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Things we learned about LLMs in 2024

simonwillison.net

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Re: Things we learned about LLMs in 2024

#591

Post by mvdtnz »

Philpax wrote: post #515
mvdtnz wrote: post #412

Earlier quoted context omitted.

It's really important to go and read the code that the author of this article actually produces with LLMs. He posted on hacker news a few months ago, a post called something like "everything I've made with ChatGPT in the month of September" or something. He's producing little toy applications that don't even begin to resemble real production code. He thinks these "tools" are useful because they help him write pointle…

Do you know who Simon is?

Only from his neverending stream of hacker news posts.

Re: Things we learned about LLMs in 2024

#592

Post by monero-xmr »

specialist wrote: post #586
adwn wrote: post #486

Earlier quoted context omitted.

One argument against that line of thinking is that energy production has negative externalities. If you use a lot of electricity, its price goes up, which incentivizes more electricity production, which generates more negative externalities. It will also raise the costs for other consumers of electricity. Now that alone is not yet an argument against crypto currencies, and one person's frivolous squandering of resour…

Unintentionally, the energy demands of cryptocurrencies, and data centers in general, have finally motivated utilities (and their regulators) to finally start building out the massive new grid capacity needed for our glorious renewable energy future. Acknowledging that facilitating scams (eg pig butchering) are cryptocurrency's primary (sole?) use case, I'm willing to look the other way if we end up with the grid we…

To pretend romance / affinity scams and crime were created by crypto is absurd. It’s fair to argue crypto made crime more efficient, but it also made the responsible parties quicker to patch holes.

The primary use case of crypto is to protect wealth from a greedy, corrupt, money-printing state. Everything else is a sideshow

Re: Things we learned about LLMs in 2024

#593

Post by deterministic »

nashadelic wrote: post #469
beoberha wrote: post #449

Earlier quoted context omitted.

LLMs being able to detect bugs in my own code is absolutely mind blowing to me. These things are “just” predicting the next token, but somehow are able to take in code that has never been written before and somehow understand it and find what’s wrong with it. I think I’m more amazed by them because I know how they work. They shouldn’t be able to do this, but the fact that they can is absolutely jaw dropping science f…

I actually get annoyed when experienced folks say this isn't AGI, its next word predict and not human-like intelligence. But we don't know how human intelligence works. Is it also just a matrix of neuron weights? Maybe it ends up looking like humans are also just next-word/thought predictors. Maybe that is what AGI will be.

A human can learn from just a few examples of chairs what a chair is. Machine learning requires way more training than that. So there does seem to be a difference in how human intelligence works.

Re: Things we learned about LLMs in 2024

#594

Post by specialist »

monero-xmr wrote: post #592
specialist wrote: post #586

Earlier quoted context omitted.

Unintentionally, the energy demands of cryptocurrencies, and data centers in general, have finally motivated utilities (and their regulators) to finally start building out the massive new grid capacity needed for our glorious renewable energy future. Acknowledging that facilitating scams (eg pig butchering) are cryptocurrency's primary (sole?) use case, I'm willing to look the other way if we end up with the grid we…

To pretend romance / affinity scams and crime were created by crypto is absurd. It’s fair to argue crypto made crime more efficient, but it also made the responsible parties quicker to patch holes. The primary use case of crypto is to protect wealth from a greedy, corrupt, money-printing state. Everything else is a sideshow

> primary use case of crypto is to protect wealth

Merely trading governments for corporations.

> Everything else is a sideshow

Agreed. Crypto is endlessly amusing.

Re: Things we learned about LLMs in 2024

#595

Post by monero-xmr »

specialist wrote: post #594
monero-xmr wrote: post #592

Earlier quoted context omitted.

To pretend romance / affinity scams and crime were created by crypto is absurd. It’s fair to argue crypto made crime more efficient, but it also made the responsible parties quicker to patch holes. The primary use case of crypto is to protect wealth from a greedy, corrupt, money-printing state. Everything else is a sideshow

> primary use case of crypto is to protect wealth Merely trading governments for corporations. > Everything else is a sideshow Agreed. Crypto is endlessly amusing.

What corporation made bitcoin?

Re: Things we learned about LLMs in 2024

#596

Post by BeetleB »

aleph_minus_one wrote: post #555
BeetleB wrote: post #242

Earlier quoted context omitted.

I think most tech folks struggle with it because they treat LLMs as computer programs, and their experience is that SW should be extremely reliable - imagine using a calculator that was wrong 5% of the time - no one would accept that! Instead, think of an LLM as the equivalent of giving a human a menial task. You know that they're not 100% reliable, and so you give them only tasks that you can quickly verify and corr…

> Instead, think of an LLM as the equivalent of giving a human a menial task. You know that they're not 100% reliable, and so you give them only tasks that you can quickly verify and correct. The problem is: for the tasks that I can give the LLM (or human) that I can easily verify and correct, the LLM fails with the majority of them, for example - programming tasks of my area of expertise (which is more "mathematical…

Your first task is definitely not what I would call a "menial" task.

Your second task is not a "task", but a knowledge search. LLMs are not good with searches (unless augmented - like RAG).

Re: Things we learned about LLMs in 2024

#597

Post by specialist »

monero-xmr wrote: post #595
specialist wrote: post #594

Earlier quoted context omitted.

> primary use case of crypto is to protect wealth Merely trading governments for corporations. > Everything else is a sideshow Agreed. Crypto is endlessly amusing.

What corporation made bitcoin?

Apologies, I assumed you knew what cryptocurrency is and how it works. My bad.

I'm really not well suited to explain this stuff. Here's an article for a general (layperson) audience to help you on your journey. https://www.cbsnews.com/news/cryptocurrency-bitcoin-virtual-...

Happy hunting!

Re: Things we learned about LLMs in 2024

#598

Post by monero-xmr »

specialist wrote: post #597
monero-xmr wrote: post #595

Earlier quoted context omitted.

What corporation made bitcoin?

Apologies, I assumed you knew what cryptocurrency is and how it works. My bad. I'm really not well suited to explain this stuff. Here's an article for a general (layperson) audience to help you on your journey. https://www.cbsnews.com/news/cryptocurrency-bitcoin-virtual-... Happy hunting!

I have been intimately involved with cryptocurrency since 2010

Re: Things we learned about LLMs in 2024

#599

Post by robertlagrant »

torginus wrote: post #189
robertlagrant wrote: post #70

Earlier quoted context omitted.

> If AGI is possible, then the entire human economy stops making sense as far as money goes, What does this mean in terms of making me coffee or building houses?

Nothing and the hilarious thing is that the AI figureheads admit that technology (as in defined by new theorems produced and new code written), will do pathetically little to move the needle on human happiness forward. The guy running Anthropic thinks the future is in biotech, developing the cure to all diseases, eternal youth etc. Which is technology all right, but it's unclear to me how these chatbots (or other AI…

I think it's definitely the case that AI has certain really useful niches, but it is hard to know the ones that will really make enough money to make training an AI worth it. E.g. "parse a million company statements into these criteria and invest based on an algorithm on those criteria" might be very valuable. Maybe someone's doing it. But I struggle to think it'd be worth billions.

Re: Things we learned about LLMs in 2024

#600

Post by capr »

ninth_ant wrote: post #540
pixl97 wrote: post #255

Earlier quoted context omitted.

>don’t ask a language model to diagnose your medical condition Honestly they are very decent at it if you give them accurate information in which to make the diagnosis. The typical problem people have is being unable to feed accurate information to the model. They'll cut out parts they don't want to think about or not put full test results in for consideration.

If the LLM is trained on accurate medical data and you provide accurate symptoms data, then the LLM can be a useful tool to output the information in a human-readable way. This is not a diagnosis. Any reasonably capable person can read webmd and apply the symptoms listed and compare them to what the patient describes. This is widely regarded as dangerous because the input data as well as the patient data are limited…

compared to what? most doctors never heard of Bayes rule
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