Oh what a sweet, sweet tune to end a Sunday to
Did someone not know this like, pretty much from day one?
Not the idiot executives that blew all their budget on AI and made up for it with mass layoffs - the people interested in it. Was that not clear that there was no “reasoning” going on?
Well, two responses I have seen to the claim that LLMs are not reasoning are:
- we are all just stochastic parrots lmao
- maybe intelligence is an emergent ability that will show up eventually (disregard the inability to falsify this and the categorical nonsense that is our definition of “emergent”).
So I think this research is useful as a response to these, although I think “fuck off, promptfondler” is pretty good too.
No, there’s an actual paper where that term originated that goes into great deal explaining what it means and what it applies to. It answers those questions and addresses potential objections people might respond with.
There’s no need for–and, frankly, nothing interesting about–“but, what is truth, really?” vibes-based takes on the term.
Only in the philosophical sense of all of physics being a giant stochastic system.
But that’s equally useful as saying that we’re Turing machines? Yes, if you draw a broad category of “all things that compute in our universe” then you can make a reasonable (but disputable!) argument that both me and a Python interpreter are in the same category of things. That doesn’t mean that a Python interpreter is smart/sentient/will solve climate change/whatever Sammy Boi wants to claim this week.
Or, to use a different analogy, it’s like saying “we’re all just cosmic energy, bro”. Yes we are, pass the joint already and stop trying to raise billions of dollars for your energy woodchipper.
My best guess is it generates several possible replies and then does some sort of token match to determine which one may potentially be the most accurate. Not sure if I’d call that “reasoning” but I guess it could potentially improve results in some cases. With OpenAI not being so open it is hard to tell though. They’ve been overpromising a lot already so it may as well be just complete bullshit.
My best guess is it generates several possible replies and then does some sort of token match to determine which one may potentially be the most accurate.
Didn’t the previous models already do this?
they do say that, yes. it’s as bullshit as all the other claims they’ve been making
A lot of people still don’t, from what I can gather from some of the comments on “AI” topics. Especially the ones that skew the other way with its “AI” hysteria is often an invite from people who know fuck all about how the tech works. “Nudifier” or otherwise generative images or explicit chats with bots that portray real or underage people being the most common topics that attract emotionally loaded but highly uninformed demands and outrage. Frankly, the whole “AI” topic in the media is so massively overblown on both fronts, but I guess it is good for traffic and nuance is dead anyway.
Indeed, although every one of us who have seen a tech hype train once or twice expected nothing less.
PDAs? Quantum computing. Touch screens. Siri. Cortana. Micropayments. Apps. Synergy of desktop and mobile.
From the outset this went from “hey that’s kind of neat” to quite possibly toppling some giants of tech in a flash. Now all we have to do is wait for the boards to give huge payouts to the pinheads that drove this shitwagon in here and we can get back to doing cool things without some imaginary fantasy stapled on to it at the explicit instruction of marketing and channel sales.
Xml also used to be a tech hype for a bit.
And i still remember how media outlets hyped up second life, forgot about it and a few months later discovered it again and more hype started. It was fun.
there’s a lot of people (especially here, but not only here) who have had the insight to see this being the case, but there’s also been a lot of boosters and promptfondlers (ie. people with a vested interest) putting out claims that their precious word vomit machines are actually thinking
so while this may confirm a known doubt, rigorous scientific testing (and disproving) of the claims is nonetheless a good thing
We suspect this research is likely part of why Apple pulled out of the recent OpenAI funding round at the last minute.
Perhaps the AI bros “think” by guessing the next word and hoping it’s convincing. They certainly argue like it.
🔥
This has been said multiple times but I don’t think it’s possible to internalize because of how fucking bleak it is.
The VC/MBA class thinks all communication can be distilled into saying the precise string of words that triggers the stochastically desired response in the consumer. Conveying ideas or information is not the point. This is why ChatGPT seems like the holy grail to them, it effortlessly1 generates mountains of corporate slop that carry no actual meaning. It’s all form and no substance, because those people – their entire existence, the essence of their cursed dark souls – has no substance.
1 batteries not included
I think you’re right. But they’re wrong. And only the chowderheads who don’t interact with customers or service personnel would believe that crap. Now, that’s not to say they can’t raise a generation that does believe that crap.
Hence the bleakness.
When you ask an LLM a reasoning question. You’re not expecting it to think for you, you’re expecting that it has crawled multiple people asking semantically the same question and getting semantically the same answer, from other people, that are now encoded in its vectors.
That’s why you can ask it. because it encodes semantics.
guy who totally gets what these words mean: “an llm simply encodes the semantics into the vectors”
all you gotta do is, you know, ground the symbols, and as long as you’re writing enough Lisp that should be sufficient for GAI
because it encodes semantics.
Please enlighten me on how? I admit I don’t know all the internals of the transformer model, but from what I know it encodes precisely only syntactical information, i.e. what next syntactical token is most likely to follow based on a syntactical context window.
How does it encode semantics? What is the semantics that it encodes? I doubt they have denatotational or operational semantics of natural language, I don’t think something like that even exists, so it has to be some smaller model. Actually, it would be enlightening if you could tell me at least what the semantical domain here is, because I don’t think there’s any naturally obvious choice for that.