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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.
It's not semantics, I just reject that this is what happens in bio-evolution in non-degenerate cases, at least if we think it's about IGF. What is x? IGF as number of «offspring equivalents»? Number of gene copies? Does this describe observed dynamics – do we see a universal tendency to increase the number of specimen, the vast increase in total mass of cell nuclei relative to the rest of the environment, or something? What about bizarre fitness-reducing stuff like Fisherian runaway? No, we see a walk through phenotype-space that both seeks local minima of distributions and changes them to induce another pivot in the search for a local mimimum. It's all survivor's bias; it has fitness-related structure, but there is no external, persistent IGF measure in the way there can be, say, an LLM's perplexity for a fixed training set. So these formalisms like IGF-optimization are imperfect approximations of what's going on in replicator dynamics, mainly useful on short stretches in static environments. The conditions of there not being a «real» IGF optimization pressure and there being one are not equivalent, they become increasingly distinct with more time steps.
Now I'm not flexing my normiedom here. I think there actually can be a neat non-circular formalism for evolution-as-a-whole: maybe something along the lines of Lotka's or Jeremy England's theory of life, a process of physical structures optimizing for capture of free energy from thermodynamic gradients and its dissipation. This is more neatly analogous to SGD, and also explains the rise of intelligence, human civilization and is, incidentally, the ideology of e/acc types who welcome our eventual transition or substitution to artificial minds who'll be even more efficient at exploiting thermodynamics.
Right, though note that inner and outer alignment are also not obviously helpful abstractions.
You can probably see now why I'm pissed at doomers like Besinger who say that this timeline is one of the worst possible ones and that we've merely learned «how to build processes analogous to evolution that spit out minds». No, our processes are better than evolution. In fact I think we are immensely doubly blessed that a) SGD+deep neural nets work as well as they do and b) our first foray into impressive general intelligence was this non-agentic LLMs paradigm. We have learned how to optimize minds for serving an approximation of a human value-laden world model, before we have learned to summon task-agnostic optimization demons; now we have at least a good pentagram to trap the demon in, and perhaps it will work magic even without one. (One could even say it's an alignment anthropic shadow – maybe we could have built AIXI-approximating optimizers first, were we to stumble on some mathematical insights, were Eliezer to read another book… but rats use this idea only selectively, to support their preconceived hypotheses).
It is. Or, well, I think evolution did fine for the ancestral environment, but we've long been a species with culture. Information determining our behavior is mainly outside the genome; so even biodeterminists admit that our genetic differences (and inductive biases) can be strongly predictive only in a shared culture, with near-homogenous conditions. All traditional cultures reinforce IGF pursuit to some extent, this is a product of bona fide cultural evolution acting on specimens via lifetime reinforcement learning; the social value of natalism does optimize for something like IGF directly over human synapses. Of course that's still «IGF» proxy as assessed by the internalized opinion of priest caste or the public; an objective IGF measure (putting away my doubts about its existence) would have been drastically more powerful.
So we should care less about whether ML models learn what we teach them to do, and care more about whether we are teaching them what we want. Data is far more of a weak link than the learning rule.
…By the way, wasn't that an idea in Three Worlds Collide? Superhappies had a single-level information substrate, their heredity and psychology were both encoded by DNA-like stuff, so they were very much in tune with themselves. I wonder if Eliezer can see how this is similar to our work with SGD.
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