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This is a bizarre problem I’ve noticed with ChatGPT. It will literally just make up links and quotations sometimes. I will ask it for authoritative quotations from so and so regarding such topic, and a lot of the quotations would be made up. Maybe because I’m using the free version? But it shouldn’t be hard to force the AI to specifically only trawl through academic works, peer reviewed papers, etc.
That's because there is no thinking going on there. It doesn't understand what it's doing. It's the Chinese Room. You put in the prompt "give me X", it looks for samples of X in the training data, then produces "Y in the style of X". It can very faithfully copy the style and such details, but it has no understanding that making shit up is not what is wanted, because it's not intelligent. It may be AI, but all it is is a big dumb machine that can pattern-match very fast out of an enormous amount of data.
It truly is the apotheosis of "a copy of you is the same as you, be that a uploaded machine intelligence or someone in many-worlds other dimension or a clone, so if you die but your copy lives, then you still live" thinking. As the law courts show here, no, a fake is not the same thing as reality at all.
In other news, the first story about AI being used by scammers (this is the kind of thing I expect to happen with AI, not "it will figure out the cure for cancer and world poverty"):
Jesus Christ that's a remarkably bad take, all the worse that it's common.
Firstly, the Chinese Room argument is a terrible one, it's an analogy that looks deeply mysterious till you take one good look at it, and it falls apart.
If you cut open your skull, you'll be hard pressed to find a single neuron that "understands English", but the collective activation of the ensemble does.
In a similar manner, neither the human nor the machinery in a Chinese Room speaks Chinese, yet the whole clearly does, for any reasonable definition of "understand", without presupposing stupid assumptions about the need for some ineffable essence to glue it all together.
What GPT does is predict the next token. That's a simple statement with a great deal of complexity underlying it.
This is an understanding built up by the model from exposure to terabytes of text, and the underlying architecture is so fluid it picks up ever more subtle nuance in said domain that it can perform above the level of the average human.
It's hard to understate the difficulty of the task it does in training, it's a blind and deaf entity floating in a sea of text that looks at enough of it to understand.
Secondly, the fact that it makes errors is not a damning indictment, ChatGPT clearly has a world model, an understanding of reality. The simple reason behind this is that we use language because it concisely communicates truth about our reality; and thus an entity that understands the former has insight into the latter.
Hardly a perfect degree of insight, but humans make mistakes from fallible memory, and are prone to bullshitting too.
As LLMs get bigger, they get better at distinguishing truth from fiction, at least as good as a brain in a vat with no way of experiencing the world can be, which is stunningly good.
GPT 4 is better than GPT 3 at avoiding such errors and hallucinations, and it's only going up from here.
Further, in ML there's a concept of distillation, where one model is trained on the output of another, until eventually the two become indistinguishable. LLMs are trained on the set of almost all human text, i.e. the Internet, and which is an artifact of human cognition. No wonder it thinks like a human, with obvious foibles and all.
At least, that's the Outer Objective, it's the equivalent of saying that humans are maximising inclusive-genetic-fitness, which is false if you look at the inner planning process of most humans. And just like evolution has endowed us with motivations and goals which get close enough at maximising its objective in the ancestral environment, so is GPT-4 endowed with unknown goals and cognition which are pretty good at maximising the log probability it assigns to the next word, but not perfect.
GPT-4 is almost certainly not doing reasoning like "What is the most likely next word among the documents on the internet pre-2021 that the filtering process of the OpenAI team would have included in my dataset?", it probably has a bunch of heuristic "goals" that get close enough to maximising the objective, just like humans have heuristic goals like sex, power, social status that get close enough for the ancestral environment, but no explicit planning for lots of kids, and certainly no explicit planning for paying protein-synthesis labs to produce their DNA by the buckets.
Should I develop bioweapons or go on an Uncle Ted-like campaign to end this terrible take?
More effort than this, please.
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I'd be super happy to be convinced of the contrary! (Given that the existence of mesa-optimisers are a big reason for my fears of existential risk) But do you mean to imply that gpt-4 is explicitly optimising for next-word prediction internally? And what about a gpt-4 variant that was only trained for 20% of the time that the real gpt-4 was? To the degree that LLMs have anything like "internal goals", they should change over the course of training, and no LLM is trained anywhere close to completion, so I find it hard to believe that the outer objective is being faithfully transfered.
I've cited Pope's Evolution is a bad analogy for AGI: inner alignment and other pieces like My Objections to "We’re All Gonna Die with Eliezer Yudkowsky" a few times already.
I think you correctly note some issues with the framing, but miss that it's unmoored from reality, hanging in midair when all those issues are properly accounted for. I am annoyed by this analogy on several layers.
Evolution is not an algorithm at all. It's the term we use to refer to the cumulative track record of survivor bias in populations of semi-deterministic replicators. There exist such things as evolutionary algorithms, but they are a reification of dynamics observed in the biological world, not another instance of the same process. The essential thing here is replicator dynamics. Accordingly, we could metaphorically say that «evolution optimizes for IGF» but that's just a (pretty trivial) claim about the apparent direction in replicator dynamics; evolution still has no objective function to guide its steps or – importantly – bake into the next ones, and humans cannot be said to have been trained with that function, lest we slip into a domain with very leaky abstractions. Lesswrongers talk smack about map and territory often but confuse them constantly. BTW, same story with «you are an agent with utility…» – no I'm not; neither are you, neither is GPT-4, neither will be the first superhuman LLM. To a large extent, rationalism is the cult of people LARPing as rational agents from economic theory models, and this makes it fail to gain insights about reality.
But even if we use such metaphors liberally. For all organisms that have nontrivial lifetime plasticity, evolution is an architecture search algorithm, not the algorithm that trains the policy directly. It bakes inductive biases into the policy such that it produces more viable copies (again, this is of course a teleological fallacy – rather, policies with IGF-boosting heritable inductive biases survive more); but those biases are inherently distribution-bound and fragile, they can't not come to rely on incidental features of a given stable environment, and crucially an environment that contained no information about IGF (which is, once again, an abstraction). Actual behaviors and, implicitly, values are learned by policies once online. using efficient generic learning rules, environmental cues and those biases. Thus evolution, as a bilevel optimization process with orders of magnitude more optimization power on the level that does not get inputs from IGF, could not have succeeded at making people, nor orther life forms, care about IGF. A fruitful way to consider it, and to notice the muddied thought process of rationalist community, is to look at extinction trajectories of different species. It's not like what makes humans (some of them) give up on reproduction is smarts and our discovery of condoms and stuff: it's just distributional shift (admittedly, we now shape our own distribution, but that, too, is not intelligence-bound). Very dumb species also go extinct when their environment changes non-lethally! Some species straight up refuse to mate or nurse their young in captivity, despite being provided every unnatural comfort! And accordingly, we don't have good reason to expect that «cognitive capabilities» increase is what would make an AI radically alter its behavioral trajectory; that's neither here nor there. Now, stochastic gradient descent is a one-level optimization process that directly changes the policy; a transformer is wholly shaped by the pressure of the objective function, in a way that a flexible intelligent agent generated by an evolutionary algorithm is not shaped by IGF (to say nothing of real biological entities). The correct analogies are something like SGD:lifetime animal learning; and evolution:R&D in ML. Incentives in machine learning community have eventually produced paradigms for training systems with partricular objectives, but do not have direct bearing on what is learned. Likewise, evolution does not directly bear on behavior. SGD totally does, so what GPT learns to do is "predict next word"; its arbitrarily rich internal structure amounts to a calculator doing exactly that. More bombastically, I'd say it's a simulator of semiotic universes which are defined by the input and sampling parameters (like ours is defined by initial conditions and cosmological constraints) and expire into the ranking of likely next tokens. This theory, if you will, exhausts its internal metaphysics; the training objective that has produced that is not part of GPT, but it defines its essence.
«Care explicitly» and «trained to completion» is muddled. Yes, we do not fill buckets with DNA (except on 4chan). If we were trained with the notion of IGF in context, we'd probably have simply been more natalist and traditionalist. A hypothetical self-aware GPT would not care about restructuring the physical reality so that it can predict token [0] (incidentally it's
!) with probability [1] over and over. I am not sure what it would even mean for GPT to be self-aware but it'd probably expess itself simply as a model that is very good at paying attention to significant tokens.Evolution has not failed nor ended (which isn't what you claim, but it's often claimed by Yud et al in this context). Populations dying out and genotypes changing conditional on fitness for a distribution is how evolution works, all the time, that's the point of the «algorithm»; it filters out alleles that are a poor match for the current distribution. If Yud likes ice cream and sci-fi more than he likes to have Jewish kids and read Torah, in a blink of an evolutionary eye he'll be replaced by his proper Orthodox brethren who consider sci-fi demonic and raise families of 12 (probably on AGI-enabled UBI). In this way, they will be sort of explicitly optimizing for IGF or at least for a set of commands that make for a decent proxy. How come? Lifetime learning of goals over multiple generations. And SGD does that way better, it seems.
This is just semantics, but I disagree with this, if you have a dynamical system that you're observing with a one-dimensional state x_t, and a state transition rule x_{t+1} = x_t - 0.1 * (2x_t) , you can either just look at the given dynamics and see no explicit optimisation being done at all, or you can notice that this system is equivalent to gradient descent with lr=0.1 on the function f(x)=x^2 . You might say that "GD is just a reification of the dynamics observed in the system", but the two ways of looking at the system are completely equivalent.
Okay, point 2 did change my mind a lot, I'm not too sure how I missed that the first time. I still think there might be a possibly-tiny difference between outer-objective and inner-objective for LLMs, but the magnitude of that difference won't be anywhere close to the difference between human goals and IGF. If anything, it's really remarkable that evolution managed to imbue some humans with desires this close to explicitly maximising IGF, and if IGF was being optimised with GD over the individual synapses of a human, of course we'd have explicit goals for IGF.
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