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.

Likely this podcast episode where Bostrom essentially says that he's concerned that with current trends there might be too much opposition to AI, though he still thinks we should place more concern than our current level of concern: 

Hopefully this gets curated because I’d like for there to be a good audio version of this.

I don’t particularly care about the “feels good” part, I care a lot more about the “extended period of time focused on an important task without distractions” part.

Whether it’s a shitpost or not (or wtv tier it is), I strongly believe more people should put more effort into freeing their workspace from distractions in order to gain more focus and productivity in their work. Context-switching and distractions are the mind killer. And, “flow state while coding never gets old.”

Also, use the Kolb's experiential cycle or something like it for deliberate practice.

you need to be flow state maxxing. you curate your environment, prune distractions. make your workspace a temple, your mind a focused laser. you engineer your life to guard the sacred flow. every notification is an intruder, every interruption a thief. the world fades, the task is the world. in flow, you're not working, you're being. in the silent hum of concentration, ideas bloom. you're not chasing productivity, you're living it. every moment outside flow is a plea to return. you're not just doing, you're flowing. the mundane transforms into the extraordinary. you're not just alive, you're in relentless, undisturbed pursuit. flow isn't a state, it's a realm. once you step in, ordinary is a distant shore. in flow, you don't chase time, time chases you, period.

Edit: If you disagree with the above, explain why.

Clarification on The Bitter Lesson and Data Efficiency

I thought this exchange provided some much-needed clarification on The Bitter Lesson that I think many people don't realize, so I figured I'd share it here:

Lecun responds:

Then, Richard Sutton agrees with Yann. Someone asks him:

There are those who have motivated reasoning and don’t know it.

Those who have motivated reasoning, know it, and don’t care.

Finally, those who have motivated reasoning, know it, but try to mask it by including tame (but not significant) takes the other side would approve of.

I'm curious to know how much the code could be faster through using a faster programming language. For example, MOJO. @Arthur Conmy