Quick Takes

one problem with UBI as a solution for AI economic disruption: at the moment when AI can first replace a human job, it will probably cost only epsilon less than the human. the cost will be mostly capital (datacenters, chips, electric plants, etc), rather than labor. so we can only afford to give the human epsilon UBI. as time goes on, eventually the AI gets cheap enough that humans can get substantial UBI, possibly exceeding their original income, as the AIs become more productive than the humans were. but there's a big gap in the middle that we need to bridge somehow. the best case scenario is that different industries get automated at different times so that the gaps don't line up, and we can redistribute the surplus from the first industries to be automated to fill the gap for later industries. the worst case is that all the gaps happen at once and we all starve to death because the surplus is not enough to keep people alive.

I would like to announce that after consulting with a flesh and blood, artisanal human lawyer, I am officially retracting my retraction. Turns out I was totally right the entire time about NY state AI bill S7263 - the bill doesn't ban chatbots from giving advice about e.g. Medicine even in its current form, and the tweet & quote tweeters are incorrect. Not only that, but I also think that the conversation I had in the original thread is itself representative of the pitfalls of TPOT/"Inadequate Equilibria" modeling of government which I complained about. An evenhanded fullpost about the debacle is forthcoming

A weird thing I have been thinking about recently is the idea that "a computer can only do one thing at once at the top layer of abstraction". Where "one thing" is loosely "run one program, one algorithm, one task/optimisation loop etc". The base of this idea is this: Suppose you had a Turing machine. For it to run two programs simultaneously (rather than just one followed by the other), it would need two heads. Furthermore, the heads must have separate state and transition tables. At which point you actually have two Turing machines reading and writing to the same tape. You can tell because, for them to share information between each other (suppose one of them is running a database and the other a webpage that displays information from the database) they actually have to write to the tape, creating a sequential lock between sending and receiving. This is not a problem a single Turing machine would ever have. By this logic of course a laptop/phone is actually many computers running at once, which is pretty accurate given my understanding of multithreading issues. And even then it actually is quite close to "one thing" at the topmost level--There's usually one active app or screen (it's possible this is more to deal with the limitations of human users than the device itself). Same with humans, we can multitask but only by task switching.

I think that people overrate bayesian reasoning and underrate "figure out the right ontology".  Most of the way good thinking happens IMO is by finding and using a good ontology for thinking about some situation, not by probabilistic calculation. When I learned calculus, for example, it wasn't mostly that I had uncertainty over a bunch of logical statements, which I then strongly updated on learning the new theorems, it was instead that I learned a bunch of new concepts, which I then applied to reason about the world.  I think AI safety generally has much better concepts for thinking about the future of AI than others, and this is a key source of alpha we have. But, there are obviously still a huge number of disagreements remaining within AI safety. I would guess that debates would be more productive if we more explicitly focused on the ontology/framing that each other are using to reason about the situation, and then discussed to what extent that framing captures the dynamics we think are important.  I think it would be good if more people say things like "I think that's a bad concept, because it obscures consideration X, which is important for thinking about the situation".  Here are some widely used concepts I think are bad and I wish became less load bearing in AI safety discourse:  * "Fast" and "slow" takeoff; takeoff speeds in general. I think these concepts are very unclear and not super natural. There are various operationalizations of these (e.g. Paul's "slow takeoff" = a gdp doubling over the course of the 4 years before the first single year that gdp doubles). This is obviously arbitrary, and I don't see why world's that meet this definition are worthwhile reasoning about separately from worlds that don't meet this definition. I also think its easy to smuggle in lots of other correlations here, like slow takeoff = people are woken up, or slow takeoff = alignment is much easier, etc.  * I feel more excited about talking about things like "I think

I'm writing a newsletter on current events, long-term trends, and topical debates roughly every other day. Recent posts include: * A summary of a debate on AI progress, featuring Ajeya Cotra, Peter Wildeford, Eli Lifland, Matthew Barnett, and others. * The three types of problems with population decline. * We underestimate the pace of progress because much of it isn't salient. * The reason we don't see more automation is simply that AI isn't good enough. * Economists are unusually good at taking human agency into account.

Bernie's Sanders quoted the March 2023 Pause Giant AI Experiments Open Letter's language "governments should step in and institute a moratorium" in a video today as justification for his legislation calling for a moratorium on the construction of new data centers, even though a moratorium on new data centers is not the kind of moratorium that the letter called for. Bernie quotes the pause letter at 7:12:

In order to believe X falsely, one has to construct a plausible-ish world where X is the case. This distorted-world construction can happen piecemeal, in a sort of auto-sum-threshold attack. In other words, suppose you want to rationalize your belief in X. You could simply abdicate all logic, and assert X while also asserting everything else that comes from your ordinary truth-seeking beliefs. (Well, that's not simple, because what do we mean by believing in X, then? Something about actions? I'll leave this as an open question here.) However, that method is kinda hard sometimes. For one thing, if you are trying to truth-seek somewhat, you might follow the rule of believing logical implications of your beliefs. That could be out of pure ethics, or it could be for instrumental reasons. So you would very quickly run into contradictions. For another thing, you'd look silly, and have a hard time mind-melding with people on X-related things. Better might be to construct a "logical counterfactual" where X is the case. You construct a plausible world nearest to our own, but where X is the case. That world may be logically / conceptually / strategically incoherent, but it could be less incoherent. X is false, and X is related to many things that you know about. So there are many logical rends to mend. This can happen piecemeal. You don't have to construct the whole X world in one go. Indeed, doing so would draw a lot of attention, and in particular self-attention. If you try to mend a bunch of rends all in one go, where they all relate to your believe in X and would all be solved by just believing not-X, you might strongly tend to just make that update. Instead you can do it piecemeal. You mend one rend today, one tomorrow. Any given contradiction can be smoothed over * by coming up with just slightly implausible explanations, * by just slightly questioning some logical links, * by just slightly revising your concepts about X to take a little dent from the contradi