TYPE III AUDIO (All episodes)

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client: ea_forum
project_id: summaries
narrator: cs
---

Original article:
https://forum.effectivealtruism.org/posts/DNWpFLrtrJXe4mted/ea-and-lw-forum-summaries-9th-jan-to-15th-jan-23

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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client: ea_forum
project_id: curated
narrator: pw
qa: mds
narrator_time: 2h20m
qa_time: 0h45m
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I previously wrote an entry for the Open Philanthropy Cause Exploration Prize on why preventing violence against women and girls is a global priority. For an introduction to the area, I have written a brief summary below.  In this post, I will extend that work, diving deeper into the literature and the landscape of organisations in the field, as well as creating a cost-effectiveness model for some of the most promising preventative interventions. Based on this, I will offer some concrete recommendations that different stakeholders should take - from individuals looking to donate, to funders, to charity evaluators and incubators.

Original article:
https://forum.effectivealtruism.org/posts/uH9akQzJkzpBD5Duw/what-you-can-do-to-help-stop-violence-against-women-and

Narrated for the Effective Altruism Forum by TYPE III AUDIO.

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client: ea_forum
project_id: curated
feed_id: ai, ai_safety, ai_safety__forecasting
narrator: pw
qa: mds
narrator_time: 4h45m
qa_time: 1h30m
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In this post, we point out that short AI timelines would cause real interest rates to be high, and would do so under expectations of either unaligned or aligned AI. However, 30- to 50-year real interest rates are low. We argue that this suggests one of two possibilities:

1.  Long(er) timelines. Financial markets are often highly effective information aggregators (the “efficient market hypothesis”), and therefore real interest rates accurately reflect that transformative AI is unlikely to be developed in the next 30-50 years.
2.  Market inefficiency. Markets are radically underestimating how soon advanced AI technology will be developed, and real interest rates are therefore too low. There is thus an opportunity for philanthropists to borrow while real rates are low to cheaply do good today; and/or an opportunity for anyone to earn excess returns by betting that real rates will rise.

In the rest of this post we flesh out this argument.

Original article:
https://forum.effectivealtruism.org/posts/8c7LycgtkypkgYjZx/agi-and-the-emh-markets-are-not-expecting-aligned-or

Narrated for the Effective Altruism Forum by TYPE III AUDIO.

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---
client: ea_forum
project_id: summaries
narrator: cs
---

Original article:
https://forum.effectivealtruism.org/posts/JZuCg7TtfzzaX9bBY/ea-and-lw-forum-summaries-holiday-edition-19th-dec-8th-jan

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.

Share feedback on this narration.

---
client: 80000_hours
project_id: articles
narrator: pw
qa: km
narrator_time: 2h00m
qa_time: 0h30m
---

Should I quit my job? Which of my offers should I take? Which long-term options should I explore?
These decisions will affect how you spend years of your time, so the stakes are high. But they’re also an area where you shouldn’t expect your intuition to be a reliable guide. This means it’s worth taking a more systematic approach.
What might a good career decision process look like?
A common approach is to make a pro and con list, but it’s possible to do a lot better. Pro and con lists make it easy to put too much weight on an unimportant factor. More importantly, they don’t encourage you to make use of the most powerful decision-making methods, which can greatly improve the quality of your decisions.

Original article:
https://80000hours.org/career-decision/article/

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client: lesswrong
project_id: curated
narrator: pw
qa: km
narrator_time: 1h05m
qa_time: 0h10m
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Here’s a story you may recognize. There's a bright up-and-coming young person - let's call her Alice. Alice has a cool idea. It seems like maybe an important idea, a big idea, an idea which might matter. A new and valuable idea. It’s the first time Alice has come up with a high-potential idea herself, something which she’s never heard in a class or read in a book or what have you.

So Alice goes all-in pursuing this idea. She spends months fleshing it out. Maybe she writes a paper, or starts a blog, or gets a research grant, or starts a company, or whatever, in order to pursue the high-potential idea, bring it to the world.

And sometimes it just works!

… but more often, the high-potential idea doesn’t actually work out. Maybe it turns out to be basically-the-same as something which has already been tried. Maybe it runs into some major barrier, some not-easily-patchable flaw in the idea. Maybe the problem it solves just wasn’t that important in the first place.

Original article:
https://www.lesswrong.com/posts/mfPHTWsFhzmcXw8ta/the-feeling-of-idea-scarcity

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---
client: lesswrong
project_id: curated
feed_id: ai, ai_safety, ai_safety__technical
narrator: pw
qa: km
narrator_time: 1h05m
qa_time: 0h10m
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Crossposted from the AI Alignment Forum. May contain more technical jargon than usual.

In terms of content, this has a lot of overlap with Reward is not the optimization target. I'm basically rewriting a part of that post in language I personally find clearer, emphasising what I think is the core insight.

When thinking about deception and RLHF training, a simplified threat model is something like this:

•  A model takes some actions.
•  If a human approves of these actions, the human gives the model some reward.
•  Humans can be deceived into giving reward in situations where they would otherwise not if they had more knowledge.
•  Models will take advantage of this so they can get more reward.
• Models will therefore become deceptive.

Before continuing, I would encourage you to really engage with the above. Does it make sense to you? Is it making any hidden assumptions? Is it missing any steps? Can you rewrite it to be more mechanistically correct?

I believe that when people use the above threat model, they are either using it as shorthand for something else or they misunderstand how reinforcement learning works. Most alignment researchers will be in the former category. However, I was in the latter.

Original article:
https://www.lesswrong.com/posts/TWorNr22hhYegE4RT/models-don-t-get-reward

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---
client: lesswrong
project_id: curated
feed_id: ai, ai_safety, ai_safety__technical
narrator: pw
qa: km
narrator_time: 2h15m
qa_time: 0h35m
---
A few collaborators and I recently released a new paper: Discovering Latent Knowledge in Language Models Without Supervision. For a quick summary of our paper, you can check out this Twitter thread.

In this post I will describe how I think the results and methods in our paper fit into a broader scalable alignment agenda. Unlike the paper, this post is explicitly aimed at an alignment audience and is mainly conceptual rather than empirical.

Tl;dr: unsupervised methods are more scalable than supervised methods, deep learning has special structure that we can exploit for alignment, and we may be able to recover superhuman beliefs from deep learning representations in a totally unsupervised way.

Original article:
https://www.lesswrong.com/posts/L4anhrxjv8j2yRKKp/how-discovering-latent-knowledge-in-language-models-without

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---
client: ea_forum
project_id: curated
narrator: pw
qa: km
narrator_time: 2h45m
qa_time: 0h50m
---
At Anima International, we recently decided to suspend our campaign against live fish sales in Poland indefinitely. After a few years of running the campaign, we are now concerned about the effects of our efforts, specifically the possibility of a net negative result for the lives of animals. We believe that by writing about it openly we can help foster a culture of intellectual honesty, information sharing and accountability. Ideally, our case can serve as a good example on reflecting on potential unintended consequences of advocacy interventions.

Original article:
https://forum.effectivealtruism.org/posts/snnfmepzrwpAsAoDT/why-anima-international-suspended-the-campaign-to-end-live

This is a linkpost for https://animainternational.org/blog/why-anima-international-suspended-the-campaign-to-end-live-fish-sales-in-poland

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---
client: 80000_hours
project_id: articles
narrator: pw
qa: mds
narrator_time: 1h10m
qa_time: 0h15m
---
We believe that some of the career paths open to you likely have over 100 times more positive impact than other paths you might take.
Why? In our key ideas series, we’ve shown that you can have more impact by:

1. Finding a bigger and/or more neglected problem to work on
2. Finding a path that gives you a bigger opportunity to contribute
3. Finding work that fits you better

We’ve also shown that there are big differences for each factor:

• Some problems seem hundreds of times more neglected relative to their scale than others.
• Some career paths let you make 100 times as big a contribution to solving those problems as others — via giving you more leverage or letting you support more effective solutions.
• You can have many times more impact in a path that’s a good fit.

On top of that, you can further increase your impact by having a good career strategy, such as by striking the right balance between investing in yourself and having an impact right away.

Original article:
https://80000hours.org/articles/careers-differ-in-impact/

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