EA - Some Things I Heard about AI Governance at EAG by utilistrutil

Link to original articleWelcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Some Things I Heard about AI Governance at EAG, published by utilistrutil on February 28, 2023 on The Effective Altruism Forum.IntroPrior to this EAG, I had only encountered fragments of proposals for AI governance: "something something national compute library," "something something crunch time," "something something academia vs industry," and that was about the size of it. I'd also heard the explicit claim that AI governance is devoid of policy proposals (especially vis-a-vis biosecurity), and I'd read Eliezer's infamous EAG DC Slack statement:My model of how AI policy works is that everyone in the field is there because they don't understand which technical problems are hard, or which political problems are impossible, or both . . .At this EAG, a more charitable picture of AI governance began to cohere for me. I was setting about recalling and synthesizing what I learned, and I realized I should share—both to provide a data point and to solicit input. Please help fill out my understanding of the area, refer me to information, and correct my inaccuracies!Eight one-on-ones contributed to this picture of the governance proposal landscape, along with Katja's and Beth's presentations, Buck's and Richard Ngo's office hours, and eavesdropping on Eliezer corrupting the youth of EAthens. I'm sure I only internalized a small fraction of the relevant content in these talks, so let me know about points I overlooked. (My experience was that my comprehension and retention of these points improved over time: as my mental model expanded, new ideas were more likely to connect to it.) The post is also sprinkled with my own speculations. I'm omitting trad concerns like stop-the-bots-from-spreading-misinformation.Crunch Time FriendsThe idea: Help aligned people achieve positions in government or make allies with people in those positions. When shit hits the fan, we activate our friends in high places, who will swiftly smash and unplug.My problem: This story, even the less-facetious versions that circulate, strikes me as woefully under-characterized. Which positions wield the relevant influence, and are timelines long enough for EAs to enter those positions? How exactly do we propose they react? Additionally, FTX probably updated us away from deceptive long-con type strategies.Residual questions: Is there a real and not-ridiculous name for this strategy?Slow Down ChinaThe chip export controls were so so good. A further move would be to reduce the barriers to high-skill immigration from China to induce brain drain. Safety field-building is proceeding, but slowly. China is sufficiently far behind that these are not the highest priorities.Compute RegulationsI'm told there are many proposals in this category. They range in enforcement from "labs have to report compute usage" to "labs are assigned a unique key to access a set amount of compute and then have to request a new key" to "labs face brick wall limits on compute levels." Algorithmic progress motivates the need for an "effective compute" metric, but measuring compute is surprisingly difficult as it is.A few months ago I heard that another lever—in addition to regulating industry—is improving the ratio of compute in academia vs industry. Academic models receive faster diffusion and face greater scrutiny, but the desirability of these features depends on your perspective. I'm told this argument is subject to "approximately 17 million caveats and question marks."Evaluations & AuditsThe idea: Develop benchmarks for capabilities and design evaluations to assess whether a model possesses those capabilities. Conditional on a capability, evaluate for alignment benchmarks. Audits could verify evaluations.Industry self-regulation: Three labs dominate the industry, an arrangement that promises to continue for a while, facilit...