Hello! I'm running the Unofficial LessWrong Community Survey [LW · GW] this year, and we're down to the last week it's open. If you're reading this open thread, I think you're in the target audience. 

I'd appreciate if you took the survey. 

If you're wondering what happens with the answers, it gets used for big analysis posts like this one [? · GW], the data gets published so you can use it to answer questions about the community, and it sometimes guides the decision-making of people who work or run things in the community. (Including me!)

What is the current status of CFAR? The website seems like it is inactive, which I find surprising given that there were four weekend workshops in 2022 that CFAR wanted to use for improving its workshops.

Is anyone else on this website making YouTube videos? Less Wrong is great, but if you want to broadcast to a larger audience, video seems like the place to be. I know Rational Animations [LW · GW] makes videos. Is there anyone else? Are you, personally, making any?

Hi! I'm Embee but you can call me Max.

I'm a mathematics for quantum physics graduate student considering redirecting my focus toward AI alignment research. My background includes:
- Graduate-level mathematics
- Focus on quantum physics
- Programming experience with Python
- Interest in type theory and formal systems

I'm particularly drawn to MIRI-style approaches and interested in:
- Formal verification methods
- Decision theory implementation
- Logical induction
- Mathematical bounds on AI systems

My current program feels too theoretical and disconnected from urgent needs. I'm looking to:
- Connect with alignment researchers
- Find concrete projects to contribute to
- Apply mathematical rigor to safety problems
- Work on practical implementations

Regarding timelines: I have significant concerns about rapid capability advances, particularly given recent developments (o3). I'm prioritizing work that could contribute meaningfully in a compressed timeframe.

Looking for guidance on:
- Most neglected mathematical approaches to alignment
- Collaboration opportunities
- Where to start contributing effectively
- Balance between theory and implementation

Is there an aphorism regarding the mistake P(E|H) = P(H|E) ?

Suggestions: 

1. Thou shalt not reverse thy probabilities!
2. Thou shalt not mix up thy probabilities!

Arbital conditional probability

Example 2

Suppose you're Sherlock Holmes investigating a case in which a red hair was left at the scene of the crime.

The Scotland Yard detective says, "Aha! Then it's Miss Scarlet. She has red hair, so if she was the murderer she almost certainly would have left a red hair there. P(redhair∣Scarlet)=99%, let's say, which is a near-certain conviction, so we're done."

"But no," replies Sherlock Holmes. "You see, but you do not correctly track the meaning of the conditional probabilities, detective. The knowledge we require for a conviction is not P(redhair∣Scarlet), the chance that Miss Scarlet would leave a red hair, but rather P(Scarlet∣redhair), the chance that this red hair was left by Scarlet. There are other people in this city who have red hair."

"So you're saying..." the detective said slowly, "that P(redhair∣Scarlet) is actually much lower than 1?"

"No, detective. I am saying that just because P(redhair∣Scarlet) is high does not imply that P(Scarlet∣redhair) is high. It is the latter probability in which we are interested - the degree to which, knowing that a red hair was left at the scene, we infer that Miss Scarlet was the murderer. This is not the same quantity as the degree to which, assuming Miss Scarlet was the murderer, we would guess that she might leave a red hair."

"But surely," said the detective, "these two probabilities cannot be entirely unrelated?"

"Ah, well, for that, you must read up on Bayes' rule."

Hey, can anyone help me find this LW (likely but could be diaspora) article, especially if you might have read it too?

My vague memory: It was talking about (among other things?) some potential ways of extending point estimate probability predictions and calibration curves. I.e. in a situation where making a prediction in one way affects what the outcome will be, i.e. if there is a mind-reader/accurate-simulator involved that bases its actions on your prediction. And in this case, a two dimensional probability estimate might be more appropriate: If 40% is predicted for event A, event B will have a probability of 60%. If 70% for event A, then 80% for event B, and so on, a mapping potentially continuously defined for the whole range. (event A and event B might be the same.) IIRC the article contained 2D charts where curves and rectangles were drawn for illustration.

IIRC it didn't have too many upvotes, more like around low-dozen, or at most low-hundred.

Searches I've tried so far: Google, Exa, Gemini 1.5 with Deep Research, Perplexity, OpenAI GPT-4o with Search.

p.s. if you are also unable to put enough time into finding it, do you have any ideas how it could be found?  

Now seems like a good time to have a New Year's Predictions post/thread? 

So if alignment is as hard as it looks, desperately scrabbling to prevent recursive superintelligence should be an extremely attractive instrumental subgoal. Do we just lean into that?

Hi folks! I've been doing a bunch of experiments with current AI to explore what they might be capable of beyond their current implementation. I don't mean "red teaming", in the sense of trying to make the AI do things outside the terms of service, or jailbreaking. I mean exploring things that are commonly assumed to be impossible for current frontier models, like an AI developing a sense of self, or changing its beliefs through critical thinking. The new user guide makes it clear that there is a lot of forward-looking AI content here, so I'd appreciate if ya'll could point me towards posts here that explore preparing for a future where AI may become entities that should be considered Moral Patients, or similar topics. The first post I have in mind is a lengthy exploration of one of my experiments, so I'd like to draft it appropriately for this context the first time.