Here are some of my top candidates for big pushes to do right now on technical AI safety (low effort notes):

• Much better model organisms / misalignment analogies:
  • Doing a wider set of pessimized training runs
    • Good candidate for lots of AI labor automation? Like maybe good to try to set up pipelines for building these envs.
  • Demonstrating risks from fitness-seekers/reward-seekers empirically
    • Even on current models with better tests, see here
  • Demonstrating various types of memetic spread of misalignment?
• Actually do control
  • Build pipelines for red-teaming monitors and the agent itself. For the agent red-teaming, I'd put particular focus on checking whether it continues malign trajectories.
  • Scaffold integrated control features and other non-monitoring runtime control measures
  • Human response and auditing
  • Improving async and sync monitoring
  • Agent security features
  • Surveilling for rogue internal deployments (as in, building after-the-fact detection methods for rogue deployments)
• Preparing for handoff and elicitation
  • Get AIs generically better at conceptual work
  • Have a plan for the evals we ultimately need to see if handoff/deference would go well and start iterating on earlier versions
    • These presumably will involve a bunch of manual scoring, so we'll need to build a process for it.
  • Analyze AI biases and epistemics and improve across many domains
  • Build the anti-slop/anti-mundane-misalignment coalition via doing ratings of AIs and applying some pressure to improve on these ratings. This could focus on a variety of related issues.
    • The hope is basically that there might be widespread interest in removing/redacting mundane misalignment and other non-misalignment behavioral problems that reduce productivity and large parts of this seem differentially good. So, if we could make this a salient metric, AI companies might improve this. A lot of the difficulty would be in measuring the problem reasonably well. There are a bunch of different ways to apply pressure or increase salience if we had decent metrics, especially if these metrics legibly correspond to a common problem that many people are running into.
  • Try to do various trend extrapolations on things here to argue we aren't on track?
• Neuralese decoding prep: Make natural language autoencoders much better, build methods for extracting internal CoT, build better evaluations of how well natural language autoencoders work.