3 min read

My current research and request for collaborators

I wrote this as a bio for EAG Bay Area 2024. I'm sharing this here because it gives an overview of what I've been working on and might reach someone who wants to chat or collaborate.

Hey! I'm Jacques. I'm an independent technical alignment researcher with a background in physics and experience in government (social innovation, strategic foresight, mental health and energy regulation). Twitter/X.


  • Collaborating with Quintin Pope on our Supervising AIs Improving AIs agenda (making automated AI science safe and controllable). The current project involves a new method allowing unsupervised model behaviour evaluations. Our agenda.
  • I'm a research lead in the AI Safety Camp for a project on stable reflectivity (testing models for metacognitive capabilities that impact future training/alignment).
  • Accelerating Alignment: augmenting alignment researchers using AI systems. A relevant talk I gave. Relevant survey post.
  • Other research that currently interests me: multi-polar AI worlds (and how that impacts post-deployment model behaviour), understanding-based interpretability, improving evals, designing safer training setups, interpretable architectures, and limits of current approaches (what would a new paradigm that addresses these limitations look like?).
  • Used to focus more on model editing, rethinking interpretability, causal scrubbing, etc.


  • How do you expect AGI/ASI to actually develop (so we can align our research accordingly)? Will scale plateau? I'd like to get feedback on some of my thoughts on this.
  • How can we connect the dots between different approaches? For example, connecting the dots between Influence Functions, Evaluations, Probes (detecting truthful direction), Function/Task Vectors, and Representation Engineering to see if they can work together to give us a better picture than the sum of their parts.
  • Debate over which agenda actually contributes to solving the core AI x-risk problems.
  • What if the pendulum swings in the other direction, and we never get the benefits of safe AGI? Is open source really as bad as people make it out to be?
  • How can we make something like the d/acc vision (by Vitalik Buterin) happen?
  • How can we design a system that leverages AI to speed up progress on alignment? What would you value the most?
  • What kinds of orgs are missing in the space?


  • Examples of projects I'd be interested in:
  • 1. Extending either the Weak-to-Strong Generalization paper or the Sleeper Agents paper,
  • 2. Understanding the impacts of synthetic data on LLM training
  • 3. working on ELK-like research for LLMs
  • 4. experiments on influence functions (studying the base model and its SFT, RLHF, iterative training counterparts; I heard that Anthropic is releasing code for this "soon")
  • 5. Figure out how to resolve the problems that arise in RLHF/RLAIF as you increase model size
  • 6. Studying the interpolation/extrapolation distinction in LLMs
  • 7. People seem overly focused on LLMs rather than what comes after. I want to understand how dangerous levels of agency could arise in the next big advance. I think one true danger that we need to concern ourselves with is autonomously agentic AI agents that can update their own goals (that go outside the bounds of humanity). "Many applications will be much more autonomous, difficult to monitor or even understand, and potentially fully close loop, i.e the agent has a complex enough action space that it can copy itself, buy compute, run itself, etc."
  • I’m also interested in talking to grantmakers for feedback on some projects I’d like to get funding for.
  • I'm slowly working on a guide for practical research productivity for alignment researchers to tackle low-hanging fruits that can quickly improve productivity in the field. I'd like feedback from people with solid track records and productivity coaches.


  • Strong math background, can understand Influence Functions enough to extend the work.
  • Strong machine learning engineering background. Can run ML experiments and fine-tuning runs with ease. Can effectively create data pipelines.
  • Strong application development background. I have various project ideas that could speed up alignment researchers; I'd be able to execute them much faster if I had someone to help me build my ideas fast.