alt Hacker NewsYou'd be laughed at if you said that ChatGPT could help you with graduate level mathematics in 2024, but this year, AI models on simple prompts are solving previously unsolved Erdos problems.
It seems silly to imagine that there is some fundamental barrier between human intelligence and AI, and that AI could never do many of the things that humans can do. Inferring intent, gauging sentiments, factoring in cultural values, etc. all the things cited as stuff humans can do but AI can't, AI can currently do if given enough context. But more importantly, all those things aren't magical tasks that can only occur inside a human skull, they are a product of information processing, its just the information processing that has been hard to make computers good at, but so far it appears AI keeps getting better.
I'm all for humans having special value that is not attached to their ability to perform useful work. However denying the abilities of AI models seems to be a common mistake many people are making, and sadly reality catches up to these people before they can emotionally prepare.
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Yes. It's as if they think AI will forever be LLM only and won't develop world models that incorporate current state assessment, dynamic next-state prediction, cause-and-effect reasoning, object permanence, etc. I'm not in the AI industry but I assume there's got to be lots of research and work being done on this.
Fable has really spooked me, honestly. It's another big jump, but not in the actual coding. I was pretty comfortable with the "you do the implementation, I do the meta work and steering", and ... no steering required, no meta work required. Here's the backlog, let me know when it's complete, I guess I'm going to go touch grass until I have to review and refine... probably tomorrow?
Reminds me of the first time I saw a coding agent stumble through an issue in 2023 maybe? and went "this is a big deal", similarly when OG gpt started making jokes that actually kinda worked.
Updated modern version of the classic "make me a greentext", apologies for slop-posting, but it seems relevant:
> be me
> senior software engineer
> in charge of making sure the tickets get, in fact, implemented
> occasionally have to open the IDE and write some code myself
> one day i open the IDE and the ticket is already closed
> the agent did it overnight
> no steering, no review notes, nothing left for me to do
> distress.jpg
> ask my manager what to do
> he says "just focus on the high-level architecture stuff"
> i say "what high-level architecture stuff"
> he says "i don't know, you're the senior engineer"
> rage.jpg
> quit my job
> become a prompt engineer, nice and simple, just tell it what to build
> first day on the job, sit down to write the prompt
> AI already wrote itAs mentioned in the article, the point of language is to communicate with other humans, and you need a human to do that.
Mathematics is famously rigorously defined, it's roughly analog to AI beating humans at chess. Sure it's impressive, but it's also something you'd expect machines to be good at.
> AI can currently do if given enough context
It's worth noting that you can substitute "dollars" for "context" in that sentence, which seems to be where many of these impressive achievements are coming from. As ever, it's unclear whether these models will get cheaper while remaining better, since all of the recent breakthroughs appear to be of the "think more" kind. For translation specifically, I'd be very surprised if the "think more" LLMs would help given the per-unit cost expected of the output.