AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe. Subscribe now RAND thinks the best AI strategy for the US is to keep all options open: …It’s not clear exactly what is optimal, so the US should spend money now to preserve options… RAND has published a lengthy paper about what the US should do to “secure geopolitical advantage on an uncertain path to superintelligence”. The main takeaway is that because so much of how the next phase of the AI takeoff will occur is unknown, the best thing is for the US to maintain a “Freedom of Action” strategy. The purpose of this strategy is “to build and preserve the ability to secure U.S. geopolitical advantage and ensure humanity’s survival with agency through the transition to Superintelligence”. The four key ingredients of the Freedom of Action Strategy: Build a human-AI ecosystem: Invest in AI safety; build tools to preserve human agency; invest in sharing the benefits of the technology; prepare society for AI-driven disruption; shape the incentives of the AI ecosystem. Build an AI-security architecture: Develop the tools to understand the frontier of AI systems both at home and abroad, including visibility into underlying compute; build technology to verify potential agreements; create regulatory expertise to govern the technology. Adapt legacy national security enterprises for the AI era: Overhaul all the security institutions of the country so that they’re ready for the changes AI brings on and able to capitalize on AI capabilities. Invest in the capacity of citizens, firms, and governments to respond: Give decisionmakers information to help them think about AI; develop and diffuse positive uses of AI to deal with its changes; build break-glass plans for responding to crises; raise AI literacy. Seven archetypal strategies in three families: Coexistence: Dominance; the US leads development of AI and suppresses others. Co-Development; the US leads a consortium (including China) to co-develop safe superintelligence with shared governance, pooled compute, and verification and monitoring. Preparedness; the US mobilizes a coexistence effort via informal coordination, “including voluntary commitments, phased deployment, independent evaluation, incident reporting, and the domestic capacity to absorb and recover from AI incidents.” Denial: Moratorium; the US pursues “a verifiable global halt to AI development above specified thresholds”. Deterrence; the US “halts its own frontier development above defined thresholds and uses the full toolkit of national power to prevent and, if necessary, destroy foreign programs”. Continuity of Society; “When every preventive strategy has failed or been judged infeasible, and coexistence is deemed impossible, the remaining objective is survival with some human populations retaining agency: The United States creates geographically distributed, biologically self-sustaining settlements hardened against AI-enabled threats”. Acceleration: The strategy implicitly believes that constraining AI development is more dangerous than AI development itself, so the US “relies on markets, competition, and rapid-iteration to produce safety as a byproduct of useful AI”. The five main uncertainties: Danger proximity: At what point does AI become so dangerous the risks aren’t worth the benefits? “If danger is close, strategies that buy time, build defenses, and invest in resiliency should be favored. If danger is not close or if humans are in greater danger without progress on advanced A, the case for accelerated development strengthens.” Coexistence feasibility: Is it possible for humans and AI systems to find a stable equilibrium? “If coexistence is feasible, strategies that build and deploy advanced AI become more desirable. If coexistence is infeasible, the case shifts toward restraining or suppressing development or other means of protecting human civilization.” Restraint feasibility: Can humans cooperate to restrain AI development? “If restraint is feasible, cooperative strategies become available. If it is not, restraint-based approaches are reachable only through more-coercive enforcement.” Decisive strategic advantage: Can a single actor build a decisive strategic lead over its competitors? “If decisive advantage is achievable and the United States assesses that it can maintain dominance, a unilateral strategy may be sensible. If a decisive advantage is impossible, because of proliferation or other causes, no single actor can leverage AI to dictate global terms.” Suppression feasibility: Can domestic or rival AI programs be suppressed by technology controls? “If suppression is feasible, Deterrence becomes possible. If it is not, actors can press ahead with dangerous development even when others restrain, without fear of being restrained themselves.” Why this matters - taking AI seriously requires spending money: The big takeaway from this RAND piece is that the US needs to spend a lot of money if it wants to pre-position itself to take advantage of continued progress in artificial intelligence - even if the main pre-positioning is about retaining optionality. Right now, it feels like the US strategy can mostly be described as the “acceleration” one described here by RAND, which is analogous to me to sitting in a car and spending all your resources on making the car go faster and upgrading the engine, and nothing on proactive safety measures like seatbelts or headlights or brakes. Read more: A U.S. Strategy to Secure Geopolitical Advantage on an Uncertain Path to Superintelligence (RAND). * Human researchers grow mice with partially human brain matter: …When is a mouse not a mouse? When a chunk of its brain is human brain tissue… A group of researchers have figured out how to grow chunks of human-like brains inside baby mice with deliberately depleted mouse brains, then use these animals as potential experimental platforms for studying human brain issues. “We establish a transplantation platform using a genetic strategy to effectively deplete glutamatergic neurons from mouse neocortex and hippocampus (apallial) and neonatally engraft the cortical cavity with human stem-cell-derived cortical organoids (hCO) to generate xenocortical mice,” the authors write. “Human cortical neurons integrate with the mouse nervous system, and in vivo cortical graft-wide calcium imaging and electrophysiological analyses revealed patterns of organized activity resembling developing circuits.” The mice with human brains did ok: In studies, the humans compared control mice (mice with normal brains), with mice with deliberately reduced brains (apallial mice), with mice with transplanted human brain tissue (xenocortical mice, or XCX). “Unsupervised machine learning applied to spontaneous mouse behaviour using motion sequencing (MoSeq) revealed distinct behavioural repertoires in control and apallial mice, with the XCX group positioned intermediate between, yet distinct from, both the control and apallial groups”, they write. “Data indicate that the human neural graft develops into an electrically active, functionally integrated network… gene set enrichment analysis of XCX L5-ET neurons revealed enrichment for human-specific L5-ET genes identified in the primary motor cortex, as well as genes characteristic of L5-ET neurons in human frontoinsular cortex, which contains VENs—a specialized population implicated in social cognition and neuropsychiatric disease”. Maze test: The researchers tested out the mice in a maze environment which is designed such that they think “the mice must maintain a memory trace of the previously explored environments”, and when they put the mice into it they observed “control and XCX, but not apallial, mice performed above chance levels”. This suggests the human brain tissue was capable of serving some memory functions. Why this matters - medicine and superintelligence: The primary use here is being able to better test out medical therapies for human brains on mice, which is an important toolkit in developing new medicines. But I also think it’s a demonstration of the large space of intelligences that might be buildable in the future by human and AI scientists; here, we have a kind of chimera brain fusing mouse and human brains together. How far could such experiments go? Could we imagine doing such experiments on other living beings? On humans? There are tremendous medical and ethical issues here, but the proof it works in mice surely suggests at some point it may be tried in others. Read more: Developmental xenocortication using human-derived organoids in mice (Nature). * Towards a research agenda for pacing AI progress: …What can the world do to flesh out pacing and make better decisions about it? A lot, actually… With all the recent talk of AI pacing and AI slowdowns there has been a lot of enthusiasm for the general idea but the actual research field for how to pace AI progress (as opposed to stopping it entirely) is relatively underdeveloped. Now, researchers are starting to think about what a pacing research agenda might look like, and a new paper lays out some of the thinking that could be developed here. “Making the right choices about the pace of AI development will be critical for everything from national security to public health. Indeed, the choices governments, AI developers and other key actors make in the next few years may well ripple outward for decades or centuries,” write the researchers in a new group paper. “The world will pace progress one way or another. Absent better tools, it might do so haphazardly, through improvised reactions overfit to prior expectations, in ways that fail to actually address risks, through institutions that outlast their use. Our hope is that, with proper research, pacing can become progressively more deliberate, targeted, proportionate, decisive, and legitimate.” Who did the research: ACS Research, University of Toronto, Arb Research, Paradigm 3 Institute, the Wharton School at the University of Pennsylvania, Goodheart Labs, Trajectory Institute, Harvard University, and the University of Cambridge. Arguments against pacing: Delays benefits like healthcare and other science-driven breakthroughs; can potentially increase concentration of power; may give a false sense of security and generate various AI capability overhangs due to the cheapening costs of compute during the pacing period; pacing early can be less efficient than pacing late given ability to use AIs to do more surgical and effective pacing; it’s hard to undo a pacing regime if you get it wrong (e.g, nuclear power). Arguments for pacing: Gives us more time to deal with threats driven by AI advances (e.g, cyber and bio); it’s hard to predict AI progress so unpredictable scary stuff can happen; AI progress can lead to lose-lose situations for the world (e.g, governments relinquishing control over some military decisions and handing them to AI systems, leading to human disempowerment). Understanding incentives: “The practical effects of an intervention will depend substantially on how the affected parties respond to them, including the actors who enforce the intervention”. Governments have variable incentives, both wanting to compete with one another to gain an advantage, but also needing to be responsive to the concerns of citizens and how that relates to domestic politics. Infrastructure providers, especially semiconductor and cloud companies, have the greatest incentives to push ahead and the most to lose by pacing. This means that “rather than rely on voluntary, unilateral interventions, some contexts will require coordinated pacing. This is more onerous, because it requires coordination during the various stages of pacing, and then mechanisms to ensure the coordination persists”. Along with this, there needs to be research [truncated for AI cost control]