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. 

Here’s new one: https://x.com/jacquesthibs/status/1796275771734155499?s=61&t=ryK3X96D_TkGJtvu2rm0uw 

Sam added in SEC filings (for AltC) that he’s YC’s chairman. Sam Altman has never been YC’s chairman. From an article posted on April 15th, 2024:

“Annual reports filed by AltC for the past 3 years make the same claim. The recent report: Sam was currently chairman of YC at the time of filing and also "previously served" as YC's chairman.”

The journalist who replied to me said: “Whether Sam Altman was fired from YC or not, he has never been YC's chair but claimed to be in SEC filings for his AltC SPAC which merged w/Oklo. AltC scrubbed references to Sam being YC chair from its website in the weeks since I first reported this.”

The article: https://archive.is/Vl3VR 

If we’re taking the perspective of the entire community, this bears less weight, but: it likely becomes close-to-impossible to criticize OpenAI from that point forward. I’m not even anti-OpenAI, I just try to be truth-seeking where I think people are dropping the ball, and I think there’s almost 0 chance I’ll be able to work with OpenAI in the future given my comments on Twitter.

I think this warrants more discussion, but I think the post would be more valuable if it did try to answer to Beren's post as well as the same statements @Quintin Pope has made about the topic.

Curious to hear what you have to say about this blog post ("Alignment likely generalizes further than capabilities").

Thanks for the comment, makes sense. Applying the boundary to AI systems likely leads to erroneous thinking (though may be narrowly useful if you are careful, in my opinion).

It makes a lot of sense to imagine future AIs having learned behaviours for using their compute efficiently without relying on some outside entity.

I agree with the fragility example.

Something I've been thinking about lately: For 'scarcity of compute' reasons, I think it's fairly likely we end up in a scaffolded AI world where one highly intelligent model (that requires much more compute) will essentially delegate tasks to weaker models as long as it knows that the weaker (maybe fine-tuned) model is capable of reliably doing that task.

Like, let's say you have a weak doctor AI that can basically reliably answer most medical questions. However, it knows when it is less confident in a diagnosis, so it will reach out to the powerful AI when it needs a second opinion from the much more intelligent AI (that requires more compute).

Somewhat related, there's a worldview that Noah Smith proposed, which is that maybe human jobs don't actually end up automated because there's an opportunity cost in giving up compute that a human can do (even if the AI can do it for cheaper) because you could instead use that compute for something much more important. Imagine, "Should I use the AI to build a Dyson sphere, or should I spread that compute across tasks humans can already do?"

Great. Yeah, I also expect that it is hard to get current models to work well on this. However, I will mention that the DeepSeekMath model does seem to outperform GPT-4 despite having only 7B parameters. So, it may be possible to create a +70B fine-tune that basically destroys GPT-4 at math. The issue is whether it generalizes to the kind of math we'd commonly see in alignment research.

Additionally, I expect at least a bit can be done with scaffolding, search, etc. I think the issue with many prompting methods atm is that they are specifically trying to get the model to arrive at solutions on their own. And what I mean by that is that they are starting from the frame of "how can we get LLMs to solve x math task on their own," instead of "how do we augment the researcher's ability to arrive at (better) proofs more efficiently using LLMs." So, I think there's room for product building that does not involve "can you solve this math question from scratch," though I see the value in getting that to work as well.

My question for as to why they can’t share all the examples was not answered, but Helen gives background on what happened here: https://open.spotify.com/episode/4r127XapFv7JZr0OPzRDaI?si=QdghGZRoS769bGv5eRUB0Q&context=spotify%3Ashow%3A6EBVhJvlnOLch2wg6eGtUa

She does confirm she can’t give all of the examples (though points to the ones that were reported), however. Which is not nothing, but eh. However, she also mentioned it was under-reported how much people were scared of Sam and he was creating a very toxic environment.

Alignment Math people: I would appreciate it if someone could review this video of Terrence Tao giving a presentation on machine-assisted proofs to give feedback on what they think an ideal alignment assistant could do in this domain.

In addition, I'm thinking of eventually looking at models like DeepSeek-Prover to see if they can be beneficial for assisting alignment researchers in creating proofs:

Proof assistants like Lean have revolutionized mathematical proof verification, ensuring high accuracy and reliability. Although large language models (LLMs) show promise in mathematical reasoning, their advancement in formal theorem proving is hindered by a lack of training data. To address this issue, we introduce an approach to generate extensive Lean 4 proof data derived from high-school and undergraduate-level mathematical competition problems. This approach involves translating natural language problems into formal statements, filtering out low-quality statements, and generating proofs to create synthetic data. After fine-tuning the DeepSeekMath 7B model on this synthetic dataset, which comprises 8 million formal statements with proofs, our model achieved whole-proof generation accuracies of 46.3% with 64 samples and 52% cumulatively on the Lean 4 miniF2F test, surpassing the baseline GPT-4 at 23.0% with 64 samples and a tree search reinforcement learning method at 41.0%. Additionally, our model successfully proved 5 out of 148 problems in the Lean 4 Formalized International Mathematical Olympiad (FIMO) benchmark, while GPT-4 failed to prove any. These results demonstrate the potential of leveraging large-scale synthetic data to enhance theorem-proving capabilities in LLMs. Both the synthetic dataset and the model will be made available to facilitate further research in this promising field.