Exploring how cognitive science can improve AI safety, governance and prioritization.
I’d be excited to intern for any research project.
Always happy to chat!
Daniel_Friedrich
Karma: 131
More evidence X-risk amplifies action against current AI harms
A quick review of resource depletion, waste and overpopulation
AI safety and consciousness research: A brainstorm
A tool for searching rationalist & EA webs
A new place to discuss cognitive science, ethics and human alignment
Recently, I made RatSearch for googling within EA-adjecent webs. Now, you can try the GPT bot version! (GPT plus required)
The bot is instructed to interpret what you want to know in relation to EA and search for it on the Forums. If it fails, it searches through the whole web, while prioritizing the orgs listed by EA News.Cons: ChatGPT uses Bing, which isn’t entirely reliable when it comes to indexing less visited webs.
Pros: It’s fun for brainstorming EA connections/perspective, even when you just type a raw phrase like “public transport” or “particle physics”
Neutral: I have yet to experiment whether it works better when you explicitly limit the search using the site: operator—try AltruSearch 2. It seems better at digging deeper within the EA ecosystem; AltruSearch 1 seems better at digging wider.
Update (12/8): The link now redirects to an updated version with very different instructions. You can still access the older version here.
Sounds nice, what’s the approximate schedule? Will a cohort form if I apply 2 months from now?
Balancing safety and waste
Suggestion: Integrated search in LessWrong, EA Forum, Alignment Forum and perhaps Progress Forum posts.
If Big Tech finds these kinds of salaries cost-effective to solve their problems, I would consider it a strong argument in favor of this project.
I imagine Elon Musk could like this project given that he believes in small effective teams of geniuses.
I’d say “polymaths” is a good label for people I’d expect to make progress like Yudkowsky, Bostrom, Hanson and von Neumann.
Edit: This may be fame-selection (engineers don’t often get credit, particularly in teams) or self-selection (interest in math+society).
The Manhattan and Enigma projects seem like examples where this kind of strategy just worked out. Some consideration that come to mind:
There could be selection effects.
From what I can find, members of these teams weren’t lured in by a lot of money. However, the salience of the AI threat in society is tiny, compared to that of WWII and large incentives could compensate that.
I’ve read money can sometimes decrease intrinsic motivation, that drives exploration & inventions, however these findings are being rebutted by newer studies. Apart from that, my guess would be that getting those teams together is the key part and if large money can facilitate that, great.
A wild idea that might help in case a similar phenomenon works in the sub-population of geniuses & which could make this project more appealing to donors: Limit a portion of these salaries, so that the recipients could only use them for socially beneficial uses.
The idea of existential risk cuts against the oppression/justice narrative, in that it could kill everyone equally. So they have to opposite it.
That seems like an extremely unnatural thought process. Climate change is the perfect analogy—in these circles, it’s salient both as a tool of oppression and an x-risk.
I think far more selection of attitudes happens through paying attention to more extreme predictions, rather than through thinking / communicating strategically. Also, I’d guess people who spread these messages most consciously imagine a systemic collapse, rather than a literal extinction. As people don’t tend to think about longtermistic consequences, the distinction doesn’t seem that meaningful.
AI x-risk is more weird and terrifying and it goes against the heuristics that “technological progress is good”, “people have always feared new technologies they didn’t understand” and “the powerful draw attention away from their power”. Some people, for whom AI x-risk is hard to accept happen to overlap with AI ethics. My guess is that the proportion is similar in the general population—it’s just that some people in AI ethics feel particularly strong & confident about these heuristics.
Btw I think climate change could pose an x-risk in the broad sense (incl. 2nd-order effects & astronomic waste), just one that we’re very likely to solve (i.e. the tail risks, energy depletion, biodiversity decline or the social effects would have to surprise us).
I’d love to see a deeper inquiry into which problems of EAs are most effectively reduced by which interventions. The suggestion there’s a lack of “skilled therapists used to working with intelligent, introspective clients” is a significant novel consideration for me as an aspiring psychologist and this kind of hybrid research could help me calibrate my intuitions.
I got access to Bing Chat. It seems:
- It only searches through archived versions of websites (it doesn’t retrieve today’s news articles, it accessed an older version of my Wikipedia user site)
- During archivation, it only downloads the content one can see without any engagement with the website (tested on Reddit “see spoiler” buttons which reveal new content in the code. It could retrieve info from posts that gained less attention but weren’t hidden behind the spoiler button)
I. e. it’s still in a box of sorts, unless it’s much more intelligent than it pretends.Edit: A recent ACX post argues text-predicting oracles might be safer, as their ability to form goals is super limited, but it provides 2 models how even they could be dangerous: By simulating an agent or via a human who decides to take bad advice like “run the paperclip maximizer code”. Scott implies thinking it would spontaneously form goals is extreme, linking a post by Veedrac. The best argument there seems to be: It only has memory equivalent to 10 human seconds. I find this convincing for the current models but it also seems limiting for the intelligence of these systems, so I’m afraid for future models, the incentives are aligned with reducing this safety valve.
The core idea sounds very interesting: Increasing rationality likely has effects which can be generalized, therefore having a measure could help evaluate wider social outreach causes.
Defining intelligence could be an AI-complete problem, but I think the problem is complicated enough as a simple factor analysis (i. e. even without knowing what we’re talking about :). I think estimating impact once we know the increase in any measure of rationality is the easier part of the problem—for ex. knowing how much promoting long-termist thinking increases support for AI regulation, we’re only a few steps from getting the QALY. The harder part for people starting out in social outreach might be to estimate how many people they can get on board of thinking more long-termistically with their specific intervention.
So I think it might be very useful to put together a list of all attempts to calculate the impact of various social outreach strategies for anyone who’s considering a new one to be able to find some reference points because the hardest estimates here also seem to be the most important (e. g. the probability Robert Wright would decrease oversuspicion between powers). My intuition tells me differences in attitudes are something intuition could predict quite well, so the wisdom of the crowd could work well here.
The best source I found when I tried to search whether someone tried to put changing society into numbers recently is this article by The Sentience Institute.
Also, this post adds some evidence based intervention suggestions to your list.
What can an EA academic do to improve the incentives in the research side of academia? To help reward quality or even positive impact?
Let me dox myself as the addressee. :) Many thanks for the response. I really value that you take seriously the possible overlap of policies and research agendas covered by AI safety and your own approach.
I totally agree that “control is a proxy goal” and I believe the AI safety mainstream does as well, as it’s the logical consequence of Bostrom’s principle of epistemic deference. Once we have an AI that reliably performs tasks in the way they were intended, the goal should be to let it shape the world according to the wisest interpretation of morality it will find. If you tried to formalize this framing, as well as the proposal to inject it with “universal loving care”, I find it very likely that you would build the same AI.
So I think our crux doesn’t concern values, which is a great sign of a tractable disagreement.
I also suppose we could agree on a simple framework of factors that would be harmful on the path to this goal from the perspectives of:a) safety (AI self-evolves to harm)
b) power / misuse (humans do harm with AI)
c) sentience (AI is harmed)
d) waste (we fail to prevent harm)Here’s my guess on how the risks compare. I’d be most curious whether you’d be able to say if the model I’ve sketched out seems to track your most important considerations, when evaluating the value of AI safety efforts—and if so, which number would you dispute with the most certainty.
One disclaimer: I think it’s more helpful to think about specific efforts, rather than comparing the AI safety movement on net. Policy entails a lot of disagreement even within AI safety and a lot of forces clashed at the negotiations around the existing policies. I mentioned that I like the general, value-uncertain framework of the EU AI act but the resulting stock of papers isn’t representative of typical AI safety work.
In slight contrast, the community widely agrees that technical AI safety research would be good if successful. I’d argue that would manifest in a robust a decrease of risk in all of the highlighted perspectives (a-d). Interpretability, evals and scaling all enable us to resolve the disagreements in our predictions regarding the morality of emergent goals and of course, work on “de-confusion” about the very relationship between goals, intelligence and morality seems beneficial regardless of our predictions and to also quite precisely match your own focus. :)
So far, my guess is that we mostly disagree on
1) Do the political AI safety efforts lead to the kind of centralization of power that could halt our cosmic potential?
I’d argue the emerging regulation reduces misuse / power risks in general. Both US and EU regulations combine monitoring of tech giants with subsidies, which is a system that should accelerate beneficial models, while decelerating harmful ones. This system, in combination with compute governance, should also be effective in the misuse risks posed by terrorists and random corporations letting superhuman AIs with random utility functions evolve with zero precautions.
2) Would [a deeply misaligned] AGI be “stupid” to wipe out humans, in its own interest?
I don’t see a good reason to but I don’t think this is the important question. We should really be asking: Would a misaligned AGI let us fulfill the ambition of longtermism (of optimally populating cosmos with flourishing settlements)?
3) Is it “simple stuff” to actually put something like “optimal morality” or “universal loving care” into code of a vastly more intelligent entity, which is so robust that we can entrust it our cosmic potential?
Looking forward to the sequel!
I’d be particularly interested in any takes on the probability that civilization will be better equipped to deal with the alignment problem in, say, 100 years. My impression is that there’s an important and not well-examined balance between:Decreasing runaway AI risk & systemic risks by slowing down AI
Increasing the time of perils
Possibly increasing its intensity by giving malicious actors more time to catch up in destructive capabilities
But also possibly increasing the time for reflection on defense before a worse time of perils.
Possibly decreasing the risk of an aligned AI with bad moral values (conditional on this risk being lower in year 2123)
Possibly increasing the risk of astronomic waste (conditional on this risk being higher if AI is significantly slowed down)
Yes, OpenAI’s domain name is in the list because they have a blog
When coming up with a similar project,* I thought the first step should be to conduct exploratory interviews with EAs that would reveal their hypotheses about the psychological factors that may go into one’s decision to take AI safety seriously. My guess would be that ideological orientation would explain the most variance.
*which I most likely won’t realize (98 %)
Edit: My project has been accepted for the CHERI summer research program, so I’ll keep you posted!
Great to see real data on the web interest! In the past weeks, I investigated the same topic myself, while taking a psychological perspective & paying attention to the EU AI act, reaching the same conclusion (just published here).