Terrence Tao shared his thoughs on Mastodon: https://mathstodon.xyz/@tao/113132502735585408
this post was submitted on 12 Sep 2024
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I think I've used it if this is the latest available, and it's terrible. It keeps feeding me wrong information, and when you correct it, it says you're right... But if you ask it again, it again feeds you the wrong information.
if you ask it again, it again feeds you the wrong information
Well, it's a LLM, they can't learn anything without rebuilding the whole model from scratch, which I wouldn't exactly call learning anyway... all they “know” is what word is most likely to follow a certain sequence of words according to their model.
Any other facts or information are completely inconsequential for their operation and results.
I just love how people seem to want to avoid using the word lie.
It’s either misinformation, or alternative facts, or hallucinations.
Granted, a lie does tend to have intent behind it, so with ChatGPT, it’s probably better to say falsehood, instead. But either way, it’s not fact, it’s not truth, and people, especially schools, should stop using it as a credible source.
There was a recent paper that argues 'bullshitting' is the most apt analogy. I.e. telling something to satisfy the other person without caring about the truth content of what you say
What about thw term "incorrect facts"?
Being wrong is not the same as lying. When LLMs start giving wrong answers on purpose to mislead people we would have a big problem.
The thought of a maliciously deceptive AGI is terrifying to me. Many, many people will trust it until it's too late.
That's not what reasoning is. Training is understanding what they're talking about and being able to draw logical conclusions based on what they've learned. It's being able to say, I didn't know but wait a second and I'll look it up," and then summing that info up in original language.
All Open AI did was make it less stupid and slap a new coat of paint on it, hoping nobody asks too many questions.
And this is something data scientists have already been doing with existing LLMs.
Dang, OpenAI just pulled an Apple. Do something other people have already done with the same results (but importantly before they made a big fuss about it), claim it's their innovation, give it a bloated name so people imagine it's more than it is and produce a graph comparing themselves to themselves, hoping nobody will look at the competition.
Just like Apple, they have their own selling point, but instead they seem to prefer making up stuff while forgetting why people use em.
On a side note they also pulled an Elon. Where's my AI companion that can comment on video in realtime and sing to me??? Ya had it "working" "live" a couple months ago, WHERE IS IT?!?
Meanwhile a bald turtle and his AI anime daughter on twitch can do exactly this, and he's building her at home on nvidia GPUs.
(Vedal987 and Neuro-sama, if you're curious)
Pulled an Apple?
I know you hate apple because android is way better but people loved their ipods, iphones, airpods and apple watches. Sure those things were made before but Apple did make them better. So I don’t know what your point is.
Assuming I'm an android fan for pointing out that Apple does shady PR. I literally mention that Apple devices have their selling point. And it isn't UNMATCHED PERFORMANCE or CUTTING EDGE TECHNOLOGY as their adds seems to suggest. It's a polished experience and beautiful presentation; that is unmatched. Unlike the hot mess that is android. Android also has its selling points, but this reply is already getting long. Just wanted to point out your pettiness and unwillingness to read more than a sentence.
So they slapped some reinforcement learning on top of their LLM and are claiming that gives it “reasoning capabilities”? Or am I missing something?
It's like 3 lms on top of eachother in a trenchcoat, and appau a calculator so it gets math right
No the article is badly worded. Earlier models already have reasoning skills with some rudimentary CoT, but they leaned more heavily into it for this model.
My guess is they didn't train it on the 10 trillion words corpus (which is expensive and has diminishing returns) but rather a heavily curated RLHF dataset.
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