Tofly's Shortform

Yudkowsky addresses some of these objections in more detail in "Intelligence Explosion Microeconomics".

Firstly, if we are talking actual computational complexity, then the mathematical background is already implicitly talking about the fastest possible algorithm to do X.

That the problem is NP-hard means that it will be difficult, no matter how intelligent the AI is.

Whether or not P=NP is an unsolved problem.

Predicting how a protien will fold is in BQP, which might be easier than NP. (another unsolved problem)

Computational complexity classes often don't matter in practice. If you are solving the travelling salesman problem, you rarely need the shortest path, a short path is good enough. Secondly, the P vs NP is worst case. There are some special cases of the travelling salesman that are easy to solve. Taking an arbitrary protein and predicting how it will fold might be computationally intractable, but the work here is done by the word "arbitrary". There are some protiens that are really hard to predict, and some that are easier. Can molecular nanotech be made using only the easily predicted protiens?

(Also, an algorithm doesn't have an intelligence level, it has an intelligence to compute relation. Once you have invented minmax, increasing the recursion depth takes next to no insight into intelligence. Given a googleplex flop computer, your argument obviously fails, because any fool could bootstrap intelligence on that.)

I have an intuition that there should be some “best architecture”, at least for any given environment, and that this architecture should be relatively “simple”.

I agree. I think that AIXI, shows that there is a simple optimal design with unlimited compute. There being no simple optimal design with finite compute would be somewhat surprising. (I think logical induction is something like only exponentially worse than any possible mathematical reasoner in use of compute)

But this is a different argument than "as soon as artificial intelligence surpasses human intelligence, recursive self-improvement will take place, creating an entity we can't hope to comprehend, let alone oppose."

A model in which both are true. Suppose that there was a design of AI that was optimal for its compute. And suppose this design was reasonably findable, ie a bunch of smart humans could find this design with effort. And suppose this design was really, really smart.

(Humans often get the answer wrong even on the cases where the exact maths takes a trivial amount of compute, like the doctors with a disease that has prevalence 1 in 1000, and the 90% reliable test) The gap between humans and optimal use of compute is likely huge.

So either humans figure out the optimal, and implement it. Or humans hack together something near human level. The near human level AI might fiddle with its own workings, trying to increase its intelligence, and then it figures out the optimal design.

In this world the prediction that vastly superhuman AI arrives not long after AI reaches human level. Its just that the self improvement isn't that recursive.

Regarding your "intuition that there should be some “best architecture”, at least for any given environment, and that this architecture should be relatively “simple”.", I think:

1) I'd say "task" rather than "environment", unless I wanted to emphasize that I think selection pressure trumps the orthogonality thesis (I'm ambivalent, FWIW).

2) I don't see why it should be "simple" (and relative to what?) in every case, but I sort of share this intuition for most cases...

3) On the other hand, I think any system with other agents probably is much more complicated (IIUC, a lot of people think social complexity drove selection pressure for human-level intelligence in a feedback loop). At a "gears level" the reason this creates an insatiable drive for greater complexity is that social dynamics can be quite winner-takes-all... if you're one step ahead of everyone else (and they don't realize it), then you can fleece them.

I don't think asymptotic reasoning is really the right tool for the job here.

We *know* things level off eventually because of physical limits (https://en.wikipedia.org/wiki/Limits_of_computation).

But fast takeoff is about how fast we go from where we are now to (e.g.) a superintelligence with a decisive strategic advantage (DSA). DSA probably doesn't require something near the physical limits of computation.

The most "optimistic" model in that post is linear. That is a model where making something as smart as you is a task of fixed difficulty. The benifit you gain by being smarter counterbalances the extra difficulty of making something smarter. In all the other models, making something as smart as you gets harder as you get smarter. (I am not talking about biological reproduction here, or about an AI blindly copying itself, I am talking about writing code that is as smart as you are from scratch).

Suppose we gave a chicken and a human access to the same computer, and asked each to program something at least as smart as they were. I would expect the human to do better than the chicken. Likewise I would bet on a team of IQ 120 humans producing an AI smarter than they are over a team of IQ 80 humans producing something smarter than they are. (Or anything, smarter than a chicken really).

A few extra IQ points will make you a slightly better chessplayer, but is the difference between not inventing minmax and not being able to write a chess playing program at all, and inventing minmax.

Making things smarter than yourself gets much easier as you get smarter, which is why only smart humans have a serious chance of managing it.

Instead of linear, try squareroot, or log.

The problem with this scenario is the part where the AI “solves the protein folding problem”. That the problem is NP-hard means that it will be difficult, no matter how intelligent the AI is.

There's no good reason to assume protein folding to be NP-hard. DeepMind seems to make good progress on it. 

I wrote once about levels of AI self-improvement and come to a similar conclusion: any more advance version of such AI will require more and more extensive testing, to ensure its stability and alignment, and the complexity of the testing task will grow very quickly, thus slowing down any intelligent explosion. This, however, doesn't preclude creation of Dangerous AI (capable to solve the task of human extinction and just slight superhuman in some domains).

Are language models utility maximizes?

I think there's two different major "phases" of a language model, training and runtime.  During training, the model is getting "steered" toward some objective function - first getting the probability of the next token "right", and then getting positive feedback from humans during rlhf (I think? I should read exactly how rlhf works).  Is this utility maximization?  It doesn't feel like it - I think I'll put my thoughts on this in another comment.

During runtime, at first glance, the model is kind of "deterministic" (wrong word), in that it's "just multiplying matrices", but maybe it "learned" some utility maximizers during training and they're embedded within it.  Not sure if this is actually possible, or if it happens in practice, and if the utility maximizers are dominate the agent or can be "overruled" by other parts of it.