Man, people in the "it's just maths and probably" camp are in for a world of hurt when they learn that everything is just maths and probability.
The observation that LLMs are just doing math gets you nowhere, everything is just doing math.
perching_aix · 54m ago
I largely agree, and upon reading this article is sadly also in that camp of applying this perspective to be dismissive.
However, I find it incredibly valuable generally to know things aren't magic, and that there's a method to the madness.
For example, I had a bit of a spat with a colleague who was 100% certain that AI models are not only unreliable because from a human perspective, insignificant changes to their inputs can cause significant changes to their outputs, but because, in their idea, they were actually random, in the nondeterministic sense. That I was speaking in hypotheticals when I took an issue with this, as he recalled my beliefs about superdeterminism, and inferred that "yeah if you know where every atom is in your processor and the state they're in, then sure, maybe they're deterministic, but that's not a useful definition of deterministic".
Me "knowing" that they're not only not any more special than any other program, but that it's just a bunch of matrix math, provided me with the confidence and resiliency necessary to reason my colleague out of his position, including busting out a local model to demonstrate the reproducibility of model interactions first hand, that he was then able to replicate on his end on a completely different hardware. Even learned a bit about the "magic" involved myself along the way (that different versions of ollama may give different results, although not necessarily).
captn3m0 · 24m ago
I also had to argue with a lawyer on the same point - he held a firm belief that “Modern GenAI systems” are different from older ML systems in that they are non-deterministic and random. And that this inherent randomness is what makes them both unexplainable (you can’t guarantee what it would type) and useful (they can be creative).
pxc · 36m ago
> [The] article is sadly also in that camp of applying this perspective to be dismissive.
TFA literally and unironically includes such phrases as "AI is awesome".
It characterizes AI as "useful", "impressive" and capable of "genuine technological marvels".
In what sense is the article dismissive? What, exactly, is it dismissive of?
lucaslazarus · 1h ago
On a tangentially-related note: does anyone have a good intuition for why ChatGPT-generated images (like the one in this piece) are getting increasingly yellow? I often see explanations attributing this to a feedback loop in training data but I don't see why that would persist for so long and not be corrected at generation time.
minimaxir · 56m ago
They aren't getting increasingly yellow (I don't think the base model has been updated since the release of GPT-4o Image Generation), but the fact that they are always so yellow is bizarre and I am still shocked OpenAI shipped it knowing that effect exists, especially since it has the practical effect of instantly being able to clock it as an AI image generation.
Generally when training image encoders/decoders, the input images are normalized so some base commonality is possible (when playing around with Flux Kontext image-to-image I've noticed subtle adjustments in image temperature), but the fact that it's piss yellow is baffling. The autoregressive nature of the generation would not explain it either.
4b11b4 · 53m ago
You're just mapping from distribution to distribution
- one of my professors
hackinthebochs · 52m ago
LLMs are modelling the world, not just "predicting the next token". They are not akin to "stochastic parrots". Some examples here[1][2][3]. Anyone claiming otherwise at this point is not arguing in good faith. There are so many interesting things to say about LLMs, yet somehow the conversation about them is stuck in 2021.
LLMs are still trained to predict the next token: gradient descent just inevitably converges on building a world model as the best way to do it.
Masked language modeling and its need to understand inputs both forwards and backwards is a more intuitive way for having a model learn a representation of the world, but causal language modeling goes brrrrrrrr.
blahburn · 40m ago
Yeah, but it’s kinda magic
israrkhan · 1h ago
A computer (or a phone) is not magic, its just billions of transistors.
or perhaps we can further simplify and call it just sand?
The observation that LLMs are just doing math gets you nowhere, everything is just doing math.
However, I find it incredibly valuable generally to know things aren't magic, and that there's a method to the madness.
For example, I had a bit of a spat with a colleague who was 100% certain that AI models are not only unreliable because from a human perspective, insignificant changes to their inputs can cause significant changes to their outputs, but because, in their idea, they were actually random, in the nondeterministic sense. That I was speaking in hypotheticals when I took an issue with this, as he recalled my beliefs about superdeterminism, and inferred that "yeah if you know where every atom is in your processor and the state they're in, then sure, maybe they're deterministic, but that's not a useful definition of deterministic".
Me "knowing" that they're not only not any more special than any other program, but that it's just a bunch of matrix math, provided me with the confidence and resiliency necessary to reason my colleague out of his position, including busting out a local model to demonstrate the reproducibility of model interactions first hand, that he was then able to replicate on his end on a completely different hardware. Even learned a bit about the "magic" involved myself along the way (that different versions of ollama may give different results, although not necessarily).
TFA literally and unironically includes such phrases as "AI is awesome".
It characterizes AI as "useful", "impressive" and capable of "genuine technological marvels".
In what sense is the article dismissive? What, exactly, is it dismissive of?
Generally when training image encoders/decoders, the input images are normalized so some base commonality is possible (when playing around with Flux Kontext image-to-image I've noticed subtle adjustments in image temperature), but the fact that it's piss yellow is baffling. The autoregressive nature of the generation would not explain it either.
- one of my professors
[1] https://arxiv.org/abs/2405.15943
[2] https://x.com/OwainEvans_UK/status/1894436637054214509
[3] https://www.anthropic.com/research/tracing-thoughts-language...
Masked language modeling and its need to understand inputs both forwards and backwards is a more intuitive way for having a model learn a representation of the world, but causal language modeling goes brrrrrrrr.
or perhaps we can further simplify and call it just sand?
or maybe atoms?