The podcast explores the importance of technical expertise in AI governance, covering areas like hardware engineering, software development, and information security. It discusses engineering technical levers, forecasting AI development, technical standards development, and grantmaking to boost AI governance interventions.
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Quick takeaways
Technical work in AI governance enhances AI governance interventions through engineering solutions and information security measures.
Focusing on technical levers and information security can make AI coordination/regulation enforceable and secure AI development processes.
Deep dives
The Importance of Technical Work in AI Governance
Technical work in AI governance involves using technical expertise to improve the chances of success for AI governance interventions. This type of work focuses on creating knowledge to enhance decision-making in AI governance interventions, potentially more impactful than direct technical safety solutions.
Engineering Technical Levers for AI Coordination and Regulation
Engineering technical solutions like on-chip devices for AI specialized chips, deadman switches on AI hardware, and software auditing of ML models can aid in enforcing AI coordination and regulation. These solutions aim to make the enforcement of regulations more politically acceptable and effective.
Information Security and AI Governance
Information security plays a crucial role in preventing theft of unsafe ML models, ensuring authentic data verification for regulators, and maintaining cybersecurity standards within AI companies. Strengthened cybersecurity measures can address risks associated with AI development and deployment, enhancing overall governance and safety in the AI sector.
People who want to improve the trajectory of AI sometimes think their options for object-level work are (i) technical safety work and (ii) non-technical governance work. But that list misses things; another group of arguably promising options is technical work in AI governance, i.e. technical work that mainly boosts AI governance interventions. This post provides a brief overview of some ways to do this work—what they are, why they might be valuable, and what you can do if you’re interested. I discuss: Engineering technical levers to make AI coordination/regulation enforceable (through hardware engineering, software/ML engineering, and heat/electromagnetism-related engineering) Information security Forecasting AI development Technical standards development Grantmaking or management to get others to do the above well Advising on the above.