Predicting the future is hard, so it’s no surprise that we occasionally miss important developments.

However, several times recently, in the contexts of Covid forecasting and AI progress, I noticed that I missed some crucial feature of a development I was interested in getting right, and it felt to me like I could’ve seen it coming if only I had tried a little harder. (Some others probably did better, but I could imagine that I wasn't the only one who got things wrong.)

Maybe this is hindsight bias, but if there’s something to it, I want to distill the nature of the mistake.

First, here are the examples that prompted me to take notice:

Predicting the course of the Covid pandemic:

• I didn’t foresee the contribution from sociological factors (e.g., “people not wanting

Caspar Oesterheld came up with two of the most important concepts in my field of work: Evidential Cooperation in Large Worlds and Safe Pareto Improvements. He also came up with a potential implementation of evidential decision theory in boundedly rational agents called decision auctions, wrote a comprehensive review of anthropics and how it interacts with decision theory which most of my anthropics discussions built on, and independently decided to work on AI some time late 2009 or early 2010.

Needless to say, I have a lot of respect for Caspar’s work. I’ve often felt very confused about what to do in my attempts at conceptual research, so I decided to ask Caspar how he did his research. Below is my writeup from the resulting conversation.

How Caspar came up with surrogate goals

The process

• Caspar

I stayed up too late collecting way-past-deadline papers and writing report cards. When I woke up at 6, this anxious email from one of my g11 Computer Science students was already in my Inbox.

Student: Hello Mr. Carle, I hope you've slept well; I haven't.

I've been seeing a lot of new media regarding how developed AI has become in software programming, most relevantly videos about NVIDIA's new artificial intelligence software developer, Devin.

Things like these are almost disheartening for me to see as I try (and struggle) to get better at coding and developing software. It feels like I'll never use the information that I learn in your class outside of high school because I can just ask an AI to write complex programs, and it will do it...

This is the second in a sequence of four posts taken from my recent report: Why Did Environmentalism Become Partisan?

Many of the specific claims made here are investigated in the full report. If you want to know more about how fossil fuel companies’ campaign contributions, the partisan lean of academia, or newspapers’ reporting on climate change have changed since 1980, the information is there.

Introduction

Environmentalism in the United States today is unusually partisan, compared to other issues, countries, or even the United States in the 1980s. This contingency suggests that the explanation centers on the choices of individual decision makers, not on broad structural or ideological factors that would be consistent across many countries and times.

This post describes the history of how particular partisan alliances were made involving...

Linked is my MSc thesis, where I do regret analysis for an infra-Bayesian^[1] generalization of stochastic linear bandits.

The main significance that I see in this work is:

• Expanding our understanding of infra-Bayesian regret bounds, and solidifying our confidence that infra-Bayesianism is a viable approach. Previously, the most interesting IB regret analysis we had was Tian et al which deals (essentially) with episodic infra-MDPs. My work here doesn't supersede Tian et al because it only talks about bandits (i.e. stateless infra-Bayesian laws), but it complements it because it deals with a parameteric hypothesis space (i.e. fits into the general theme in learning-theory that generalization bounds should scale with the dimension of the hypothesis class).
• Discovering some surprising features of infra-Bayesian learning that have no analogues in classical theory. In particular, it

cancer neoantigens

For cells to become cancerous, they must have mutations that cause uncontrolled replication and mutations that prevent that uncontrolled replication from causing apoptosis. Because cancer requires several mutations, it often begins with damage to mutation-preventing mechanisms. As such, cancers often have many mutations not required for their growth, which often cause changes to structure of some surface proteins.

The modified surface proteins of cancer cells are called "neoantigens". An approach to cancer treatment that's currently being researched is to identify some specific neoantigens of a patient's cancer, and create a personalized vaccine to cause their immune system to recognize them. Such vaccines would use either mRNA or synthetic long peptides. The steps required are as follows:

1. The cancer must develop neoantigens that are sufficiently distinct from human surface