281–290 of 318 posts
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Where do they get the bespoke training data from? And how much? I don’t really know anything about this.
meta has reallocated a significant protion of their staff to genrating this
I'ts not like they really inspire much confidence in their future predictions.
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No, well known in the current multiverse branch where we still occasionally use things like math and scientific analysis instead of people’s vibe checks and pelican SVGs. Here’s the paper from OpenAI where Dario himself was a co-author: https://arxiv.org/pdf/2001.08361 > We have observed consistent scalings of language model log-likelihood loss with non-embedding parameter count N, dataset size D, and optimized train…
instead of people’s vibe checks and pelican SVGs. Right, what happened is everyone went to Fable and asked it to make the very best bicycle pelican SVG, no mistakes. And Fable's bicycle pelican SVGs were such timeless masterpieces, we all instantly got AI psychosis. Happily, you were immune to this.
> it is clear that actual intelligence has plateaued significantly. > Moving forward, the industry cannot continue to train bigger and bigger models since their intelligence not only plateaus but often will get worse These are wild claims - why are we concluding that bigger models and more data = more hallucination? That’s actually the opposite of what’s been happening over the last couple years. Some models may stil…
Yeah not only is it totally unsubstantiated, the benchmarks are getting less useful to really show the difference between these models. Big model smell is still a thing and GLM 5.2 while impressive is not Fable class. Here is something I would like people to chew on. Perhaps the smartest researchers in the world across multiple labs know more about this than we do? Perhaps they are aware of issues like the data wall…
Perhaps the smartest researchers in the world across multiple labs follow the money, and don't make waves that go against them getting their paychecks?
That's part of what makes them smartest.
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Are the smartest researchers in the world out there saying there isn't a wall? I don't know of any people doing the actual R&D who frequently make outrageous claims.
The entirety of Anthropic believe ai is going to eat everything, not just software, and result in major societal disruption within a year. They do not have a sliver of a doubt on this. Article has no idea, is completely wrong.
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In cognitive science, it appears your brain has two modes of thinking: - A very parallel type of computation that is fast and generally accurate and integrates hundreds of variables. It’s sometimes labeled as intuition or system 1 thinking. - A much slower, step by step, analytical type, commonly linked with your pre-frontal cortex (one of the newest parts of the brain). Sometimes called system 2 thinking. Maybe the…
An LLM is not thinking, assuming and relating it to thought and universal truths is nonsense.
> it is clear that actual intelligence has plateaued significantly. > Moving forward, the industry cannot continue to train bigger and bigger models since their intelligence not only plateaus but often will get worse These are wild claims - why are we concluding that bigger models and more data = more hallucination? That’s actually the opposite of what’s been happening over the last couple years. Some models may stil…
My impression is that the fundamental issue is that LLMs attempt to extract reasoning (executive execution) from data (relationship between tokens). There's an open question about whether this is theoretically possible, but it doesn't seem like it to me. Human generated data is an effect of reasoning. Attempting to extract executive function from it is kind of like taking an anti-derivative of a function. This has al…
Still inputs, that in the end changed something about synapses and their activation. And whether doesn't have a strong enough local effect to be material to the those operations, can be ignored too. E.g. gravity might kill you via a fall or a tide drowning you, but might have zero influence in your thinking at the brain operation level, aside from some influence that can be expressed in weights and such.
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I’m not disagreeing with you but at the same time, models don’t “know” anything in that binary sense. I’m not trying to get in the woods here, I genuinely mean that what you pass off as a simple explanation is actually incredibly nuanced. A fact appeared once in training data , a fact never appeared in the training data, a fact appeared ten times, a fact appeared a thousand times. Which does the model know? Facts are…
I often wonder how humans "know" things. I suspect (ignorant armchair) we have some ability to signal strength of those facts, via repetition. Without this layer of introspection i imagine LLMs can never avoid hallucination. It obviously breaks down with humans too, given we so easily hallucinate and confuse things we "know". However i still suspect we're more reliable at probing information we've experienced vs not.…
Isn't that precisely what the LLM training does? It signals strength of those facts, via repetition.
Hallucination rate scores are a little tricky to interpret because they're conditional on the model not knowing the answer. That means they don't measure the probability of your encountering a hallucination in everyday use, since that also depends on the probability of the model not knowing the answer, as well as how well your distribution of tasks aligns with the distribution tested in the eval. I'd also hesitate to…
Those numbers are abysmal. Should we really be using LLMs to write our code? I have a theory- LLMs can spit out code that gets the job done and looks ok, maybe even great, but contains small “anomalies” that compound over time. An enterprise app developed entirely with LLM-happy devs might end up virtually unmaintainable. I’m not sure how to explain it, but the more I see LLM-written code the more I feel it’s bad cod…
For most enterprise apps, being "unmaintainable" would be an improvement.
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I can't help but feel that people continually underestimate how bad human written code becomes over time. The exception is probably single-person passion projects or open source projects that maintain quality governance over time. I strongly suspect most closed source code developed under commercial or internal pressure is pretty awful after a few years of development. All LLM code has to do is suck less than existin…
Sucky human-written code is still based on human understanding, which can change over time, be readjusted or solidified. People implement something wrong once, then update their perspective, then in the future does it right. LLMs doesn't have this benefit. You forget to add the correct to the system prompt, and the LLM will repeat the same mistake over and over, and worse than that, their mistakes aren't based on the…
Not random across their whole training set. Random across related concepts bundled together in the training set. Which is not that dissimilar to human mistakes.
A human's mistakes are also based on going from one option in their training and not another, where the two are close together but one is not appropriate and doesn't fully cover the expected result.
That's obvious in a typo (you get close to the target word but miss it just so), but also in off by one errors (you're still in the proximity of the correct loop you should have written), all the way to picking the wrong architecture or patter n (you still chose among patterns for the worse fit you've picked, you don't suddenly start using cooking recipes).
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As a side gig, I write novel software that solves problems no existing software does, that existing LLMs have difficulty reproducing, purely for the purpose of existing as LLM training data. There are journalists being hired to write Atlantic-worthy articles that exist only as LLM training data, because they're getting paid more than the Atlantic would pay them for it. It's insane. Yes, they are hiring the experts th…
> There are journalists being hired to write Atlantic-worthy articles that exist only as LLM training data, because they're getting paid more than the Atlantic would pay them for it. I kinda doubt that quality-assertion of "Atlantic-worthy." While I have no doubt such articles are written solely as training data, I'd expect their quality to be much less than the real thing, since there's no public to critique them, p…
> professional ethics to uphold