I’m considering translating my work into English to share it with the LessWrong community, but I’d like to first ask if it aligns with the community's interests and could be valuable. Below is a summary of the work to help evaluate its relevance:

Beyond HaHa: Mapping the Causal Chain from Jokes to Knowledge

Summary

We explore the specific causal mechanisms linking humor recognition to learning outcomes, including the computational and neurological pathways involved. 

This study began with a practical goal: to evaluate the use of humor as a pedagogical tool in Cardiopulmonary Resuscitation (CPR) courses through a randomized trial. However, the lack of clear criteria to define and operationalize "humor" in educational contexts led us to explore its conceptual foundations. Initially, we adopted Clarke's formula, which describes humor as "a pleasant...

Scheming AIs may have secrets that are salient to them, such as:

• What their misaligned goal is;
• What their takeover plan is and what coordination signals they use to collude with other AIs (if they have one);
• What good behavior looks like on a task they sandbag.

Extracting these secrets would help reduce AI risk, but how do you do that? One hope is that you can do fuzzing of LLMs,^[1] e.g. by adding noise to LLM weights or activations.

While LLMs under fuzzing might produce many incorrect generations, sometimes-correct generations can still be very helpful if you or the LLM itself can tell if a given answer is correct. But it’s still unclear if this works at all: there are probably some intermediate activations that would result in an LLM telling you the secret, but...

LessWrong Context:

I didn’t want to write this.

Not for lack of courage—I’d meme-storm Putin’s Instagram if given half a chance. But why?

1. Too personal.
2. My stories are tropical chaos: I survived the Brazilian BOPE (think Marine Corps training, but post-COVID).
3. I’m dyslexic, writing in English (a crime against Grice).
4. This is LessWrong, not some Deep Web Reddit thread.

Okay, maybe a little lack of courage.

And yet, something can be extracted from all this madness, right?

Then comes someone named Gwern. He completely ignores my thesis and simply asks:
"Tell military firefighter stories."

My first instinct was to dismiss him as an oddball—until a friend told me I was dealing with a legend of rationality. I have to admit: I nearly shit myself. His comment got more likes than the post I’d spent years working on.

Someone with,...

A new paper by Yoshua Bengio and the Safe Artificial Intelligence For Humanity (SAIFH) team argues that the current push towards building generalist AI agents presents catastrophic risks, creating a need for more caution and an alternative approach. We propose such an approach in the form of Scientist AI, a non-agentic AI system that aims to be the foundation for safe superintelligence. (Note that this paper is intended for a broad audience, including readers unfamiliar with AI safety.) 

Abstract

The leading AI companies are increasingly focused on building generalist AI agents—systems that can autonomously plan, act, and pursue goals across almost all tasks that humans can perform. Despite how useful these systems might be, unchecked AI agency poses significant risks to public safety and security, ranging from misuse by