“If anyone with insider knowledge wants to write about the impact of Effective Altruism in the technology industry please get in touch with me claire@quillette.com. 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.
Don’t forget that we train language models on the internet! The more truthful your dataset is, the more truthful the models will be! Let’s revamp the internet for truthfulness, and we’ll subsequently improve truthfulness in our AI systems!!
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).
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 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 AIstoensure 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.
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.
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.
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 claire@quillette.com. 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.
One may infer that they do not care about truth, at least not relative to other considerations.
I’ve also started working on a repo in order to make Community Notes more efficient by using LLMs.
Don’t forget that we train language models on the internet! The more truthful your dataset is, the more truthful the models will be! Let’s revamp the internet for truthfulness, and we’ll subsequently improve truthfulness in our AI systems!!
I shared a tweet about it here: https://x.com/JacquesThibs/status/1724492016254341208?s=20
Consider liking and retweeting it if you think this is impactful. I’d like it to get into the hands of the right people.
More information about the alleged manipulative behaviour of Sam Altman
Source
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.
Update, board members seem to be holding their ground more than expected in this tight situation:
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.
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.
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.
According to the debate week announcement, Scott Alexander will be writing a summary/conclusion post.
Perfect, thanks!
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.
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.
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.