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Just the bottom lines from our key ideas series.

https://80000hours.org/key-ideas/summary/

TLDR: Get good at something that lets you effectively contribute to big and neglected global problems.

What ultimately makes for an impactful career? You can have more positive impact over the course of your career by aiming to:

1. Help solve a more pressing problem. Many global issues should get more attention, but as individuals we should look for the biggest gaps in existing efforts. To do that, you can compare issues in terms of scale, neglectedness, and tractability. It turns out that some issues receive hundreds of times less attention relative to how big and solvable they seem. This means which issue you choose to work on is likely the biggest driver of your impact. In particular, our generation may see the rise of transformative technologies, which could lead to existential risks and make now a crucial moment that could affect the future for many generations to come — but our current institutions are doing little to address these issues. We have a list of global issues we think are particularly pressing for more people to work on right now.

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client: lesswrong
project_id: curated
feed_id: ai, ai_safety
narrator: pw
qa: km
narrator_time: 2h20m
qa_time: 45m
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Excerpt:
During my Master's and Ph.D. (still ongoing), I have spoken with many academics about AI safety. These conversations include chats with individual PhDs, poster presentations and talks about AI safety.

I think I have learned a lot from these conversations and expect many other people concerned about AI safety to find themselves in similar situations. Therefore, I want to detail some of my lessons and make my thoughts explicit so that others can scrutinize them.

TL;DR: People in academia seem more and more open to arguments about risks from advanced intelligence over time and I would genuinely recommend having lots of these chats. Furthermore, I underestimated how much work related to some aspects AI safety already exists in academia and that we sometimes reinvent the wheel. Messaging matters, e.g. technical discussions got more interest than alarmism and explaining the problem rather than trying to actively convince someone received better feedback.

Original article:
https://www.lesswrong.com/posts/SqjQFhn5KTarfW8v7/lessons-learned-from-talking-to-greater-than-100-academics

Narrated for LessWrong by TYPE III AUDIO.

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feed_id: ai, ai_safety, ai_safety__governance
narrator_time: 4h
narrator: pw
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client: 80000_hours
project_id: 80000_hours
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https://80000hours.org/career-reviews/china-related-ai-safety-and-governance-paths/#strong-networking-abilities-especially-for-policy-roles

Expertise in China and its relations with the world might be critical in tackling some of the world’s most pressing problems. In particular, China’s relationship with the US is arguably the most important bilateral relationship in the world, with these two countries collectively accounting for over 40% of global GDP.1 These considerations led us to publish a guide to improving China–Western coordination on global catastrophic risks and other key problems in 2018. Since then, we have seen an increase in the number of people exploring this area.

China is one of the most important countries developing and shaping advanced artificial intelligence (AI). The Chinese government’s spending on AI research and development is estimated to be on the same order of magnitude as that of the US government,2 and China’s AI research is prominent on the world stage and growing.

Because of the importance of AI from the perspective of improving the long-run trajectory of the world, we think relations between China and the US on AI could be among the most important aspects of their relationship. Insofar as the EU and/or UK influence advanced AI development through labs based in their countries or through their influence on global regulation, the state of understanding and coordination between European and Chinese actors on AI safety and governance could also be significant.

That, in short, is why we think working on AI safety and governance in China and/or building mutual understanding between Chinese and Western actors in these areas is likely to be one of the most promising China-related career paths. Below we provide more arguments and detailed information on this option.

If you are interested in pursuing a career path described in this profile, contact 80,000 Hours’ one-on-one team and we may be able to put you in touch with a specialist advisor.

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project_id: articles
narrator: pw
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narrator_time: 2h
qa_time: 25m
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Excerpt:
You have 80,000 hours in your career: 40 hours per week, 50 weeks per year, for 40 years.

That’s a huge amount of time. And it means that your career is not only a major driver of your happiness — it’s probably also your biggest opportunity to have a positive impact on the world.

So how can you best spend those hours?

We’re a nonprofit that aims to help you answer this question, and in this article we’ll explain how we can help.

Original article:
https://80000hours.org/start-here/

Narrated for 80,000 Hours by TYPE III AUDIO.

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client: ea_forum
project_id: summaries
narrator: cs
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Original article:
https://forum.effectivealtruism.org/s/W4fhpuN26naxGCBbN/p/cwa5m5pJQh857GE7C

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).

Published by TYPE III AUDIO on behalf of the Effective Altruism Forum.

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project_id: curated
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https://www.lesswrong.com/posts/6LzKRP88mhL9NKNrS/how-my-team-at-lightcone-sometimes-gets-stuff-done

Disclaimer: I originally wrote this as a private doc for the Lightcone team. I then showed it to John and he said he would pay me to post it here. That sounded awfully compelling. However, I wanted to note that I’m an early founder who hasn't built anything truly great yet. I’m writing this doc because as Lightcone is growing, I have to take a stance on these questions. I need to design our org to handle more people. Still, I haven’t seen the results long-term, and who knows if this is good advice. Don’t overinterpret this. 

Suppose you went up on stage in front of a company you founded, that now had grown to 100, or 1000, 10 000+ people. You were going to give a talk about your company values. You can say things like “We care about moving fast, taking responsibility, and being creative” -- but I expect these words would mostly fall flat. At the end of the day, the path the water takes down the hill is determined by the shape of the territory, not the sound the water makes as it swooshes by. To manage that many people, it seems to me you need clear, concrete instructions. What are those? What are things you could write down on a piece of paper and pass along your chain of command, such that if at the end people go ahead and just implement them, without asking what you meant, they would still preserve some chunk of what makes your org work? 

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https://www.lesswrong.com/posts/rP66bz34crvDudzcJ/decision-theory-does-not-imply-that-we-get-to-have-nice

Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

(Note: I wrote this with editing help from Rob and Eliezer. Eliezer's responsible for a few of the paragraphs.)

A common confusion I see in the tiny fragment of the world that knows about logical decision theory (FDT/UDT/etc.), is that people think LDT agents are genial and friendly for each other.[1]

One recent example is Will Eden’s tweet about how maybe a molecular paperclip/squiggle maximizer would leave humanity a few stars/galaxies/whatever on game-theoretic grounds. (And that's just one example; I hear this suggestion bandied around pretty often.)

I'm pretty confident that this view is wrong (alas), and based on a misunderstanding of LDT. I shall now attempt to clear up that confusion.

To begin, a parable: the entity Omicron (Omega's little sister) fills box A with $1M and box B with $1k, and puts them both in front of an LDT agent saying "You may choose to take either one or both, and know that I have already chosen whether to fill the first box". The LDT agent takes both.

"What?" cries the CDT agent. "I thought LDT agents one-box!"

LDT agents don't cooperate because they like cooperating. They don't one-box because the name of the action starts with an 'o'. They maximize utility, using counterfactuals that assert that the world they are already in (and the observations they have already seen) can (in the right circumstances) depend (in a relevant way) on what they are later going to do.

A paperclipper cooperates with other LDT agents on a one-shot prisoner's dilemma because they get more paperclips that way. Not because it has a primitive property of cooperativeness-with-similar-beings. It needs to get the more paperclips.

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feed_id: ai, ai_safety, ai_safety__forecasting
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https://www.lesswrong.com/posts/6Xgy6CAf2jqHhynHL/what-2026-looks-like#2022

Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

This was written for the Vignettes Workshop.[1] The goal is to write out a detailed future history (“trajectory”) that is as realistic (to me) as I can currently manage, i.e. I’m not aware of any alternative trajectory that is similarly detailed and clearly more plausible to me. The methodology is roughly: Write a future history of 2022. Condition on it, and write a future history of 2023. Repeat for 2024, 2025, etc. (I'm posting 2022-2026 now so I can get feedback that will help me write 2027+. I intend to keep writing until the story reaches singularity/extinction/utopia/etc.)

What’s the point of doing this? Well, there are a couple of reasons:

• Sometimes attempting to write down a concrete example causes you to learn things, e.g. that a possibility is more or less plausible than you thought.
• Most serious conversation about the future takes place at a high level of abstraction, talking about e.g. GDP acceleration, timelines until TAI is affordable, multipolar vs. unipolar takeoff… vignettes are a neglected complementary approach worth exploring.
• Most stories are written backwards. The author begins with some idea of how it will end, and arranges the story to achieve that ending. Reality, by contrast, proceeds from past to future. It isn’t trying to entertain anyone or prove a point in an argument.
• Anecdotally, various people seem to have found Paul Christiano’s “tales of doom” stories helpful, and relative to typical discussions those stories are quite close to what we want. (I still think a bit more detail would be good — e.g. Paul’s stories don’t give dates, or durations, or any numbers at all really.)[2]
• “I want someone to ... write a trajectory for how AI goes down, that is really specific about what the world GDP is in every one of the years from now until insane intelligence explosion. And just write down what the world is like in each of those years because I don't know how to write an internally consistent, plausible trajectory. I don't know how to write even one of those for anything except a ridiculously fast takeoff.” --Buck Shlegeris

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Original article:
https://forum.effectivealtruism.org/s/W4fhpuN26naxGCBbN/p/tm3RMfxetLsmcwftQ

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).

Published by TYPE III AUDIO on behalf of the Effective Altruism Forum.

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project_id: articles
narrator: pw
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https://80000hours.org/problem-profiles/preventing-catastrophic-pandemics/

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