Neel Nanda is an AI researcher who writes on mechanistic interpretability of transformers, plus personal essays on friendship, productivity, and doing things.
Commenters recommend his interpretability work, including the Othello-GPT study and his getting-started guide, and cite his posts on productivity and wasted motion.
You mentioned that you're not sure about how to improve the one-sided empathy situation with your SO. So I thought it might be helpful to share some pertinent info that I've come across on HN in the past. I feel that the article and comment discussion on the "Intentionally making close friends" post (https://news.ycombinator.com/item?id=33774353) may offer some actionable advice. Also, see the authors other post specifically on "debugging others" (https://www.neelnanda.io/blog/30-debugging-others).
My own advice is to be direct with your SO about your needs / expectations not being met. I went through the same thing with my SO. We went to couples therapy for many years with no improvement, until eventually they realized it was serious enough of a problem to lead to the end of the relationship, prompting them to put in the necessary effort to change. Another poster mentioned Gottman "bids for attention" which is spot on as well. Both partners in the relationship need to show up for each other to provide emotional support. Some people are simply more used to receiving support and don't know how / aren't used to providing it. It should be made clear that this is unacceptable and needs to change, but also that you are willing to give it time and work with your SO to help them gradually improve their empathetic capacity so that they can be a better partner to you (and better person in general) and keep your relationship healthy.
I think it's also important to note that balance…
LLM based AIs have lots of "features" which are kind of synonymous with "concepts" - these can be anything from `the concept of an apostrophe in the word don't`, to `"George Wash" is usually followed by "ington" in the context of early American History`. Inside of the LLMs neural network, these are mapped to some circuitry-in-software-esque paths.
We don't really have a good way of understanding how these features are generated inside of the LLMs or how their circuitry is activated when outputting them, or why the LLMs are following those circuits. Because of this, we don't have any way to debug this component of an LLM - which makes them harder to improve. Similarly, if LLMs/AIs ever get advanced enough, we'll want to be able to identify if they're being wilfully deceptive towards us, which we can't currently do. For these reasons, we'd like to understand what is actually happening in the neural network to produce & output concepts. This domain of research is usually referred to as "interpretability".
OpenAI (and also DeepMind and Anthropic) have found a few ways to inspect the inner circuitry of the LLMs, and reveal a handful of these features. They do this by asking questions of the model, and then inspecting which parts of the LLM's inner circuitry "lights up". They then ablate (turn off) circuitry to see if those features become less frequently used in the AIs response as a verification step.
The graphs and highlighted words are visual representations of concepts…
Have a look at this research paper[1] and follow-up post[2]. The upshot is that the researchers trained a model of the same shape that the main language models are to play Othello. The model started with no knowledge of the game, and was fed a bunch of move sequences (e.g. "c4 c3 d3 e3"). The researchers were then able to take that model, and figure out, by looking only at the activations, what the board state was. Changing the activations so that they modeled a different board state caused the model to play moves that made sense with the new board state, and did not make sense with the original board state.
And then the follow-up research[2] established that, not only can you determine the board state by looking at the activations, but that you can trivially do so. Specifically, if you look at the residual stream after layer 4, you can build a linear classifier for whether each of the 64 squares is empty, and whether each of the 64 squares contains a token owned by the player whose turn it is. If you bump the values in the direction implied by each of those classifiers, you can make the model output moves in the same way as if the tokens on those squares had the opposite color. There's even a colab notebook[3] you can play with.
That research was on Othello, not chess, but I'm pretty sure that "LLMs are able to develop models of the world if doing so helps them predict the next token better" generalizes to chess too.
[1] https://arxiv.org/pdf/2210.13382.pdf
[2]…
> This is speculation at best. Models are black boxes, even to those who make them. We can't discern a "meaningful internal representation" in a model, anymore than a human brain.
They are not pure black boxes. They are too complex to decipher, but it doesn't mean we can't look at activations and get some very high level idea of what is going on.
For world models specifically, the paper that first demonstrated that LLM has some kind of a world model corresponding to the task it is trained on came out in 2023: https://www.neelnanda.io/mechanistic-interpretability/othello. Now you might argue that this doesn't prove anything about generic LLMs, and that is true. But I would argue that, given this result, and given what LLMs are capable of doing, assuming that they have some kind of world model (even if it's drastically simplified and even outright wrong around the edges) should be the default at this point, and people arguing that they definitely don't have anything like that should present concrete evidence ot that effect.
> We're just highly advanced "stochastic parrots" with a game loop.
If that is your assertion, then what's the point of even talking about "stochastic parrots" at all? By this definition, _everything_ is that, so it ceases to be a meaningful distinction.
I want to highlight this blog article which in my opinion gives more actionable advice than all of the books listed (I read 6/8 of those) on the topic of having good social life
https://www.neelnanda.io/blog/mini-blog-post-23-taking-social-initiative
tldr; taking social initiative is like a cheat code.
The easiest way to make friends is to start organizing. I started applying this approach to my social life and I created two new friends groups from scratch.
1. Me and my brother started organizing lasertag matches. Basically anyone is invited. We even post open invitation to all our friends. Right now we have 20 people in our group chat and we do lasertags + beers once a month.
2. I created a group of friends from highschool - like a reunion shit but once every month. When I reached out to people everyone was hyped. I had only one of these meetings atm but everyone was happy and said they want more. We will be having a second one soon.
If I needed more friends that's what I would be focusing on. Organizing a cyclic get together of people. It's like a cheat code for having a lot of friends.
Right now I am a bit of time constrained but I look forward to organizing something different in the future.
I am building https://linklonk.com and I think it adds a social proof to content discovery while preserving anonymity. Here is how it works:
- When you upvote an item, you connect stronger to users that also posted that item and to RSS feeds that posted this. For example, if you upvote "Post 43: Intentionally Making Close Friends — Neel Nanda"(https://linklonk.com/item/827619236936941568) then you will get connected to 6 users that upvoted that article and the RSS feed https://www.neelnanda.io/blog?format=rss
- The recommendation algorithm shows you other items upvoted by users and feeds you are connected to.
- At the top of you recommendations you see content from users and feeds that you are most strongly connected to (ie, those who have posted more useful content for you).
With this mechanism you discover users and feeds that post great content just by rating content, without having to know the users personally. Yet, when you see your recommendations you know that they are coming not from random people, but from people who found useful for you content before. I think, that is a more meaningful version of social proof than the aggregate counts of likes in the traditional social media.