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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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