Here’s a screenshot of Elon Musk’s post on X that gives some insight into how they are trying to train/RAG Grok:
It’s also implied in this NYTimes article: Grok Chatbot Mirrored X Users’ ‘Extremist Views’ in Antisemitic Posts, xAI Says.
I think this ties in well with Grok 4’s examination of Musk’s timeline on X to determine the correct response. There’s some weird version of attention going on here, where the model itself is using attention, and RAG uses cosine similarity to find things, which is attention.
I also think there is something sociotechnical going on with LLMs and what I’d like to call a deep attractor for religion in human nature. As context windows get bigger, deeper associations can be found and expressed in the models. I think that conspiracy theories is one that is relatively close to the surface, but the patterns associated with revelation and enlightenment are also in there. My guess is that they are deep enough that they are largely unaffected by Reinforcement Learning from Human Feedback. The increasing size of the context window may be why we are seeing things like this:
- They Asked ChatGPT Questions. The Answers Sent Them Spiraling. – The New York Times
- Why Is ChatGPT Telling People to Email Me?
In a way it’s kind of like being the leader of a cult. It must be an amazing experience to have a following. But you can only lead in the direction the followers want to go. And there is a lot of path dependency – where you go depends a lot on the initial message and the type of person it appeals to. In the case of Musk, he got his base when he started going after the “woke mind virus,” (click on the links above and below and look at the replies) which attracted a following that leads directly to mecha-Hitler. With the AI-revelation folks, it’s a more personalized trajectory, but still the same mechanism. You wind up leading where the model can go.
Kimi (huggingface) looks like a nice model with open weights: Moonshot AI – Open Platform. I’m really interested in the base model
viztales 