability to directly speak to the LLM with your voice is huge.

Re: Terence Tao on O1

#512

Post by ben_w »

codr7 wrote: post #502
ben_w wrote: post #433

Earlier quoted context omitted.

Indeed, even if I were to minimise what LLMs can do, they are still achieving what "targeted advertising" very obviously isn't.

They're both short sighted attempts at extracting profit while ignoring all negative consequences.

To extent I agree, I think that's true for all tech since the plough, fire, axles.

But I would otherwise say that most (though not all*) AI researchers seem to be deeply concerned about the set of all potential negative consequences, including mutually incompatible outcomes where we don't know which one we're even heading towards yet.

* And not just Yann LeCun — though, given his position, it would still be pretty bad even if it was just him dismissing the possibility of anything going wrong

Re: Terence Tao on O1

#513

Post by sweeter »

hcks wrote: post #392
sweeter wrote: post #207

Earlier quoted context omitted.

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

Yea, obviously not, but the smaller problems this bigger project was composed of were things that you could see anywhere. I made heavy use of string manipulation that could be generally applied to basically anything

Re: Terence Tao on O1

#514

Post by IshKebab »

almostuseful wrote: post #484
IshKebab wrote: post #466

Earlier quoted context omitted.

> whoever came up with this was able identify what can be traded off to make the previously impossible, possible, unlocking new scales and possibilities. In fairness, that is an extremely standard trick so it's reasonably unlikely that the author came up with it themselves.

But the first time someone came up with that idea it was an act of creativity.

Yeah I agree. Though it is hard to tell if LLMs are capable of "easy" creativity like that though because anything that easy has already been done many times in its training set.

You've have to invent some new domain I guess and see if it could be creative within that domain. Difficult to think of a good test though.

Re: Terence Tao on O1

#515

Post by markusde »

red_trumpet wrote: post #447
markusde wrote: post #295

Earlier quoted context omitted.

The examples in this book are extraordinarily simple, and covers material that many proof assistants were designed to be extremely good at expressing. I wouldn't be surprised if a LLM could automate the exercises in this book completely. Writing nontrivial proofs in a theorem prover is a different beast. In my experience (as someone who writes mechanized mathematical proofs for a living) you need to not only know the…

> think about all of the ways your proof is going to be used beforehand What do you mean by that? I don't know much about theorem provers, but my POV would be that a proof is used to verify a statement. What other uses are there one should consider?

The issue is-- there are lots of way to write down a statement.

One common example is if you're going to internalize or externalize a property of a data structure: eg. represent it with a dependent type, or a property about a non-dependent type. This comes with design tradeoffs: some lemmas might expect internalized representations only, some rewrites might only be usable (eg. no horrifying dependent type errors) with externalized representations. For math in particular, which involves rich hierarchies of data structures, your choice about internalization might can impacts about what structures from your mathematical library you can use, or the level of fragile type coercion magic that needs to happen behind the scenes.

Re: Terence Tao on O1

#516

Post by garyal »

dartos wrote: post #498
mewpmewp2 wrote: post #427

Earlier quoted context omitted.

The projects I do are mostly frontend in React and backend with TypeScript/Node.js. I have around 10+ years of professional experience although I did on/off hobby coding before that since 15 years ago. It's mostly API endpoints, calling a database, third party APIs, data transformation, aggregation type of things. Then either UI according to what designers provide or whatever I want to do for my side projects. I thin…

Yes I find copilot is nice for things like tansack query. It’s like better snippets. At my job we have this pretty clean SOA type architecture backed by a mongo db. Copilot has trouble building the more complicated, domain specific queries on its own, I’ve found. I do occasionally ask chatgpt how to write a certain query in a general case and apply that to what I’m writing. I also don’t really like mongosh’s docs.

Hi there - I'm a PM at MongoDB that works on the MongoDB Shell. I'm curious to hear your thoughts on the issues you're currently facing with mongosh docs and how we could make them better for you. Thanks for taking the time to leave feedback!

Re: Terence Tao on O1

#517

Post by vessenes »

jero1000 wrote: post #421
vessenes wrote: post #299

Earlier quoted context omitted.

I see this theory a lot but mostly from people who haven’t tried pair coding with a quality llm. In fact these llms give experienced developers super powers; you can be crazy productive with them. If you think we are close to the maximum useful software in the world already, then maybe. I do not believe that. Seeing software production and time costs drop one to two orders of magnitude means we will have very differe…

I abandoned 3D art after witnessing DALL-E 2's capabilities, and I've observed the ripple effects across creative fields. Initially, photographers and fellow artists dismissed AI as a non-threat. That turned out to be misguided optimism. Now, with Midjourney producing such impressive work, the majority of us have become largely obsolete. These days, I'm noticing developers exhibiting the same denial. From my perspect…

I was a paid wedding photographer in the 1990s, and I used a Rolleiflex TLR with 120 Roll film. I recently attended a friend's wedding, and took with me a Fuji GFX100 series camera, effortlessly shooting pictures I could never have taken with a Rollei from terrible angles at like 5x the resolution with far, far more dynamic range than 120 film ever had.

30 years after I gave up the Rollei, I'm not obsolete as a photographer, and when there's a quality diffusion model that could take a few of my photos from the event at 100 megapixels, and get prompted by me as to what I want to see out of them creatively, I will still not be obsolete, even as a photographer, but most certainly not obsolete as an artist. In fact, I'll have more tools available for my art, with new skills needed, and different workflows.

As to abandoning 3D art -- your call. If you love it, why not see how these new tools open up your art? If you don't love some of the new tools, no problem, don't use them. I still shoot medium format film some times. If you were planning on a long term creative career without staying on top of technical advances in your field, that has not been possible for at least a few centuries.

Re: Terence Tao on O1

#518

Post by delusional »

kaoD wrote: post #373
macrolime wrote: post #218

Earlier quoted context omitted.

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 sav…

I'm a senior engineer (as in, really senior, not only years of experience). I can get familiar with unfamiliar APIs in a few hours and then I can be sure I'm doing the right thing, instead of silently failing to meet edge cases and introducing bugs because I couldn't identify what was wrong in the LLM output (because, well, I'm unfamiliar with the API in the first place). In other words: LLMs don't solve any notewort…

I feel sort of the same way but I'm desperate to understand what I'm missing. So many people sing such high praises. Billions are being invested. People are proclaiming the end of software developers. What I'm looking at can't be the product they are talking about.

I'm perfectly happy reading man pages personally. Half the fun of programming to me is mastering the API to get something out of it nobody expected was in there. To study the documentation (or implementation) to identify every little side effect. The details are most of the fun to me.

I don't really intend to use the AI for myself, but I do really wish to see what they see.

Re: Terence Tao on O1

#519

Post by scubbo »

mewpmewp2 wrote: post #267
scubbo wrote: post #163

Earlier quoted context omitted.

> Do you really think people are just blindly pushing code they can't make heads or tails of? That they haven't tested? Do you seriously think people are just one shotting code and blasting it into prod? Yes, and I see proof of it _literally every day_ in Code Reviews where I ask juniors to describe or justify their choices and they shrug and say "That's what Copilot told me to put".

That sounds more like poor hiring decisions.

That sounds more like moving the goalposts. The claim (via sarcastic comment) was that people do not simply push code that they do not understand - and I provided a counter-example. No-one in that conversation disagrees that that's a bad practice - but until and unless I have full mandate to hire and fire whoever I want to work with, or to change jobs at will, I'm going to have to work with people whose development practices I disagree with.

Re: Terence Tao on O1

#520

Post by thewanderer1983 »

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…

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 credentials primarily reflect one’s ability to succeed in a structured educational environment, which is just one aspect of intelligence.

    Value of Real-World Experience:
        Correctness: The argument that real-world experience is crucial is accurate. Many industries value practical experience and skills over formal education. For example, in technology and business sectors, hands-on experience, problem-solving abilities, and adaptability are often more important than academic qualifications alone. This is supported by numerous studies and industry practices that prioritize experience and performance over degrees.

    Critique of Credentialism:
        Correctness: Thewanderer’s critique of credentialism is valid. Over-reliance on academic credentials can overlook the diverse talents and skills that individuals without formal degrees may possess. This perspective is supported by the growing recognition of alternative education paths, such as vocational training, apprenticeships, and self-directed learning, which can also lead to successful careers.

    Inclusivity and Egalitarianism:
        Correctness: Promoting inclusivity and valuing diverse forms of knowledge is a correct and progressive stance. Intelligence and capability are not confined to those with advanced degrees. Many successful individuals in various fields do not have formal academic credentials but have achieved significant accomplishments through experience, self-learning, and practical skills.

    Encouragement of Self-Worth:
        Correctness: Encouraging individuals to value their own experiences and knowledge is a positive and correct approach. It fosters confidence and self-worth, which are important for personal and professional growth. Recognizing the value of diverse experiences and perspectives contributes to a more inclusive and equitable society.
In summary, Thewanderer’s argument is correct in several key aspects:

    It accurately distinguishes between academic credentials and broader measures of intelligence.
    It correctly emphasizes the importance of real-world experience.
    It validly critiques the overemphasis on academic credentials.
    It promotes an inclusive and egalitarian view of intelligence.
    It encourages self-worth and confidence in one’s abilities.
These points collectively support a well-rounded and accurate perspective on intelligence and capability.
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