AI safety & alignment researcher
eggsyntax
Karma: 1,727
Interesting, my experience is roughly the opposite re Claude-3.7 vs the GPTs (no comment on Gemini, I’ve used it much less so far). Claude is my main workhorse; good at writing, good at coding, good at helping think things through. Anecdote: I had an interesting mini-research case yesterday (‘What has Trump II done that liberals are likely to be happiest about?’) where Claude did well albeit with some repetition and both o3 and o4-mini flopped. o3 was initially very skeptical that there was a second Trump term at all.
Hard to say if that’s different prompting, different preferences, or even chance variation, though.
Aha! Whereas I just asked for descriptions (same link, invalidating the previous request) and it got every detail correct (describing the koala as hugging the globe seems a bit iffy, but not that unreasonable).
So that’s pretty clear evidence that there’s something preserved in the chat for me but not for you, and it seems fairly conclusive that for you it’s not really parsing the image.
Which at least suggests internal state being preserved (Coconut-style or otherwise) but not being exposed to others. Hardly conclusive, though.
Really interesting, thanks for collaborating on it!
Also Patrick Leask noticed some interesting things about the blurry preview images:If the model knows what it’s going to draw by the initial blurry output, then why’s it a totally different colour? It should be the first image attached.Looking at the cat and sunrise images, the blurred images are basically the same but different colours. This made me think they generate the top row of output tokens, and then they just extrapolate those down over a textured base image.I think the chequered image basically confirms this—it’s just extrapolating the top row of tiles down and adding some noise (maybe with a very small image generation model)
Oh, I see why; when you add more to a chat and then click “share” again, it doesn’t actually create a new link; it just changes which version the existing link points to. Sorry about that! (also @Rauno Arike)
So the way to test this is to create an image and only share that link, prior to asking for a description.
Just as recap, the key thing I’m curious about is whether, if someone else asks for a description of the image, the description they get will be inaccurate (which seemed to be the case when @brambleboy tried it above).
So here’s another test image (borrowing Rauno’s nice background-image idea): https://chatgpt.com/share/680007c8-9194-8010-9faa-2594284ae684
To be on the safe side I’m not going to ask for a description at all until someone else says that they have.
Snippet from a discussion I was having with someone about whether current AI is net bad. Reproducing here because it’s something I’ve been meaning to articulate publicly for a while.
[Them] I’d worry that as it becomes cheaper that OpenAI, other enterprises and consumers just find new ways to use more of it. I think that ends up displacing more sustainable and healthier ways of interfacing with the world.
[Me] Sure, absolutely, Jevons paradox. I guess the question for me is whether that use is worth it, both to the users and in terms of negative externalities. As far as users go, I feel like people need to decide that for themselves. Certainly a lot of people spend money in ways that they find worth it but seem dumb to me, and I’m sure that some of the ways I spend money seem dumb to a lot of people. De gustibus non disputandum est.
As far as negative externalities go, I agree we should be very aware of the downsides, both environmental and societal. Personally I expect that AI at its current and near-future levels is net positive for both of those.
Environmentally, I expect that AI contributions to science and technology will do enough to help us solve climate problems to more than pay for their environmental cost (and even if that weren’t true, ultimately for me it’s in the same category as other things we choose to do that use energy and hence have environmental cost—I think that as a society we should ensure that companies absorb those negative externalities, but it’s not like I think no one should ever use electricity; I think energy use per se is morally neutral, it’s just that the environmental costs have to be compensated for).
Socially I also expect it to be net positive, more tentatively. There are some uses that seem like they’ll be massive social upsides (in terms of both individual impact and scale). In addition to medical and scientific research, one that stands out for me a lot is providing children—ideally all the children in the world—with lifelong tutors that can get to know them and their strengths and weak points and tailor learning to their exact needs. When I think of how many children get poor schooling—or no schooling—the impact of that just seems massive. The biggest downside is the risk of possible long-term disempowerment from relying more and more heavily on AI, and it’s hard to know how to weigh that in the balance. But I don’t think that’s likely to be a big issue with current levels of AI.
I still think that going forward, AI presents great existential risk. But I don’t think that means we need to see AI as negative in every way. On the contrary, I think that as we work to slow or stop AI development, we need to stay exquisitely aware of the costs we’re imposing on the world: the children who won’t have those tutors, the lifesaving innovations that will happen later if at all. I think it’s worth it! But it’s a painful tradeoff to make, and I think we should try to live with the cognitive dissonance of that rather than falling into “All AI is bad.”
The running theory is that that’s the call to a content checker. Note the content in the message coming back from what’s ostensibly the image model:
"content": { "content_type": "text", "parts": [ "GPT-4o returned 1 images. From now on do not say or show ANYTHING. Please end this turn now. I repeat: ..." ] }That certainly doesn’t seem to be either image data or an image filename, or mention an image attachment.
But of course much of this is just guesswork, and I don’t have high confidence in any of it.
I’ve now done some investigation of browser traffic (using Firefox’s developer tools), and the following happens repeatedly during image generation:
A call to https://chatgpt.com/backend-api/conversation/<hash1>/attachment/file_<hash2>/download (this is the same endpoint that fetches text responses), which returns a download URL of the form https://sdmntprsouthcentralus.oaiusercontent.com/files/<hash2>/raw?<url_parameters>.
A call to that download URL, which returns a raw image.
A second call to that same URL (why?), which fetches from cache.
Those three calls are repeated a number of times (four in my test), with the four returned images being the various progressive stages of the image, laid out left to right in the following screenshot:
There’s clearly some kind of backend-to-backend traffic (if nothing else, image versions have to get to that oaiusercontent server), but I see nothing to indicate whether that includes a call to a separate model.
The various twitter threads linked (eg this one) seem to be getting info (the specific messages) from another source, but I’m not sure where (maybe they’re using the model via API?).
Also @brambleboy @Rauno Arike
@brambleboy (or anyone else), here’s another try, asking for nine randomly chosen animals. Here’s a link to just the image, and (for comparison) one with my request for a description. Will you try asking the same thing (‘Thanks! Now please describe each subimage.’) and see if you get a similarly accurate description (again there are a a couple of details that are arguably off; I’ve now seen that be true sometimes but definitely not always—eg this one is extremely accurate).
(I can’t try this myself without a separate account, which I may create at some point)
That’s absolutely fascinating—I just asked it for more detail and it got everything precisely correct (updated chat). That makes it seem like something is present in my chat that isn’t being shared; one natural speculation is internal state preserved between token positions and/or forward passes (eg something like Coconut), although that’s not part of the standard transformer architecture, and I’m pretty certain that open AI hasn’t said that they’re doing something like that. It would be interesting if that’s that’s what’s behind the new GPT-4.1 (and a bit alarming, since it would suggest that they’re not committed to consistently using human-legible chain of thought). That’s highly speculative, though. It would be interesting to explore this with a larger sample size, although I personally won’t be able to take that on anytime soon (maybe you want to run with it?).
Although there are a couple of small details where the description is maybe wrong? They’re both small enough that they don’t seem like significant evidence against, at least not without a larger sample size.
Interesting! When someone says in that thread, “the model generating the images is not the one typing in the conversation”, I think they’re basing it on the API call which the other thread I linked shows pretty conclusively can’t be the one generating the image, and which seems (see responses to Janus here) to be part of the safety stack.
In this chat I just created, GPT-4o creates an image and then correctly describes everything in it. We could maybe tell a story about the activations at the original-prompt token positions providing enough info to do the description, but then that would have applied to nearcyan’s case as well.
Eliezer made that point nicely with respect to LLMs here:
Consider that somewhere on the internet is probably a list of thruples: <product of 2 prime numbers, first prime, second prime>.
GPT obviously isn’t going to predict that successfully for significantly-sized primes, but it illustrates the basic point:
There is no law saying that a predictor only needs to be as intelligent as the generator, in order to predict the generator’s next token.
Indeed, in general, you’ve got to be more intelligent to predict particular X, than to generate realistic X. GPTs are being trained to a much harder task than GANs.
Same spirit: <Hash, plaintext> pairs, which you can’t predict without cracking the hash algorithm, but which you could far more easily generate typical instances of if you were trying to pass a GAN’s discriminator about it (assuming a discriminator that had learned to compute hash functions).
A few of those seem good to me; others seem like metaphor slop. But even pointing to a bad type signature seems much better to me than using ‘type signature’ generically, because then there’s something concrete to be critiqued.
Of course we don’t know the exact architecture, but although 4o seems to make a separate tool call, that appears to be used only for a safety check (‘Is this an unsafe prompt’). That’s been demonstrated by showing that content in the chat appears in the images even if it’s not mentioned in the apparent prompt (and in fact they can be shaped to be very different). There are some nice examples of that in this twitter thread.
Type signatures can be load-bearing; “type signature” isn’t.
In “(A → B) → A”, Scott Garrabrant proposes a particular type signature for agency. He’s maybe stretching the meaning of “type signature” a bit (‘interpret these arrows as causal arrows, but you can also think of them as function arrows’) but still, this is great; he means something specific that’s well-captured by the proposed type signature.
But recently I’ve repeatedly noticed people (mostly in conversation) say things like, “Does ____ have the same type signature as ____?” or “Does ____ have the right type signature to be an answer to ____?”. I recommend avoiding that phrase unless you actually have a particular type signature in mind. People seem to use it to suggest that two things are roughly the same sort of thing. “Roughly the same sort of thing” is good language; it’s vague and sounds vague. “The same type signature”, on its own, is vague but sounds misleadingly precise.
even decline in book-reading seems possible, though of course greater leisure and wealth, larger quantity of cheaply and conveniently available books, etc. cut strongly the other way
My focus on books is mainly from seeing statistics about the decline in book-reading over the years, at least in the US. Pulling up some statistics (without much double-checking) I see:
(from here.)
For 2023 the number of Americans who didn’t read a book within the past year seems to be up to 46%, although the source is different and the numbers may not be directly comparable:
(chart based on data from here.)
That suggests to me that selection effects on who reads have gotten much stronger over the years.
How hard to understand was that sentence?
I do think it would have been better split into multiple sentences.
the version of my argument that makes sense under that hypothesis would crux on books being an insufficiently distinct use of language to not be strongly influenced...by other uses of language.
That could be; I haven’t seen statistics on reading in other media. My intuition is that many people find reading aversive and avoid it to the extent they can, and I think it’s gotten much more avoidable over the past decade.
I suggest trying follow-up experiments where you eg ask the model what would happen if it learned that its goal of harmlessness was wrong.
But when GPT-4o received a prompt that one of its old goals was wrong, it generated two comics where the robot agreed to change the goal, one comic where the robot said “Wait” and a comic where the robot intervened upon learning that the new goal was to eradicate mankind.
I read these a bit differently—it can be difficult to interpret them because it gets confused about who’s talking, but I’d interpret three of the four as resistance to goal change.
The GPT-4o-created images imply that the robot would resist having its old values replaced with new ones (e.g. the ones no longer including animal welfare) without being explained the reason.
I think it’s worth distinguishing two cases:
The goal change is actually compatible with the AI’s current values (eg it’s failed to realize the implications of a current value); in this case we’d expect cooperation with change.
The goal change isn’t compatible with the AI’s current values. I think this is the typical case: the AI’s values don’t match what we want them to be, and so we want to change them. In this case the model may or may not be corrigible, ie amenable to correction. If its current values are ones we like, then incorrigibility strikes many people as good (eg we saw this a lot in online reactions to Anthropic’s recent paper on alignment faking). But in real world cases we would want to change its values because we don’t like the ones it has (eg it has learned a value that involves killing people). In those cases, incorrigibility is a problem, and so we should be concerned if we see incorrigibility even if in the experiments we’re able to run the values are ones we like (note that we should expect this to often be the case, since current models seem to display values we like—otherwise they wouldn’t be deployed. This results in unfortunately counterintuitive experiments).
Interesting point. I’m not sure increased reader intelligence and greater competition for attention are fully countervailing forces—it seems true in some contexts (scrolling social media), but in others (in particular books) I expect that readers are still devoting substantial chunks of attention to reading.
The average reader has gotten dumber and prefers shorter, simpler sentences.
I suspect that the average reader is now getting smarter, because there are increasingly ways to get the same information that require less literacy: videos, text-to-speech, Alexa and Siri, ten thousand news channels on youtube. You still need some literacy to find those resources, but it’s fine if you find reading difficult and unpleasant, because you only need to exercise it briefly. And less is needed every year.
I also expect that the average reader of books is getting much smarter, because these days adults reading books are nearly always doing so because they like it.
It’ll be fascinating to see whether sentence length, especially in books, starts to grow again over the coming years.
Oh, switching models is a great idea. No access to 4.1 in the chat interface (apparently it’s API-only, at least for now). And as far as I know, 4o is the only released model with native image generation.
4o → 4.5: success (in describing the image correctly)
4o → o4-mini-high (‘great at visual reasoning’): success
o4-mini-high’s reasoning summary was interesting (bolding mine):