TYPE III AUDIO (All episodes)

---
narrator_time: 4h
narrator: pw
qa: km
client: lesswrong
feed_id: ai, ai_safety, ai_safety__forecasting
---

https://www.lesswrong.com/posts/K4urTDkBbtNuLivJx/why-i-think-strong-general-ai-is-coming-soon

I think there is little time left before someone builds AGI (median ~2030). Once upon a time, I didn't think this.

This post attempts to walk through some of the observations and insights that collapsed my estimates.

The core ideas are as follows:

1. We've already captured way too much of intelligence with way too little effort.
2. Everything points towards us capturing way more of intelligence with very little additional effort.
3. Trying to create a self-consistent worldview that handles all available evidence seems to force very weird conclusions.

Some notes up front:

• I wrote this post in response to the Future Fund's AI Worldview Prize. Financial incentives work, apparently! I wrote it with a slightly wider audience in mind and supply some background for people who aren't quite as familiar with the standard arguments.
• I make a few predictions in this post. Unless otherwise noted, the predictions and their associated probabilities should be assumed to be conditioned on "the world remains at least remotely normal for the term of the prediction; the gameboard remains unflipped."
• For the purposes of this post, when I use the term AGI, I mean the kind of AI with sufficient capability to make it a genuine threat to humanity's future or survival if it is misused or misaligned. This is slightly more strict than the definition in the Future Fund post, but I expect the difference between the two definitions to be small chronologically.
• For the purposes of this post, when I refer to "intelligence," I mean stuff like complex problem solving that's useful for achieving goals. Consciousness, emotions, and qualia are not required for me to call a system "intelligent" here; I am defining it only in terms of capability.

Share feedback on this narration.

---
narrator_time: 45m
narrator: pw
qa: km
client: lesswrong
---

https://www.lesswrong.com/posts/8vesjeKybhRggaEpT/consider-your-appetite-for-disagreements

Poker

There was a time about five years ago where I was trying to get good at poker. If you want to get good at poker, one thing you have to do is review hands. Preferably with other people.

For example, suppose you have ace king offsuit on the button. Someone in the highjack opens to 3 big blinds preflop. You call. Everyone else folds. The flop is dealt. It's a rainbow Q75. You don't have any flush draws. You missed. Your opponent bets. You fold. They take the pot and you move to the next hand.

Once you finish your session, it'd be good to come back and review this hand. Again, preferably with another person. To do this, you would review each decision point in the hand. Here, there were two decision points.

The first was when you faced a 3BB open from HJ preflop with AKo. In the hand, you decided to call. However, this of course wasn't your only option. You had two others: you could have folded, and you could have raised. Actually, you could have raised to various sizes. You could have raised small to 8BB, medium to 10BB, or big to 12BB. Or hell, you could have just shoved 200BB! But that's not really a realistic option, nor is folding. So in practice your decision was between calling and raising to various realistic sizes.

Share feedback on this narration.

---
narrator_time: 3h30m
narrator: pw
qa: km
client: lesswrong
---

https://www.lesswrong.com/posts/REA49tL5jsh69X3aM/introduction-to-abstract-entropy

This post, and much of the following sequence, was greatly aided by feedback from the following people (among others): Lawrence Chan, Joanna Morningstar, John Wentworth, Samira Nedungadi, Aysja Johnson, Cody Wild, Jeremy Gillen, Ryan Kidd, Justis Mills and Jonathan Mustin. Illustrations by Anne Ore.

Introduction & motivation

In the course of researching optimization, I decided that I had to really understand what entropy is.[1] But there are a lot of other reasons why the concept is worth studying:

• Information theory:
  • Entropy tells you about the amount of information in something.
  • It tells us how to design optimal communication protocols.
  • It helps us understand strategies for (and limits on) file compression.
• Statistical mechanics:
  • Entropy tells us how macroscopic physical systems act in practice.
  • It gives us the heat equation.
  • We can use it to improve engine efficiency.
  • It tells us how hot things glow, which led to the discovery of quantum mechanics.
• Epistemics (an important application to me and many others on LessWrong):
  • The concept of entropy yields the maximum entropy principle, which is extremely helpful for doing general Bayesian reasoning.
• Entropy tells us how "unlikely" something is and how much we would have to fight against nature to get that outcome (i.e. optimize).
• It can be used to explain the arrow of time.
• It is relevant to the fate of the universe.
• And it's also a fun puzzle to figure out!

Share feedback on this narration.

---
narrator_time: 1h20m
narrator: pw
qa: km
client: lesswrong
---

https://www.lesswrong.com/posts/fFY2HeC9i2Tx8FEnK/my-resentful-story-of-becoming-a-medical-miracle

This is a linkpost for https://acesounderglass.com/2022/10/13/my-resentful-story-of-becoming-a-medical-miracle/

You know those health books with “miracle cure” in the subtitle? The ones that always start with a preface about a particular patient who was completely hopeless until they tried the supplement/meditation technique/healing crystal that the book is based on? These people always start broken and miserable, unable to work or enjoy life, perhaps even suicidal from the sheer hopelessness of getting their body to stop betraying them. They’ve spent decades trying everything and nothing has worked until their friend makes them see the book’s author, who prescribes the same thing they always prescribe, and the patient immediately stands up and starts dancing because their problem is entirely fixed (more conservative books will say it took two sessions). You know how those are completely unbelievable, because anything that worked that well would go mainstream, so basically the book is starting you off with a shit test to make sure you don’t challenge its bullshit later?

Well 5 months ago I became one of those miraculous stories, except worse, because my doctor didn’t even do it on purpose. This finalized some already fermenting changes in how I view medical interventions and research. Namely: sometimes knowledge doesn’t work and then you have to optimize for luck.

I assure you I’m at least as unhappy about this as you are. 

Share feedback on this narration.

---
narrator_time: 1h30m
narrator: pw
qa: km
client: lesswrong
---

https://www.lesswrong.com/posts/gNodQGNoPDjztasbh/lies-damn-lies-and-fabricated-options

This is an essay about one of those "once you see it, you will see it everywhere" phenomena.  It is a psychological and interpersonal dynamic roughly as common, and almost as destructive, as motte-and-bailey, and at least in my own personal experience it's been quite valuable to have it reified, so that I can quickly recognize the commonality between what I had previously thought of as completely unrelated situations.

The original quote referenced in the title is "There are three kinds of lies: lies, damned lies, and statistics."

Background 1: Gyroscopes
Gyroscopes are weird.

Except they're not.  They're quite normal and mundane and straightforward.  The weirdness of gyroscopes is a map-territory confusion—gyroscopes seem weird because my map is poorly made, and predicts that they will do something other than their normal, mundane, straightforward thing.

In large part, this is because I don't have the consequences of physical law engraved deeply enough into my soul that they make intuitive sense.

I can imagine a world that looks exactly like the world around me, in every way, except that in this imagined world, gyroscopes don't have any of their strange black-magic properties.  It feels coherent to me.  It feels like a world that could possibly exist.

"Everything's the same, except gyroscopes do nothing special."  Sure, why not.

But in fact, this world is deeply, deeply incoherent.  It is Not Possible with capital letters. And a physicist with sufficiently sharp intuitions would know this—would be able to see the implications of a world where gyroscopes "don't do anything weird," and tell me all of the ways in which reality falls apart.

Share feedback on this narration.

---
narrator_time: 4h30m
narrator: pw
qa: km
feed_id: ai, ai_safety, ai_safety__technical, ai_safety__governance
client: lesswrong
---

https://www.lesswrong.com/posts/rCJQAkPTEypGjSJ8X/how-might-we-align-transformative-ai-if-it-s-developed-very

This post gives my understanding of what the set of available strategies for aligning transformative AI would be if it were developed very soon, and why they might or might not work. It is heavily based on conversations with Paul Christiano, Ajeya Cotra and Carl Shulman, and its background assumptions correspond to the arguments Ajeya makes in this piece (abbreviated as “Takeover Analysis”). 

I premise this piece on a nearcast in which a major AI company (“Magma,” following Ajeya’s terminology) has good reason to think that it can develop transformative AI very soon (within a year), using what Ajeya calls “human feedback on diverse tasks” (HFDT) - and has some time (more than 6 months, but less than 2 years) to set up special measures to reduce the risks of misaligned AI before there’s much chance of someone else deploying transformative AI. 

Share feedback on this narration.

---
narrator_time: 1h30m
narrator: pw
qa: km
client: lesswrong
---

https://www.lesswrong.com/posts/7cHgjJR2H5e4w4rxT/quintin-s-alignment-papers-roundup-week-1

Introduction

I've decided to start a weekly roundup of papers that seem relevant to alignment, focusing on papers or approaches that might be new to safety researchers. Unlike the Alignment Newsletter, I'll be spending relatively little effort on summarizing the papers. I'll just link them, copy their abstracts, and potentially describe some of my thoughts on how the paper relates to alignment. Hopefully, this will let me keep to a weekly schedule.

The purpose of this series isn't so much to share insights directly with the reader, but instead to make them aware of already existing research that may be relevant to the reader's own research.

Share feedback on this narration.

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

Original article:
https://forum.effectivealtruism.org/s/W4fhpuN26naxGCBbN/p/YxiXZcddn4kEqGdr9

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: ea_forum
project_id: summaries
narrator: cs
---

Original article:
https://forum.effectivealtruism.org/s/W4fhpuN26naxGCBbN/p/Hi5z6tm9d2keHALgv

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.

---
narrator_time: 3h00m
narrator: pw
qa: km
client: lesswrong
feed_id: ai, ai_safety
---

Share feedback on this narration.