Favorite post of the year so far!

Better link: https://www.bloomberg.com/opinion/articles/2024-07-10/jefferies-funded-some-fake-water 

My favored version of this project would involve >50% of the work going into the econ literature and models on investor incentives, with attention to

• Principal-agent problems
• Information asymmetry
• Risk preferences
• Time discounting

And then a smaller fraction of the work would involve looking into AI labs, specifically. I'm curious if this matches your intentions for the project or whether you think there are important lessons about the labs that will not be found in the existing econ literature.

How does the fiduciary duty of companies to investors work?

OpenAI instructs investors to view their investments "in the spirit of a donation," which might be relevant for this question.

I would really like to see a post from someone in AI policy on "Grading Possible Comprehensive AI Legislation." The post would lay out what kind of safety stipulations would earn a bill an "A-" vs a "B+", for example. 

I'm imagining a situation where, in the next couple years, a big omnibus AI bill gets passed that contains some safety-relevant components. I don't want to be left wondering "did the safety lobby get everything it asked for, or did it get shafted?" and trying to construct an answer ex-post. 

I don't know how I hadn't seen this post before now! A couple weeks after you published this, I put out my own post arguing against most applications of analogies in explanations of AI risk. I've added a couple references to your post in mine. 

Adult brains are capable of telekinesis, if you fully believe in your ability to move objects with your mind. Adults are generally too jaded to believe such things. Children have the necessary unreserved belief, but their minds are not developed enough to exercise the ability.

File under 'noticing the start of an exponential': A.I. Helped to Find a Vast Source of the Copper That A.I. Needs to Thrive

Scott Alexander says:

Suppose I notice I am a human on Earth in America. I consider two hypotheses. One is that everything is as it seems. The other is that there is a vast conspiracy to hide the fact that America is much bigger than I think - it actually contains one trillion trillion people. It seems like SIA should prefer the conspiracy theory (if the conspiracy is too implausible, just increase the posited number of people until it cancels out).

I am often confused by the kind of reasoning at play in the text I bolded. Maybe someone can help sort me out. As I increase the number of people in the conspiracy world, my prior in that world also decreases. If my prior falls faster than the number of people in the considered world grows, I will not be able to construct a conspiracy-world that allows the thought experiment to bite. 

Consider the situation where I arrive at the airport, where I will wait in line at security. Wouldn't I be more likely to discover a line 1000 people long than 100 people long? I am 10x more likely to exist in the longer line. The problem is that our prior on 1000 people security lines might be very low. The reasoning on display in the above passage would invite us to simply crank up the length of the line, say, to 1 million people. I suspect that SIA proponents don't show up at the airport expecting lines this long. Why? Because the prior on a million-person line is more than a thousand times lower than the prior on a 100-person line.

This also applies to some presentations of Pascal's mugging.

Jacob Steinhardt on predicting emergent capabilities:

There’s two principles I find useful for reasoning about future emergent capabilities:

1. If a capability would help get lower training loss, it will likely emerge in the future, even if we don’t observe much of it now.
2. As ML models get larger and are trained on more and better data, simpler heuristics will tend to get replaced by more complex heuristics. . . This points to one general driver of emergence: when one heuristic starts to outcompete another. Usually, a simple heuristic (e.g. answering directly) works best for small models on less data, while more complex heuristics (e.g. chain-of-thought) work better for larger models trained on more data.

The nature of these things is that they're hard to predict, but general reasoning satisfies both criteria, making it a prime candidate for a capability that will emerge with scale.