I'd like to have conversations with people who work or are knowledgeable about energy and security. Whether that's with respect to energy grids, nuclear power plants, solar panels, etc. I'm exploring a startup idea to harden the world's critical infrastructure against powerful AI. (I am also building a system to make formal verification more deployable at scale so that it may reduce loss of control and misuse scenarios.)

I've given workshops on using AIs for productivity/research to various research organizations like MATS. I'm happy to offer a bit of my time to share my expertise on that if that would make the meeting more interesting for you (or any other topics you'd like to hear my perspective on).

Context about me: I'm Jacques. I started working on technical AI safety research in January 2022. Before that, I had been engaging with AI ethics in a more personal capacity, worked as a data scientist at the Canada Energy Regulator, and earned a BSc/master's in Physics. I'm currently based in Montreal.

Please schedule a meeting if interested (or DM if you know someone I should talk to): https://calendly.com/jacquesthibodeau/45-minute-meeting

Quillette founder seems to be planning to write an article regarding EA's impact on on tech:

"If anyone with insider knowledge wants to write about the impact of Effective Altruism in the technology industry please get in touch with me [email protected]. We pay our writers and can protect authors' anonymity if desired."

It would probably be impactful if someone in the know provided a counterbalance to whoever will undoubtedly email her to disparage EA with half-truths/lies.

If you work at a social media website or YouTube (or know anyone who does), please read the text below:

Community Notes is one of the best features to come out on social media apps in a long time. The code is even open source. Why haven't other social media websites picked it up yet? If they care about truth, this would be a considerable step forward beyond. Notes like “this video is funded by x nation” or “this video talks about health info; go here to learn more” messages are simply not good enough.

If you work at companies like YouTube or know someone who does, let's figure out who we need to talk to to make it happen. Naïvely, you could spend a weekend DMing a bunch of employees (PMs, engineers) at various social media websites in order to persuade them that this is worth their time and probably the biggest impact they could have in their entire career.

If you have any connections, let me know. We can also set up a doc of messages to send in order to come up with a persuasive DM.

More information about the alleged manipulative behaviour of Sam Altman

Source

Hey everyone, my name is Jacques, I'm an independent technical alignment researcher (primarily focused on evaluations, interpretability, and scalable oversight). I'm now focusing more of my attention on building an Alignment Research Assistant. I'm looking for people who would like to contribute to the project. This project will be private unless I say otherwise.

Side note: I helped build the Alignment Research Dataset ~2 years ago. It has been used at OpenAI (by someone on the alignment team), (as far as I know) at Anthropic for evals, and is now used as the backend for Stampy.ai.

If you are interested in potentially helping out (or know someone who might be!), send me a DM with a bit of your background and why you'd like to help out. To keep things focused, I may or may not accept.

I have written up the vision and core features for the project here. I expect to see it evolve in terms of features, but the vision will likely remain the same. I'm currently working on some of the features and have delegated some tasks to others (tasks are in a private GitHub project board).

I'm also collaborating with different groups. For now, the focus is to build core features that can be used individually but will eventually work together into the core product. In 2-3 months, I want to get it to a place where I know whether this is useful for other researchers and if we should apply for additional funding to turn it into a serious project.

In case this is useful to anyone in the future: LTFF does not provide funding for for-profit organizations. I wasn't able to find mentions of this online, so I figured I should share.

I was made aware of this after being rejected today for applying to LTFF as a for-profit. We updated them 2 weeks ago on our transition into a non-profit, but it was unfortunately too late, and we'll need to send a new non-profit application in the next funding round.

Hey everyone, in collaboration with Apart Research, I'm helping organize a hackathon this weekend to build tools for accelerating alignment research. This hackathon is very much related to my effort in building an "Alignment Research Assistant."

Here's the announcement post:

2 days until we revolutionize AI alignment research at the Research Augmentation Hackathon!

As AI safety researchers, we pour countless hours into crucial work. It's time we built tools to accelerate our efforts! Join us in creating AI assistants that could supercharge the very research we're passionate about.

Date: July 26th to 28th, online and in-person
Prizes: $2,000 in prizes

Why join?

* Build tools that matter for the future of AI
* Learn from top minds in AI alignment
* Boost your skills and portfolio

We've got a Hackbook with an exciting project to work on waiting for you! No advanced AI knowledge required - just bring your creativity!

Register now: Sign up on the website here, and don't miss this chance to shape the future of AI research!

We put out a proposal for automating AI safety research on Manifund. We got our first $10k. I figured I'd share this here if you or someone you might know would like to fund our work! Thanks!

Coordinal Research: Accelerating the research of safely deploying AI systems.

Project summary

What are this project's goals? How will you achieve them?

Coordinal Research (formerly Lyra Research, merging with Vectis AI) wants to accelerate the research of safe and aligned AI systems. We're complementing existing research in these directions through two key approaches:

1. Developing tools that accelerate the rate at which human researchers can make progress on alignment.
2. Building automated research systems that can assist in alignment work today.

Automation and agents are here, and are being used to accelerate AI capabilities. AI Safety research is lagging behind in adopting these technologies, and many technical safety agendas would benefit from having their research output accelerated. Models are capable enough today to replace software engineers, conduct literature reviews, and generate novel research ideas. Given that the fundamental nature of the workforce is likely to change drastically in the coming years, and that these technologies are being used to increase automation of capabilities research, Coordinal wants to close this gap between capabilities and alignment sooner rather than later, before it grows wider.

With the right scaffolding, frontier models can be used to accelerate AI safety research agendas. There now exist myriad academic papers and projects, as well as for-profit, capabilities-focused or business-driven startups building out agentic and autonomous systems. We want to ensure adoption of these tools for capabilities research does not significantly outpace adoption for safety work.

Support for this project will directly fund 1) building out and iterating on our existing functional MVPs that address these goals, and 2) bootstrapping our nonprofit organization to assist and produce high-quality technical AI safety research. 

Our technical tools center on two key components:

1. A structured, AI-aided research workflow we call Seed that helps researchers refine ideas, connect to open questions in the field, create detailed research project specifications, and an initial codebase optimized for leveraging AI coding agents and significantly speeding up idea-to-implementation feedback loops.
2. Our core automated scaffold accepts any research plan or task and conducts background research, implements software, evaluates experimental results, and writes research reports based on those results.

We've already made significant progress:

• We have a demo of the automated scaffold, which is live at coordinal.org (happy to share a demo key privately, feel free to reach out).

• We have a demo of Seed, which should be up and running fairly soon.

• We’ve curated over 400 open questions in AI safety that can be used directly with our automated scaffold, which we are gearing up to run when we have the funding and compute necessary.

Some people might feel that we are working on this too soon, but we believe building automated AI safety R&D infrastructure early ensures we're ready when more capable systems arrive. As Ryan Greenblatt has said, "It seems likely we'll run into integration delays and difficulties speeding up security and safety work in particular."

This is fundamentally a short timelines bet–we believe the alignment community must develop these tools before integration delays become critical bottlenecks. It’s impossible to perfectly time an exponential–you are either too early or too late.

For responses to questions and criticisms, please refer to our FAQ.

How will this funding be used?

The funding will be used to pay for:

• Our salaries for 3 months.
• Compute.

Our concrete goals for the next 3 months are:

• Build out our MVPs, iterating on them through experimentation as well as researcher testing and feedback.
• Make our research workflow tooling fully available to safety researchers, such that they can refine their current project plans and research directions.
• Solve open questions posed by other members of the AI safety community via our automated scaffold, maximally taking advantage of existing capabilities to understand how much is possible as well as meaningfully contribute to safety research.

The minimal funding goal will primarily be used for compute as we iterate on and develop our primary product. We are currently at a rate where we are spending roughly $100 per reasonably sized research target, without GPU resources. This will largely be used to offset our cloud and API bills as we build out proofs of concept to demonstrate the organization’s value. This grant would significantly enhance our capacity for progress in the upcoming months and enable us to secure additional funding and computing resources from other grant-making organizations, which will be more likely to fund our organization once we show serious examples of automated research. We are confident that, with a dedicated effort, we can persuade them within three months to support us.

Target funding will be used to fully build out the organization formally, including providing salaries and other overhead coverage for our team for 6 months. We are currently working on this via personal runway; this amount would allow us to fully dedicate ourselves to the organization and its goals full-time.

Any amount over our target funding would likely go fully towards compute. We fully believe we will be able to effectively convert funding into compute into research in a productive manner within the next few months, and this will be our primary operating cost.

Who is on your team? What's your track record on similar projects?

Our team formed during the Catalyze Impact AI safety incubator program, combining our vision for automated research and our strong research and engineering backgrounds in AI/ML and AI safety.

Ronak Mehta Homepage, Google Scholar, Resume/CV, LinkedIn

I’ve been actively working on this project over the last few months, building out the proof of concept and all associated technical pieces including core scaffolding, devops, and testing. Last summer and through the fall, I participated in MATS 6.0 working on problems in provably safe and guaranteed-safe AI (finetuning compact proofs work coming soon, a benchmark for theorem proving in lean). Before that, I worked at an LLM startup for a year as an ML research engineer following graduation with a PhD in Machine Learning and Computer Science from the University of Wisconsin-Madison.

Jacques Thibodeau GitHub, Lesswrong profile, LinkedIn, Website and Resume.

Independent AI safety researcher actively focusing on research automation and automated evaluations to uncover unwanted side-effects. MATS scholar (2022) with background as a Data Scientist. Currently mentoring SPAR projects on automating interpretability. Created the Alignment Research Literature Dataset used by OpenAI, Anthropic, and StampyAI. Conducted research on model editing techniques (ROME) with mentorship from former OpenAI researchers (William Saunders and Alex Gray). Built an award-winning data science search tool (BERDI) for environmental data.

What are the most likely causes and outcomes if this project fails?

1. Technical limitations of current AI systems. If fully automated research isn't possible yet, our tools would still provide significant acceleration for specific subtasks (literature review, experiment coding, data analysis). We hope to identify which tasks in research are most amenable to automation now as a fallback, while also designing our system in such a way that as underlying models improve, temporary limitations do not mean long-term failure.

2. Compute limitations. It may turn out that a large amount of compute is necessary for even minimal proofs of concept. We are already reaching a point where we can effectively deploy up to $100 per research project in compute. We expect that in a few weeks this could increase significantly as we allow our framework to take advantage of more expensive bare metal and cloud infrastructure. We are actively seeking out other sources of compute and API credit offsets, and applying for other funding sources. We hope to partner with existing aligned institutions for compute, AGI labs for tokens, academic institutions with GPUs, and compute providers for subsidized resources.

Note that OpenAI’s new PaperBench paper (for replicating ML papers) estimated that, “...on average it costs $400 in API credits to run an o1 IterativeAgent 12-hour rollout on a single paper in PaperBench. For the 20 papers, this sums to $8000 USD per eval run. Grading costs an additional $66 USD per paper on average with o3-mini SimpleJudge.”

3. Researcher adoption barriers. It is reasonable to be skeptical of automated research given researchers’ current experiences with LLMs. Still, we believe we can convince them over time as we demonstrate how our tools can automate their research agendas. While we do intend to use the system internally to directly produce valuable safety research, we also want to work directly with a smaller set of researchers, demonstrating clear value before broader deployment.

4. Capabilities concerns. We plan on keeping core infrastructure fully private, and providing access to the system only to vetted alignment researchers. We're only open-sourcing the direct research outputs (papers, findings, benchmarks, generated code) rather than the automation infrastructure itself. We plan on developing safety cases for automated alignment research that address potential risks and mitigation strategies, and hope to collaborate directly on what these look like with relevant stakeholders (e.g., governmental bodies, eval organizations).

How much money have you raised in the last 12 months, and from where?

We are a new organization that formed during the Catalyze Impact AI Safety incubator program. We have not yet secured any funding, but are applying to the usual suspects that fund technical AI safety work.

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.

I'm exploring the possibility of building an alignment research organization focused on augmenting alignment researchers and progressively automating alignment research (yes, I have thought deeply about differential progress and other concerns). I intend to seek funding in the next few months, and I'd like to chat with people interested in this kind of work, especially great research engineers and full-stack engineers who might want to cofound such an organization. If you or anyone you know might want to chat, let me know! Send me a DM, and I can send you some initial details about the organization's vision.

Here are some things I'm looking for in potential co-founders:

Need

• Strong software engineering skills

Nice-to-have

• Experience in designing LLM agent pipelines with tool-use
• Experience in full-stack development
• Experience in scalable alignment research approaches (automated interpretability/evals/red-teaming)

I quickly wrote up some rough project ideas for ARENA and LASR participants, so I figured I'd share them here as well. I am happy to discuss these ideas and potentially collaborate on some of them.

Alignment Project Ideas (Oct 2, 2024)

1. Improving "A Multimodal Automated Interpretability Agent" (MAIA)

Overview

MAIA (Multimodal Automated Interpretability Agent) is a system designed to help users understand AI models by combining human-like experimentation flexibility with automated scalability. It answers user queries about AI system components by iteratively generating hypotheses, designing and running experiments, observing outcomes, and updating hypotheses.

MAIA uses a vision-language model (GPT-4V, at the time) backbone equipped with an API of interpretability experiment tools. This modular system can address both "macroscopic" questions (e.g., identifying systematic biases in model predictions) and "microscopic" questions (e.g., describing individual features) with simple query modifications.

This project aims to improve MAIA's ability to either answer macroscopic questions or microscopic questions on vision models.

2. Making "A Multimodal Automated Interpretability Agent" (MAIA) work with LLMs

MAIA is focused on vision models, so this project aims to create a MAIA-like setup, but for the interpretability of LLMs.

Given that this would require creating a new setup for language models, it would make sense to come up with simple interpretability benchmark examples to test MAIA-LLM. The easiest way to do this would be to either look for existing LLM interpretability benchmarks or create one based on interpretability results we've already verified (would be ideal to have a ground truth). Ideally, the examples in the benchmark would be simple, but new enough that the LLM has not seen them in its training data.

3. Testing the robustness of Critique-out-Loud Reward (CLoud) Models

Critique-out-Loud reward models are reward models that can reason explicitly about the quality of an input through producing Chain-of-Thought like critiques of an input before predicting a reward. In classic reward model training, the reward model is trained as a reward head initialized on top of the base LLM. Without LM capabilities, classic reward models act as encoders and must predict rewards within a single forward pass through the model, meaning reasoning must happen implicitly. In contrast, CLoud reward models are trained to both produce explicit reasoning about quality and to score based on these critique reasoning traces. CLoud reward models lead to large gains for pairwise preference modeling on RewardBench, and also lead to large gains in win rate when used as the scoring model in Best-of-N sampling on ArenaHard.

The goal for this project would be to test the robustness of CLoud reward models. For example, are the CLoud RMs (discriminators) more robust to jailbreaking attacks from the policy (generator)? Do the CLoud RMs generalize better?

From an alignment perspective, we would want RMs that generalize further out-of-distribution (and ideally, always more than the generator we are training).

4. Synthetic Data for Behavioural Interventions

Simple synthetic data reduces sycophancy in large language models by (Google) reduced sycophancy in LLMs with a fairly small number of synthetic data examples. This project would involve testing this technique for other behavioural interventions and (potentially) studying the scaling laws. Consider looking at the examples from the Model-Written Evaluations paper by Anthropic to find some behaviours to test.

5. Regularization Techniques for Enhancing Interpretability and Editability

Explore the effectiveness of different regularization techniques (e.g. L1 regularization, weight pruning, activation sparsity) in improving the interpretability and/or editability of language models, and assess their impact on model performance and alignment. We expect we could apply automated interpretability methods (e.g. MAIA) to this project to test how well the different regularization techniques impact the model.

In some sense, this research is similar to the work Anthropic did with SoLU activation functions. Unfortunately, they needed to add layer norms to make the SoLU models competitive, which seems to have hidden away the superposition in other parts of the network, making SoLU unhelpful in making the models more interpretable

That said, we hope to find that we can increase our ability to interpret these models through regularization techniques. A technique like L1 regularization should help because it encourages the model to learn sparse representations by penalizing non-zero weights or activations. Sparse models tend to be more interpretable as they rely on a smaller set of important features.

Methodology:

1. Identify a set of regularization techniques (e.g., L1 regularization, weight pruning, activation sparsity) to be applied during fine-tuning.
2. Fine-tune pre-trained language models with different regularization techniques and hyperparameters.
3. Evaluate the fine-tuned models using interpretability tools (e.g., attention visualization, probing classifiers) and editability benchmarks (e.g., ROME).
4. Analyze the impact of regularization on model interpretability, editability, and performance.
5. Investigate the relationship between interpretability, editability, and model alignment.

Expected Outcomes:

• Quantitative assessment of the effectiveness of different regularization techniques for improving interpretability and editability.
• Insights into the trade-offs between interpretability, editability, and model performance.
• Recommendations for regularization techniques that enhance interpretability and editability while maintaining model performance and alignment.

6. Quantifying the Impact of Reward Misspecification on Language Model Behavior

Investigate how misspecified reward functions influence the behavior of language models during fine-tuning and measure the extent to which the model's outputs are steered by the reward labels, even when they contradict the input context. We hope to better understand language model training dynamics. Additionally, we expect online learning to complicate things in the future, where models will be able to generate the data they may eventually be trained on. We hope that insights from this work can help us prevent catastrophic feedback loops in the future. For example, if model behavior is mostly impacted by training data, we may prefer to shape model behavior through synthetic data (it has been shown we can reduce sycophancy by doing this).

Prior works:

• The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Models by Alexander Pan, Kush Bhatia, Jacob Steinhardt
• Survival Instinct in Offline Reinforcement Learning by Anqi Li, Dipendra Misra, Andrey Kolobov, Ching-An Cheng
• Simple synthetic data reduces sycophancy in large language models by (Google), Jerry Wei, Da Huang, Yifeng Lu, Denny Zhou, Quoc V. Le
• Scaling Laws for Reward Model Overoptimization by (OpenAI), Leo Gao, John Schulman, Jacob Hilton
• On the Sensitivity of Reward Inference to Misspecified Human Models by Joey Hong, Kush Bhatia, Anca Dragan

Methodology:

1. Create a diverse dataset of text passages with candidate responses and manually label them with coherence and misspecified rewards.
2. Fine-tune pre-trained language models using different reward weighting schemes and hyperparameters.
3. Evaluate the generated responses using automated metrics and human judgments for coherence and misspecification alignment.
4. Analyze the influence of misspecified rewards on model behavior and the trade-offs between coherence and misspecification alignment.
5. Use interpretability techniques to understand how misspecified rewards affect the model's internal representations and decision-making process.

Expected Outcomes:

• Quantitative measurements of the impact of reward misspecification on language model behavior.
• Insights into the trade-offs between coherence and misspecification alignment.
• Interpretability analysis revealing the effects of misspecified rewards on the model's internal representations.

7. Investigating Wrong Reasoning for Correct Answers

Understand the underlying mechanisms that lead to language models producing correct answers through flawed reasoning, and develop techniques to detect and mitigate such behavior. Essentially, we want to apply interpretability techniques to help us identify which sets of activations or token-layer pairs impact the model getting the correct answer when it has the correct reasoning versus when it has the incorrect reasoning. The hope is to uncover systematic differences as to when it is not relying on its chain-of-thought at all and when it does leverage its chain-of-thought to get the correct answer.

[EDIT Oct 2nd, 2024] This project intends to follow a similar line of reasoning as described in this post and this comment. The goal is to study chains-of-thought and improve faithfulness without suffering an alignment tax so that we can have highly interpretable systems through their token outputs and prevent loss of control. The project doesn't necessarily need to rely only on model internals.

Related work:

1. Decomposing Predictions by Modeling Model Computation by Harshay Shah, Andrew Ilyas, Aleksander Madry
2. Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models by Peter Hase, Mohit Bansal, Been Kim, Asma Ghandeharioun
3. On Measuring Faithfulness or Self-consistency of Natural Language Explanations by Letitia Parcalabescu, Anette Frank
4. Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting by Miles Turpin, Julian Michael, Ethan Perez, Samuel R. Bowman
5. Measuring Faithfulness in Chain-of-Thought Reasoning by Tamera Lanham et al.

Methodology:

1. Curate a dataset of questions and answers where language models are known to provide correct answers but with flawed reasoning.
2. Use interpretability tools (e.g., attention visualization, probing classifiers) to analyze the model's internal representations and decision-making process for these examples.
3. Develop metrics and techniques to detect instances of correct answers with flawed reasoning.
4. Investigate the relationship between model size, training data, and the prevalence of flawed reasoning.
5. Propose and evaluate mitigation strategies, such as data augmentation or targeted fine-tuning, to reduce the occurrence of flawed reasoning.

Expected Outcomes:

• Insights into the underlying mechanisms that lead to correct answers with flawed reasoning in language models.
• Metrics and techniques for detecting instances of flawed reasoning.
• Empirical analysis of the factors contributing to flawed reasoning, such as model size and training data.
• Proposed mitigation strategies to reduce the occurrence of flawed reasoning and improve model alignment.

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.

My current speculation as to what is happening at OpenAI

How do we know this wasn't their best opportunity to strike if Sam was indeed not being totally honest with the board?

Let's say the rumours are true, that Sam is building out external orgs (NVIDIA competitor and iPhone-like competitor) to escape the power of the board and potentially go against the charter. Would this 'conflict of interest' be enough? If you take that story forward, it sounds more and more like he was setting up AGI to be run by external companies, using OpenAI as a fundraising bargaining chip, and having a significant financial interest in plugging AGI into those outside orgs.

So, if we think about this strategically, how long should they wait as board members who are trying to uphold the charter?

On top of this, it seems (according to Sam) that OpenAI has made a significant transformer-level breakthrough recently, which implies a significant capability jump. Long-term reasoning? Basically, anything short of 'coming up with novel insights in physics' is on the table, given that Sam recently used that line as the line we need to cross to get to AGI.

So, it could be a mix of, Ilya thinking they have achieved AGI while Sam places a higher bar (internal communication disagreements) + the board not being alerted (maybe more than once) about what Sam is doing, e.g. fundraising for both OpenAI and the orgs he wants to connect AGI to + new board members who are more willing to let Sam and GDB do what they want being added soon (another rumour I've heard) + ???. Basically, perhaps they saw this as their final opportunity to have any veto on actions like this.

Here's what I currently believe:

• There is a GPT-5-like model that already exists. It could be GPT-4.5 or something else, but another significant capability jump. Potentially even a system that can coherently pursue goals for months, capable of continual learning, and effectively able to automate like 10% of the workforce (if they wanted to).
• As of 5 PM, Sunday PT, the board is in a terrible position where they either stay on board and the company employees all move to a new company, or they leave the board and bring Sam back. If they leave, they need to say that Sam did nothing wrong and sweep everything under the rug (and then potentially face legal action for saying he did something wrong); otherwise, Sam won't come back.
• Sam is building companies externally; it is unclear if this goes against the charter. But he does now have a significant financial incentive to speed up AI development. Adam D'Angelo said that he would like to prevent OpenAI from becoming a big tech company as part of his time on the board because AGI was too important for humanity. They might have considered Sam's action going in this direction.
• A few people left the board in the past year. It's possible that Sam and GDB planned to add new people (possibly even change current board members) to the board to dilute the voting power a bit or at least refill board seats. This meant that the current board had limited time until their voting power would become less important. They might have felt rushed.
• The board is either not speaking publicly because 1) they can't share information about GPT-5, 2) there is some legal reason that I don't understand (more likely), or 3) they are incompetent (least likely by far IMO).
• We will possibly never find out what happened, or it will become clearer by the month as new things come out (companies and models). However, it seems possible the board will never say or admit anything publicly at this point.
• Lastly, we still don't know why the board decided to fire Sam. It could be any of the reasons above, a mix or something we just don't know about.

Other possible things:

• Ilya was mad that they wouldn't actually get enough compute for Superalignment as promised due to GPTs and other products using up all the GPUs.
• Ilya is frustrated that Sam is focused on things like GPTs rather than the ultimate goal of AGI.

Would newer people find it valuable to have some kind of 80,000 hours career chatbot that had access to the career guide, podcast notes, EA forum posts, job postings, etc, and then answered career questions? I’m curious if it could be designed to be better than just a raw read of the career guide or at least a useful add-on to the career guide.

Potential features:

• It could collect your conversation and convert most of it into an application for a (human) 1-on-1 meeting.
• You could have a speech-to-text option to ramble all the things you’ve been thinking of.
• ???

If anyone from 80k is reading this, I’d be happy to build this as a paid project.

Attempt to explain why I think AI systems are not the same thing as a library card when it comes to bio-risk.

To focus on less of an extreme example, I’ll be ignoring the case where AI can create new, more powerful pathogens faster than we can create defences, though I think this is an important case (some people just don’t find it plausible because it relies on the assumption that AIs being able to create new knowledge).

I think AI Safety people should make more of an effort to walkthrough the threat model so I’ll give an initial quick first try:

1) Library. If I’m a terrorist and I want to build a bioweapon, I have to spend several months reading books at minimum to understand how it all works. I don’t have any experts on-hand to explain how to do it step-by-step. I have to figure out which books to read and in what sequence. I have to look up external sources to figure out where I can buy specific materials.

Then, I have to somehow find out how to to gain access to those materials (this is the most difficult part for each case). Once I gain access to the materials, I still need to figure out how to make things work as a total noob at creating bioweapons. I will fail. Even experts fail. So, it will take many tries to get it right, and even then, there are tricks of the trade I’ll likely be unaware of no matter which books I read. Either it’s not in a book or it’s incredibly hard to find so you’ll basically never find it.

All this while needing a high enough degree of intelligence and competence.

2) AI agent system. You pull up your computer and ask for a synthesized step-by-step plan on how to cause the most death or ways to cripple your enemy. Many agents search through books and the internet while also using latent knowledge about the subject. It tells you everything you truly need to know in a concise 4-page document.

Relevant theory, practical steps (laid out with images and videos on how to do it), what to buy and where/how to buy it, pre-empting any questions you may have, explaining the jargon in a way that is understandable to nearly anyone, can take actions on the web to automatically buy all the supplies you need, etc.

You can even share photos of the entire process to your AI as it continues to guide you through the creation of the weapon because it’s multi-modal.

You can basically outsource all cognition to the AI system, allowing you to be the lazy human you are (we all know that humans will take the path of least-resistance or abandon something altogether if there is enough friction).

That topic you always said you wanted to know more about but never got around to it? No worries, your AI system has lowered the bar sufficiently that the task doesn’t seem as daunting anymore and laziness won’t be in the way of you making progress.

Conclusion: a future AI system will have the power of efficiency (significantly faster) and capability (able to make more powerful weapons than any one person could do on their own). It has the interactivity that Google and libraries don’t have. It’s just not the same as information scattered in different sources.

Is someone planning on doing an overview post of all the AI Pause discussion? I’m guessing some people would appreciate it if someone took the time to make an unbiased synthesis of the posts and discussions.

I'm working on an ultimate doc on productivity I plan to share and make it easy, specifically for alignment researchers.

Let me know if you have any comments or suggestions as I work on it.

Roam Research link for easier time reading.

Google Docs link in case you want to leave comments there.

I gave talk about my Accelerating Alignment with LLMs agenda about 1 month ago (which is basically a decade in AI tools time). Part of the agenda covered (publicly) here.

I will maybe write an actual post about the agenda soon, but would love to have some people who are willing to look over it. If you are interested, send me a message. I am currently applying for grants and exploring the possibility of building an org focused on speeding up this agenda and avoid spreading myself too thin.

I recently sent in some grant proposals to continue working on my independent alignment research. It gives an overview of what I'd like to work on for this next year (and more really). If you want to have a look at the full doc, send me a DM. If you'd like to help out through funding or contributing to the projects, please let me know.

Here's the summary introduction:

12-month salary for building a language model system for accelerating alignment research and upskilling (additional funding will be used to create an organization), and studying how to supervise AIs that are improving AIs to ensure stable alignment.

Summary

• Agenda 1: Build an Alignment Research Assistant using a suite of LLMs managing various parts of the research process. Aims to 10-100x productivity in AI alignment research. Could use additional funding to hire an engineer and builder, which could evolve into an AI Safety organization focused on this agenda. Recent talk giving a partial overview of the agenda.
• Agenda 2: Supervising AIs Improving AIs (through self-training or training other AIs). Publish a paper and create an automated pipeline for discovering noteworthy changes in behaviour between the precursor and the fine-tuned models. Short Twitter thread explanation.
• Other: create a mosaic of alignment questions we can chip away at, better understand agency in the current paradigm, outreach, and mentoring.

As part of my Accelerating Alignment agenda, I aim to create the best Alignment Research Assistant using a suite of language models (LLMs) to help researchers (like myself) quickly produce better alignment research through an LLM system. The system will be designed to serve as the foundation for the ambitious goal of increasing alignment productivity by 10-100x during crunch time (in the year leading up to existentially dangerous AGI). The goal is to significantly augment current alignment researchers while also providing a system for new researchers to quickly get up to speed on alignment research or promising parts they haven’t engaged with much.

For Supervising AIs Improving AIs, this research agenda focuses on ensuring stable alignment when AIs self-train or train new AIs and studies how AIs may drift through iterative training. We aim to develop methods to ensure automated science processes remain safe and controllable. This form of AI improvement focuses more on data-driven improvements than architectural or scale-driven ones.

I’m seeking funding to continue my work as an independent alignment researcher and intend to work on what I’ve just described. However, to best achieve the project’s goal, I would want additional funding to scale up the efforts for Accelerating Alignment to develop a better system faster with the help of engineers so that I can focus on the meta-level and vision for that agenda. This would allow me to spread myself less thin and focus on my comparative advantages. If you would like to hop on a call to discuss this funding proposal in more detail, please message me. I am open to refocusing the proposal or extending the funding.

Near-Term AI capabilities probably bring low-hanging fruits for global poverty/health

I'm an alignment researcher, but I still think we should be vigilant about how models like GPT-N could potentially be used to make the world a better place. I like the work that Ought is doing with respect to the academic field (and, hopefully, alignment soon as well). However, my guess is that there are low-hanging fruits popping up because of this new technology, and the non-profit sector has yet to catch up.

This shortform is a Call To Action for any EA entrepreneur, you could potentially boost efficiency of the non-profit sector with the use of these tools. Of course, be careful since GPT-3 will hallucinate sometimes. But putting it in a larger system with checks and balances could 1) make non-profits save time and money 2) make previously inefficient or non-viable non-profits become a top charity.

I could be wrong about this, but my expectation is that there will be a lag between the time people can use GPT effectively for the non-profit sector and when they actually do.