Import AI 456: RSI and economic growth; radical optionality for AI regulation; and a neural computer

Import AI 456: RSI and economic growth; radical optionality for AI regulation; and a neural computer What laws does superintelligence demand? 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. Regulate? Don’t regulate. There’s a third way: Radical Optionality: …Governments should invest in the tools now that they might need in a future crisis… Researchers with the Institute for Law & AI have written about “radical optionality”, an approach whereby governments might give themselves the tools that they may need in the future if powerful AI starts to massively disrupt the world. “At its core, radical optionality is about preserving democratic governments’ ability to make good decisions about how to govern transformative AI systems as circumstances evolve. In the short term, this means avoiding overregulation while rapidly building the institutions, information channels and legal authorities needed to respond competently to a broad range of scenarios.” The key idea - invest now for an uncertain future: Given the immense stakes of AI development, “governments should be willing to spend an extraordinary amount of money, effort, and political capital on preserving optionality”, they write. In other words: It’s such a big deal you should be fine spending a bunch of money now with an uncertain return. “Governments should be wary of counterproductive interventions, but not much concerned with the actual pecuniary cost of any realistic measure that seems likely to have net-positive results”. Specifics: They also recommend several specific interventions in a few categories: Information-gathering authorities: Transparency requirements, where companies need to publish information about their AI systems. Reporting requirements, where companies are compelled to share certain information with a government agency. Once these are in place, establish an auditing regime so some third-party can verify the veracity of what the transparency and reporting rules target. Whistleblower protections: Ensure that employees at frontier labs can report information about risks. Information-sharing within and between governments: Ensure that governments can effectively coordinate and facilitate discussions, especially those dealing with sensitive information about the progress of AI. This may be especially important for strengthening and protecting supply chains deemed critical to AI development. Flexible rules and definitions: Avoiding premature regulation by potentially making conditional “if-then” regulatory commitments, or an approach whereby a high-level target is set (e.g., mitigating risk) and companies are free to define the specifics of how they do that. This is bound up in the need to come up with flexible definitions, or definitions that can evolve over time. Assessments and evaluations: Develop government and third-party capacity to assess the capabilities and safety aspects of AI systems. Improve security of model weights and algorithmic secrets: Invest more in locking down the weights of neural nets as well as the algorithmic secrets behind some of the best systems. This can be achieved through promulgating voluntary standards for physical and cybersecurity. Hiring and talent: A meta-investment which would help with all of the above is investing…