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.
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.
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.
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.
Can you give concrete use-cases that you imagine your project would lead to helping alignment researchers? Alignment researchers have wildly varying styles of work outputs and processes. I assume you aim to accelerate a specific subset of alignment researchers (those focusing on interpretability and existing models and have an incremental / empirical strategy for solving the alignment problem).
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 know rather than treating it as isolated from everything (flashcards can perpetuate the problem of learning things in isolated form).
Deep processing is the foundation of all learning. It is the ability to connect, process, organize and relate information. The opposite of deep processing is rote memorization. If it doesn’t feel like you are engaging ~90% of your brain power when you are learning/reading something, you are likely not encoding the information into your long-term memory effectively.
Only use Flashcards as a last resort. Flashcards are something a lot of people use because they feel comfortable going through them. However, if your goal is to be efficient in your learning, you should only use flashcards when it’s something that requires rote learning. Video worth watching on Spaced Repetition.
My current approach for learning about alignment: I essentially have a really big Roam Research page called "AI Alignment" where I break down the problem into chunks like "Jargon I don't understand," "Questions to Answer," "Different people's views on alignment," etc. As I fill in those details, I add more and more information in the "Core of the Alignment Problem" section. I have a separate page called "AI Alignment Flow Chart" which I'm using as a structure for backcasting on how we solved alignment and identifying the crucial things we need to solve and things I need to better understand. I also sometimes have a specific page for something like Interpretability when I'm trying to do a deep dive on a topic, but I always try to link it to the other things I've written in my main doc.
And this video concisely covers a lot of important learning concepts.
Look at the beginning of the video for an explanation of encoding, storage (into long-term memory), and retrieval/rehearsal to make sure you remember long-term.
Outside of learning:
Get enough sleep. 8 hours-ish.
Exercise like HIIT.
Make sure you have good mental health.
Meditation is likely useful. I personally use it to recharge my battery when I feel a crash coming and I think it’s useful for training yourself to work productively for longer periods of time. This one I’m less sure of, but seems to work for me.
Learning (all of these take time to master, don’t expect you will use them in the most effective way right out of the gate):
Use inquiry-based (curiosity-based) learning. Have your learning be guided by questions you have, like:
”Why is this important?”
”How does it relate to this other concept?”
Learn by scope. Start with the big picture and gradually break things down where it is important.
Chunking. Group concepts together and connect different chunks by relationship.
Create stories to remember things.
Focus on relationships between concepts. This is crucial.
Spaced repetition (look at my other notes on how SR is overrated but still useful)
Apply your learning by creating things (like a forum post applying the new concept to something and explaining it)
Ever since I was little, I have relied on my raw brain power to get to where I am. Unfortunately, I could never bring myself to do what other smart kids were doing. Flashcards, revision? I would either get bored out of my mind or struggle because I didn’t know how to do it well. Mindmaps? It felt OK while I was doing it the few times I tried, but I would never revise it, and, honestly, I sucked at it.
But none of that mattered. I could still do well enough even though my learning system was terrible. However, I didn’t get the top grades, and I felt frustrated.
I read a few books and watched the popular YouTubers on how to learn things best. Spaced Repetition and Active Recall kept coming up. All these intelligent people were using it, and I truly believed it worked. However, whenever I tried it, I either ended up with too many flashcards to have the time to review, or I couldn't build a habit out of it. Flashcards also felt super inefficient when studying physics.
I did use Cal Newport’s stuff for some classes and performed better by studying the same amount of time, but as soon as things got intense (exam season/lots of homework), I would revert to my old (ineffective) study techniques like reading the textbook aimlessly and highlighting stuff. As a result, I would never truly develop the skill (yes, skill!) of studying well. But, just like anything, you can get better at creating mindmaps for proper learning and long-term memory.
I never got a system down, and I feel I’m losing out on gains in my career. How do I learn things efficiently? I don’t want to do the natural thing of putting in more hours to get more done. 1) My productivity will be capped by my inefficient system, 2) I still want to live life, and 3) it probably won’t work anyways.
So, consider this my public accountability statement to take the time to develop the skills necessary to become more efficient in my work. No more aimlessly reading LessWrong posts about AI alignment. There are more efficient ways to learn.
I want to contribute to AI alignment in a bigger way, and something needs to change. There is so much to learn, and I want to catch up as efficiently as possible instead of just winging it and trying whatever approach seems right.
Had I continued working on things I don’t care deeply about, I might have never decided to put in the effort to create a new system (which will probably take a year of practicing my learning skills). Maybe I would have tried for a few weeks and then reverted to my old habits. I could have kept coasting in life and done decently well in work and my personal life. But we need to solve alignment, and building these skills now will allow me to reap major benefits in a few years.
(Note: a nice bonus for developing a solid learning system is that you can pass it on to your children. I’m excited to do that one day, but I’d prefer to start doing this now so that I know that *I* can do it, and I’m not just telling my future kids nonsense.)
My goal will be to create a “How to Create an Efficient Learning System” guide tailored for professionals and includes examples in AI alignment. Please let me know if there are some things you’d like me to explore in that guide.
Before I go, I’ll mention that I’m also interested in eventually taking what I learn from constructing my own learning system and creating something that allows others to do the same, but with much less effort. I hope to make this work for the alignment community in particular (which relates to my accelerating alignment project), but I’d also like to eventually expand to people working on other cause areas in effective altruism.
Important part: Use GPT to facilitate the process of pushing you to higher-order learning as fast as possible.
Here’s Bloom’s Taxonomy for higher-order learning:
For example, you want to ask GPT to come up with analogies and such to help you enter higher-order thinking by thinking about whether the analogy makes sense.
Is the analogy truly accurate?
Does it cover the main concept you are trying to understand?
Then, you can extend the analogy to try to make it better and more comprehensive.
This allows you to offload the less useful task (e.g. coming up with the analogy), and spending more time in the highest orders of learning (the evaluation phase; “is this analogy good? where does it break down?”).
You still need to use your cognitive load to encode the knowledge effectively. Look for desirable difficulty.
Use GPT to create a pre-study of the thing you would like to learn.
Have it create an outline of the order of the things you should learn.
Have it give you a list of all the jargon words in a field and how they relate so that you can quickly get up to speed on the terminology and talk to an expert.
Coming up with chunks of the topic you are exploring.
You can give GPT text that describes what you are trying to understand, the relationships between things and how you are chunking them.
Then, you can ask GPT to tell you what are some weak areas or some things that are potentially missing.
GPT works really well as a knowledge “gap-checker”.
When you are trying to have GPT output some novel insights or complicated nuanced knowledge, it can give vague answers that aren’t too helpful. This is why, it is often better to treat GPT as a gap-checker and/or a friend that is prompting you to come up with great insights.
Reference: I’ve been using ChatGPT/GPT-4 a lot to gain insights on how to accelerate alignment research. Some of my conclusions are similar to what was described in the video below.
How learning efficiently applies to alignment research
As we are trying to optimize for actually solving the problem, [LW · GW] we should not fall into the trap of learning just to learn. We should instead focus on learning efficiently with respect to how it helps us generate insights that lead to a solution for alignment. This is also the framing we should have in mind when we are building tools for augmenting alignment researchers.
With the above in mind, I expect that part of the value of learning efficiently involves some of the following:
Efficient learning involves being hyper-focused on identifying the core concepts and how they all relate to one another. This mode of approaching things seems like it helps us attack the core of alignment much more directly and bypasses months/years of working on things that are only tangential.
Developing a foundation of a field seems key to generating useful insights. The goal is not to learn everything but to build a foundation that allows you to bypass spending way too much time tackling sub-optimal sub-problems or dead-ends for way too long. Part of the foundation-building process should reduce the time it shapes you into an exceptional alignment researcher rather than a knower-of-things.
As John Wentworth says [LW · GW] with respect to the Game Tree of Alignment: "The main reason for this exercise is that (according to me) most newcomers to alignment waste years on tackling not-very-high-value sub-problems or dead-end strategies."
Lastly, many great innovations have not come from unique original ideas. There's an iterative process passed amongst researchers and it seems often the case that the greatest ideas come from simply merging ideas that were already lying around. Learning efficiently (and storing those learnings for later use) allows you to increase the number of ideas you can merge together. If you want to do that efficiently, you need to improve your ability to identify which ideas are worth storing in your mental warehouse to use for a future merging of ideas.
Curiosity is certainly a powerful tool for learning! I think any learning system which isn't taking advantage of it is sub-optimal. Learning should be guided by curiosity.
The thing is, sometimes we need to learn things we aren't so curious about. One insight I Iearned from studying learning is that you can do specific things to make yourself more curious about a given thing and harness the power that comes with curiosity.
Ultimately, what this looks like is to write down questions about the topic and use them to guide your curious learning process. It seems that this is how efficient top students end up learning things deeply in a shorter amount of time. Even for material they care little about, they are able to make themselves curious and be propelled forward by that.
That said, my guess is that goodharting the wrong metric can definitely be an issue, but I'm not convinced that relying on what makes you naturally curious is the optimal strategy for solving alignment. Either way, it's something to think about!
By the way, I've just added a link to a video by a top competitive programmer on how to learn hard concepts. In the video and in the iCanStudy course, both talk about the concept of caring about what you are learning (basically, curiosity). Gaining the skill to care and become curious is an essential part of the most effective learning. However, contrary to popular belief, you don't have to be completely guided by what makes you naturally curious! You can learn how to become curious (or care) about any random concept.
Added my first post (of, potentially, a sequence) on effective learning here [LW · GW]. I think there are a lot of great lessons at the frontier of the literature and real-world practice on learning that go far beyond the Anki approach that a lot of people seem to take these days. The important part is being effective and efficient. Some techniques might work, but that does not mean it is the most efficient (learning the same thing more deeply in less time).
Note that I also added two important videos to the root shortform:
While spaced repetition is good, many people end up misusing it as a crutch instead of defaulting to trying to deeply understand a concept right away. As you get better at properly encoding the concept, you extend the forgetting curve to the point where repetition is less needed.
Here's some additional notes on the fundamentals on being an effective learner:
Encoding and Retrieval (What it take to learn)
Working memory is the memory that we use. However, if it is not encoded properly or at all, we will forget it.
Encode well first (from working memory to long-term memory), then frequently and efficiently retrieve from long-term memory.
If studying feels easy, means that you aren't learning or holding on to the information. It means that you are not encoding and retrieving effectively.
You want it to be difficult when you are studying because this is how it will encode properly.
Spacing, Interleaving, and Retrieval (SIR)
These are three rules that apply to every study technique in the course (unless told otherwise). You can apply SIR to all techniques.
Spacing: space your learning out.
Pre-study before class, then learn in class, and then a week later revise it with a different technique.
A rule of thumb you can follow is to wait long enough until you feel like you are just starting to forget the material.
As you get better at encoding the material effectively as soon as you are exposed to it, you will notice that you will need to do less repetition.
How to space reviews:
Beginner Schedule (less reviews need as you get better at encoding)
End of week
End of month
After learning something for the first time, review it later on the same day.
Review everything from the last 2-3 days mid-week.
Do an end of week revision on the week's worth of content.
End of month revision on entire month's worth of content.
Review of what's necessary as time goes on.
(If you're trying to do well on an exam or a coding interview, you can do the review 1 or 2 weeks before the assessment.)
Reviewing time duration:
No less than 30 minutes per subject for end-of-week
No less than 1.5 hours per subject for end-of-month.
Schedule the reviews in your Calendar and add a reminder!
Interleaving: hitting a topic or concept from multiple different angles (mindmaps, teaching).
The idea is that there is the concept you want to learn, but also there is a surrounding range that you also want to learn (not just the isolated concept).
Could be taking a concept and asking a question about it. Then, asking a question from another angle. Then, asking how it relates to another concept.
Try to use a multitude of these techniques in your studying, never studying or revising anything the same way more than once.
Math, it could be thinking about the real-world application of it.
Examples of interleaving:
Teach an imaginary student
Draw a mindmap
Draw an image instead of using words to find a visual way of expressing information
Answer practice questions
Create your own challenging test questions
Create a test question that puts what you've learned into a real-world context
Take a difficult question that you found in a practice test and modify it so that the variables are different, or an extra step is added
Form a study group and quiz each other - for some subjects you can even debate the topic, with one side trying to prove that the other person is missing a point or understanding it incorrectly
For languages, you can try to speak or write a piece of dialogue or speech, as well as some variations. How might someone respond? How would you respond back? Are there any other responses that would be appropriate?
Retrieval: taking info from your long-term memory and bringing it into your working memory to recall, solve problems and answer questions.
Taking a concept and retrieving it from your long-term memory.
Don't just retrieve right away, you can look at your notes, take a few minutes and retrieve.
Or it also happens when you are learning something. Let's say you are listening to a lecture. Are you just writing everything down or are you taking some time to think and process what is being said and then writing down notes? The second one is better.
When you are learning something, you want to apply interleaving by learning from different sources and mediums. So, practice become great at learning while listening, while watching, while reading. These are all individual modes of learning you can get better at and they will all help you better retain the material if you use them all while learning.
I use the app Concepts on my iPad to draw mindmaps. Drawing mindmaps with pictures and such is way more powerful (better encoding into long-term memory) than typical mindmap apps where you just type words verbatim and draw arrows. It's excellent since it has a (quasi-) infinite canvas. This is the same app that Justin Sung uses.
When I want to go in-depth into a paper, I will load it into OneNote on my iPad and draw in the margin to better encode my understanding of the paper.
I've been using the Voice Dream Reader app on my iPhone and iPad to get through posts and papers much faster (I usually have time to read most of an Alignment Forum post on my way to work and another on the way back). Importantly, I stop the text-to-speech when I'm trying to understand an important part. I use Pocket to load LW/AF posts into it and download PDFs on my device and into the app for reading papers. There's a nice feature in the app that automatically skips citations in the text, so reading papers isn't as annoying. The voices are robotic, but I just cycled through a bunch until I found one I didn't mind (I didn't buy any, but there are premium voices). I expect Speechify has better voices, but it's more expensive, and I think people find that the app isn't as good overall compared to Voice Dream Reader. Thanks to Quintin Pope for recommending the app to me.
So I think what I'm getting here is that you have an object-level disagreement (not as convinced about doom), but you are also reinforcing that object-level disagreement with signalling/reputational considerations (this will just alienate people). This pattern feels ugh and worries me. It seems highly important to separate the question of what's true from the reputational question. It furthermore seems highly important to separate arguments about what makes sense to say publicly on-your-world-model vs on-Eliezer's-model. In particular, it is unclear to me whether your position is "it is dangerously wrong to speak the truth about AI risk" vs "Eliezer's position is dangerously wrong" (or perhaps both).
I guess that your disagreement with Eliezer is large but not that large (IE you would name it as a disagreement between reasonable people, not insanity). It is of course possible to consistently maintain that (1) Eliezer's view is reasonable, (2) on Eliezer's view, it is strategically acceptable to speak out, and (3) it is not in fact strategically acceptable for people with Eliezer's views to speak out about those views. But this combination of views does imply endorsing a silencing of reasonable disagreements which seems unfortunate and anti-epistemic.
My own guess is that the maintenance of such anti-epistemic silences is itself an important factor contributing to doom. But, this could be incorrect.
This was posted on the day of the open letter and I was indeed confused about what to think of the situation.
I think something I failed to properly communicate is that I was worried that this was a bad time to pull the lever even if I’m concerned about risks from AGI. I was worried the public wouldn’t take alignment seriously because they cause a panic much sooner than people were ready for.
I care about being truthful, but I care even more about not dying so my comment was mostly trying to communicate that I didn’t think this was the best strategic decision for not dying.
I was seeing a lot of people write negative statements about the open letter on Twitter and it kind of fed my fears that this was going to backfire as a strategy and impact all of our work to make ai risk taken seriously.
In the end, the final thing that matters is that we win (i.e. not dying from AGI).
I’m not fully sure what I think now (mostly because I don’t know about higher order effects that will happen 2-3 years from now), but I think it turned out a lot strategically better than I initially expected.
This reminds me of the internet-libertarian chain of reasoning that anything that government does is protected by the threat of escalating violence, therefore any proposals that involve government (even mild ones, such as "once in a year, the President should say 'hello' to the citizens") are calls for murder, because... (create a chain of escalating events starting with someone non-violently trying to disrupt this, ending with that person being killed by cops)...
Yes, a moratorium on AIs is a call for violence, but only in the sense that every law is a call for violence.
Given funding is a problem in AI x-risk at the moment, I’d love to see people to start thinking of creative ways to provide additional funding to alignment researchers who are struggling to get funding.
For example, I’m curious if governance orgs would pay for technical alignment expertise as a sort of consultant service.
Also, it might be valuable to have full-time field-builders that are solely focused on getting more high-net-worth individuals to donate to AI x-risk.
Setting aside the question of whether people are overly confident about their claims regarding AI risk, I'd like to talk about how we talk about it amongst ourselves.
We should avoid jokingly saying "we're all going to die" because I think it will corrode your calibration to risk with respect to P(doom) and it will give others the impression that we are all more confident about P(doom) than we really are.
I think saying it jokingly still ends up creeping into your rational estimates on timelines and P(doom). I expect that the more you joke about high P(doom), the more likely you will end up developing an unjustified high P(doom). And I think if you say it enough, you can even convince yourself that you are more confident in your high P(doom) than you really are.
Joking about it in public also potentially diminishes your credibility. They may or may not know if you are joking, but that doesn't matter.
For all the reasons above, I've been trying to make a conscious effort to avoid this kind of talk.
From my understanding, being careful with the internal and external language you use is something that is recommended in therapy. Would be great if someone could point me to examples of this.
What are some important tasks you've found too cognitively taxing to get in the flow of doing?
One thing that I'd like to consider for Accelerating Alignment [LW · GW] is to build tools that make it easier to get in the habit of cognitively demanding tasks by reducing the cognitive load necessary to do the task. This is part of the reason why I think people are getting such big productivity gains from tools like Copilot.
One way I try to think about it is like getting into the habit of playing guitar. I typically tell people to buy an electric guitar rather than an acoustic guitar because the acoustic is typically much more painful for your fingers. You are already doing a hard task of learning an instrument, try to reduce the barrier to entry by eliminating one of the causes of friction. And while you're at it, don't put your guitar in a case or in a place that's out of your way, make it ridiculously easy to just pick up and play. In this example, it's not cognitively taxing, but it is some form of tax that produces friction.
It is possible that we could have much more people tackling the core of alignment if it was less mentally demanding to get to that point and contribute to a solution. It's possible that some level of friction for some tasks is making it so people are more likely to opt for what is easy (and potentially leads to fake progress on a solution to alignment). One such example might be understanding some difficult math. Another might be communicating your research in a way that is understandable to others.
I think it's worth thinking in this frame when coming up with ways to accelerate alignment research by augmenting researchers.
For developing my hail mary alignment approach, the dream would be to be able to load enough of the context of the idea into a LLM that it could babble suggestions (since the whole doc won't fit in the context window, maybe randomizing which parts beyond the intro are included for diversity?), then have it self-critique those suggestions automatically in different threads in bulk and surface the most promising implementations of the idea to me for review. In the perfect case I'd be able to converse with the model about the ideas and have that be not totally useless, and pump good chains of thought back into the fine-tuning set.
Wrote up a short (incomplete) bullet point list of the projects I'd like to work on in 2023:
Main time spent (initial ideas, will likely pivot to varying degrees depending on feedback; will start with one):
Fine-tune GPT-3/GPT-4 on alignment text and connect the API toLoom, VSCode (CoPilot for alignment research) and potentially notetaking apps like Roam Research. (1-3 months, depending on bugs and if we continue to add additional features.)
Create an audio-to-post pipeline where we can easily help alignment researchers create posts through conversations rather than staring at a blank page. (1-4 months, depending on collaboration with Conjecture and others; and how many features we add.)
Leaving the door open and experimenting with ChatGPT and/or GPT-4 to use them for things we haven't explored yet. Especially GPT-4, we can guess in advance what it will be capable of, but we'll likely need to experiment a lot to discover how to use it optimally given it might have new capabilities GPT-3 doesn't have. (2 to 6 weeks.)
Work with Janus, Nicholas Dupuis, and others on building tools for accelerating alignment research using language models (in prep for and integrating GPT-4). These will serve as tools for augmenting the work of alignment researchers. Many of the tool examples are covered in the grant proposal, myrecent post [LW · GW], and anupcoming post, and Nicholas'doc on Cyborgism (we've recently spun up a discord to discuss these things with other researchers; send DM for link). This work is highly relevant to OpenAI's main alignment proposal.
This above work involves:
Working on setting the foundation for automating alignment and making proposal verification viable. (1 week of active work for a post I'm working on, and then some passive work while I build tools.)
Studying the epistemology of effective research helps generate research that leads us to solve alignment. For example, promoting flow and genius moments, effective learning (I'm taking a course on this and so far it is significantly better than the "Learning How to Learn" course) andhow it can translate to high-quality research [LW(p) · GW(p)], etc. (5 hours per week)
It's very hard to predict how the tool-building will go because I expect to be doing a lot of iteration to land on things that are optimally useful rather than come up with a specific plan and stick to it. My goal here is to implement design thinking and approaches that startups use. This involves taking the survey responses, generating a bunch of ideas, create an MVP, test it out with alignment researchers, and then learn from feedback.
Finish a sequence I'm working on with others. We are currently editing the intro post and refining the first post. We went through 6 weeks of seminars for a set of drafts and we are now working to build upon those. (6 to 8 weeks)
Other Projects outside of the grant (will dedicate about 1 day per week, but expect to focus more on some of these later next year, depending on how Accelerating Alignment goes. If not, I'll likely find some mentees or more collaborators to work on some of them.)
Support the Shard Theory team in running experiments using RL and language models. I'll be building off of my MATS colleagues'work. (3 to 5 months for running experiments and writing about them. Would consider spending a month or so on this and then mentoring someone to continue.)
Applying theTuned Lens to better understand what transformers are doing. For example, what is being written and read from the residual stream and how certain things like RL lead to non-myopic behaviour. Comparing self-supervised models to RL fine-tuned models. (2 to 4 months by myself, probably less if I collaborate.)
Building off of Causal Tracing and Causal Scrubbing to develop more useful causal interpretability techniques. Inthis linked doc, I discuss this in the second main section: "Relevance For Alignment." (3 days to wrap up first post. For exploring, studying and writing about new causal methods, anywhere from 2 months to 4 months.)
Provide support for governance projects. I've been mentoring someone looking to explore AI Governance for the past few months (they are now applying for an internship at GovAI). They are currently writing up a post on "AI safety" governance in Canada. I'll be providing mentorship on a few posts I've suggested they write. Here's my recentgovernance post [LW · GW]. (2-3 hours per week)
Update and wrap up theGEM proposal. Adding new insights to it, including the new Tunes Lens that Nora has been working on. (1 week)
Applying quantilizers to Large Language Models. This project is still in the discovery phase for a MATS colleague of mine. I'm providing comments at the moment, but it may turn into a full-time project later next year.
Mentoring through theAI Safety Mentors and Mentees [EA · GW] program. I'm currently mentoring someone who is working on Shard Theory and Infra-Bayesianism relevant work.
Two other projects I would find interesting to work on:
Causal Scrubbing to remove specific capabilities from a model. For example, training a language model on The Pile and a code dataset. Then, applying causal scrubbing to try and remove the model's ability to generate code while still achieving the similar loss on The Pile.
Working on a new grant proposal right now. Should be sent this weekend. If you’d like to give feedback or have a look, please send me a DM! Otherwise, I can send the grant proposal to whoever wants to have a look once it is done (still debating about posting it on LW).
Outside of that, there has been a lot of progress on the Cyborgism discord (there is a VSCode plugin called Worldspider that connects to the various APIs, and there has been more progress on Loom). Most of my focus has gone towards looking at the big picture and keeping an eye on all the developments. Now, I have a better vision of what is needed to create an actually great alignment assistant and have talked to other alignment researchers about it to get feedback and brainstorm. However, I’m spread way too thin and will request additional funding to get some engineer/builder to start building the ideas out so that I can focus on the bigger picture and my alignment work.
If I can get my funding again (previous funding ended last week) then my main focus will be building out the system I have in my for accelerating alignment work + continue working on the new agenda [LW · GW] I put out with Quintin and others. There’s some other stuff I‘d like to do, but those are lower priority or will depend on timing. It’s been hard to get the funding application done because things are moving so fast and I’m trying not to build things that will be built by default. And I’ve been talking to some people about the possibility of building an org so that this work could go a lot faster.
I often find information about AI development on X (f.k.a.Twitter) and sometimes other websites. They usually don't warrant their own post, so I'll use this thread to share. I'll be placing a fairly low filter on what I share.
There's someone on X (f.k.a.Twitter) called Jimmy Apples (🍎/acc) and he has shared some information in the past that turned out to be true (apparently the GPT-4 release date and that OAI's new model would be named "Gobi"). He recently tweeted, "AGI has been achieved internally." Some people think that the Reddit comment below may be from the same guy (this is just a weak signal, I’m not implying you should consider it true or update on it):
Predicting the GPT-4 launch date can easily be disproven with the confidence game. It's possible he just created a prediction for every day and deleted the ones that didn't turn out right.
For the Gobi prediction it's tricky. The only evidence is the Threadreader and a random screenshot from a guy who seems clearly related to jim. I am very suspicious of the Threadreader one. On one hand I don't see a way it can be faked, but it's very suspicious that the Gobi prediction is Jimmy's only post that was saved there despite him making an even bigger bombshell "prediction". It's also possible, though unlikely, that the Information's article somehow found his tweet and used it as a source for their article.
What kills Jimmy's credibility for me is his prediction back in January (you can use the Wayback Machine to find it) that OAI had finished training GPT-5, no not a GPT-5 level system, the ACTUAL GPT-5 in October 2022 and that it was 125T parameters.
Also goes without saying, pruning his entire account is suspicious too.
I’ll try to find them, but this was what people were saying. They also said he deleted past tweets so that evidence may forever be gone.
I remember one tweet where Jimmy said something like, “Gobi? That’s old news, I said that months ago, you need to move on to the new thing.” And I think he linked the tweet though I’m very unsure atm. Need to look it up, but you can use the above for a search.
Not sure exactly what this means, but Jimmy Apples has now tweeted the following:
My gut is telling me that he apple-bossed too close to the sun (released info he shouldn't have, and now that he's concerned about his job or some insider's job), and it's time for him to stop sharing stuff (the apple being bitten symbolizing that he is done sharing info).
This is because the information in my shortform was widely shared on X and beyond.
He also deleted all of his tweets (except for the retweets).
Or that he was genuinely just making things up and tricking us for fun, and a cryptic exit is a perfect way to leave the scene. I really think people are looking way too deep into him and ignoring the more outlandish predictions he's made (125T GPT-4 and 5 in October 2022), along with the fact there is never actual evidence of his accurate ones, only 2nd hand very specific and selective archives.
He did say some true things before. I think it's possible all of the new stuff is untrue, but we're getting more reasons to believe it's not entirely false. The best liars sprinkle in truth.
I think, as a security measure, it's also possible that even people within OpenAI know all the big details of what's going on (this is apparently the case for Anthropic). This could mean, for OpenAI employees, that some details are known while others are not. Employees themselves could be forced to speculate on some things.
Either way, I'm not obsessing too much over this. Just sharing what I'm seeing.
AGI is "something that can solve quantum gravity"?
That's not just a criterion for general intelligence, that's a criterion for genius-level intelligence. And since general intelligence in a computer has advantages of speed, copyability, little need for down time, that are not possessed by general intelligence, AI will be capable of contributing to its training, re-design, agentization, etc, long before "genius level" is reached.
This underlines something I've been saying for a while, which is that superintelligence, defined as AI that definitively surpasses human understanding and human control, could come into being at any time (from large models that are not publicly available but which are being developed privately by Big AI companies). Meanwhile, Eric Schmidt (former Google CEO) says about five years until AI is actively improving itself, and that seems generous.
So I'll say: timeline to superintelligence is 0-5 years.
capable of contributing to its training, re-design, agentization, etc, long before "genius level" is reached
In some models of the world this is seen as unlikely to ever happen, these things are expected to coincide, which collapses the two definitions of AGI. I think the disparity between sample efficiency of in-context learning and that of pre-training is one illustration for how these capabilities might come apart, in the direction that's opposite to what you point to: even genius in-context learning doesn't necessarily enable the staying power of agency, if this transient understanding can't be stockpiled and the achieved level of genius is insufficient to resolve the issue while remaining within its limitations (being unable to learn a lot of novel things in the course of a project).
Occasionally reading what OSS AI gurus say, they definitely overhype their stuff constantly. The ones who make big claims and try to hype people up are often venture entrepreneur guys rather than actual ML engineers.
The open source folks I mostly keep an eye on are the ones who do actually code and train their own models. Some are entrepreneurs, but they know a decent amount. Not top engineers, but they seem to be able to curate datasets and train custom models.
There’s some wannabe script kiddies too, but once you lurk enough, you become aware of who are actually decent engineers (you’ll find some at Vector Institute and Jeremy Howard is pro- open source, for example). I wouldn’t totally discount them having an impact, even though some of them will overhype.
I think it would be great if alignment researchers read more papers
But really, you don't even need to read the entire paper. Here's a reminder to consciously force yourself to at least read the abstract. Sometimes I catch myself running away from reading an abstract of a paper even though it is very little text. Over time I've just been forcing myself to at least read the abstract. A lot of times you can get most of the update you need just by reading the abstract. Try your best to make it automatic to do the same.
To read more papers, consider using Semantic Scholar and arXiv Explorer. Semantic Scholar can be quite nice because because once you save papers in folders, it will automatically recommend you similar papers every day or week. You can just go over the list of abstract of papers in your Research Dashboard every week to keep up-to-date.
I’ve always been interested in people just becoming hyper-obsessed in pursuing a goal. One easy example is with respect to athletes. Someone like Kobe Bryant was just obsessed with becoming the best he could be. I’m interested in learning what we can from the experiences of the hyper-obsessed and what we can apply to our work in EA / Alignment.
I bought a few books on the topic, I should try to find the time to read them. I’ll try to store some lessons in this shortform, but here’s a quote from Mr. Beast’s Joe Rogan interview:
Most of my growth came from […] basically what I did was I found these other 4 lunatics and we basically talked every day for a thousand days in a row. We did nothing but just hyper-study [Youtube] and how to go viral. We’d have skype calls and some days I’d hop on the call at 7 am and hop off the call at 10 pm, and then do it again the next day.
We didn’t do anything, we had no life. We all hit a million subscribers like within a month. It’s crazy, if you envision a world where you are trying to be great at something and it’s you where you are fucking up, well you in two years might learn from 20 mistakes. But if you have others where you can learn from their mistakes, you’ve learned like 5x the amount of stuff. It helps you grow exponentially way quicker.
We’re talking about every day, all day. We had no friends outside of the group, we had no life. Nevermind 10,000 hours, we did like 50,000 hours.
As an independent researcher who is not currently at one of the hubs, I think it’s important for me to consider this point a lot. I’m hoping to hop on discord voice calls and see if I can make it a habit to make progress with other people who want to solve alignment.
I’m not saying I should aim for absolutely no life, but I’m hoping to learn what I can that‘s practically applicable to what I do.
I think people might have the implicit idea that LLM companies will continue to give API access as the models become more powerful, but I was talking to someone earlier this week that made me remember that this is not necessarily the case. If you gain powerful enough models, you may just keep it to yourself and instead spin AI companies with AI employees to make a ton of cash instead of just charging for tokens.
For this reason, even if outside people build the proper brain-like AGI setup with additional components to squeeze out capabilities from LLMs, they may be limited by:
1. open-source models
2. the API of the weaker models from the top companies
3. the best API of the companies that are lagging behind
One error people can make when thinking about takeoff speeds is assuming that because we are in a world with some gradual takeoff, it now means we are in a "slow takeoff" world. I think this can lead us to make some mistakes in our strategy. I usually prefer thinking in the following frame: “is there any point in the future where we’ll have a step function that prevents us from doing slow takeoff-like interventions for preventing x-risk?”
In other words, we should be careful to assume that some "slow takeoff" doesn't have an abrupt change after a couple of years. You might get some gradual takeoff where slow takeoff interventions work and then...BAM...orders of magnitude of more progress. Let's be careful not to abandon fast takeoff-like interventions as soon as we think we are in a slow-takeoff world.
I’m collaborating on a new research agenda. Here’s a potential insight about future capability improvements:
There has been some insider discussion (and Sam Altman has said) that scaling has started running into some difficulties. Specifically, GPT-4 has gained a wider breath of knowledge, but has not significantly improved in any one domain. This might mean that future AI systems may gain their capabilities from places other than scaling because of the diminishing returns from scaling. This could mean that to become “superintelligent”, the AI needs to run experiments and learn from the outcome of those experiments to gain more superintelligent capabilities.
So you can imagine the case where capabilities come from some form of active/continual/online learning, but that was only possible once models were scaled up enough to gain capabilities in that way. And so that as LLMs become more capable, they will essentially become capable of running their own experiments to gain alphafold-like capabilities across many domains.
Of course, this has implications for understanding takeoffs / sharp left turns.
Top Diplomacy players seem to focus on gigabrain strategies rather than deception
Diplomacy players will no longer want to collaborate with you if you backstab them once. This is so pervasive they'll still feel you are untrustworthy across tournaments. Therefore, it's mostly optimal to be honest and just focus on gigabrain strategies. That said, a smarter agent could do stuff like saying specific phrasing to make one player mad at another player and then tilt really hard. Wording could certainly play a role in dominating other players.
Why did the model "backstab" the human? How is it coming up and using plans?
It seems that the model is coming up with a plan at one point and time and honestly telling the user that's the plan they have. The plan can predict several steps ahead. The thing is, the model can decide to change that plan on the very next turn, which sometimes leads to what we would consider as backstabbing.
They only 'enforce' consistency (with a classifier) when comparing what the model intends to do in the next action and what its message implies it will do. If the classifier notices that the intent from the reasoning engine and the implied intent from the message it's about to send diverge, the system will avoid sending that message. However, as I understand it, they are not penalizing the model for developing a new plan at t+1. This is what leads to the model making an honest deal on one turn and then backstabbing that person on the next turn. It just decided to change plans.
At no point is the model "lying"; it's just updating its plan. Cicero will straight up tell you that it's going to backstab you if that is part of its plan because the model is forced to communicate its intent 'honestly.'
Current interpretability techniques and future systems
At the moment, it seems that the main worry for interpretability is that the model has some kind of deceptive module inside of it. This is certainly an issue worth investigating for future powerful AI. What might not be clear is what we should do if deception is some emergent behaviour part of a larger system we place a language model within.
In the case of Cicero, the language model is only translating the intent of the strategic reasoning engine; it is not coming up with plans. However, future AI systems will likely have language models as more of a central component, and we might think that if we just do interpretability on that model's internals and we find no deception, it means we're good. However, this might not be the case. It may be that once we place that model in a bigger system, it leads to some form of deceptive behaviour. For Cicero, that looks like the model choosing one thing at turn 1 and then doing something different from the first intended plan at turn 2.
The model is not including how specific messages will maximize EV
The language model essentially translates the intent from the reasoning engine into chat messages. It is not, however, modeling how it could phrase things to deceptively gain someone's trust, how asking questions would impact play, etc.
Clarification about the dialogue model
Note that the dialogue model feeds into the strategic reasoning engine to enforce human-like actions based on the previous conversations. If they don't do this, the players will think something like, "no human plays like this," and this may be potentially bad (not clear to me as exactly why; maybe increases the likelihood of being exploited?).
Should we be worried?
Eh, I'd be a lot more worried if the model was a GPT-N model that can come up with long-term plans that uses language to manipulate players into certain actions. I expect a model like this to be even more capable at winning, but straight up optimize for galaxy-brain strategies that focus on manipulating and tilting players. The problem arises when people build a Cicero-like AI with a powerful LLM as the core, tack on some safety filters, and assume it's safe. Either way, I would certainly not use any of these models to make high-stakes decisions.
AI labs should be dedicating a lot more effort into using AI for cybersecurity as a way to prevent weights or insights from being stolen. Would be good for safety and it seems like it could be a pretty big cash cow too.
If they have access to the best models (or specialized), it may be highly beneficial for them to plug them in immediately to help with cybersecurity (perhaps even including noticing suspicious activity from employees).
I don’t know much about cybersecurity so I’d be curious to hear from someone who does.
Small shortform to say that I’m a little sad I haven’t posted as much as I would like to in recent months because of infohazard reasons. I’m still working on Accelerating Alignment with LLMs and eventually would like to hire some software engineer builders that are sufficiently alignment-pilled.
Fyi, if there are any software projects I might be able to help out on after May, let me know. I can't commit to anything worth being hired for but I should have some time outside of work over the summer to allocate towards personal projects.
Call To Action: Someone should do a reading podcast of the AGISF material to make it even more accessible (similar to the LessWrong Curated Podcast and Cold Takes Podcast). A discussion series added to YouTube would probably be helpful as well.
“We assume the case that AI (intelligences in general) will eventually converge on one utility function. All sufficiently intelligent intelligences born in the same reality will converge towards the same behaviour set. For this reason, if it turns out that a sufficiently advanced AI would kill us all, there’s nothing that we can do about it. We will eventually hit that level of intelligence.
Now, if that level of intelligence is doesn’t converge towards something that kills us all, we are safer in a world where AI capabilities (of the current regime) essentially go from 0 to 100 because an all-powerful AI is not worried about being shut down given how capable it is. However, if we increase model capabilities slowly, we will hit a point where AI systems are powerful-but-weak-enough to be concerned about humanity being able to shut it down and kill humanity as a result. For this reason, AI safetyists may end up causing the end of humanity by slowing down progress at a point where it shouldn’t be.
If AI systems change regime, then it is more likely worse if it FOOMs.”
That’s my short summary of the video below. They said they’ve talked to a few people in AI safety about this, apparently one being a CEO of an AI Safety org.
What are people’s current thoughts on London as a hub?
OAI and Anthropic are both building offices there
2 (?) new AI Safety startups based on London
The government seems to be taking AI Safety somewhat seriously (so maybe a couple million gets captured for actual alignment work)
MATS seems to be on the path to be sending somewhat consistent scholars to London
A train ride away from Oxford and Cambridge
Anything else I’m missing?
I’m particularly curious about whether it’s worth it for independent researchers to go there. Would they actually interact with other researchers and get value from it or would they just spend most of their time alone or collaborating with a few people online? Could they get most of the value from just spending 1-2 months in both London/Berkeley per year doing work sprints and the rest of the time somewhere else?
AFAIK, there's a distinct cluster of two kinds of independent alignment researchers:
those who want to be at Berkeley / London and are either there or unable to get there for logistical or financial (or social) reasons
those who very much prefer working alone
It very much depends on the person's preferences, I think. I personally experienced a OOM-increase in my effectiveness by being in-person with other alignment researchers, so that is what I choose to invest in more.
I'm still in some sort of transitory phase where I'm deciding where I'd like to live long term. I moved to Montreal, Canada lately because I figured I'd try working as an independent researcher here and see if I can get MILA/Bengio to do some things for reducing x-risk.
Not long after I moved here, Hinton started talking about AI risk too, and he's in Toronto which is not too far from Montreal. I'm trying to figure out the best way I could leverage Canada's heavyweights and government to make progress on reducing AI risk, but it seems like there's a lot more opportunity than there was before.
This area is also not too far from Boston and NYC, which have a few alignment researchers of their own. It's barely a day's drive away. For me personally, there's the added benefit that it is also just a day's drive away from my home (where my parents live).
Montreal/Toronto is also a nice time zone since you can still work a few hours with London people, and a few hours with Bay Area people.
That said, it's obvious that not many alignment researchers are here and eventually end up at one of the two main hubs.
When I spent time at both hubs last year, I think I preferred London. And now London is getting more attention than I was expecting:
Anthropic is opening up an office in London.
The Prime Minister recently talk to the orgs about existential risk.
Apollo Research and Leap Labs are based in London.
SERI MATS is still doing x.1 iterations in London.
Conjecture is still there.
Demis now leading Google DeepMind.
It's not clear how things will evolve going forward, but I still have things to think about. If I decide to go to London, I can get a Youth Mobility visa for 2 years (I have 2 months to decide) and work independently...but I'm also considering building an org for Accelerating Alignment too and I'm not sure if I could get that setup in London.
I think there is value in being in person, but I think that value can fade over time as an independent researcher. You just end up in a routine, stop talking to as many people, and just work. That's why, for now, I'm trying to aim for some kind of hybrid where I spend ~2 months per year at the hubs to benefit from being there in person. And maybe 1-2 work retreats. Not sure what I'll do if I end up building an org.
Someone should create a “AI risk arguments” flowchart that serves as a base for simulating a conversation with skeptics or the general public. Maybe a set of flashcards to go along with it.
I want to have the sequence of arguments solid enough in my head so that I can reply concisely (snappy) if I ever end up in a debate, roundtable or on the news. I’ve started collecting some stuff since I figured I should take initiative on it.
Text-to-Speech tool I use for reading more LW posts and papers
I use Voice Dream Reader. It's great even though the TTS voice is still robotic. For papers, there's a feature that let's you skip citations so the reading is more fluid.
I've mentioned it before, but I was just reminded that I should share it here because I just realized that if you load the LW post with "Save to Voice Dream", it will also save the comments so I can get TTS of the comments as well. Usually these tools only include the post, but that's annoying because there's a lot of good stuff in the LW comments and I often never get around to them. But now I will likely read (+listen) to more of them.
I honestly feel like some software devs should probably still keep their high-paying jobs instead of going into alignment and just donate a bit of time and programming expertise to help independent researchers if they want to start contributing to AI Safety.
I think we can probably come up with engineering projects that are interesting and low-barrier-to-entry for software engineers.
I also think providing “programming coaching” to some independent researchers could be quite useful. Whether that’s for getting them better at coding up projects efficiently or preparing for research engineer type roles at alignment orgs.
I talk a bit more about this, here [EA(p) · GW(p)]:
and here [LW · GW] (post about gathering data for alignment):
Heads up, we are starting to work on stuff like this in a discord server (DM for link) and I’ll be working on this stuff full-time from February to end of April (if not longer). We’ve talked about data collection a bit over the past year, but have yet to take the time to do anything serious (besides the alignment text dataset). In order to make this work, we’ll have to make it insanely easy on the part of the people generating the data. It’s just not going to happen by default. Some people might take the time to set this up for themselves, but very few do.
Glad to see others take interest in this idea! I think this kind of stuff has a very low barrier to entry for software engineers who want to contribute to alignment, but might want to focus on using their software engineering skills rather than trying to become a full-on researcher. It opens up the door for engineering work that is useful for independent researchers, not just the orgs.
When an agent interacts with the world, there are two possible ways the agent makes mistakes:
Its values were not aligned with the outer objective, and so it does something intentionally wrong,
Its world model was incorrect, so it makes an accidental mistake.
Thus, the training process of an AGI will improve its values or its world model, and since it eventually gets diminishing marginal returns from both of these, both the world model and the values must improve together. Therefore, it is very likely that the agent will have a sufficiently good world model to understand that it is in a training loop before it has fully aligned inner values.
So, what if we prevented the model from recognizing it is in a training loop (e.g. preventing/delaying situational awareness) until we are certain it has fully aligned inner values? In other words, we could use some stronger forms of model editing to remove specific knowledge (or prevent the model from gaining that knowledge) from the model. Perhaps you penalize the model from learning things that are not useful for fully embedding aligned inner values (Tool AI-ish). Maybe even apply negative gradient steps to "unlearn" things.
Precursor checking: Another general type of training rationale that I think is worth calling attention to is what I’ll call “precursor checking,” which is the concept of using some method of gaining information about a model’s internals—e.g. transparency/interpretability or AI cognitive science—to check for some precursor to bad behavior rather than the bad behavior itself. This could involve substituting in some narrower, easier to check training goal—that still falls within the broader actual training goal—as the target for the training rationale. For example, if your training rationale involves ensuring that you don’t get a deceptive model that’s actively trying to trick its training process [LW · GW], then rather than explicitly trying to look for such deception (which could be especially hard since a deceptive model might actively try to avoid detection), you could instead try to ensure that your model has a short horizon length in terms of how far ahead its planning. Such a plan might work better, since horizon length might be easier to guarantee in a training rationale while still being consistent with the desired training goal and hopefully ruling out the possibility of deception. One issue with this sort of approach, however, is that you have to guarantee that whatever precursor for bad behavior you’re looking for is in fact a necessary condition for that bad behavior—if it turns out that there’s another way of getting that bad behavior that doesn’t go through the precursor, that could be a problem.
Counterarguments to this might be:
The model might not be able to have fully aligned inner values that remain robust as capabilities eventually generalize far out of distribution.
It will exceptionally difficult to know if we've actually removed this knowledge/capability from the model (even if it's possible).
I'd be interested in hearing people's thoughts/criticisms on this.
The model can be “narrower.” It doesn’t need to understand biology, physics, or human society that well. In practice we’d probably fine-tune from an LLM that does understand all of those things, but we could apply some targeted brain damage to the model as a safety precaution. More generally, the model only has to exceed human-level in a few domains, while it can be worse than humans in most others.
There's this Twitter thread that I saved a while ago that is no longer up. It's about generating ideas for startups. However, I think the insight from the thread carries over well enough to thinking about ideas for Accelerating Alignment. Particularly, being aware of what is on the cusp of being usable so that you can take advantage of it as soon as becomes available (even do the work beforehand).
For example, we are surprisingly close to human-level text-to-speech (have a look at Apple's new model for audiobooks). Open-source models or APIs might come out as soon as later this year or next year.
It's worth doing some thinking about how TTS (and other new tech) will fit into current workflows as well as how it will interact with future possible tools also on the cusp.
Paul Buchheit says that people at the leading edge of a rapidly changing field "live in the future." Combine that with Pirsig and you get:
Live in the future, then build what's missing.
Once you're living in the future in some respect, the way to notice startup ideas is to look for things that seem to be missing. If you're really at the leading edge of a rapidly changing field, there will be things that are obviously missing. What won't be obvious is that they're startup ideas. So if you want to find startup ideas, don't merely turn on the filter "What's missing?" Also turn off every other filter, particularly "Could this be a big company?" There's plenty of time to apply that test later. But if you're thinking about that initially, it may not only filter out lots of good ideas, but also cause you to focus on bad ones.
Most things that are missing will take some time to see. You almost have to trick yourself into seeing the ideas around you.
Anyway, here's the Twitter thread I saved (it's very much in the startup world of advice, but just keep in mind how the spirit of it transfers to Accelerating Alignment):
How to come up with new startup ideas
I've helped 750+ startup founders. I always try to ask: "How did you come up with your idea?" Here are their answers:
First, they most commonly say: "Solve your own problems. Meaning, live on the edge of tech and see what issues you encounter. Then build a startup to solve it." I agree, and I love that. But it's not the whole answer you want. Where do these problems actually come from?
I’ll start by defining what a good startup idea looks like to me. It offers a meaningful benefit, such as:
A big reduction of an intense/frequent frustration
A big reduction in the cost of an expensive problem
A big increase in how entertaining/emotional a thing is I call these 3x ideas—ideas compelling enough to overcome the friction to try 'em.
Btw, some people say startups must be "10x better to succeed." This is misleading. For an app to be 10x better, than, say, Uber, it would have to straight up teleport you to your destination.
Examples of real 3x ideas:
Dropbox/Box: Cheaply share files without coordination or friction
Instacart: Get groceries delivered—without a big cost premium
Uber: Get a cab 3x faster, in 3x more locations, and for cheaper
So, where do these 3x ideas come from? From the creation of new infrastructure—either technological or legal. I keep an eye on this. For example:
1. New technologies
Fast mobile processors
2. Changes in the law
Legalization of marijuana
Patents expiring (And 1,000 more infrastructure examples.)
When new technological/legal infrastructure emerges, startups pounce to productize the new 3x possibilities. Those possibilities fall into categories:
1. Cost reductions:
Cheaper broadband enables cloud storage (Dropbox)
Cheaper batteries enables electric cars (Tesla)
2. Better functionality:
Smartphones and 3G spawned the mobile era
3. Brand new categories:
The legalization of marijuana spawned weed stores and weed delivery apps
As the CEO of Box wrote: “We bet on four mega-trends that would shift the power to cloud: faster internet, cheaper compute and storage, mobile, and better browsers. Even so, we underestimated the scale of each tailwind. Always bet on the mega-trends.”
So takeaway number one is that new infrastructure spawns startups. But, we're not done. For those ideas to survive in the market, I believe you need another criterion: Cultural acceptance. Society has to be ready for you:
Here are startups that became possible through changes in societal behavior.
1. Pop culture making behaviors less cool:
Cigarettes go out of style, so we get nicotine and vaping
Heavy drinking goes out of style, so we get low-alcohol seltzers
2. Mobile apps making it more normal to trust strangers:
The rise of Uber, Airbnb, Tinder, and couchsurfing better acclimated society to trusting people they’ve only met over the Internet.
This next part is important:
Notice how cultural acceptance results from (1) new media narratives and (2) the integration of technology into our lives, which changes behaviors. And note how those startup ideas were already feasible for a while, but couldn't happen *until cultural acceptance was possible.*
Implication: Study changes in infrastructure plus shifts in cultural acceptance to identify what’s newly possible in your market. Here's an example:
1. Uber saw that widespread smartphone adoption with accurate GPS data made it possible to replace taxis with gig workers. Cultural acceptance was needed here—because it was unorthodox to step into a stranger's car and entrust them with your safety.
2. Hims saw that the Propecia hair loss drug's patent was expiring, and capitalized on it by selling it via an online-first brand. Not much cultural acceptance was needed here since people were already buying the drug.
Okay, so let's turn all this into a framework. Here's just one way to find startup ideas.
Step 1: Spot upcoming infrastructure:
Subscribe to industry blogs/podcasts, try products, read congressional bills, read research, and talk to scientists and engineers.
Step 2: Determine if market entry is now possible:
As you’re scanning the infrastructure, look for an emergent 3x benefit that your startup could capture.
Step 3: Explore second-order ideas too:
If other startups capture a 3x idea before you do, that may be okay.
First, there may be room for more than one (Uber and Lyft, Google and Bing, Microsoft Teams and Slack).
Second, when another startup captures a 3x benefit, it typically produces many downstream 2x ideas. This is a key point.
For example, now that millions use 3x products like Slack, Zoom, and Uber, what tools could make them less expensive, more reliable, more collaborative?
Tons. So many downstream ideas emerge. 2x ideas may be smaller in scale but can still be huge startups. And they might be partially pre-validated.
To recap: One way to find startup ideas is to study infrastructure (3x ideas) and observe what emerges from startups that tackle that infrastructure (2x ideas).
Should EA / Alignment offices make it ridiculously easy to work remotely with people?
One of the main benefits of being in person is that you end up in spontaneous conversations with people in the office. This leads to important insights. However, given that there's a level of friction for setting up remote collaboration, only the people in those offices seem to benefit.
If it were ridiculously easy to join conversations for lunch or whatever (touch of a button rather than pulling up a laptop and opening a Zoom session), then would it allow for a stronger cross-pollination of ideas?
I'm not sure how this could work in practice, but it's not clear to me that we are in an optimal setting at the moment.
There have been some digital workspace apps, but those are not ideal, in my opinion.
The thing you need to figure out is how to make it easy for remote people to join in when there's a convo happening, and make it easy for office workers to accept. The more steps, the less likely it will become a habit or happen at all.
Then again, maybe this is just too difficult to fix and we'll be forced to be in person for a while. Could VR change this?
Detail about the ROME paper I've been thinking about
In the ROME paper, when you prompt the language model with "The Eiffel Tower is located in Paris", you have the following:
Subject token(s): The Eiffel Tower
Relationship: is located in
Once a model has seen a subject token(s) (e.g. Eiffel Tower), it will retrieve a whole bunch of factual knowledge (not just one thing since it doesn’t know you will ask for something like location after the subject token) from the MLPs and 'write' into to the residual stream for the attention modules at the final token to look at the context, aggregate and retrieve the correct information.
In other words, if we take the "The Eiffel Tower is located in", the model will write different information about the Eiffel Tower into the residual stream once it gets to the layers with "factual" information (early-middle layers). At this point, the model hasn't seen "is located in" so it doesn't actually know that you are going to ask for the location. For this reason, it will write more than just the location of the Eiffel Tower into the residual stream. Once you are at the point of predicting the location (at the final token, "in"), the model will aggregate the surrounding context and pull the location information that was 'written' into the residual stream via the MLPs with the most causal effect.
What is stored in the MLP is not the relationship between the facts. This is obvious because the relationship is coming after the subject tokens. In other words, as we said before, the MLPs are retrieving a bunch of factual knowledge, and then the attention modules are picking the correct (forgive the handwavy description) fact given what was retrieved and the relationship that is being asked of it.
My guess is that you could probably take what is being 'written' into the residual stream and directly predict properties of the subject token from the output of the layers with the most causal effect to predict a fact.
I'm unsure if I didn't emphasize it in the post enough, but part of the point of my post on ROME [LW · GW] was that many AI researchers seemed to assume that transformers are not trained in a way that prevents them from understanding that A is B = B is A.
As I discussed in the comment above,
What is stored in the MLP is not the relationship between the facts. This is obvious because the relationship is coming after the subject tokens. In other words, as we said before, the MLPs are retrieving a bunch of factual knowledge, and then the attention modules are picking the correct (forgive the handwavy description) fact given what was retrieved and the relationship that is being asked of it.
This means that the A token will 'write' some information into the residual stream, while the B token will 'write' other information into the residual. Some of that information may be the same, but not all. And so, if it's different enough, the attention heads just won't be able to pick up on the relevant information to know that B is A. However, if you include the A token, the necessary information will be added to the residual stream, and it will be much more likely for the model to predict that B is A (as well as A is B).
From what I remember in the case of ROME, as soon as I added the edited token A to the prompt (or make the next predicted token be A), then the model could essentially predict B is A.
I write what it means in the context of ROME, below (found here [LW · GW] in the post):
So, part of the story here is that the transformer stores the key for one entity (Eiffel Tower) separately from another (Rome). And so you'd need a second edit to say, "the tower in Rome is called the Eiffel Tower."
Intuitively, as a human, if I told you that the Eiffel Tower is in Rome, you'd immediately be able to understand both of these things at once. While for the ROME method, it's as if it's two separate facts. For this reason, you can’t really equate ROME with how a human would naturally update on a fact. You could maybe imagine ROME more like doing some brain surgery on someone to change a fact.
The directional nature of transformers could make it so that facts are stored somewhat differently than what we’d infer from our experience with humans. What we see as one fact may be multiplefacts for a transformer. Maybe bidirectional models are different. That said, ROME could be seen as brain surgery which might mess up things internally and cause inconsistencies.
It looks like the model is representing its factual knowledge in a complex/distributed way, and that intervening on just one node does not propagate the change to the rest of the knowledge graph.
Why is this surprising at all then? My guess is that symmetry is intuitive to us, and we're used to LLMs being capable of surprising and impressive things, so it's weird to see something seemingly basic missing.
I actually have a bit of an updated (evolving) opinion on this:
Upon further reflection, it’s not obvious to me that humans and decoder-only transformers are that different. Could be that we both store info unidirectionally, but humans only see B->A as obvious because our internal loop is so optimized that we don’t notice the effort it takes.
Like, we just have a better system message than LLMs and that system message makes it super quick to identify relationships. LLMs would probably be fine doing the examples in the paper if you just adjusted their system message a little instead of leaving it essentially blank.
How do you imagine the system message helping? If the information is stored hetero-associatively (K -> V) like how it is in a hash map, is there a way to recall in the reverse direction (V -> K) other than with a giant scan?
Yeah, I'd have to think about it, but I imagined something like, "Given the prompt, quickly outline related info to help yourself get the correct answer." You can probably output tokens that quickly help you get the useful facts as it is doing the forward pass.
In the context of the paper, now that I think about it, I think it becomes nearly impossible unless you can somehow retrieve the specific relevant tokens used for the training set. Not sure how to prompt those out.
When I updated the models to new facts using ROME, it wasn't possible to get the updated fact unless the updated token was in the prompt somewhere. As soon as it is found in the prompt, it retrieves the new info where the model was edited.
Diversifying your dataset with the reverse prompt to make it so it has the correct information in whichever way possible feels so unsatisfying to me...feels like there's something missing.
As I said, this is a bit of an evolving opinion. Still need time to think about this, especially regarding the differences between decoder-only transformers and humans.
Preventing capability gains (e.g. situational awareness) that lead to deception
Note: I'm at the crackpot idea stage of thinking about how model editing could be useful for alignment.
One worry with deception is that the AI will likely develop a sufficiently good world model to understand it is in a training loop before it has fully aligned inner values.
The thing is, if the model was aligned, then at some point we'd consider it useful for the model to have a good enough world model to recognize that it is a model. Well, what if you prevent the model from being able to gain situational awareness only after it has properly embedded aligned values? In other words, you don't lobotomize the model permanently from ever gaining situational awareness (which would be uncompetitive), but you lobotomize it until we are confident it is aligned and won't suddenly become deceptive once it gains situational awareness.
I'm imagining a scenario where situational awareness is a module in the network or you're able to remove it from the model without completely destroying the model and having interpretability tools powerful enough to be confident that the trained model is aligned. Once you are confident this is the case, you might be in a world where you are no longer worried about situational awareness.
Anyway, I expect that there are issues with this, but wanted to write it up here so I can remove it from another post I'm writing. I'd need to think about this type of stuff a lot more to add it to the post, so I'm leaving it here for now.