In 2015, I posted some investing advice for people who only spend a few hours per year on investing.

I intended to review it after five years, but a pandemic distracted me. It looks like this whole decade will end up being too busy for me to write everything that I want to write. But I’ve become able to write faster recently, maybe due to the feeling of urgency about AI transforming the world soon. So I’m getting a few old ideas for blog posts off of my to-do list, in order to be able to devote most of my attention to AI when the world becomes wild.

My advice worked poorly enough that I’m too discouraged to quantify the results.

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I’ve been using Modere’s Curb, a supplement intended to produce healthy GLP-1 levels.

I started taking it in late October, hoping to lose enough weight (3 to 5 pounds?) that I could stop taking Rauwolfia to handle my blood pressure.

My weight dropped 2 pounds in late November, to 149. Since then my weight has been more stable than before. Any remaining trend has been too small to measure. I suspect that the timing of my weight loss is due to getting more exercise than usual the last week in November.

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Book review: Uncontrollable: The Threat of Artificial Superintelligence and the Race to Save the World, by Darren McKee.

This is by far the best introduction to AI risk for people who know little about AI. It’s appropriate for a broader class of readers than most laymen-oriented books.

It was published 14 months ago. In this rapidly changing field, most AI books say something that gets discredited by the time they’re that old. I found no clear example of such obsolescence in Uncontrollable (but read on for a set of controversial examples).

Nearly everything in the book was familiar to me, yet the book prompted me to reflect better, thereby changing my mind modestly – mostly re-examining issues that I’ve been neglecting for the past few years, in light of new evidence.

The rest of this review will focus on complaints, mostly about McKee’s overconfidence. The features that I complain about reduce the value of book by maybe 10% compared to the value of an ideal book. But that ideal book doesn’t exist, and I’m not wise enough to write it.

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The standout announcement from the recent Foresight Vision Weekend came from Openwater, who presented a novel cancer treatment.

I’ve been a bit slow to write about it, in part because my initial reaction was that it’s too good to be true, and most big claims of medical advances are not true.

TL;DR: They’ve developed a cheap ultrasound device that can selectively kill cancer cells by exploiting their unique resonant frequencies, similar to how an opera singer can shatter a wine glass.

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Book review: Genesis: Artificial Intelligence, Hope, and the Human Spirit, by Henry A. Kissinger, Eric Schmidt, and Craig Mundie.

Genesis lends a bit of authority to concerns about AI.

It is a frustrating book. It took more effort for me read than it should have taken. The difficulty stems not from complex subject matter (although the topics are complex), but from a peculiarly alien writing style that transcends mere linguistic differences – though Kissinger’s German intellectual heritage may play a role.

The book’s opening meanders through historical vignettes whose relevance remains opaque, testing my patience before finally addressing AI.

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TL;DR:

  • Corrigibility is a simple and natural enough concept that a prosaic AGI can likely be trained to obey it.
  • AI labs are on track to give superhuman(?) AIs goals which conflict with corrigibility.
  • Corrigibility fails if AIs that have goals which conflict with corrigibility.
  • AI labs are not on track to find a safe alternative to corrigibility.

This post is mostly an attempt to distill and rewrite Max Harm’s Corrigibility As Singular Target Sequence so that a wider audience understands the key points. I’ll start by mostly explaining Max’s claims, then drift toward adding some opinions of my own.

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Book review: Boom: Bubbles and the End of Stagnation, by Byrne Hobart and Tobias Huber.

Hobart and Huber (HH) claim that bubbles are good.

I conclude that this claim is somewhat true. That’s partly because they redefine the concept of a bubble in ways that help make it true.

Boom is densely packed with relevant information. Alas, it’s not full of connections between the various pieces of information and any important conclusions. Nor does it excel at convincing me that its claims are true.

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Two months ago I attended Eric Drexler’s launch of MSEP.one. It’s open source software, written by people with professional game design experience, intended to catalyze better designs for atomically precise manufacturing (or generative nanotechnology, as he now calls it).

Drexler wants to draw more attention to the benefits of nanotech, which involve large enough exponents that our intuition boggles at handling them. That includes permanent health (Drexler’s new framing of life extension and cures for aging).

He hopes that a decentralized network of users will create a rich library of open-source components that might be used to build a nanotech factory. With enough effort, it could then become possible to design a complete enough factory that critics would have to shift from their current practice of claiming nanotech is impossible, to arguing with expert chemists over how well it would work.

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Here are some scattered comments about the 2024 elections.

I was glad to have Manifold Markets and Election Betting Odds to watch the results. I want numbers, not encumbered by the storytellers and emotions of the news media. I also watched the odds that Nate Silver tried to update, but that was a total flop.

Peak Polarization

I see many weak hints that the polarization of the US has subsided compared to 2020.

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