The list of posts is getting unwieldy, so I'll post the up-to-date stuff at the beginning:

Humans inconsistencies:

Reward function learning:

Understanding humans:

Framework:

Three human problems and one AI issue

Acausal trade:

Introduction
Double decrease
Pre-existence deals
Full decision algorithms
Breaking acausal trade
Trade in different types of utility functions
Being unusual
Conclusion

Oracle designs:

Three Oracle designs

Extracting human values:

Corrigibility:

Indifference:

AIs in virtual worlds:

True answers from AI:

Miscellanea:

Migrating my old post over from Less Wrong.

I recently went on a two day intense solitary "AI control retreat", with the aim of generating new ideas for making safe AI. The "retreat" format wasn't really a success ("focused uninterrupted thought" was the main gain, not "two days of solitude" - it would have been more effective in three hour sessions), but I did manage to generate a lot of new ideas. These ideas will now go before the baying bloodthirsty audience (that's you, folks) to test them for viability.

A central thread running through could be: if you want something, you have to define it, then code it, rather than assuming you can get if for free through some other approach.

To provide inspiration and direction to my thought process, I first listed all the easy responses that we generally give to most proposals for AI control. If someone comes up with a new/old brilliant idea for AI control, it can normally be dismissed by appealing to one of these responses:

@. The AI is much smarter than us. @. It’s not well defined. @. The setup can be hacked.

By the agent.
By outsiders, including other AI.
Adding restrictions encourages the AI to hack them, not obey them. @. The agent will resist changes. @. Humans can be manipulated, hacked, or seduced. @. The design is not stable.
Under self-modification.
Under subagent creation.
Unrestricted search is dangerous. @. The agent has, or will develop, dangerous goals.

Important background ideas:

I decided to try and attack as many of these ideas as I could, head on, and see if there was any way of turning these objections. A key concept is that we should never just expect a system to behave "nicely" by default (see eg here). If we wanted that, we should define what "nicely" is, and put that in by hand.

I came up with sixteen main ideas, of varying usefulness and quality, which I will be posting in the coming weekdays in comments (the following links will go live after each post). The ones I feel most important (or most developed) are:

Anti-pascaline agent
Anti-restriction-hacking (EDIT: I have big doubts about this approach, currently)
Creating a satisficer (EDIT: I have big doubts about this approach, currently)
Crude measures
False miracles
Intelligence modules
Models as definitions
Added: Utility vs Probability: idea synthesis

While the less important or developed ideas are:

Please let me know your impressions on any of these! The ideas are roughly related to each other as follows (where the arrow Y→X can mean "X depends on Y", "Y is useful for X", "X complements Y on this problem" or even "Y inspires X"):

EDIT: I've decided to use this post as a sort of central repository of my new ideas on AI control. So adding the following links:

Short tricks:

High-impact from low impact:

High impact from low impact, best advice:

Overall meta-thoughts:

Pareto-improvements to corrigible agents:

Predicted corrigibility: pareto improvements

AIs in virtual worlds:

Low importance AIs:

Counterfactual agents detecting agent's influence

Wireheading:

Superintelligence and wireheading

AI honesty and testing:

Goal completion: