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client: ea_forum
project_id: curated
narrator: pw
qa: mds
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Apply now to start a nonprofit in Biosecurity or Large-Scale Global Health

In this post we introduce our top five charity ideas for launch in 2023, in the areas of Biosecurity and Large-Scale Global Health. These are the result of five months’ work from our research team, and a six-stage iterative process that includes collaboration with partners and ideas from within and outside of the EA community. 

We’re looking for people to launch these ideas through our July - August 2023 Incubation Program. The deadline for applications is March 12, 2023. 

[APPLY NOW]

We provide cost-covered two-month training, stipends, ongoing mentorship, and grants up to $200,000 per project. You can learn more on our website. We also invite you to join our event on February 20, 6PM UK Time. Sam Hilton, our Director of Research, will introduce the ideas and answer your questions. Sign up here. 

Original article:

https://forum.effectivealtruism.org/posts/xWRweQmmEKoLFwGyu/ce-announcing-our-2023-charity-ideas-apply-now-2

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client: lesswrong
project_id: curated
feed_id: ai, ai_safety, ai_safety__technical, ai_safety__governance
narrator: pw
qa: mds
narrator_time: 5h30m
qa_time: 2h15m
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There is a lot of disagreement and confusion about the feasibility and risks associated with automating alignment research. Some see it as the default path toward building aligned AI, while others expect limited benefit from near term systems, expecting the ability to significantly speed up progress to appear well after misalignment and deception. Furthermore, progress in this area may directly shorten timelines or enable the creation of dual purpose systems which significantly speed up capabilities research.

OpenAI recently released their alignment plan. It focuses heavily on outsourcing cognitive work to language models, transitioning us to a regime where humans mostly provide oversight to automated research assistants. While there have been a lot of objections to and concerns about this plan, there hasn’t been a strong alternative approach aiming to automate alignment research which also takes all of the many risks seriously.

The intention of this post is not to propose an end-all cure for the tricky problem of accelerating alignment using GPT models. Instead, the purpose is to explicitly put another point on the map of possible strategies, and to add nuance to the overall discussion.

Original article:
https://www.lesswrong.com/posts/bxt7uCiHam4QXrQAA/cyborgism

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client: joe_carlsmith
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feed_id: ai, ai_safety, ai_safety__technical, ai_safety__forecasting
narrator: pw
qa: km
narrator_time: 18h00m
qa_time: 5h30m
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This report examines what I see as the core argument for concern about existential risk from misaligned artificial intelligence. I proceed in two stages. First, I lay out a backdrop picture that informs such concern. On this picture, intelligent agency is an extremely powerful force, and creating agents much more intelligent than us is playing with fire -- especially given that if their objectives are problematic, such agents would plausibly have instrumental incentives to seek power over humans. Second, I formulate and evaluate a more specific six-premise argument that creating agents of this kind will lead to existential catastrophe by 2070. On this argument, by 2070: (1) it will become possible and financially feasible to build relevantly powerful and agentic AI systems; (2) there will be strong incentives to do so; (3) it will be much harder to build aligned (and relevantly powerful/agentic) AI systems than to build misaligned (and relevantly powerful/agentic) AI systems that are still superficially attractive to deploy; (4) some such misaligned systems will seek power over humans in high-impact ways; (5) this problem will scale to the full disempowerment of humanity; and (6) such disempowerment will constitute an existential catastrophe. I assign rough subjective credences to the premises in this argument, and I end up with an overall estimate of ~5% that an existential catastrophe of this kind will occur by 2070. (May 2022 update: since making this report public in April 2021, my estimate here has gone up, and is now at >10%.)

Original article:
https://arxiv.org/abs/2206.13353

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client: lesswrong
project_id: curated
narrator: pw
qa: mds
---

This is a linkpost for https://escapingflatland.substack.com/p/childhoods

Let’s start with one of those insights that are as obvious as they are easy to forget: if you want to master something, you should study the highest achievements of your field. If you want to learn writing, read great writers, etc.

But this is not what parents usually do when they think about how to educate their kids. The default for a parent is rather to imitate their peers and outsource the big decisions to bureaucracies. But what would we learn if we studied the highest achievements?

Thinking about this question, I wrote down a list of twenty names—von Neumann, Tolstoy, Curie, Pascal, etc—selected on the highly scientific criteria “a random Swedish person can recall their name and think, Sounds like a genius to me”. That list is to me a good first approximation of what an exceptional result in the field of child-rearing looks like. I ordered a few piles of biographies, read, and took notes. Trying to be a little less biased in my sample, I asked myself if I could recall anyone exceptional that did not fit the patterns I saw in the biographies, which I could, and so I ordered a few more biographies.

This kept going for an unhealthy amount of time.

I sampled writers (Virginia Woolf, Lev Tolstoy), mathematicians (John von Neumann, Blaise Pascal, Alan Turing), philosophers (Bertrand Russell, René Descartes), and composers (Mozart, Bach), trying to get a diverse sample.

In this essay, I am going to detail a few of the patterns that have struck me after having skimmed 42 biographies. I will sort the claims so that I start with more universal patterns and end with patterns that are less common.

Original article:
https://www.lesswrong.com/posts/CYN7swrefEss4e3Qe/childhoods-of-exceptional-people

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project_id: curated
narrator: pw
qa: mds
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Hi everyone,

I've been reading up on H5N1 this weekend, and I'm pretty concerned. Right now my hunch is that there is a non-zero chance that it will cost more than 10,000 people their lives.

To be clear, I think it is unlikely that H5N1 will become a pandemic anywhere close to the size of covid.

Nevertheless, I think our community should be actively following the news and start thinking about ways to be helpful if the probability increases. I am creating this thread as a place where people can discuss and share information about H5N1. We have a lot of pandemic experts in this community, do chime in!

Original article:

https://forum.effectivealtruism.org/posts/QMMFyAX3ajf9vF5sb/h5n1-thread-for-information-sharing-planning-and-action

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client: lesswrong
project_id: curated
feed_id: ai, ai_safety, ai_safety__technical
narrator: pw
qa: mds
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(Epistemic status: attempting to clear up a misunderstanding about points I have attempted to make in the past. This post is not intended as an argument for those points.)

I have long said that the lion's share of the AI alignment problem seems to me to be about pointing powerful cognition at anything at all, rather than figuring out what to point it at.

It’s recently come to my attention that some people have misunderstood this point, so I’ll attempt to clarify here.

Original article:
https://www.lesswrong.com/posts/NJYmovr9ZZAyyTBwM/what-i-mean-by-alignment-is-in-large-part-about-making

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client: ea_forum
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feed_id: ai, ai_safety, ai_safety__forecasting
narrator: pw
qa: mds
narrator_time: 1h20m
qa_time: 0h15m
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This is a linkpost for https://epochai.org/blog/literature-review-of-transformative-artificial-intelligence-timelines

We summarize and compare several models and forecasts predicting when transformative AI will be developed.

Highlights

• The review includes quantitative models, including both outside and inside view, and judgment-based forecasts by (teams of) experts.
• While we do not necessarily endorse their conclusions, the inside-view model the Epoch team found most compelling is Ajeya Cotra’s “Forecasting TAI with biological anchors”, the best-rated outside-view model was Tom Davidson’s “Semi-informative priors over AI timelines”, and the best-rated judgment-based forecast was Samotsvety’s AGI Timelines Forecast.
• The inside-view models we reviewed predicted shorter timelines (e.g. bioanchors has a median of 2052) while the outside-view models predicted longer timelines (e.g. semi-informative priors has a median over 2100). The judgment-based forecasts are skewed towards agreement with the inside-view models, and are often more aggressive (e.g. Samotsvety assigned a median of 2043).

Original article:

https://forum.effectivealtruism.org/posts/4Ckc2zNrAKQwnAyA2/literature-review-of-transformative-artificial-intelligence

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---
A Chemical Hunger (a), a series by the authors of the blog Slime Mold Time Mold (SMTM), argues that the obesity epidemic is entirely caused (a) by environmental contaminants.
In my last post, I investigated SMTM’s main suspect (lithium).[1] This post collects other observations I have made about SMTM’s work, not narrowly related to lithium, but rather focused on the broader thesis of their blog post series.
I think that the environmental contamination hypothesis of the obesity epidemic is a priori plausible. After all, we know that chemicals can affect humans, and our exposure to chemicals has plausibly changed a lot over time. However, I found that several of what seem to be SMTM’s strongest arguments in favor of the contamination theory turned out to be dubious, and that nearly all of the interesting things I thought I’d learned from their blog posts turned out to actually be wrong. I’ll explain that in this post.

Original article:
https://www.lesswrong.com/posts/NRrbJJWnaSorrqvtZ/on-not-getting-contaminated-by-the-wrong-obesity-ideas

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client: ea_forum
project_id: summaries
narrator: cs
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Original article:
https://forum.effectivealtruism.org/posts/Qzfew7EBPgdCzsxED/ea-and-lw-forum-weekly-summary-30th-jan-5th-feb-2023

This is part of a weekly series summarizing the top posts on the EA Forum — you can see the full collection here. The first post includes some details on purpose and methodology. Feedback, thoughts, and corrections are welcomed.

Narrated by Coleman Jackson Snell. Summaries written by Zoe Williams (Rethink Priorities).

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client: 80000_hours
project_id: articles
narrator: pw
qa: mds
narrator_time: 3h00m
qa_time: 0h45m
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Lots of people say they want to “make a difference,” “do good,” “have a social impact,” or “make the world a better place” — but they rarely say what they mean by those terms.
By clarifying your definition, you can better target your efforts, and make a difference more effectively.
But how should you define social impact?
Thousands of years of philosophy have gone into that question. We’re going to try to sum up that thinking; introduce a practical, rough-and-ready definition of social impact; and explain why we think it’s a good definition to focus on.
This is a bit ambitious for one article, so to the philosophers in the audience, please forgive the enormous simplifications. We hope the usefulness of the definition will make up for it.

Original article:
https://80000hours.org/articles/what-is-social-impact-definition/

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