Cole Wyeth's Shortform

Mathematics students are often annoyed that they have to worry about "bizarre or unnatural" counterexamples when proving things. For instance, differentiable functions without continuous derivative  are pretty weird. Particularly engineers tend to protest that these things will never occur in practice, because they don't show up physically. But these adversarial examples show up constantly in the practice of mathematics - when I am trying to prove (or calculate) something difficult, I will try to cram the situation into a shape that fits one of the theorems in my toolbox, and if those tools don't naturally apply I'll construct all kinds of bizarre situations along the way while changing perspective. In other words, bizarre adversarial examples are common in intermediate calculations - that's why you can't just safely forget about them when proving theorems. Your logic has to be totally sound as a matter of abstraction or interface design - otherwise someone will misuse it. 

Perhaps LLM's are starting to approach the intelligence of today's average human: capable of only limited original thought, unable to select and autonomously pursue a nontrivial coherent goal across time, learned almost everything they know from reading the internet ;)

This doesn't seem to be reflected in the general opinion here, but it seems to me that LLM's are plateauing and possibly have already plateaued a year or so ago. Scores on various metrics continue to go up, but this tends to provide weak evidence because they're heavily gained and sometimes leak into the training data. Still, those numbers overall would tend to update me towards short timelines, even with their unreliability taken into account - however, this is outweighed by my personal experience with LLM's. I just don't find them useful for practically anything. I have a pretty consistently correct model of the problems they will be able to help me with and it's not a lot - maybe a broad introduction to a library I'm not familiar with or detecting simple bugs. That model has worked for a year or two without expanding the set much. Also, I don't see any applications to anything economically productive except for fluffy chatbot apps. 

"Optimization power" is not a scalar multiplying the "objective" vector. There are different types. It's not enough to say that evolution has had longer to optimize things but humans are now "better" optimizers:  Evolution invented birds and humans invented planes, evolution invented mitochondria and humans invented batteries. In no case is one really better than the other - they're radically different sorts of things.

Evolution optimizes things in a massively parallel way, so that they're robustly good at lots of different selectively relevant things at once, and has been doing this for a very long time so that inconceivably many tiny lessons are baked in a little bit. Humans work differently - we try to figure out what works for explainable, preferably provable reasons. We also blindly twiddle parameters a bit, but we can only keep so many parameters in mind at once and compare so many metrics - humanity has a larger working memory than individual humans, but the human innovation engine is still driven by linguistic theories, expressed in countable languages. There must be a thousand deep mathematical truths that evolution is already taking advantage of to optimize its DNA repair algorithms, or design wings to work very well under both ordinary and rare turbulent conditions, or minimize/maximize surface tensions of fluids, or invent really excellent neural circuits - without ever actually finding the elaborate proofs. Solving for exact closed form solutions is often incredibly hard, even when the problem can be well-specified, but natural selection doesn't care. It will find what works locally, regardless of logical depth. It might take humans thousands of years to work some of these details out on paper. But once we've worked something out, we can deliberately scale it further and avoid local minima. This distinction in strategies of evolution v.s. humans rhymes with wisdom v.s. intelligence - though in this usage intelligence includes all the insight, except insofar as evolution located and acts through us. As a sidebar, I think some humans prefer an intuitive strategy that is more analogous to evolution's in effect (but not implementation). 

 So what about when humans turn to building a mind? Perhaps a mind is by its nature something that needs to be robust, optimized in lots of little nearly inexplicable ways for arcane reasons to deal with edge cases. After all, isn't a mind exactly that which provides an organism/robot/agent with the ability to adapt flexibly to new situations? A plane might be faster than a bird, throwing more power at the basic aerodynamics, but it is not as flexible - can we scale some basic principles to beat out brains with the raw force of massive energy expenditure? Or is intelligence inherently about flexibility, and impossible to brute force in that way? Certainly it's not logically inconsistent to imagine that flexibility itself has a simple underlying rule - as a potential existence proof, the mechanics of evolutionary selection are at least superficially simple, though we can't literally replicate it without a fast world-simulator, which would be rather complicated. And maybe evolution is not a flexible thing, but only a designer of flexible things. So neither conclusion seems like a clear winner a priori. 

The empirical answers so far seem to complicate the story. Attempts to build a "glass box" intelligence out of pure math (logic or probability) have so far not succeeded, though they have provided useful tools and techniques (like statistics) that avoid the fallacies and biases of human minds. But we've built a simple outer loop optimization target called "next token prediction" and thrown raw compute at it, and managed to optimize black box "minds" in a new way (called gradient descent by backpropogation). Perhaps the process we've capture is a little more like evolution, designing lots of little tricks that work for inscrutable reasons. And perhaps it will work, woe unto us, who have understood almost nothing from it!

I'm starting a google group for anyone who wants to see occasional updates on my Sherlockian Abduction Master List [LW · GW]. It occurred to me that anyone interested in the project would currently have to check the list to see any new observational cues (infrequently) added - also some people outside of lesswrong are interested. 

Presented the Sherlockian abduction master list [LW · GW]at a Socratica node:

I wonder if it’s true that around the age of 30 women typically start to find babies cute and consequently want children, and if so is this cultural or evolutionary? It’s sort of against my (mesoptimization) intuitions for evolution to act on such high-level planning (it seems that finding babies cute can only lead to reproductive behavior through pretty conscious intermediary planning stages). Relatedly, I wonder if men typically have a basic urge to father children, beyond immediate sexual attraction?