This is a list of projects to consider for folks who want to use Wise AI to steer the world towards positive outcomes.
Some of these projects are listed because they're impactful. Others are listed because I believe they would be good projects for someone to get started.
Please note that this post is titled "potentially useful projects" for a reason. Some of these projects are likely to have much higher impact than others. Whilst I've really tried to avoid projects with net-negative EV, it's entirely possible that a few would be anyway. Please don't ignore your own judgment just because I've listed a project!
I'm sure my views about what projects are valuable will change quite significantly as I dive deeper into the topic, but I still think it's worthwhile for me to put some kind of list out there.
Field-building:
At this stage, I consider field-building work around Wise AI as especially high priority. I feel that there’s starting to be some real energy around this area, however, very little of this will aid us in avoiding catastrophic risks. Fields tend to be more flexible in their initial stages, but gradually settle into an orthodoxy. It's important to intervene when it's still possible to make a difference:
- Prioritisation research: Wisdom can mean a lot of different things; there are multiple types of wisdom. It seems quite important to try to identify priorities whilst the field is still fluid and easier to influence via this kind of work. One key way AI safety analysis will differ from that of people outside the field is that AI safety analysis is much more likely to take into account the externalities of enabling a capability, as opposed to naively ignoring this. Whilst this work is high priority, you may want to refrain unless you think it’s a particularly good fit for your skillset, given the difficulty of this work. It’s very easy to miss a crucial consideration that breaks your analysis and maybe even reverses the sign.
- Summarising important papers or other resources (example): Producing a good summary can take a lot of work, but this doesn’t seem to be the hardest skill to learn. There are two primary reasons why you might want to do this: a) increasing the impact of the original research b) building up the field by raising awareness and by making it easier to get oriented.
- Produce and verify some AI-generated reports related to artificial wisdom: AI-generated reports are often very good. The main downside is that you don’t know if they are reliable, but this isn't much of an issue if someone just decides to put in the work.
- Literature review of previous work on artificial wisdom: Whilst artificial reports can be very helpful, Deep Research still can’t match human judgement in terms of what is important. This would take longer, but it's probably worth it.
- Summary of how different cultures and disciplines conceive of wisdom: Whilst less direct than reviewing work on artificial wisdom, this would still save few folk a lot of time. Very easily to subtly distort another culture's ontology though.
- Assist with the communication of these ideas: There is some quite valuable work to be done here. PM me if you’re interested in this, though I suspect quality of communication is more important than quantity.
Theoretical questions:
- To what extent is wisdom just the absence of unwisdom? Even if being wise requires more than the mere avoidance of being unwise, avoiding unwisdom may still be the lowest hanging fruit.
- In what ways is current AI wise? In what ways is it unwise? It's much easier to form a plan to fill in the gaps once we know what they are.
- Insofar as current AIs are wise, what is it about their training that makes them wise? Similarly, insofar as they are unwise, what is the cause?
- How might AI wisdom differ from human wisdom? How can we bridge this gap?
- How might neural networks represent wisdom internally? Is there likely to be a “wisdom direction”, and, if so, how would we find it? Maybe we can identify the various components of wisdom?
- What are the impacts of choosing a particular broad approach to learning wisdom? For example, imitation learning vs RL. What balance is optimal?
- In what circumstances might a wise advisor lead to a reckless or malicious actor becoming responsible or prosocial? This determines the extent to which wise AI would be safe to proliferate.
- How can we evaluate an AI system for wisdom? Is this even possible? How accurately would we need to be able to measure wisdom in order to train an AI system to be wise? How does this differ from evaluating wisdom in humans?
- Is the idea of a “wisdom explosion” a coherent concept? Or perhaps it is conceivable, but feedback loops are too weak in this world to make it work.
- Is a super-intelligence system automatically a super-wise system? If so, for what definitions of intelligence and wisdom
- What biases or mistakes can cause someone to be smart rather than wise?
- What level of wisdom do we need in order to navigate these challenges?
- What would the world look like if it were devoting serious attention to this?
- What are the strengths and weaknesses of LLMs when it comes to philosophy?
Concrete work:
- Improving model specs: Read some model specs and make suggestions about how to improve the wisdom of the models. You might object that this would only be incremental, but I suspect incremental gains are valuable anyway.
- User interface design: What is the best user interface for obtaining wise advice when it's really important to make the right decision? It seems unlikely that it would be just a simple chatbot interface.
- Journaling your attempts to make use of AI advice: The hope would be that the journaling would identify issues or considerations that would be easy to miss in Theory Land. For this reason, I see subjective research as massively underrated. It may not be as rigorous as empirical research, but it provides a firehose of bits and this is more valuable than rigor in the early stages of a field.
- Attempting to train wise AI advisors and journaling your experience: As per the previous point, at least at this stage, I’d much rather have an informal journal someone kept whilst attempting to train wise AI advisors than a few extra bits of empirical info.
- Identify and experiment with various toy scenarios to figure out which ones would be most valuable for developing or testing wisdom: This work could be directly applied, but it would help clarify the strategy for improving wisdom as well.
- Social media advisor bot: Create a bot to advise you on your social media posts. Whether they’re posts that you’re likely to regret in the future, according to your own values, either because you’re being rude or because you’re strawmanning. If this were to improve the quality of the social media discourse, it could increase the chance of society being able to navigate somewhere positively.
- Tools for reducing cognitive biases: There’s a decent chance that being wise is primarily about avoiding unwisdom, so why not lean into it?
- Decision sanity-checker: “If catastrophes often involve someone making egregiously bad decisions (knowingly or otherwise), might there be a role for LLMs to help catch these decisions before they are finalized? What information and infrastructure would be needed? - There will likely be decisions that such a tool could catch, where avoiding making a bad decision could be massively beneficial for the world.
- Crux identification: AI tool that determines the main reasons that people (or groups disagree). Much more tractable then directly figuring out the correct answer.
- Start a group that aims to provide analyses on questions of strategic importance with the assistance of AI tools: Might be closer to a bite-sized piece of work than trying to directly train AI to advise on decisions.
Less important:
- Improving the Artificial Wisdom Wikipedia article: This could be a good project for learning more about the area, but the impact of this is quite unclear, with as AI models potentially make a good Wikipedia article less important.
- Running a traditional empirical experiment on AI wisdom: I expect this will be valuable down the line. Right now, subjective research provides more bits, but when we get to the point of having to try to decide between competing schools of thought, we'll need something more rigorous to differentiate. If you’re especially keen on traditional empirical research, you might just want to focus on other areas of AI safety for now.
List of projects for related areas:
• AI Impacts Essay Competition: Covers the automation of philosophy and wisdom.
• Fellowship on AI for Human Reasoning - Future of Life Foundation: AI tools for coordination and epistemics
• AI for Epistemics - Benjamin Todd - He writes: "The ideal founding team would cover the bases of: (i) forecasting / decision-making expertise (ii) AI expertise (iii) product and entrepreneurial skills and (iv) knowledge of an initial user-type. Though bear in mind that if you have a gap in one of these areas now, you could probably fill it within a year" and then provides a list of projects.
• Project ideas: Epistemics - Forethought - focuses on improving epistemics in general, not just AI solutions.