> when OpenAI imposed ChatGPT on the world... OpenAI offered ChatGPT to the world. A large, monied cross-section of the world had yet to throw its capital behind the Large Language Model technology that made the ChatBot possible. While it is fair to see AI development now as a global imposition, OpenAI did not have the agency as a 2022 startup to impose on the scale we see now.
> A large, monied cross-section of the world I asked Mistral, and it guestimated that Altman, Thiel, Musk, and Hoffman had like $20.3B together when they founded it. Sound to me that the founding of OpenAI was exactly the point when the monied world threw its dollars behind AI.
What Emily Bender meant by "stochastic parrots"
261–270 of 280 posts
Re: What Emily Bender meant by "stochastic parrots"
#262Re: What Emily Bender meant by "stochastic parrots"
#263I paid a bit of attention to this paper and the phrase 'stochastic parrots' when it came out and i thought this was worth saying and doing at that time. their suggestions about financial and environmental costs are worth studying, their concern about carefully evaluating datasets to feed to the model rather than feeding the entire internet is fully justified. so - to everyone saying this was a bad paper; if you have…
Those costs have to be compared to the way things are currently done without AI. They never are. Ever.
And even when they are: they sure seem to bet against Moore's Law or just the general tendency for things to get better/efficient over time.
It's frankly remarkable how capable the models have become that we can run locally now on a decent laptop.
The same thing happened with image generation. I've had arguments with people that image generators are killing the environment, but I can do it in 20-30 seconds on my GPU. No one bats an eyelash when I play 20-30 minutes or even hours of a video game on my GPU, but the images are burning down the planet.
It's slightly maddening.
Re: What Emily Bender meant by "stochastic parrots"
#264> when OpenAI imposed ChatGPT on the world... OpenAI offered ChatGPT to the world. A large, monied cross-section of the world had yet to throw its capital behind the Large Language Model technology that made the ChatBot possible. While it is fair to see AI development now as a global imposition, OpenAI did not have the agency as a 2022 startup to impose on the scale we see now.
Re: What Emily Bender meant by "stochastic parrots"
#265This all sounds like a lot of backpedaling and “well actually” kind of stuff. “Stochastic parrot got picked up and interpreted by other people as a minimization or an insult. It was not meant that way. Other people might be using it that way but that’s not how I intended it”. Yeah that’s because it was chosen to be an insulting phrase.. Parroting is only ever used as a pejorative phrase. But sure, everyone else mindl…
Yes, and I don't understand how people like this paper authors mostly disregard all these achievements. It is obvious now that our common definition of "understanding" either is flawed, or at least needs redefining and precisioning.
Re: What Emily Bender meant by "stochastic parrots"
#266Earlier quoted context omitted.
I'm afraid the precise connection you're making isn't totally obvious to me. As far as prediction - I mean sure the cortex and LLMs do prediction, but then so can RNNs or diffusion models or any other generative model. Really any ML architecture is learning to compress its environment in pursuit of modelling. More broadly, the predictive brain model would suggest that all of the brain, not just the neocortex, is dedi…
> More broadly, the predictive brain model would suggest that all of the brain, not just the neocortex, is dedicated to prediction. What would you say makes LLMs similar to the neocortex, rather than the basal ganglia or Broca's area? The whole brain is most definitely not dedicated to prediction, and I don't think "prediction" is a very useful model anyways. You could say the hippocampus is for "prediction" if you r…
Agreed that the neocortex uses fewer layers because of looping - I also suspect it's partly because neurons are more complex than the neurons used in ANNs, so in principle they should be capable of more sophisticated computation in a single forward pass (especially considering that handling multiple neurotransmitters could mean superposed functions).
The point about there being one correct way to model something does seem backed up by the Platonic Representation hypothesis (https://arxiv.org/pdf/2405.07987). I've even seem some work that shows you can find a bijective map between the latent spaces of different transformers (https://arxiv.org/pdf/2505.12540).
Your point about Finsler spaces is fascinating - I hadn't come across the term before. It'd be interesting to see if there's work that specifically allows latents to exhibit that kind of directional metric behaviour, and whether that improves generalisation or something.
On the entire brain being dedicated to prediction, that's more of a high-level comment. I was thinking of work like Andy Clark on predictive processing, which suggests that even regions of the brain which we think of as receptive (eg. the visual cortex) may be implementing a generative/predictive model which is corrected by sensory input.
Re: What Emily Bender meant by "stochastic parrots"
#267Earlier quoted context omitted.
This is a useful piece on that: https://andymasley.substack.com/p/the-ai-water-issue-is-fake
How do you know this doesn't suffer from Gell-Mann Amnesia? The first version had so many glaring errors that have been "corrected" (removed), and I don't have the energy to comb through this one. I am highly skeptical of layperson debunking like this.
Most of the changes seem to be because Masley found one counter-example (Newton County, Georgia) where AI datacenters do seem to be increasing water costs; the only deletions AFAICS is toning down language where Masley used to say "there are no examples" to "there is one counter-example". I don't see any other major corrections that have been removed.
Here's an annotated diff of the two texts: https://bafybeie7b3zs2gqifpvn7ee7y7326wcexwnsbhnur5coymu3m6w...
Re: What Emily Bender meant by "stochastic parrots"
#268Earlier quoted context omitted.
It requires some level of semantic understanding, like what a paren is and what it means to balance them. The issue in this discussion is that "predict the next token" is a problematically reductive description of what's going on. It's like saying compilers are programs that emit bytes or that humans are mammals that make sounds. It's not strictly false but it's not capturing the depth of what's happening either. A s…
In fact LLMs are trained to predict the next token in the training set. Of course sometimes a new text input doesn't match the training set, or it matches two or more places in the training set. LLMs use a neural network to interpolate, so that's fine. Please look this up if you have any doubts. Ok. Now. I think you're adding something to the description above. Maybe what you're describing is something "emergent," or…
"interpolate" in what vector space, pray tell? What does "interpolate" even mean, when I prompt it "write me a story about a sentient banana in the style of Hemingway and oh make it a commentary on class consciousness"? You can't assemble such a thing by cutting and pasting pieces of other text. That kind of "interpolation" has to happen at the semantic level - ipso facto, there is a semantic level.
Not to mention that no, they don't predict the next token in the training set. Give any LLM the first paragraph of any Wikipedia article - almost certainly in the training set, and uniquely so - and it won't predict the next word correctly, a lot of the time. But it will predict a word that is grammatically correct, stylistically apropos, and most likely factually correct. So what's it really doing, hm?
LLMs aren't even large enough to contain their training data - not even remotely close. It can't "stitch together things it saw" because it doesn't remember them. It only remembers the ideas used to construct them. The learned abstraction is the entire point of the exercise. LLMs would be useless if they were overfit the way you say they are.
Re: What Emily Bender meant by "stochastic parrots"
#269Earlier quoted context omitted.
>A bird doesn't learn gravity or aerodynamics, it has no 'sense of physics'. That's not what I said. What I said was that it's physics that provides the ground truth. >you could, at least in theory, falsify the entire experience of the bird It wouldn't be a bird anymore, but a dysfunctional cyborg with false perceptions. >There is nothing in a bird's brain that directly percieves reality. Yes, of course there is. Ani…
>It wouldn't be a bird anymore, but a dysfunctional cyborg with false perceptions. The criticism in the paper is of the architecture of LLMs, isn't it? The paper contends """Text generated by an LM is not grounded in communicative intent, any model of the world , or any model of the reader’s state of mind. [...] an LM is a system for haphazardly stitching together sequences of linguistic forms it has observed in its…
It doesn't matter if you are one. If you were an AI researcher and encountered a model that saw things for what they really are you would deem it to be malfunctioning and discard it. Any functional model will always be at least one degree further removed from a raw sensory experience that agrees with yours, than you. Its perception will be invariably filtered through the lens of labeled human output.
>I happen to be in possession of some of these and I think this needs a "usually, under ordinary conditions".
I won't dwell much on what you mean, since you said it's unimportant, but it takes a lot for sensory organs to malfunction, and even when they do, they produce corrupted, not false, information; black blotches, not pink elephants. I assume you were thinking of alcohol or something; drugs that affect perception affect the brain, not the other organs.
An interesting edge case is stuff like entoptic phenomena, but those aren't false perceptions; they're, if you will, hypertrue perceptions (something so true, we would rather not see it).
>but "red" and "blue" are the bread and butter of my visual consciousness. These correspond to firings of my sensory neurons much more than they correspond to anything in reality. If that's not interpretation, what is it/what is interpretation?
"Red" and "blue" are your brain's interpretation of the signals it receives from the eye. Notice how "red" and "blue" are fully abstract words, decoupled from anything physical, whereas if I were to refer to the actual encoding of visual information that passes through the optic nerve, I'd have no choice but to reference physical processes (probably talk about voltage and action potentials; I honestly don't know how the optic nerve works). That's because a retinal cell is a simple transducer. The eye doesn't interpret, it merely converts and encodes. Interpretation is a higher level operation.
You cannot compare the simplicity of the mechanism of a whole eye to the indescribable complexity that is between the sentence "roses are red and violets are blue" written on a book, and the raw perception of red roses and blue violets. But a model will only ever be exposed to that distant interpretation, not to roseness or redness.
>I expect we agree that I can show a multimodal model a real and a CGI picture and it can tell me which is which.
Of course, but obviously that's not something it knows inherently. There's nothing about the image intrinsically that says it's fake; someone has to label it such that the model can associate it with unreality (according to our own parameters). That's not how an animal works. An animal assumes what its senses perceive is real and can distinguish its own thoughts from its sensory input.
>On the flipside, I could show some meh drawings of fairies to Arthur Conan Doyle, and he'd say "Whoa, this changes everything". I consider him to be one of the great rational minds of history, but he was unable to pass your test here.
That's a slight equivocation. He would not have mistaken the drawings of fairies for raw perceptions of fairies, he would have simply been swayed by the rhetorical strength of the testimony implicit in the drawing (and perhaps an explicit one that accompanied it). If you want to make a true parallel to a multimodal model you'd have to compare a CGI fairy and a photograph of someone holding a drawing of a fairy. The CGI is as raw to the model as the signals passing through your optic nerves right now, but the drawing is one level of abstraction further away.
Re: What Emily Bender meant by "stochastic parrots"
#270Earlier quoted context omitted.
In fact LLMs are trained to predict the next token in the training set. Of course sometimes a new text input doesn't match the training set, or it matches two or more places in the training set. LLMs use a neural network to interpolate, so that's fine. Please look this up if you have any doubts. Ok. Now. I think you're adding something to the description above. Maybe what you're describing is something "emergent," or…
>LLMs use a neural network to interpolate "interpolate" in what vector space, pray tell? What does "interpolate" even mean, when I prompt it "write me a story about a sentient banana in the style of Hemingway and oh make it a commentary on class consciousness"? You can't assemble such a thing by cutting and pasting pieces of other text. That kind of "interpolation" has to happen at the semantic level - ipso facto, th…