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#1: AI takeoff, longtermism vs. existential risk, and probability discounting
- 2022/04/23
- 再生時間： 30 分
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あらすじ・解説
The remedies for all our diseases will be discovered long after we are dead; and the world will be made a fit place to live in. It is to be hoped that those who live in those days will look back with sympathy to their known and unknown benefactors. — John Stuart Mill Future Matters is a newsletter about longtermism brought to you by Matthew van der Merwe and Pablo Stafforini. Each month we collect and summarize longtermism-relevant research, share news from the longtermism community, and feature a conversation with a prominent longtermist. You can also subscribe on Substack, read on the EA Forum and follow on Twitter. Research Scott Alexander's "Long-termism" vs. "existential risk" worries that “longtermism” may be a worse brand (though not necessarily a worse philosophy) than “existential risk”. It seems much easier to make someone concerned about transformative AI by noting that it might kill them and everyone else, than by pointing out its effects on people in the distant future. We think that Alexander raises a valid worry, although we aren’t sure the worry favors the “existential risk” branding over the “longtermism” branding as much as he suggests: existential risks are, after all, defined as risks to humanity's long-term potential. Both of these concepts, in fact, attempt to capture the core idea that what ultimately matters is mostly located in the far future: existential risk uses the language of “potential” and emphasizes threats to it, whereas longtermism instead expresses the idea in terms of value and the duties it creates. Maybe the “existential risk” branding seems to address Alexander’s worry better because it draws attention to the threats to this value, which are disproportionately (but not exclusively) located in the short-term, while the “longtermism” branding emphasizes instead the determinants of value, which are in the far future. In General vs AI-specific explanations of existential risk neglect, Stefan Schubert asks why we systematically neglect existential risk. The standard story invokes general explanations, such as cognitive biases and coordination problems. But Schubert notes that people seem to have specific biases that cause them to underestimate AI risk, e.g. it sounds outlandish and counter-intuitive. If unaligned AI is the greatest source of existential risk in the near-term, then these AI-specific biases could explain most of our neglect. Max Roser’s The future is vast is a powerful new introduction to longtermism. His graphical representations do well to convey the scale of humanity’s potential, and have made it onto the Wikipedia entry for longtermism. Thomas Kwa’s Effectiveness is a conjunction of multipliers makes the important observation that (1) a person’s impact can be decomposed into a series of impact “multipliers” and that (2) these terms interact multiplicatively, rather than additively, with each other. For example, donating 80% instead of 10% multiplies impact by a factor of 8 and earning $1m/year instead of $250k/year multiplies impact by a factor of 4; but doing both of these things multiplies impact by a factor of 32. Kwa shows that many other common EA choices are best seen as multipliers of impact, and notes that multipliers related to judgment and ambition are especially important for longtermists. The first installment in a series on “learning from crisis”, Jan Kulveit's Experimental longtermism: theory needs data (co-written with Gavin Leech) recounts the author's motivation to launch Epidemic Forecasting, a modelling and forecasting platform that sought to present probabilistic data to decisionmakers and the general public. Kulveit realized that his "longtermist" models had relatively straightforward implications for the COVID pandemic, such that trying to apply them to this case (1) had the potential to make a direct, positive difference to the crisis and (2) afforded an opportunity to experimentally test those models. While the first of these effects had obvious appeal, Kulveit considers the second especially important from a longtermist perspective: attempts to think about the long-term future lack rapid feedback loops, and disciplines that aren't tightly anchored to empirical reality are much more likely to go astray. He concludes that longtermists should engage more often in this type of experimentation, and generally pay more attention to the longtermist value of information that "near-termist" projects can sometimes provide. Rhys Lindmark’s FTX Future Fund and Longtermism considers the significance of the Future Fund within the longtermist ecosystem by examining trends in EA funding over time. Interested readers should look at the charts in the original post for more details, but roughly it looks like Open Philanthropy has allocated about 20% of its budget to longtermist causes in recent years, accounting for about 80% of all longtermist grantmaking. On the assumption that Open Phil ...