Hi Cameron, nice to see you here : ) what are your thoughts on a critique like: human prosocial behavior/values only look the way they look and hold stable within-lifetimes, insofar as we evolved in + live in a world where there are loads of other agents with roughly equal power as ourselves? Do you disagree with that belief? 

This was very insightful. It seems like a great thing to point to, for the many newish-to-alignment people ideating research agendas (like myself). Thanks for writing and posting!

This is a really cool idea and I'm glad you made the post! Here are a few comments/thoughts:

H1: "If you give a human absolute power, there is a small subset of humans that actually cares and will try to make everyone’s life better according to their own wishes"

How confident are you in this premise? Power and sense of values/incentives/preferences may not be orthogonal (and my intuition is that it isn't).  Also, I feel a little skeptical about the usefulness of thinking about the trait showing up more or less in various intelligence strata within humans. Seems like what we're worried about is in a different reference class. Not sure.

H4 is something I'm super interested in and would be happy to talk about it in conversations/calls if you want to : )

Something at the root of this might be relevant to the inverse scaling competition thing where they're trying to find what things get worse in larger models. This might have some flavor of obviously wrongness -> deception via plausible sounding things as models get larger? https://github.com/inverse-scaling/prize 

interesting idea. like.. a mix of genuine sympathy/expansion of moral circle to AI, and virtue signaling/anti-corporation meme spreads to the majority population and effectively curtails AGI capabilities research? This feels like a thing that might actually do nothing to reduce corporations' efforts to get to powerful AI unless it reaches a threshold at which point there's very dramatic actions against corporations who continue to try to do that thing

Hi John. One could run useful empirical experiments right now, before fleshing out all these structures and how to represent them, if you can assume that a proxy for human representations (crude: conceptnet, less crude: similarity judgments on visual features and classes collected by humans) is a good enough proxy for "relevant structures" (or at least that these representations more faithfully capture the natural abstractions than the best machines in vision tasks for example, where human performance is the benchmark performance), right?

I had a similar idea about ontology mismatch identification via checking for isomorphic structures, and also realized I had no idea how to realize that idea. Through some discussions with Stephen Casper and Ilia Sucholutsky, we kind of pivoted the above idea into the regime of interpretability/adversarial robustness where we are hunting for interesting properties given that we can identify the biggest ways that humans and machines are representing things differently (and that humans, for now, are doing it "better"/more efficiently/more like the natural abstraction structures that exist). 

I think am working in the same building this summer (caught a split-second glance at you yesterday); I would love a chance to discuss how selection theorems might relate to an interpretability/adversarial robustness project I have been thinking about.

Thanks so much for the response, this is all clear now! 

Sorry if it's obvious from some other part of your post, but the whole premise is that sufficiently strong models *deployed in sufficiently complex environments* leads to general intelligence with optimization over various levels of abstractions. So why is it obvious that: It doesn't matter if your AI is only taught math, if it's a glorified calculator — any sufficiently powerful calculator desperately wants to be an optimizer? 

If it's only trained to solve arithmetic and there are no additional sensory modalities aside from the buttons on a typical calculator, how does increasing this AI's compute/power lead to it becoming an optimizer over a wider domain than just arithmetic? Maybe I'm misunderstanding the claim, or maybe there's an obvious reason I'm overlooking.

Also, what do you think of the possibility that when AI becomes superhuman++ in tasks, that the representations go from interpretable to inscrutable again (because it uses lower level representations that are inaccessible to humans)? I understand the natural abstraction hypothesis, and I buy it too, but even an epsilon increase in details might compound into significant prediction outcomes if a causal model is trying to use tons of representations in conjunction to compute something complex. 

Do you think it might be valuable to find a theoretical limit that shows that the amount of compute needed for such epsilon-details to be usefully incorporated is greater than ever will be feasible (or not)? 

Hi Steve, loved this post! I've been interested in viewing the steering and thought generator + assessor submodule framework as the object and generator-of-values of which which we want AI to learn a good pointer to/representation of, to simulate out the complex+emergent human values and properly value extrapolate. 

I know the way I'm thinking about the following doesn't sit quite right with your perspective, because AFAIK, you don't believe there need to be independent, modular value systems that give their own reward signals for different things (your steering subsystem and thought generator and assessor subsystem are working in tandem to produce a singular reward signal). I'd be interested in hearing your thoughts on what seems more realistic, after importing my model of value generators as more distinctive and independent modular systems in the brain.

In the past week, I've been thinking about the potential importance of considering human value generators as modular subsystems (for both compute and reward). Consider the possibility that at various stages of the evolutionary neurocircuitry-shaping timeline of humans, that modular and independently developed subsystems developed. E.g. one of the first systems, some "reptilian" vibe system, was one that rewarded sugary stuff because it was a good proxy at the time for nutritious/calorie-dense foods that help with survival. And then down the line, there was another system that developed to reward feeling high-social status, because it was a good proxy at the time for surviving as social animals in in-group tribal environments. What things would you critique about this view, and how would you fit similar core-gears into your model of the human value generating system?

I'm considering value generators as more independent and modular, because (this gets into a philosophical domain but) perhaps we want powerful optimizers to apply optimization pressure not towards the human values generated by our wholistic-reward-system, but to ones generated by specific subsystems (system 2, higher-order values, cognitive/executive control reward system) instead of reptilian hedon-maximizing system. 

This is a few-day old, extremely crude and rough-around-the-edges idea, but I'd especially appreciate your input and critiques on this view. If it were promising enough, I wonder if (inspired by John Wentworth's evolution of modularity post) training agents in a huge MMO environment and switching up reward signals in the environment (or the environment distribution itself) every few generations would lead to a development of modular reward systems (mimicking the trajectory of value generator systems developing in humans over the evolutionary timeline).