One fairly strong belief of mine is that Less Wrong's epistemic standards are not high enough to make solid intellectual progress here. So far my best effort to make that argument has been in the comment thread starting here. Looking back at that thread, I just noticed that a couple of those comments have been downvoted to negative karma. I don't think any of my comments have ever hit negative karma before; I find it particularly sad that the one time it happens is when I'm trying to explain why I think this community is failing at its key goal of cultivating better epistemics.

There's all sorts of arguments to be made here, which I don't have time to lay out in detail. But just step back for a moment. Tens or hundreds of thousands of academics are trying to figure out how the world works, spending their careers putting immense effort into reading and producing and reviewing papers. Even then, there's a massive replication crisis. And we're trying to produce reliable answers to much harder questions by, what, writing better blog posts, and hoping that a few of the best ideas stick? This is not what a desperate effort to find the truth looks like.

Here is the best toy model I currently have for rational agents. Alas, it is super messy and hacky, but better than nothing. I'll call it the BAVM model; the one-sentence summary is "internal traders concurrently bet on beliefs, auction actions, vote on values, and merge minds". There's little novel here, I'm just throwing together a bunch of ideas from other people (especially Scott Garrabrant and Abram Demski).

In more detail, the three main components are:

1. A prediction market
2. An action auction
3. A value election

You also have some set of traders, who can simultaneously trade on any combination of these three. Traders earn money in two ways:

1. Making accurate predictions about future sensory experiences on the market.
2. Taking actions which lead to reward or increase the agent's expected future value.

They spend money in three ways:

1. Bidding to control the agent's actions for the next N timesteps.
2. Voting on what actions get reward and what states are assigned value.
3. Running the computations required to figure out all these trades.

Values are therefore dominated by whichever traders earn money from predictions or actions, who will disproportionately vote for values that are formulated in the same on... (read more)

(COI note: I work at OpenAI. These are my personal views, though.)

My quick take on the "AI pause debate", framed in terms of two scenarios for how the AI safety community might evolve over the coming years:

1. AI safety becomes the single community that's the most knowledgeable about cutting-edge ML systems. The smartest up-and-coming ML researchers find themselves constantly coming to AI safety spaces, because that's the place to go if you want to nerd out about the models. It feels like the early days of hacker culture. There's a constant flow of ideas and brainstorming in those spaces; the core alignment ideas are standard background knowledge for everyone there. There are hackathons where people build fun demos, and people figuring out ways of using AI to augment their research. Constant interactions with the models allows people to gain really good hands-on intuitions about how they work, which they leverage into doing great research that helps us actually understand them better. When the public ends up demanding regulation, there's a large pool of competent people who are broadly reasonable about the risks, and can slot into the relevant institutions and make them work well.
2. AI sa

Just read Bostrom's Deep Utopia (though not too carefully). The book is structured with about half being transcripts of fictional lectures given by Bostrom at Oxford, about a quarter being stories about various woodland creatures striving to build a utopia, and another quarter being various other vignettes and framing stories.

Overall, I was a bit disappointed. The lecture transcripts touch on some interesting ideas, but Bostrom's style is generally one which tries to classify and taxonimize, rather than characterize (e.g. he has a long section trying to analyze the nature of boredom). I think this doesn't work very well when describing possible utopias, because they'll be so different from today that it's hard to extrapolate many of our concepts to that point, and also because the hard part is making it viscerally compelling.

The stories and vignettes are somewhat esoteric; it's hard to extract straightforward lessons from them. My favorite was a story called The Exaltation of ThermoRex, about an industrialist who left his fortune to the benefit of his portable room heater, leading to a group of trustees spending many millions of dollars trying to figure out (and implement) what it means to "benefit" a room heater.

Here's a (messy, haphazard) list of ways a group of idealized agents could merge into a single agent:

Proposal 1: they merge into an agent which maximizes a weighted sum of their utilities. They decide on the weights using some bargaining solution.

Objection 1: this is not Pareto-optimal in the case where the starting agents have different beliefs. In that case we want:

Proposal 2: they merge into an agent which maximizes a weighted sum of their utilities, where those weights are originally set by bargaining but evolve over time depending on how accurately each original agent predicted the future.

Objection 2: this loses out on possible gains from acausal trade. E.g. if a paperclip-maximizer finds itself in a universe where it's hard to make paperclips but easy to make staples, it'd like to be able to give resources to staple-maximizers in exchange for them building more paperclips in universes where that's easier. This requires a kind of updateless decision theory:

Proposal 3: they merge into an agent which maximizes a weighted sum of their utilities (with those weights evolving over time), where the weights are set by bargaining subject to the constraint that each agent obeys commitme... (read more)

I recently had a very interesting conversation about master morality and slave morality, inspired by the recent AstralCodexTen posts.

The position I eventually landed on was:

1. Empirically, it seems like the world is not improved the most by people whose primary motivation is helping others, but rather by people whose primary motivation is achieving something amazing. If this is true, that's a strong argument against slave morality.
2. The defensibility of morality as the pursuit of greatness depends on how sophisticated our cultural conceptions of greatness are. Unfortunately we may be in a vicious spiral where we're too entrenched in slave morality to admire great people, which makes it harder to become great, which gives us fewer people to admire, which... By contrast, I picture past generations as being in a constant aspirational dialogue about what counts as greatness—e.g. defining concepts like honor, Aristotelean magnanimity ("greatness of soul"), etc.
3. I think of master morality as a variant of virtue ethics which is particularly well-adapted to domains which have heavy positive tails—entrepreneurship, for example. However, in domains which have heavy negative tails, the pursuit of g