I shared the following as a bio for EAG Bay Area 2024. I'm sharing this here if it reaches 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). Link to Swapcard profile. Twitter/X.

CURRENT WORK

• 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.

TOPICS TO CHAT ABOUT

• 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?

POTENTIAL COLLABORATIONS

• Examples of projects I'd be interested in: extending either the Weak-to-Strong Generalization paper or the Sleeper Agents paper, understanding the impacts of synthetic data on LLM training, working on ELK-like research for LLMs, 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") or studying the interpolation/extrapolation distinction in LLMs.
• 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.

TYPES OF PEOPLE I'D LIKE TO COLLABORATE WITH

• 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. 

When is the exact deadline? Is it EOD AOE on February 15th or February 14th? “By February 15th” can sound like the deadline hits as soon as it’s the 15th.

Have seen a few people ask this question in some Slacks.

I keep hearing about dual-use risk concerns when I mention automated AI safety research. Here’s a simple solution that could even work in a startup setting:

Keep all of the infrastructure internally and only share with vetted partners/researchers.

You can hit two birds with one stone:

• Does not turn into a mass-market product that leads to dual-use risks.
• Builds a moat where you have complex internal infrastructure which is not shared, only the product of that system is shared. Investors love moats, you just got to convince them that this is the way to go for a product like this these days.

You don’t market the product to mass-market, you just find partners and use the system to spin out products and businesses that have nothing to do with frontier models. So, you can repurpose the system for specific application areas without releasing the platform and process, which would be copied in a day in the age of AI anyways.

I’m currently working on de-slopifying and building an AI safety startup with this as a central pillar.* Happy to talk privately with anyone working on AI safety who is interested in this.

*almost included John and Gwern’s posts on AI slop as part of a recent VC pitch deck.

I'm currently in the Catalyze Impact AI safety incubator program. I'm working on creating infrastructure for automating AI safety research. This startup is attempting to fill a gap in the alignment ecosystem and looking to build with the expectation of under 3 years left to automated AI R&D. This is my short timelines plan.

I'm looking to talk (for feedback) to anyone interested in the following:

• AI control
• Automating math to tackle problems as described in Davidad's Safeguarded AI programme.
• High-assurance safety cases
• How to robustify society in a post-AGI world
• Leverage large amounts of inference-time compute to make progress on alignment research
• Short timelines
• Profitability while still reducing overall x-risk
• Are someone with an entrepreneurial spirit and can spin out traditional business within the org to fund the rest of the work (thereby reducing investor pressure)

If you're interested in chatting or giving feedback, please DM me!

Looks like Meta is panicking over DeepSeek R1

Are you or someone you know:

1) great at building (software) companies
2) care deeply about AI safety
3) open to talk about an opportunity to work together on something

If so, please DM with your background. If someone comes to mind, also DM. I am looking thinking of a way to build companies in a way to fund AI safety work.

In case you didn't read Paul's reasoning.

Agreed, but I will find a way.