Three Epoch AI employees* are leaving to co-found an AI startup focused on automating work:

"Mechanize will produce the data and evals necessary for comprehensively automating work."

They also just released a podcast with Dwarkesh.

*Matthew Barnett, Tamay Besiroglu, Ege Erdil

I would find it valuable if someone could gather an easy-to-read bullet point list of all the questionable things Sam Altman has done throughout the years.

I usually link to Gwern’s comment thread (https://www.lesswrong.com/posts/KXHMCH7wCxrvKsJyn/openai-facts-from-a-weekend?commentId=toNjz7gy4rrCFd99A), but I would prefer if there was something more easily-consumable.

Given the OpenAI o3 results making it clear that you can pour more compute to solve problems, I'd like to announce that I will be mentoring at SPAR for an automated interpretability research project using AIs with inference-time compute.

I truly believe that the AI safety community is dropping the ball on this angle of technical AI safety and that this work will be a strong precursor of what's to come.

Note that this work is a small part in a larger organization on automated AI safety I’m currently attempting to build.

Here’s the link: https://airtable.com/appxuJ1PzMPhYkNhI/shrBUqoOmXl0vdHWo?detail=eyJwYWdlSWQiOiJwYWd5SURLVXg5WHk4bHlmMCIsInJvd0lkIjoicmVjRW5rU3d1UEZBWHhQVHEiLCJzaG93Q29tbWVudHMiOmZhbHNlLCJxdWVyeU9yaWdpbkhpbnQiOnsidHlwZSI6InBhZ2VFbGVtZW50IiwiZWxlbWVudElkIjoicGVsSmM5QmgwWDIxMEpmUVEiLCJxdWVyeUNvbnRhaW5lcklkIjoicGVsUlNqc0xIbWhUVmJOaE4iLCJzYXZlZEZpbHRlclNldElkIjoic2ZzRGNnMUU3Mk9xSXVhYlgifX0

Here’s the pitch:

As AIs become more capable, they will increasingly be used to automate AI R&D. Given this, we should seek ways to use AIs to help us also make progress on alignment research.

Eventually, AIs will automate all research, but for now, we need to choose specific tasks that ... (read more)

How likely is it that the board hasn’t released specific details about Sam’s removal because of legal reasons? At this point, I feel like I have to place overwhelmingly high probability on this.

So, if this is the case, what legal reason is it?

I thought Superalignment was a positive bet by OpenAI, and I was happy when they committed to putting 20% of their current compute (at the time) towards it. I stopped thinking about that kind of approach because OAI already had competent people working on it. Several of them are now gone.

It seems increasingly likely that the entire effort will dissolve. If so, OAI has now made the business decision to invest its capital in keeping its moat in the AGI race rather than basic safety science. This is bad and likely another early sign of what's to come.

I think the research that was done by the Superalignment team should continue happen outside of OpenAI and, if governments have a lot of capital to allocate, they should figure out a way to provide compute to continue those efforts. Or maybe there's a better way forward. But I think it would be pretty bad if all that talent towards the project never gets truly leveraged into something impactful.

For anyone interested in Natural Abstractions type research: https://arxiv.org/abs/2405.07987

Claude summary:

Key points of "The Platonic Representation Hypothesis" paper:

1. Neural networks trained on different objectives, architectures, and modalities are converging to similar representations of the world as they scale up in size and capabilities.

2. This convergence is driven by the shared structure of the underlying reality generating the data, which acts as an attractor for the learned representations.

3. Scaling up model size, data quantity, and task diversity leads to representations that capture more information about the underlying reality, increasing convergence.

4. Contrastive learning objectives in particular lead to representations that capture the pointwise mutual information (PMI) of the joint distribution over observed events.

5. This convergence has implications for enhanced generalization, sample efficiency, and knowledge transfer as models scale, as well as reduced bias and hallucination.

Relevance to AI alignment:

1. Convergent representations shaped by the structure of reality could lead to more reliable and robust AI systems that are better anchored to the real worl

I thought this series of comments from a former DeepMind employee (who worked on Gemini) were insightful so I figured I should share.

From my experience doing early RLHF work for Gemini, larger models exploit the reward model more. You need to constantly keep collecting more preferences and retraining reward models to make it not exploitable. Otherwise you get nonsensical responses which have exploited the idiosyncracy of your preferences data. There is a reason few labs have done RLHF successfully.

It's also know that more capable models exploit loopholes in reward functions better. Imo, it's a pretty intuitive idea that more capable RL agents will find larger rewards. But there's evidence from papers like this as well: https://arxiv.org/abs/2201.03544 

To be clear, I don't think the current paradigm as-is is dangerous. I'm stating the obvious because this platform has gone a bit bonkers.

The danger comes from finetuning LLMs to become AutoGPTs which have memory, actions, and maximize rewards, and are deployed autonomously. Widepsread proliferation of GPT-4+ models will almost certainly make lots of these agents which will cause a lot of damage and potentially cause something ind

Why aren't you doing research on making pre-training better for alignment?

I was on a call today, and we talked about projects that involve studying how pre-trained models evolve throughout training and how we could guide the pre-training process to make models safer. For example, could models trained on synthetic/transformed data make models significantly more robust and essentially solve jailbreaking? How about the intersection of pretraining from human preferences and synthetic data? Could the resulting model be significantly easier to control? How would it impact the downstream RL process? Could we imagine a setting where we don't need RL (or at least we'd be able to confidently use resulting models to automate alignment research)? I think many interesting projects could fall out of this work.

So, back to my main question: why aren't you doing research on making pre-training better for alignment? Is it because it's too expensive and doesn't seem like a low-hanging fruit? Or do you feel it isn't a plausible direction for aligning models?

We were wondering if there are technical bottlenecks that would make this kind of research more feasible for alignment research to better study ho... (read more)

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.

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 going 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 AG... (read more)

News on the next OAI GPT release:

Nagasaki, CEO of OpenAI Japan, said, "The AI ​​model called 'GPT Next' that will be released in the future will evolve nearly 100 times based on past performance. Unlike traditional software, AI technology grows exponentially." 

https://www.itmedia.co.jp/aiplus/articles/2409/03/news165.html 

The slide clearly states 2024 "GPT Next". This 100 times increase probably does not refer to the scaling of computing resources, but rather to the effective computational volume + 2 OOMs, including improvements to the architecture and learning efficiency. GPT-4 NEXT, which will be released this year, is expected to be trained using a miniature version of Strawberry with roughly the same computational resources as GPT-4, with an effective computational load 100 times greater. Orion, which has been in the spotlight recently, was trained for several months on the equivalent of 100k H100 compared to GPT-4 (EDIT: original tweet said 10k H100s, but that was a mistake), adding 10 times the computational resource scale, making it +3 OOMs, and is expected to be released sometime next year.

Note: Another OAI employee seemingly confirms this (I've followed... (read more)

I encourage alignment/safety people to be open-minded about what François Chollet is saying in this podcast:

I think many are blindly bought into the 'scale is all you need' and apparently godly nature of LLMs and may be dependent on unfounded/confused assumptions because of it.

Getting this right is important because it could significantly impact how hard you think alignment will be. Here's @johnswentworth responding to @Eliezer Yudkowsky about his difference in optimism compared to @Quintin Pope (despite believing the natural abstraction hypothesis is true):

Entirely separately, I have concerns about the ability of ML-based technology to robustly point the AI in any builder-intended direction whatsoever, even if there exists some not-too-large adequate mapping from that intended direction onto the AI's internal ontology at training time.  My guess is that more of the disagreement lies here.

I doubt much disagreement between you and I lies there, because I do not expect ML-style training to robustly point an AI in any builder-intended direction. My hopes generally don't route through targeting via ML-style training.

I do think my deltas from many other people lie there - e.g. that

Resharing a short blog post by an OpenAI employee giving his take on why we have 3-5 year AGI timelines (https://nonint.com/2024/06/03/general-intelligence-2024/):

Folks in the field of AI like to make predictions for AGI. I have thoughts, and I’ve always wanted to write them down. Let’s do that.

Since this isn’t something I’ve touched on in the past, I’ll start by doing my best to define what I mean by “general intelligence”: a generally intelligent entity is one that achieves a special synthesis of three things:

• A way of interacting with and observing a complex environment. Typically this means embodiment: the ability to perceive and interact with the natural world.
• A robust world model covering the environment. This is the mechanism which allows an entity to perform quick inference with a reasonable accuracy. World models in humans are generally referred to as “intuition”, “fast thinking” or “system 1 thinking”.
• A mechanism for performing deep introspection on arbitrary topics. This is thought of in many different ways – it is “reasoning”, “slow thinking” or “system 2 thinking”.

If you have these three things, you can build a generally intelligent agent. Here’s how:

First, you se... (read more)

Low-hanging fruit:

Loving this Chrome extension so far: YouTube Summary with ChatGPT & Claude - Chrome Web Store

It adds a button on YouTube videos where, when you click it (or keyboard shortcut ctrl + x + x), it opens a new tab into the LLM chat of your choice, pastes the entire transcript in the chat along with a custom message you can add as a template ("Explain the key points.") and then automatically presses enter to get the chat going.

It's pretty easy to get a quick summary of a YouTube video without needing to watch the whole thing and then ask follow-up questions. It seems like an easy way to save time or do a quick survey of many YouTube videos. (I would not have bothered going through the entire "Team 2 | Lo fi Emulation @ Whole Brain Emulation Workshop 2024" talk, so it was nice to get the quick summary.)

I usually like getting a high-level overview of the key points of a talk to have a mental mind map skeleton before I dive into the details.

You can even set up follow-up prompt buttons (which works with ChatGPT but currently does not work with Claude for me), though I'm not sure what I'd use. Maybe something like, "Why is this important to AI alignment?"

The default prom... (read more)

Dario Amodei believes that LLMs/AIs can be aided to self-improve in a similar way to AlphaGo Zero (though LLMs/AIs will benefit from other things too, like scale), where the models can learn by themselves to gain significant capabilities.

The key for him is that Go has a set of rules that the AlphaGo model needs to abide by. These rules allow the model to become superhuman at Go with enough compute.

Dario essentially believes that to reach better capabilities, it will help to develop rules for all the domains we care about and that this will likely be possible for more real-world tasks (not just games like Go).

Therefore, I think the crux here is if you think it is possible to develop rules for science (physics, chemistry, math, biology) and other domains s.t., the models can do this sort of self-play to become superhuman for each of the things we care about.

So far, we have examples like AlphaGeometry, which relies on our ability to generate many synthetic examples to help the model learn. This makes sense for the geometry use case, but how do we know if this kind of approach will work for the kinds of things we actually care about? For games and geometry, this seems possible, but wha... (read more)

Alignment Researcher Assistant update.

Hey everyone, my name is Jacques, I'm an independent technical alignment researcher, primarily focused on evaluations, interpretability, and scalable oversight (more on my alignment research soon!). I'm now focusing more of my attention on building an Alignment Research Assistant (I've been focusing on my alignment research for 95% of my time in the past year). I'm looking for people who would like to contribute to the project. This project will be private unless I say otherwise (though I'm listing some tasks); I understand the dual-use nature and most criticism against this kind of work.

How you can help:

• Provide feedback on what features you think would be amazing in your workflow to produce high-quality research more efficiently.
• Volunteer as a beta-tester for the assistant.
• Contribute to one of the tasks below. (Send me a DM, and I'll give you access to the private Discord to work on the project.)
• Funding to hire full-time developers to build the features.

Here's the vision for this project:

How might we build an AI system that augments researchers to get us 5x or 10x productivity for the field as a whole?

The system is designed with two main minds

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

Current Thoughts on my Learning System

Crossposted from my website. Hoping to provide updates on my learning system every month or so.

TLDR of what I've been thinking about lately:

• There are some great insights in this video called "How Top 0.1% Students Think." And in this video about how to learn hard concepts.
• Learning is a set of skills. You need to practice each component of the learning process to get better. You can’t watch a video on a new technique and immediately become a pro. It takes time to reap the benefits.
• Most people suck at mindmaps. Mindmaps can be horrible for learning if you just dump a bunch of text on a page and point arrows to different stuff (some studies show mindmaps are ineffective, but that's because people initially suck at making them). However, if you take the time to learn how to do them well, they will pay huge dividends in the future. I’ll be doing the “Do 100 Things” challenge and developing my skill in building better mindmaps. Getting better at mindmaps involves “chunking” the material and creating memorable connections and drawings.
• Relational vs Isolated Learning. As you learn something new, try to learn it in relation to the things you already kno

Recent paper I thought was cool:

In-Run Data Shapley: Data attribution method efficient enough for pre-training data attribution.

Essentially, it can track how individual data points (or clusters) impact model performance across pre-training. You just need to develop a set of validation examples to continually check the model's performance on those examples during pre-training. Amazingly, you can do this over the course of a single training run; no need to require multiple pre-training runs like other data attribution methods have required.

Other methods, like influence functions, are too computationally expensive to run during pre-training and can only be run post-training.

So, here's why this might be interesting from an alignment perspective:

• You might be able to set up a bunch of validation examples to test specific behaviour in the models so that we are hyper-aware of which data points contribute the most to that behaviour. For example, self-awareness or self-preservation.
• Given that this is possible to run during pre-training, you might understand model behaviour at such a granular level that you can construct data mixtures/curriculums that push the model towards internalizing 'hum

I'm currently ruminating on the idea of doing a video series in which I review code repositories that are highly relevant to alignment research to make them more accessible.

I do want to pick out repos with perhaps even bad documentation that are still useful and then hope on a call with the author to go over the repo and record it. At least have something basic to use when navigating the repo.

This means there would be two levels: 1) an overview with the author sharing at least the basics, and 2) a deep dive going over most of the code. The former likely contains most of the value (lower effort for me, still gets done, better than nothing, points to repo as a selection mechanism, people can at least get started).

I am thinking of doing this because I think there may be repositories that are highly useful for new people but would benefit from some direction. For example, I think Karpathy and Neel Nanda's videos have been useful in getting people started. In particular, Karpathy saw OOM more stars to his repos (e.g. nanoGPT) after the release of his videos (which, to be fair, he's famous, and a number of stars is definitely not a perfect proxy for usage).

I'm interested in any feedback ... (read more)

I’m still thinking this through, but I am deeply concerned about Eliezer’s new article for a combination of reasons:

• I don’t think it will work.
• Given that it won’t work, I expect we lose credibility and it now becomes much harder to work with people who were sympathetic to alignment, but still wanted to use AI to improve the world.
• I am not convinced as he is about doom and I am not as cynical about the main orgs as he is.

In the end, I expect this will just alienate people. And stuff like this concerns me.

I think it’s possible that the most memetically powerful approach will be to accelerate alignment rather than suggesting long-term bans or effectively antagonizing all AI use.

Looks like Meta is panicking over DeepSeek R1

Quote from Cal Newport's Slow Productivity book: "Progress in theoretical computer science research is often a game of mental chicken, where the person who is able to hold out longer through the mental discomfort of working through a proof element in their mind will end up with the sharper result."

Do we expect future model architectures to be biased toward out-of-context reasoning (reasoning internally rather than in a chain-of-thought)? As in, what kinds of capabilities would lead companies to build models that reason less and less in token-space?

I mean, the first obvious thing would be that you are training the model to internalize some of the reasoning rather than having to pay for the additional tokens each time you want to do complex reasoning.

The thing is, I expect we'll eventually move away from just relying on transformers with scale. And so... (read more)

I'm currently working on building an AI research assistant designed specifically for alignment research. I'm at the point where I will be starting to build specific features for the project and delegate work to developers who would like to contribute.

• Developers: If you are a developer who might be interested in contributing to this project, send me a DM for more details.
• Alignment Researchers: I have a long list of features I want to build. I need to prioritize the features that people actually think would help them the most. If you'd like to look over the features and provide feedback, send me a DM and I will send you the relevant list of features.

(This is the tale of a potentially reasonable CEO of the leading AGI company, not the one we have in the real world. Written after a conversation with @jdp.)

You’re the CEO of the leading AGI company. You start to think that your moat is not as big as it once was. You need more compute and need to start accelerating to give yourself a bigger lead, otherwise this will be bad for business.

You start to look around for compute, and realize you have 20% of your compute you handed off to the superalignment team (and even made a public commitment!). You end up ma... (read more)

This seems like a fairly important paper by Deepmind regarding generalization (and lack of it in current transformer models): https://arxiv.org/abs/2311.00871