[weweweoo]

Generative AI appears fantastic aid for many smaller tasks where there's enough training data, and correctness of the answer is subjective (like art), or easily verifiable by a human in the loop (small snippets of code, checking that summary of an article matches the contents of the original). Generally it helps with the tedious parts, but not with the hard parts of my job.

I don't have much belief in fully autonomous generative AI agents performing more complex tasks any time soon. It's a significant productivity boost for some jobs, but not a total replacement for humans who do more than read from a script, or write clickbait articles for media.

[harrisoned]

I agree with that. At work, we are about to implement a decent LLM and ditch Dialogflow for our chatbot. But not to talk directly to the client (it's asking for a disaster), just to recognize intentions, pretty much like Dialogflow but better.

Right now there are many small but decent models available for free, and cheap to use. If it wasn't for the hype, it would never have reached that level of optimization. Now we can make decent home assistants, text parsers and a bunch of other stuff you already mentioned.

But someone paid for that. The companies who believed this would be revolutionary will eventually have a really hard reality check. Not that they won't try and use it for critical stuff, but once they do and it fails spectacularly they will realize a lot of money went down the drain.

[elforce002]

And we'll thank them for their service.

[alecco]

> where there's enough training data

The newer models are 10x faster and cheaper, therefore synthetic data is 10x cheaper to make now.

If the ARC challenge makes an impact, there's a good chance the next generation AI will need a lot less data.