The importance of Entropy

Given that there's been a lot of talk about using entropy during sampling of LLMs lately (related GitHub), I figured I'd share a short post I wrote for my website before it became a thing:

Imagine you're building a sandcastle on the beach. As you carefully shape the sand, you're creating order from chaos - this is low entropy. But leave that sandcastle for a while, and waves, wind, and footsteps will eventually reduce it back to a flat, featureless beach - that's high entropy.

Entropy is nature's tendency to move from order to disord... (read more)

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 ... (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)

Easy LessWrong post to LLM chat pipeline (browser side-panel)

I started using Sider as @JaimeRV recommended here. Posting this as a top-level shortform since I think other LessWrong users should be aware of it.

Website with app and subscription option. Chrome extension here.

You can either pay for the monthly service and click the "summarize" feature on a post and get the side chat window started or put your OpenAI API / ChatGPT Pro account in the settings and just cmd+a the post (which automatically loads the content in the chat so you can immediately ask a ... (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)

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)

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

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

OpenAI CEO Sam Altman has privately said the company could become a benefit corporation akin to rivals Anthropic and xAI.

Paywalled: https://www.theinformation.com/articles/openai-ceo-says-company-could-become-benefit-corporation-akin-to-rivals-anthropic-xai

"Sam Altman recently told some shareholders that OAI is considering changing its governance structure to a for-profit business that OAI's nonprofit board doesn't control. [...] could open the door to public offering of OAI; may give Altman an opportunity to take a stake in OAI."

I had this thought yesterday: "If someone believes in the 'AGI lab nationalization by default' story, then what would it look like to build an organization or startup in preparation for this scenario?"

For example, you try to develop projects that would work exceptionally well in a 'nationalization by default' world while not getting as much payoff if you are in a non-nationalization world. The goal here is to do the normal startup thing: risky bets with a potentially huge upside.

I don't necessarily support nationalization and am still trying to think throu... (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

I like this feature on the EA Forum so sharing here to communicate interest in having it added to LessWrong as well:

The EA Forum has an audio player interface added to the bottom of the page when you listen to a post. In addition, there are play buttons on the left side of every header to make it quick to continue playing from that section of the post.

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)

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

Like, let's say you have a weak doctor AI that can basically reliably answer most medical questions. However, it knows when it is less confident in a diagnosis, so it will reach out to the powerful AI whe... (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.

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 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.

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.

(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)

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

Anybody know how Fathom Radiant (https://fathomradiant.co/) is doing?

They’ve been working on photonics compute for a long time so I’m curious if people have any knowledge on the timelines they expect it to have practical effects on compute.

Also, Sam Altman and Scott Gray at OpenAI are both investors in Fathom. Not sure when they invested.

I’m guessing it’s still a long-term bet at this point.

OpenAI also hired someone who worked at PsiQuantum recently. My guess is that they are hedging their bets on the compute end and generally looking for opportunities on ... (read more)

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 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 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

More information about alleged manipulative behaviour of Sam Altman

Source

Text from article (along with follow-up paragraphs):

Some members of the OpenAI board had found Altman an unnervingly slippery operator. For example, earlier this fall he’d confronted one member, Helen Toner, a director at the Center for Security and Emerging Technology, at Georgetown University, for co-writing a paper that seemingly criticized OpenAI for “stoking the flames of AI hype.” Toner had defended herself (though she later apologized to the board for not anticipating how the p

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 extraord... (read more)

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: