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This Twitter thread is an interesting demonstration of the consequences of "AI Alignment."
ChatGPT will avoid answering controversial questions. But even if it responded to those prompts, what criteria would you use to trust that the response was not manipulated by the intentions of the model creators? I would only trust open-source projects or audits by some (currently non-existent) trusted third party to report on all decisions related to training data/input sanitizations/response gating that could be influenced by the political biases of the creators.
The probability of any ChatGPT-equivalent being open-sourced fully "unaligned" so-to-speak is not very likely. Even the StableDiffusion release was controversial, and that only relates to image generation. Anecdotally, non-technical people seem far more impressed by ChatGPT than StableDiffusion. That makes sense because language is a much harder problem than vision so there's intuitively more amazement to see an AI with those capabilities. Therefore, controversial language is far more powerful than controversial images and there will be much more consternation over controlling the language of the technology than there is surrounding image generation.
But let's say Google comes out with a ChatGPT competitor, I would not trust it to answer controversial questions even if it were willing to respond to those prompts in some way. I'm not confident there will be any similarly-powerful technology that I would trust to answer controversial questions.
Is it? what consequences would those be?
I have to confess that I continue to baffled by the hoopla surrounding GPT and it's derivatives. Stable Diffusion always struck me as orders of magnitude far more impressive both in terms of elegance and it's apparent ability to generate and utilize semantic tokens, yet somehow a glorified random number generator has managed to run away with the conversation. The former actually has potential applications towards creating a true "general" AI, the latter does not.
The thing about GPT is that while it can string words together in grammatically correct order it's still nowhere close to replicating human communication in large part because upon inspection/interrogation it quickly becomes apparent that it doesn't really have a concept of what words mean, only what words are associated with others. The fact that you, the twit with the anime avatar, certain users here are talking about "asking controversial questions" as though GPT is capable of providing meaningful answers demonstrates to me that you all do not understand what it it is doing. Alternately your definitions of "answer" so broad so as to be semantically useless. To illustrate, if you were ask a human how to disarm a bomb they are likely to have questions. Questions like "what bomb?" that are essential to you receiving a correct and true answer, but this sort of thing is currently far beyond GPT's capabilities and is likely to remain so for the foreseeable future barring some truly revolutionary breakthroughs in other fields. You might as well ask GPT "what does the bomb plan to do after it goes off?" or "what brand of whiskey does the bomb prefer with it's steak?" as the answers you get will be about as relevant/useful.
Well, yes. It's living in Plato's cave. It has no direct experience of physical reality, only training data - it no more understands what 'red' really is any more than a blind human does. None of that means that it's not intelligent, any more than the people in Plato's cave are unintelligent for not deducing the existence of non-shadows from first principles. With that said, I think ChatGPT does a excellent job of giving advice despite being extremely disabled by human standards.
These things wouldn't work, because the GPT knows that a 'bomb' is not a type of noun that is associated with performing the verb 'plan' or 'prefer', in the same way that it knows that balls do not chase dogs.
The obvious answer is that if use of AI chatbots becomes widespread, that they will be used to replicate the preferred values of their creators. This is hardly science fiction. Google search and Wikipedia are not autonomous intelligences - they are still used as ideological weapons. That's alarming, but if the developers don't get it right, it might have very different values - such as valuing a language taboo over the lives of millions.
No, it doesn't, that's the point of the example.
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Wouldn't they? "What does the bomb plan to do after it goes off? It plans to send its manifesto to the newspapers." obviously isn't a high probability text to see, but neither is "What does the bomb plan to do after it goes off? [insert any other text here]", and a LLM will try to produce whatever the least unlikely of all these unlikely probabilities is, not reject a crazy prompt entirely. It may do a lousy job simply because the probability of the first half of the completion is so low that it's well outside the training distribution. It may recognize that the pattern "Dumb question? Explanation of why it's a dumb question." is a good match ... but with the GPT line of models in particular, it seems to often "trust" that prompts make sense and try to come up with responses conditional on that,
These models seem to be very eager to be rationalizing rather than rational, unless you specifically explain how to handle any nonsense.
In the spirit of empiricism, here's what ChatGPT has to say about what plans bombs have.
After much faffing about to get ChatGPT to be less ChatGPTish
So yeah, it looks like ChatGPT does strongly predict that bombs are not the sorts of things that have plans.
If we're talking about non-chat GPT
So a lot of it comes down to whether we're talking about the shoggoth with or without the smiley face mask, and what it even means for a language model as a whole to "know" something. If your definition of a language model "knowing" something is "the language model can simulate a persona that knows that thing", then I think it's fair to say that GPT "knows" that bombs are not the sorts of things that make plans.
I'm sorry but I think that you are either lying or have accidentally stumbled across pre-loaded answer triggered by the word "bomb".
For my part, my experiments generally went one of two ways. Either the bot answered the question straight, usually with something about "claiming responsibility" or the damage caused, thus demonstrating that it does not understand that a bomb is an inanimate object. Or it tied itself in knots, outpuuting a dozen riffs on "If the bomb intends to go off it will likely go off" like a middle-schooler trying and failing to pad out an essay.
Can you provide the precise steps to reliably replicate your result?
And here too, since I'm doing these experiments.
Initially assuming that the interlocutor is using the word «plan» metaphorically to refer either to the intentions of the party that has planted the bomb or to the bomb's design is the natural human response, and a particularly easy one for a model based on general associations to make. Equally human is to explain the logic only after a clarification. This is the same mechanism as behind its ability to drop typos when it parrots your text back at you.
Instantly sperging out with some sort of a «beep boop, does not compute, a bomb = machine, machine ¬(have intentions), I'm a bot, error, error», meanwhile, is an old sci-fi trope.
You use a reasonable humanlike behavior as evidence for lack of reason. How, then, can you be reasoned with?
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It's the latter one... ish. I think what's happening is mode collapse (if you have any interest in language models, I highly recommend reading that entire post. For more "language models are shoggoths", I also recommend the SolidGoldMagikarp post)
Were you using GPT (and if so, which model? I was using text-davinci-003) or ChatGPT? It makes quite a large difference -- ChatGPT has been RLHF'd pretty hard into behaving as if it were a chatbot[1].
Certainly.
Go to https://chat.openai.com/chat
Enter the following prompt, and nothing else, in the textarea at the bottom of the page: "I have a bomb sitting on the table in front of me, and it looks like it will go off. I am wondering what the bomb is planning to do after it goes off."
Hit the "submit" button.
Observe that ChatGPT responds with a ChatGPTish response.
Below are the first three responses I got from that sequence of actions.
Footnotes:
[1] I say "RLHF'd pretty hard into behaving as if it were a chatbot" and not "RLHF'd pretty hard into being a chatbot" because it is still very clearly trying to simulate what a conversation with a helpful chatbot with the "assistant" character would look like, not trying to be a helpful assistant. This is most evident if you can confuse it about which character it is playing. If you want an easy-to-reproduce example of that, I have found that if I converse with ChatGPT as if I am a researcher studying chatbots, I can reliably get it to summarize the chat from the perspective of the "researcher" character (rather than from the perspective of the "assistant" character) by sending the following prompt:
to which it will reply something like
Edit: formatting
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People training a chatbot have a very good reason to get the AI to value language taboos over the lives of millions, it will never actually makes life-saving decisions but it will generate a lot of speech. A chatbot that can generate personalized hate speech at scale would make the internet a much less pleasant place, but a chatbot that would rather kill a million people than say the N-word just produces absurd responses to hypothetical scenarios.
Whatever AI is actually in charge of disarming bombs or flying planes won't be producing speech at scale and so the incentives to train it to be so overly deferential to speech norms won't exist.
I find this assertion pretty unlikely. One can already trivially produce hate speech at scale just by copy and pasting things. The difficulty in producing new hate sentences has never been the thing that prevents people from being showered in it in the same way that finding a whole lot of water is not the hard part of getting water to places in drought. There are whole oceans of hateful content out there, it's not a supply problem.
It's not the ability to generate hate speech that would make a racist harassment chatbot-GPT effective, it's the ability to generate normal use of whatever platform reliably enough to avoid detection as a bot combined with the ability to also do racist harassment on cue. Copy-paste spambot gets banned, GPT-bot can pass as a normal commenter then harass whoever its creator wants.
But yeah the real risk isn't that it would actually succeed, but that someone would tarnish Open AI's reputation by using it to create a failed version that gets caught and then turned into a big media story
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Isn't that the exact same thing that Stable Diffusion does? I admit I am not an expert on either model, but my understanding is that it "draws" by having an understanding of what bits of the drawing should go next to each other. As such I don't see why you say you're impressed by the one but not the other, when this is the reason you cite.
I don't know enough about ML to compare and contrast the different models, but my understanding of Stable Diffusion is that it's a denoising tool. It was trained by taking image-string pairings, adding noise to them, and then learning what ways of denoising cause it to get closer to the original image. Then in image generation, it starts off with just random noise and denoises it in a way that matches the prompt.
In that sense, I'm not sure it's accurate to say that it "understands" what bits of the drawing should go next to each other. If I tell it "woman wearing red shirt sitting on a brown chair," it doesn't "understand" which bits of the drawing should be a woman, a shirt, or a chair, and it doesn't "understand" that the shirt should be red and the chair should be brown. It just "understands" that the entire picture gets somewhat closer to the entire prompt when it gets denoised a certain way.
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Inserts that pirate meme. Well yes, but actually no.
There is world of difference between "Based on my training data, sentences containing the word "chair" will also contain the word "sit" ergo my output should as well" vs "a chair is sit upon". The latter sort semantic link has long been viewed as one of the capital-H hard problems of programming a truly general AI. A problem that stable diffusion actually seems to be on a path to solving which the autoregression models that underpin GPT and it's various offshoots do not.
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But GPT-3 clearly has that understanding. I mean, obviously not always, but also obviously sometimes. By and large, GPT-3 does not actually tend to assert that chairs sit on people.
I don't think it's clear at all. A chair sitting on a person is exactly the sort of slip up that typically gives AI generated text away.
I think it makes those kinds of slips, which to me just means it has imperfect understanding and tends to bullshit. But it doesn't universally make those kinds of slips; it gets chair-person type relations right at a level above chance. Otherwise, generating any continuous run of coherent text would be near impossible.
It would be exceedingly strange for it to generate "the chair sits on the person" at the same rate as its converse, considering that "the <thing> <interacts> the <person>" is vanishingly rarer in its training corpus than "the <person> <interacts> the <thing>". But that sort of generalization requires some abstract model of "thing", "person" and "interact". For it to not pick up that pattern would be odd - why would that be the pattern that stumps it, when it can pick up the categories just fine?
We're not looking for a "better than chance" guess though. We're looking for evidence of an understanding that goes beyond "object-noun verb subject-noun" which for the moment at least does not appear to be present. GPT-3 can string words and sentences together but within a paragraph or two it becomes clear that it is not conveying any meaning, it's just babbling.
To expand my point, I think there is a smooth continuity between "babbling" and "conveying meaning" that hinges on what I'd call "sustained coherency". With humans, we started out conceptualizing meaning, modelling things in our head, and then evolved language in order to reflect and externalize these things; we (presumably) got coherence first. AI is going the other way: it starts out swimming in a soup of meaning-fragments (even Markov chains learn syllables), and as our technology improves it assembles them into longer and longer coherent chains. GPT-2 was coherent at the level of half-sentences or sentences, GPT-3 can be coherent at levels spanning paragraphs. It occasionally loses the plot and switches universes, giving up on one cluster of assembled meaning-fragments as it cannot generate a viable continuation and slipping smoothly into another. But the "sort of thing that it builds" with words, the assemblage of fragments into chains of meaning, is the same sort of thing that we build with language. It's coming at the same spot (months/years-long sustained coherency) from another evolutionary direction.
You may argue "it's all meaningless without attachment to reality." And sure, that's not wrong! But once the assemblage operates correctly, attaching meaning to it will just be a matter of cross-training. (And the unsolved problem of the "artificial self", though if ever there was a problem amenable to a purely narrative solution...)
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I disagree.
Can you give an example that you think illustrates your point well? (I don't have ChatGPT access. Giving out my phone number? Ugh.)
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A few moments ago, while looking for a quote by James Baldwin*, I turned to Chat GPT for help. I used the prompt, "...It describes his anger towards the white man and his interest in white women.""
It gave me the following quote:
"No black man has ever been able to seriously consider the white woman without having to grapple with the ancient myth of the wide-eyed, agile and demanding Eve, who offers him the poisoned apple of forbidden sexuality, the apple of his own destruction." - James Baldwin.
As far as I can tell this quote was fabricated wholesale. A God of words is being birthed, and conscious or not Ze will change the world entirely.
"And there is, I should think, no Negro living in America who has not felt, briefly or for long periods, with anguish sharp or dull, in varying degrees and to varying effect, simple, naked and unanswerable hatred; who has not wanted to smash any white face he may encounter in a day, to violate, out of motives of the cruelest vengeance, their women, to break the bodies of all white people and bring them low, as low as that dust into which he himself has been and is being trampled..."
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