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

Things we learned about LLMs in 2024

simonwillison.net

Post reply on HN
Print view View original

181–190 of 615 posts

Re: Things we learned about LLMs in 2024

#181

Post by baobabKoodaa »

whatshisface wrote: post #82
philipkglass wrote: post #44

Earlier quoted context omitted.

If AGI is possible, then the entire human economy stops making sense as far as money goes, and 'owning' part of OpenAI gives you power. That's if AGI is possible and not easily replicated . If AGI can be copied and/or re-developed like other software then the value of owning OpenAI stock is more like owning stock in copper producers or other commodity sector companies. (It might even be a poorer investment. Even AGI…

This belief comes from confusing the singularity (every atom on Earth is converted into a giant image of Sam Altman) with AGI (a store employee navigates a confrontation with an unruly customer, then goes home and wins at Super Mario).

If I recall correctly, these terms were used more or less interchangeably for a few decades, until 2020 or so, when OpenAI started making actual progress towards AGI, and it was clear that the type of AGI that could be imagined at that point, would not be of the type that would produce singularity.

Re: Things we learned about LLMs in 2024

#182

Post by antirez »

uludag wrote: post #136
antirez wrote: post #54

About "people still thinking LLMs are quite useless", I still believe that the problem is that most people are exposed to ChatGPT 4o that at this point for my use case (programming / design partner) is basically a useless toy. And I guess that in tech many folks try LLMs for the same use cases. Try Claude Sonnet 3.5 (not Haiku!) and tell me if, while still flawed, is not helpful. But there is more: a key thing with L…

I don't think people finding LLMs useless is a good representation of the general sentiment though. I feel that more than anything, people are annoyed at LLM slop. Someone uses an LLM too much to write code, they create "slop," which ends up making things worse.

Unfortunately complex tools will be misused by part of the population. There is no easy escape from that in the modernity of possibilities. Look at the Internet itself.

Re: Things we learned about LLMs in 2024

#183

Post by baobabKoodaa »

xnx wrote: post #156

Double checking, I don't think I saw anything about video generation. Not sure if those fall under the "LLM" umbrella. It came very late in the year, but the Google Veo 2 limited testing are astounding. There are at least a half-dozen other services where you can pay to generate video.

Video generation was covered in OP

Re: Things we learned about LLMs in 2024

#184

Post by Gooblebrai »

adsharma wrote: post #31

In spite of all this progress, I can't find LLMs that solve simple tasks like: Here is my resume. Make it look nice (some design hints). They can spit html and css, but not Google doc. On the other hand, Google results are dominated by SEO spam. You can probably find one usable result on page 10. The problem is not technology. It's a business model that can support the humans feeding data into the LLM.

> They can spit html and css, but not Google doc.

Wow. At this stage, I think people are just searching for excuses to complain about anything that the LLM does NOT do.

Re: Things we learned about LLMs in 2024

#185

Post by wslh »

cactusfrog wrote: post #145
nntwozz wrote: post #65

I think John Gruber summed it up nicely: https://daringfireball.net/2024/12/openai_unimaginable OpenAI’s board now stating “We once again need to raise more capital than we’d imagined” less than three months after raising another $6.6 billion at a valuation of $157 billion sounds alarmingly like a Ponzi scheme — an argument akin to “Trust us, we can maintain our lead, and all it will take is a never-ending stream of…

Every waste of money is not a Ponzi scheme.

Not every, but wasting money is one of the tricks of corruption.

Re: Things we learned about LLMs in 2024

#186

Post by karolist »

minimaxir wrote: post #96
sowbug wrote: post #84

Simon has mentioned in multiple articles how cool it is to use 64GB DRAM for GPU tasks on his MacBook. I agree it's cool, but I don't understand why it is remarkable. Is Apple doing something special with DRAM that other hardware manufacturers haven't figured out? Assuming data centers are hoovering up nearly all the world's RAM manufacturing capacity, how is Apple still managing to ship machines with DRAM that perfo…

LLMs run on the GPU, and the unified memory of Apple silicon means that the 64 GB can be used by the GPU. Consumer GPUs top out at 24 GB VRAM.

llama.cpp can run LLMs on CPU. iGPU can also use system memory, the novel thing is not that, it's that the LLM inference is mostly memory bandwidth bound and memory bandwidth of a custom built PC with really fast DDR5 RAM is around 100GB/s, nVidia consumer GPUs at the top end are around 1TB/s, with mid range GPUs at around half that. M1 Max has 400GB/s, M1 Ultra is 800GB/s, but you can have Apple Silicon Macs with up to 192GB of 800GB/s memory usable by GPU, this means much faster inference than just CPU+system memory due to bandwidth and more affordable than building a multi-GPU system to match the memory amount.

Re: Things we learned about LLMs in 2024

#187

Post by switch007 »

I've watched juniors take their output as gospel applying absolutely zero thinking and getting confused when I suggest looking at the reference manual instead

I've had PMs believe it can replace all writing of tickets and thinking about the feature, creating completely incomprehensible descriptions and acceptance criteria

I've had Slack messages and emails from people with zero sincerity and classic LLM style and the bs that entails

I've had them totally confidently reply with absolute nonsense about many technical topics

I'm grouchy and already over LLMs

Re: Things we learned about LLMs in 2024

#189

Post by torginus »

robertlagrant wrote: post #70
datadrivenangel wrote: post #33

Earlier quoted context omitted.

It's justified if AGI is possible. If AGI is possible, then the entire human economy stops making sense as far as money goes, and 'owning' part of OpenAI gives you power. That is of course, assuming AGI is possible and exponential, and that marketshare goes to a single entity instead of a set of entities. Lots of big assumptions. Seems like we're heading towards a slow-lackluster singularity though.

> 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 systems) are the quickest way to get there.

Re: Things we learned about LLMs in 2024

#190

Post by jsheard »

com2kid wrote: post #93
sowbug wrote: post #84

Simon has mentioned in multiple articles how cool it is to use 64GB DRAM for GPU tasks on his MacBook. I agree it's cool, but I don't understand why it is remarkable. Is Apple doing something special with DRAM that other hardware manufacturers haven't figured out? Assuming data centers are hoovering up nearly all the world's RAM manufacturing capacity, how is Apple still managing to ship machines with DRAM that perfo…

Apple uses HBM, basically RAM on the same die as the CPU. It has a lot more memory bandwidth than typically PC dram, but still less than many GPUs. (Although the highest end macs have bandwidth that is in the same ballpark as GPUs)

Apple does not use HBM, they use LPDDR. The way they use it is similar in principle to HBM (on-package, very wide bus) but it's not the same thing.
Post reply on HN