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Introduction
The EA Mexico Residency Fellowship marked a significant milestone in bringing together individuals committed to effective altruism (EA) worldwide, focusing on Spanish speakers and individuals from underrepresented backgrounds. This post serves as an overview of the program's outcomes and areas for improvement. By sharing our experiences, we aim to provide valuable insights for future organizers of similar initiatives.
Quick Facts
- 102 participants from 25 countries, comprising 11 fellows and 91 visitors. 36% of the fellows identified themselves as female or non-binary. 32% of the fellows were from LATAM.
- Generated 714 new connections at an average cost of 371 USD per connection.
- Weekly cost for hosting a participant was approximately 602 USD
- 42.5% of participants rated the fellowship 10/10.
- 30% of respondents valued the fellowship at over $10,000.
- 40% of participants would consider relocating to Mexico City for an established
Confidence level: I’m a computational physicist working on nanoscale simulations, so I have some understanding of most of the things discussed here, but I am not specifically an expert on the topics covered, so I can’t promise perfect accuracy.
I want to give a huge thanks to Professor Phillip Moriarty of the university of Nottingham for answering my questions about the experimental side of mechanosynthesis research.
Introduction:
A lot of people are highly concerned that a malevolent AI or insane human will, in the near future, set out to destroy humanity. If such an entity wanted to be absolutely sure they would succeed, what method would they use? Nuclear war? Pandemics?
According to some in the x-risk community, the answer is this: The AI will invent molecular nanotechnology, and then kill...
With all materials available, my credence is very likely (above 95%) that something self-replicating that is more impressive than bacteria and viruses is possible, but I have no idea how impressive the limits of possibility are.
Much of the (purported) advantage of diamondoid mechanisms is that they're (meant to be) stiff enough to operate deterministically with atomic precision. Without that, you're likely to end up much closer to biological systems—transport is more diffusive, the success of any step is probabilistic, and you need a whole ecosystem of mec...
Having a high-level overview of the AI safety ecosystem seems like a good thing, so I’ve created an Anki deck to help people familiarise themselves with the 167 key organisations, projects, and programs currently active in the field.
Why Anki?
Anki is a flashcard app that uses spaced repetition to help you efficiently remember information over the long term. It’s useful for learning and memorising all kinds of things – it was the the main tool I used to learn German within a year – and during the time that I’ve been testing out this deck I feel like it’s already improved my grasp of the AI safety landscape.
What you’ll learn
The deck is based on data from the AI Existential Safety Map run by AED – if you’re not...
The idea came about because I was looking for ways I could use Anki beyond language learning and figured this could be useful, then decided that if it seems useful for me then presumably for others too.
When I told a few people I was working on this, I generally didn’t get particularly excited feedback. It seemed like this may at least to some degree be because people are sceptical as to the quality of shared decks, which is partly why I put a lot of time into making this one as well-designed as possible.
That’s also the reason I would personally be keen to ...
TL;DR
- Edit: The original deadline was October 6th, 2023, but we reviewed applications on a rolling basis, and –as of September 25– we have now received and reviewed enough applications, so we've closed the role. We are sorry about this, and we hope to see your application in a future hiring round.
- Epoch is hiring a part-time Associate Data Analyst to annotate papers informing our research and stakeholders’ policy decisions.
- This is a fully remote independent contractor role. Contracts will be set up as a 1-month, renewable, independent contractor agreements.
- Compensation ranges from $20-$25 USD per hour, estimated at 5-25 hours of work per week.
- This is an excellent opportunity for those interested in doing impactful work while gaining exposure to cutting-edge machine learning advancements.
- Comment below or email careers@epochai.org if you have
We just finished hiring a data analyst for October. It's possible that we'll hire another candidate in the future, but the position is not currently taking applications.
At EAGs I often find myself having roughly the same 30 minute conversation with university students who are interested in policy careers and want to test their fit.
This post will go over two cheap tests, each possible to do over a weekend, that you can do to test your fit for policy work.
I am by no means the best person to be giving this advice but I received feedback that my advice was helpful, and I'm not going to let go of an opportunity to act old and wise. A lot of it is based off what worked for me, when I wanted to break into the field a few years ago. Get other perspectives too! Contradictory input in the comments from people with more seniority is...
Lightcone Infrastructure (the organization that grew from and houses the LessWrong team) has just finished renovating a 7-building physical campus that we hope to use to make the future of humanity go better than it would otherwise.
We're hereby announcing that it is generally available for bookings. We offer preferential pricing for projects we think are good for the world, but to cover operating costs, we're renting out space to a wide variety of people/projects.
How do I express interest?
- Chat with us (you can use LessWrong intercom in the bottom right)
- Submit an inquiry via this form
- Comment on this post
What kinds of things can I run here?
- Team Retreats (15-80 people)
- We offer cozy nooks with firepits, discussion rooms with endless whiteboards, plus lodging. The space is very modular, and we can
So I'm curious if community opinion is generally that the EVF purchase was "bad" and the Lightcone purchase was "good"?
I didn't get the sense that there's a community-consensus about the castle as a convenient event venue that you can resell at some point later to make up parts of the costs. Some people were very vocal in their outrage, but many thought it might be totally fine or is at least defensible even if it was a mistake.
It could be that "third wave EA" will contain a norm of accepting that people have different takes on things like that. For exampl...
TL;DR: I argue for two main theses:
- [Moderate-high confidence] It would be better to aim for a conditional pause, where a pause is triggered based on evaluations of model ability, rather than an unconditional pause (e.g. a blanket ban on systems more powerful than GPT-4).
- [Moderate confidence] It would be bad to create significant public pressure for a pause through advocacy, because this would cause relevant actors (particularly AGI labs) to spend their effort on looking good to the public, rather than doing what is actually good.
Since mine is one of the last posts of the AI Pause Debate Week, I've also added a section at the end with quick responses to the previous posts.
Which goals are good?
That is, ignoring tractability and just assuming that we succeed at the...
A conditional pause fails to prevent x-risk if either:
- The AI successfully exfiltrates itself (which is what's needed to defeat rollback mechanisms) during training or evaluation, but before deployment.
- The AI successfully sandbags the evaluations. (Note that existing conditional pause proposals depend on capability evaluations, not alignment evaluations.)
Another way evals could fail is if they work locally but it's still too late in the relevant sense because even with the pause mechanism kicking in (e.g., "from now on, any training runs that use 0.2x...
A theory of change is a set of hypotheses about explicitly articulates the cause-and-effect steps for how a project—suchproject or organization can turn inputs into a desired impact on the world (i.e. it’s their theory of how they’ll make a change). They generally include the following sections:
- Inputs / activities: What the project or organization does to create change (e.g. “distribute bednets”)
- Outputs: The tangible effects generated by the inputs (e.g. “beneficiaries have access to malaria nets”)
- Intermediate outcomes: The outputs’ effects, including benefits for the beneficiary, (e.g. “malaria nets are used” and "reduced incidence of malaria")
- Impact: What we’re ultimately solving, and why the intermediate outcomes matter (e.g. “lives saved”)
Best practices when crafting a theory of change (i.e. for creators):
- Invest sufficiently in understanding the problem context (i.e. understanding the needs and incentives of the beneficiaries and other stakeholders, as well as barriers to change and the economic & political context)
- Map the causal pathway backwards from impact to activities
- Question every causal step (is it clear why A should cause B? how might it fail?)
Hallmarks of an intervention, an organization, or a movement—will accomplish its goals.excellent theory of change (i.e. for reviewers):
- A focused suite of activities
- The evidence and assumptions behind each step are explicitly named
- The relative confidence of each step is clear
- It is
often created by starting withclear who thegoals, and then identifying the penultimateactor is in each stepthat leads
Common mistakes to the goals. The step that leads to the penultimate step is then identified, and so on, until the first steps are known. This method is called backward induction, backwards mapping or backchaining.
Further reading
Siegmann, Charlotte (2022) Collection of resources aboutavoid in theories of change are:
- Not making fundamental impact the goal (e.g.,
Effective Altruismstopping at ‘increased immunizations’ instead of ‘improved health’) - Being insufficiently detailed: (a) making large leaps between each step, (b) combining multiple major outcomes into one step (e.g. ‘government introduces and enforces regulation’).
- Setting and forgetting (instead of regularly iterating on it)
- Not building your theory of change into a measurement plan
From: Nailing the basics – Theories of change — EA Forum, May 15. (effectivealtruism.org)
One problem in AI/policy/ethics is that it seems like we sometimes have inconsistent preferences. For example, I could want to have a painting in my office, and simultaneously not want to have a painting in my office. This is a problem because classical (preference) logic can’t really deal with contradictions, so it relies on the assumption that they don't exist.
Since it seems like we sometimes experience them, I created a non-classical preference logic that can deal with them. Because effective altruism is all about logically satisfying preferences I cons... (read more)