[pavlov]

> “The enthusiasm around it reminds me of JavaScript framework wars of 10 years ago”

Hmm. If LLMs turned out like JS frameworks, that would mean that in ten years people will be saying:

“Maybe we don’t really need all this expensive ceremony, honestly this could be done with vanilla if/else heuristics…?”

[spacebanana7]

I can imagine a bloated world where 500B param models are used for tasks where 7B param modes perform adequately.

At that time, there could be complaints on hacker news about messaging apps with autocomplete models that take up gigabytes.

[pavlov]

I could see a world where people turn to an LLM for a task where the hand-rolled solution could be a simple state machine or some nested switch statements.

The irony would be that the LLM could write you that code, but if you don’t know to ask…

[falcor84]

The real irony would be if the AI uses an LLM to write the code the second time it sees the same request repeated, deploys it to effortlessly deal with all future requests, and goes back to playing Quake while collecting the full paycheck.

[phillipcarter]

A key difference is that the impact of these enormous models is obscured away. I don't feel the impact of having to run GPT-3.5-turbo at scale because it's just an API call away, and it's even more reliable now.

The main critiques outside of data privacy I've read are related to energy consumption, but even then, it's...not compelling? I read an article[0] that estimated the training of ChatGPT (3.5) to emit as much C02 as more than 3 round-trip flights between SF and NYC. That's not good! But also, really highlights that if we're to reduce emissions, there's clearly bigger targets than the largest ML models in the world.

[0]: https://themarkup.org/news/2023/07/06/ai-is-hurting-the-clim...

[dmd]

"How can I sum this column of numbers?"

"IDK, throw it at the LLM"

[azeirah]

I do stuff like this sometimes when I have some csv or something and need it in JSON.

Could easily be done with 1 line of bash or js or python or whatever but... it's easier to just let the LLM do it for me :|

[satvikpendem]

Or you could ask the AI for a script to convert it (whose resultant JSON would be free of hallucinations) and ask the AI to run the code, even, since I believe ChatGPT now has that ability, because I've definitely noticed that ChatGPT will sometimes mess up the data when converting from one format to another.

[why_only_15]

That would be awesome, but we've tried for decades and haven't gotten there with basic if/else. I do think it's pretty plausible that if you combine some very slimmed down models with strong heuristics you could get far though. At the moment I think expense doesn't really matter -- an hour of a knowledge worker's time is worth 416,000 tokens of GPT-4, the most expensive model out there. For llama-2 it's even less time. Unless you're processing truly epic numbers of tokens, by far the most important is whether we can use these things for real.

[pavlov]

> "That would be awesome, but we've tried for decades and haven't gotten there with basic if/else."

Oh, I know — I was trying to throw some shade on the state of JS frameworks rather than LLMs. With the pendulum now swinging back to vanilla DOM manipulation, it feels like the enormous effort spent on devising ways to wrap web UIs in endless variations of abstractions might have been somewhat of a waste.

[antupis]

Well these models at least tell in name what they do eg llama-2-70b-Guanaco-QLoRA-fp16.

[intelVISA]

Subtle :)