Darklight's Shortform

So, a while back I came up with an obscure idea I called the Alpha Omega Theorem and posted it on the Less Wrong forums. Given how there's only one post about it, it shouldn't be something that LLMs would know about. So in the past, I'd ask them "What is the Alpha Omega Theorem?", and they'd always make up some nonsense about a mathematical theory that doesn't actually exist. More recently, Google Gemini and Microsoft Bing Chat would use search to find my post and use that as the basis for their explanation. However, I only have the free version of ChatGPT and Claude, so they don't have access to the Internet and would make stuff up.

A couple days ago I tried the question on ChatGPT again, and GPT-4o managed to correctly say that there isn't a widely known concept of that name in math or science, and basically said it didn't know. Claude still makes up a nonsensical math theory. I also today tried telling Google Gemini not to use search, and it also said it did not know rather than making stuff up.

I'm actually pretty surprised by this. Looks like OpenAI and Google figured out how to reduce hallucinations somehow.

I recently interviewed with Epoch, and as part of a paid work trial they wanted me to write up a blog post about something interesting related to machine learning trends. This is what I came up with:

http://www.josephius.com/2022/09/05/energy-efficiency-trends-in-computation-and-long-term-implications/

Recently I tried out an experiment using the code from the Geometry of Truth paper to try to see if using simple label words like "true" and "false" could substitute for the datasets used to create truth probes. I also tried out a truth probe algorithm based on classifying with the higher cosine similarity to the mean vectors.

Initial results seemed to suggest that the label word vectors were sorta acceptable, albeit not nearly as good (around 70% accurate rather than 95%+ like with the datasets). However, testing on harder test sets showed much worse accuracy (sometimes below chance, somehow). So I can probably conclude that the label word vectors alone aren't sufficient for a good truth probe.

Interestingly, the cosine similarity approach worked almost identically well as the mass mean (aka difference in means) approach used in the paper. Unlike the mass mean approach though, the cosine similarity approach can be extended to a multi-class situation. Though, logistic regression can also be extended similarly, so it may not be particularly useful either, and I'm not sure there's even a use case for a multi-class probe. 

Anyways, I just thought I'd write up the results here in the unlikely event someone finds this kind of negative result as useful information.

I've been looking at the numbers with regards to how many GPUs it would take to train a model with as many parameters as the human brain has synapses. The human brain has 100 trillion synapses, and they are sparse and very efficiently connected. A regular AI model fully connects every neuron in a given layer to every neuron in the previous layer, so that would be less efficient.

The average H100 has 80 GB of VRAM, so assuming that each parameter is 32 bits, then you have about 20 billion per GPU. So, you'd need 10,000 GPUs to fit a single instance of a human brain in RAM, maybe. If you assume inefficiencies and need to have data in memory as well you could ballpark another order of magnitude so 100,000 might be needed.

For comparison, it's widely believed that OpenAI trained GPT4 on about 10,000 A100s that Microsoft let them use from their Azure supercomputer, most likely the one listed as third most powerful in the world by the Top500 list.

Recently though, Microsoft and Meta have both moved to acquire more GPUs that put them in the 100,000 range, and Elon Musk's X.ai recently managed to get a 100,000 H100 GPU supercomputer online in Memphis.

So, in theory at least, we are nearly at the point where they can train a human brain sized model in terms of memory. However, keep in mind that training such a model would take a ton of compute time. I haven't done to calculations yet for FLOPS so I don't know if it's feasible yet.

Just some quick back of the envelope analysis.

Okay, so I decided to do an experiment in Python code where I modify the Iterated Prisoner's Dilemma to include Death, Asymmetric Power, and Aggressor Reputation, and run simulations to test how different strategies do. Basically, each player can now die if their points falls to zero or below, and the payoff matrix uses their points as a variable such that there is a power difference that affects what happens. Also, if a player defects first in any round of any match against a non-aggressor, they get the aggressor label, which matters for some strategies that target aggressors. 

Long story short, there's a particular strategy I call Avenger, which is Grim Trigger but also retaliates against aggressors (even if the aggression was against a different player) that ensures that the cooperative strategies (ones that never defect first against a non-aggressor) win if the game goes enough rounds. Without Avenger though, there's a chance that a single Opportunist strategy player wins instead. Opportunist will Defect when stronger and play Tit-For-Tat otherwise.

I feel like this has interesting real world implications.

Interestingly, Enforcer, which is Tit-For-Tat but also opens with Defect against aggressors, is not enough to ensure the cooperative strategies always win. For some reason you need Avenger in the mix.

Edit: In case anyone wants the code, it's here.

I have some ideas and drafts for posts that I've been sitting on because I feel somewhat intimidated by the level of intellectual rigor I would need to put into the final drafts to ensure I'm not downvoted into oblivion (something a younger me experienced in the early days of Less Wrong).

Should I try to overcome this fear, or is it justified?

For instance, I have a draft of a response to Eliezer's List of Lethalities post that I've been sitting on since 2022/04/11 because I doubted it would be well received given that it tries to be hopeful and, as a former machine learning scientist, I try to challenge a lot of LW orthodoxy about AGI in it. I have tremendous respect for Eliezer though, so I'm also uncertain if my ideas and arguments aren't just hairbrained foolishness that will be shot down rapidly once exposed to the real world, and the incisive criticism of Less Wrongers.

The posts here are also now of such high quality that I feel the bar is too high for me to meet with my writing, which tends to be more "interesting train-of-thought in unformatted paragraphs" than the "point-by-point articulate with section titles and footnotes" style that people tend to employ.

Anyone have any thoughts?

I'm wondering what people's opinions are on how urgent alignment work is. I'm a former ML scientist who previously worked at Maluuba and Huawei Canada, but switched industries into game development, at least in part to avoid contributing to AI capabilities research. I tried earlier to interview with FAR and Generally Intelligent, but didn't get in. I've also done some cursory independent AI safety research in interpretability and game theoretic ideas my spare time, though nothing interesting enough to publish yet.

My wife also recently had a baby, and caring for him is a substantial time sink, especially for the next year until daycare starts. Is it worth considering things like hiring a nanny, if it'll free me up to actually do more AI safety research? I'm uncertain if I can realistically contribute to the field, but I also feel like AGI could potentially be coming very soon, and maybe I should make the effort just in case it makes some meaningful difference.

I was recently trying to figure out a way to calculate my P(Doom) using math. I initially tried just making a back of the envelope calculation by making a list of For and Against arguments and then dividing the number of For arguments by the total number of arguments. This led to a P(Doom) of 55%, which later got revised to 40% when I added more Against arguments. I also looked into using Bayes Theorem and actual probability calculations, but determining P(E | H) and P(E) to input into P(H | E) = P(E | H) * P(H) / P(E) is surprisingly hard and confusing.

So, I had a thought.  The glory system [LW · GW] idea that I posted about earlier, if it leads to a successful, vibrant democratic community forum, could actually serve as a kind of dataset for value learning.  If each post has a number attached to it that indicates the aggregated approval of human beings, this can serve as a rough proxy for a kind of utility or Coherent Aggregated Volition.

Given that individual examples will probably be quite noisy, but averaged across a large amount of posts, it could function as a real world dataset, with the post content being the input, and the post's vote tally being the output label.  You could then train a supervised learning classifier or regressor that could then be used to guide a Friendly AI model, like a trained conscience.

This admittedly would not be provably Friendly, but as a vector of attack for the value learning problem, it is relatively straightforward to implement and probably more feasible in the short-run than anything else I've encountered.

I tried asking ChatGPT, Gemini, and Claude to come up with a formula that converts between correlation space to probability space while preserving the relationship 0 = 1/n. I came up with such a formula a while back, so I figure it shouldn't be hard. They all offered formulas, all of which were shown to be very much wrong when I actually graphed them to check.