Posted by danielfalbo 3 days ago
They are cool new tools use them where you can but there is a ton of research still left to do. Just lols at the hubris silicon valley will make something so smart it extincts humankind. It'll happen from the lack of water and heated planet first :)
The stocastic parrot argument is still debated but more nuanced than before. Although the original author still stands by the statement. Evidence of internal planning per model. Anthropic Attribution Graphs Research with some rhyming did support it but gemma didn't.
The idea of "understanding" is still up for debate as well. Sure, when models are directly trained on data there is representation. Othello-GPT Studies was one way to support but that was during training so some interal representation was created. Out of distribution task will collapse to confabulation. Apple's GSM-Symbolic Research seems to support that.
Chain of thought is a helpful tool but is untrustworthy at best. Anthropic themselves have showed this https://www.anthropic.com/research/reasoning-models-dont-say...
personally, as someone building on top of gen AI for a living, i finally bit the bullet on building using LLMs. it did reduce friction in things i don't like doing and did not explore as much. by acting as a catalyst when i needed to finally address them, it helped me get going and eventually become proficient in the core tech itself.
outside of work, however, i find people around me use the services much more than i do. sometimes it felt like the "big data is like teenage sex"[1], but some aspects were quite genuine. got better appreciation after trying them to better understand other people's perspective and to design better.
with "slop" as word of the year and people wondering if a random clip is AI, now more than ever the effects in general life seems apparent. it is not as sexy as "i will lose my job soon", but the effects are here and now. while the next year will be even more interesting, i can't wait for the bubble to burst.
It is easy to see that LLMs exclusively parrot by asking them about current political topics [1], because they cannot plagiarize settled history from Wikipedia and Britannica.
But of course there also is the equivalence between LLMs and Markov chains. As far as I can see, it does not rely on absurd equivalences like encoding all possible output states in an infinite Markov chain:
https://arxiv.org/abs/2410.02724
Then there is stochastic parrot research:
https://arxiv.org/abs/2502.08946
"The stochastic parrot phenomenon is present in LLMs, as they fail on our grid task but can describe and recognize the same concepts well in natural language."
As said above, this is obvious to anyone who has interacted with LLMs. Most researchers know what is expected of them if they want to get funding and will not research the obvious too deeply.
[1] They have Internet access of course.
If Antirez has never gotten an LLM to perform an absolutely embarrassing mistake, he must be very lucky or we should stop listening to him.
Programmers' resistance has not weakened. Since the ORCL drop of 40% anti-LLM opinions are censored and downvoted here. Many people have given up, and we always get articles from the same LLM influencers.
So nice to see people who think about this seriously converge on this. Yes. Creating something smarter than you was always going to be a sketchy prospect.
All of the folks insisting it just couldn't happen or ... well, there have just been so many objections. The goalposts have walked from one side of the field to the other, and then left the stadium, went on a trip to Europe, got lost in a beautiful little village in Norway, and decided to move there.
All this time though, the prospect of instantiating a something smarter than you (and yes, it will be smarter than you even if it's at human level because of electronic speeds...) This whole idea is just cursed and we should not do the thing.
Sure, but not so sure that this has any relevance to the topic at hand. You seem to be taking the assumption that LLMs can ever reach that level for granted.
It may be possible that all it takes is scaling up and at some point some threshold gets reached past which intelligence emerges. Maybe.
Personally, I'm more on board with the idea that since LLMs display approximately 0 intelligence right now, no amount of scaling will help and we need a fundamentally different approach if we want to create AGI.
I'm not sure antirez is involved in any business decision making process at Redis Ltd.
He may not be part of "they".
Please don't post insinuations about astroturfing, shilling, brigading, foreign agents, and the like. It degrades discussion and is usually mistaken. If you're worried about abuse, email hn@ycombinator.com and we'll look at the data.
Another one, though:
Please don't comment about the voting on comments. It never does any good, and it makes boring reading.
I'll design a system for the senate that enables outside voters to first turn down the microphone's volume of a speaker if he says that another senator works for company X and then removes him from the floor. That'll be a great success for democracy and "intellectual curiosity", which is also in the guidelines.