Super cool work Yixiong - we were impressed by your professionalism in this process despite working within another group's whims on this one. Some other observations from our side that may be relevant for other folks hosting hackathons:
- Prepare starter materials: For example, for some of our early interpretability hackathons, we built a full resource base (Github) with videos, Colabs, and much more (some of it with Neel Nanda, big appreciation for his efforts in making interp more available). Our philosophy for the starter materials are: "If a participant can make a submission-worthy project by maximum cloning your repo and typing two commands or simply walk through a Google Colab, this is the ideal starter code." This means that with only small adjustments, they'll be able to make an original project. We rarely if ever see this exploited, i.e. "template code as submission" because they're able to copy-paste things around for a really strong research project.
- Make sure what they should submit is super clear: Making a really nice template goes a long way to make a submission super clear for participants. An example can be seen in our MASEC hackathon: Docs and page. If someone can just receive your submission template and know everything they need to know to submit a great project, that is really good since they'll be spending most of their time inside of that document.
- Make sure judging criteria are really good: People will use your judging criteria to determine what to prioritize in their project. This is extremely valuable for you to get right. For example, we usually use a variation on the three criteria: 1) Topic advancement, 2) AI safety impact, and 3) quality / reproducibility. A recent example was the Agent Security Hackathon:

> 1. Agent safety: Does the project move the field of agent safety forward? After reading this, do we know more about how to detect dangerous agents, protect against dangerous agents, or build safer agents than before?
> 2. AI safety: Does the project solve a concrete problem in AI safety? If this project is fully realized, would we expect the world with superintelligence to be a safer (even marginally) than yesterday?
> 3. Methodology: Is the project well-executed and is the code available so we can review it? Do we expect the results to generalize beyond the specific case(s) presented in the submission?

- Make the resources and ideas available early: As Yixiong mentions, it's really valuable for people not to be confused. If they know exactly what report format they'll submit, which idea they'll work on, and who they'll work with, this is a great way to ensure that the 2-3 days of hacking are an incredibly efficient use of their time.
- Matching people by ideas trumps by background: We've tried various ways to match individuals who don't have teams. The absolute best system we've found is to get people to brainstorm before the hackathon, share their ideas, and organize teams online. We also host team matching sessions which consist of fun-fact-intros and otherwise just discusses specific research ideas.
- Don't make it longer than a weekend: If you host a hackathon and make it longer than a weekend, most people who cannot attend outside that weekend will avoid participating because they'll feel that the ones who can participate more than the weekend can spend their weekdays to win the grand prize. Additionally, a very counter-intuitive thing happens where if you give people three weeks, they'll actually spend much less time on it than if you just give them a weekend. This can depend on the prizes or outcome rewards, of course, but is a really predictable effect, in our experience.
- Don't make it shorter than two days: Depending on your goal, one day will never be enough to create an original project. Our aims are original pilot research papers that can stand on their own and the few one-day events we've hosted have never worked very well, except for brainstorming. Often, participants won't even have any functional code or any ideas on the Sunday morning of the event but by the submission deadline have a really high quality project that wins the top prize. This seems to happen due to this very concrete exploration of ideas that happens in the IDE and on the internet where some are discarded and nothing promising comes up before 11am Sunday.

And as Yixiong mentions, we have more resources on this along with an official chapter network (besides volunteer locations) at https://www.apartresearch.com/sprints/locations. You're welcome to get in touch if you're interested in hosting at sprints@apartresearch.com. 

COI: One of our researchers hosted a cyber-evals workshop at Yixiong's AI safety track.

This seems like a great effort. We made a small survey called pain points in AI safety survey back in 2022 that we received quite a few answers to which you can see the final results of here. Beware that this has not been updated in ~2 years.

It seems like there's a lot of negative comments about this letter.  Even if it does not go through, it seems very net positive for the reason that it makes explicit an expert position against large language model development due to safety concerns. There's several major effects of this, as it enables scientists, lobbyists, politicians and journalists to refer to this petition to validate their potential work on the risks of AI, it provides a concrete action step towards limiting AGI development, and it incentivizes others to think in the same vein about concrete solutions.

I've tried to formulate a few responses to the criticisms raised:

• "6 months isn't enough to develop the safety techniques they detail": Besides it being at least 6 months, the proposals seem relatively reasonable within something as farsighted as this letter. Shoot for the moon and you might hit the sky, but this time the sky is actually happening and work on many of their proposals is already underway. See e.g. EU AI Act, funding for AI research, concrete auditing work and safety evaluation on models. Several organizations are also working on certification and the scientific work towards watermarking is sort of done? There's also great arguments for ensuring this since right now, we are at the whim of OpenAI management on the safety front.
• "It feels rushed": It might have benefitted from a few reformulations but it does seem alright?
• "OpenAI needs to be at the forefront": Besides others clearly lagging behind already, what we need are insurances that these systems go well, not at the behest of one person. There's also a lot of trust in OpenAI management and however warranted that is, it is still a fully controlled monopoly on our future. If we don't ensure safety, this just seems too optimistic (see also differences between public interview for-profit sama and online sama).
• "It has a negative impact on capabilities researchers": This seems to be an issue from <2020 and some European academia. If public figures like Yoshua cannot change the conversation, then who should? Should we just lean back and hope that they all sort of realize it by themselves? Additionally, the industry researchers from DM and OpenAI I've talked with generally seem to agree that alignment is very important, especially as their management is clearly taking the side of safety.
• "The letter signatures are not validated properly": Yeah, this seems like a miss, though as long as the top 40 names are validated, the negative impacts should be relatively controlled.

All in good faith of course; it's a contentious issue but this letter seems generally positive to me.

Oliver's second message seems like a truly relevant consideration for our work in the alignment ecosystem. Sometimes, it really does feel like AI X-risk and related concerns created the current situation. Many of the biggest AGI advances might not have been developed counterfactually, and machine learning engineers would just be optimizing another person's clicks.

I am a big fan of "Just don't build AGI" and academic work with AI, simply because it is better at moving slowly (and thereby safely through open discourse and not $10 mil training runs) compared to massive industry labs. I do have quite a bit of trust in Anthropic, DeepMind and OpenAI simply from their general safety considerations compared to e.g. Microsoft's release of Sydney. 

As part of this EA bet on AI, it also seems like the safety view has become widespread among most AI industry researchers from my interactions with them (though might just be a sampling bias and they were honestly more interested in their equity growing in value). So if the counterfactual of today's large AGI companies would be large misaligned AGI companies, then we would be in a significantly worse position. And if AI safety is indeed relatively trivial, then we're in an amazing position to make the world a better place. I'll remain slightly pessimistic here as well, though.

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There's an interesting case on the infosec mastodon instance where someone asks Sydney to devise an effective strategy to become a paperclip maximizer, and it then expresses a desire to eliminate all humans. Of course, it includes relevant policy bypass instructions. If you're curious, I suggest downloading the video to see the entire conversation, but I've also included a few screenshots below (Mastodon, third corycarson comment).

Hilarious to the degree of Manhatten scientists laughing at atmospheric combustion.

Thank you for pointing this out! It seems I wasn't informed enough about the context. I've dug a bit deeper and will update the text to: 

• Another piece reveals that OpenAI contracted Sama to use Kenyan workers with less than $2 / hour wage ($0.5 / hour average in Nairobi) for toxicity annotation for ChatGPT and undisclosed graphical models, with reports of employee trauma from the explicit and graphical annotation work, union breaking, and false hiring promises. A serious issue.

For some more context, here is the Facebook whistleblower case (and ongoing court proceedings in Kenya with Facebook and Sama) and an earlier MIT Sloan report that doesn't find super strong positive effects (but is written as such, interestingly enough). We're talking pay gaps from relocation bonuses, forced night shifts, false hiring promises, supposedly human trafficking as well? Beyond textual annotation, they also seemed to work on graphical annotation.

I recommend reading Blueprint: The Evolutionary Origins of a Good Society about the science behind the 8 base human social drives where 7 are positive and the 8th is the outgroup hatred that you mention as fundamental. I have not read much up on the research on outgroup exclusion but I talked to an evolutionary cognitive psychologist who mentioned that this is receiving a lot of scientific scrutiny as a "basic drive" from evolution's side. 

Axelrod's The Evolution of Cooperation also finds that collaborative strategies work well in evolutionary prisoner's dilemma game-theoretic simulations, though hard and immediate reciprocity for defection is also needed, which might lead to the outgroup hatred you mention.

An interesting solution here is radical voluntarism where an AI philosopher king runs the immersive reality where all humans are in and you can only be causally influenced upon if you want to. This means that you don't need to do value alignment, just very precise goal alignment. I was originally introduced to this idea Carado.

The summary has been updated to yours for both the public newsletter and this LW linkpost.  And yes, they seem exciting. Connecting FFS to interpretability was a way to contextualize it in this case, until you would provide more thoughts on the use case (given your last paragraph in the post). Thank you for writing, always appreciate the feedback!