On the quest to understand the fundamental mathematics of intelligence and of the universe with curiosity.
"A mathematician builds the most explanatory model they can which they can still prove theorems about."
"A physicist builds the simplest model they can which still explains the key phenomena."
- Authors of Principles of Deep Learning Theory book
"An engineer builds the most realistic/accurate model they can which can still be computed within budget."
- jez2718
"Claude Sonnet 4.5 was able to recognize many of our alignment evaluation environments as being tests of some kind, and would generally behave unusually well after making this observation."
How do you rate the lowered sycophancy of GPT-5, relatively speaking?
According to Jan Leike, Claude Sonnet 4.5 It’s the most aligned frontier model yet https://x.com/janleike/status/1972731237480718734
I really like the definition of rationalist from https://www.lesswrong.com/posts/2Ee5DPBxowTTXZ6zf/rationalists-post-rationalists-and-rationalist-adjacents :
"A rationalist, in the sense of this particular community, is someone who is trying to build and update a unified probabilistic model of how the entire world works, and trying to use that model to make predictions and decisions."
I recently started saying that I really love Effective Curiosity:
Maximizing the total understanding of reality by building models of as many physical phenomena as possible across as many scales of the universe as possible, that are as comprehensive, unified, simple, and empirically predictive as possible.
And I see it more as a direction. And I see it from a more collective intelligence perspective. I think modelling the whole world in fully unified way and in total accuracy is impossible, even with all of our science with all our technology, because we're all finite limited agents with limited computational resources and time, limited modelling capability, and we get stuck in various models, from various perspectives, and so on. And all we have is approximations, that predict certain parts reality to a certain degree, but never fully all of reality in perfect accuracy in all it's complexity. And we have a lot of blind spots. All models are wrong but some predictively approximate the extremely nuanced complexity of reality better than others.
And from all of this, intelligence and fundamental physics, which are subsets of this, are the most fascinating to me.
I like your definition of rationalism!
I recently started saying that I really love Effective Curiosity:
Maximizing the total understanding of reality by building models of as many physical phenomena as possible across as many scales of the universe as possible, that are as comprehensive, unified, simple, and empirically predictive as possible.
And I see it more as a direction. And I see it from a more collective intelligence perspective. I think modelling the whole world in fully unified way and in total accuracy is impossible, even with all of our science with all our technology, because we're all finite limited agents with limited computational resources and time, limited modelling capability, and we get stuck in various models, from various perspectives, and so on. And all we have is approximations, that predict certain parts reality to a certain degree, but never fully all of reality in perfect accuracy in all it's complexity. And we have a lot of blind spots. All models are wrong but some predictively approximate the extremely nuanced complexity of reality better than others.
And from all of this, intelligence and fundamental physics, which are subsets of this, are the most fascinating to me.
Lovely podcast with Max Tegmark "How Physics Absorbed Artificial Intelligence & (Soon) Consciousness"
Description: "MIT physicist Max Tegmark argues AI now belongs inside physics, and that consciousness will be next. He separates intelligence (goal-achieving behavior) from consciousness (subjective experience), sketches falsifiable experiments using brain-reading tech and rigorous theories (e.g., IIT/φ), and shows how ideas like Hopfield energy landscapes make memory “feel” like physics. We get into mechanistic interpretability (sparse autoencoders), number representations that snap into clean geometry, why RLHF mostly aligns behavior (not goals), and the stakes as AI progress accelerates from “underhyped” to civilization-shaping. It’s a masterclass on where mind, math, and machines collide."
Whaaat!?
Gemini 2.5 pro is way worse at IMO and got 30%, and DeepThink version gets gold??
But it's more finetuned for IMOlike problems, but I bet the OpenAI's model was too.
Both use "novel RL methods".
Hmm, "access to a set of high-quality solutions to previous problems and general hints and tips on how to approach IMO problems", seems like system prompt, as they claim no tool use like OpenAI.
Both models failed the 6th question which required more creativity
Deepmind's solutions are more organized, more readable, more well written than OpenAI's.
But OpenAI's style is also more compressed to save tokens, so maybe going more out of human-like language into more out of distribution territory will be the future (Neuralese).
Did OpenAI and DeepMind somehow hack the methodology, or do these new general language models truly generalize more?
opus 4.5 is 4o for nerds https://x.com/justalexoki/status/2010027088217342095