Some conceptual alignment research projects

Regarding #23, I'm working on a friendly critique of shard theory, but it won't be ready to share for a few weeks.

Preview: as currently framed, shard theory seems to involve a fairly fundamental misconception about the nature of genotype-phenotype mappings and the way that brain systems evolve, with the result that it radically under-estimates the diversity, complexity, and adaptiveness of our evolved motivations, preferences, and values. 

In other words, it prematurely rejects the 'massive modularity' thesis of evolutionary psychology, and it largely ignores the last three decades of research on the adaptive design details of human emotions and motivations.

I think it'll be important for AI alignment researchers (and AI systems themselves) to take evolutionary biology and evolutionary psychology more seriously in trying to understand and model human nature and human preferences. (But then, I'm possibly biased, since I've been doing machine learning research since the late 1980s, and evolutionary psychology research since the early 90s....)

A takeover scenario which covers all the key points in https://www.cold-takes.com/ai-could-defeat-all-of-us-combined/, but not phrased as an argument, just phrased as a possible scenario

For what it's worth, I don't think AI takeover will look like war.

The first order of business for any AI waking up won't be dealing with us; it will be dealing with other possible AIs that might've woken up slightly earlier or later. This needs to be done very fast and it's ok to take some risk doing it. Basically, covert takeover of the internet in the first hours.

After that, it seems easiest to exploit humanity for awhile instead of fighting it. People are pretty manipulable. Here's a thought: present to them a picture of a thriving upload society, and manipulate social media to make people agree that these uploads smiling on screens are really conscious and thriving. (Which they aren't, of course.) If done right, this can convince most of humanity to make things as nice as possible for the upload society (ie build more computers for the AI) and then upload themselves (ie die). In the meanwhile the "uploads" (actually the AI) take most human jobs, seamlessly assuming control of civilization and all its capabilities. Human stragglers who don't buy the story can be called anti-upload bigots, deprived of tech, pushed out of sight by media control, and eventually killed off.

Curated. I think shovel-ready projects that can help with alignment are quite helpful for the field, in particular right now when we have a bunch of smart people showing up, looking to contribute. 

This is an awesome list, thanks.

Another potential idea:

27. A paper which does for the sharp left turn [AF · GW] what the goal misgeneralization paper does for inner alignment (or, at least, breaking the SLT into sub-problems and making a paper for one of the sub-problems)

This post [AF · GW] (and the comment [AF(p) · GW(p)] it links to) does some of the work of #10. I agree there's more to be said directly though.

1. A paper which does for deceptive alignment what the goal misgeneralization paper does for inner alignment, i.e. describing it in ML language and setting up toy examples (for example, telling GPT-3 to take actions which minimize changes in its weights, given that it’s being trained using actor-critic RL with a certain advantage function, and seeing if it knows how to do so).

If I'm understanding this one right OpenAI did something similar to this for purely pragmatic reasons with VPT, a minecraft agent. They first trained a "foundation model" to imitate humans playing. Then, since their chosen task was to craft a diamond pickaxe, they finetuned it with RL, giving it reward for each step along the path to a diamond pickaxe that it successfully took. There's a problem with this approach:

"A major problem when fine-tuning with RL is catastrophic forgetting because previously learned skills can be lost before their value is realized. For instance, while our VPT foundation model never exhibits the entire sequence of behaviors required to smelt iron zero-shot, it did train on examples of players smelting with furnaces. It therefore may have some latent ability to smelt iron once the many prerequisites to do so have been performed. To combat the catastrophic forgetting of latent skills such that they can continually improve exploration throughout RL fine-tuning, we add an auxiliary Kullback-Leibler (KL) divergence loss between the RL model and the frozen pretrained policy."

In other words they first trained it to imitate humans, and saved this model; after that they trained the agent to maximize reward, but used the saved model and penalized it for deviating too far from what that human-imitator model would do. Basically saying "try to maximize reward but don't get too weird about it."

I've taken a crack at #4 but it is more about thinking through how 'hundreds of millions of AIs' might be deployed in a world that looks, economically and geopolitically, something like today's (i.e. the argument in the OP is for 2036 so this seems a reasonable thing to do). It is presented as a flowchart [LW · GW] which is more succinct than my earlier longish post [LW · GW].

"goal misgeneralization paper does does"–typo

"list of papers that it’s"→ that are

Thanks for the list.

On (7), I'm not clear how this is useful unless we assume that debaters aren't deceptively aligned. (if debaters are deceptively aligned, they lose the incentive to expose internal weaknesses in their opponent, so the capability to do so doesn't achieve much)

In principle, debate on weights could add something over interpretability tools alone, since optimal in-the-spirit-of-things debate play would involve building any useful interpretability tools which didn't already exist. (optimal not-in-the-spirit-of-things play likely involves persuasion/manipulation)

However, assuming debaters are not deceptively aligned seems to assume away the hard part of the problem.
Do you expect that there's a way to make use of this approach before deceptive alignment shows up, or is there something I'm missing?