Fresh out of the oven: CAKE is accepted at #NeurIPS2025! 🎉 TL;DR: We introduce Context-Aware Kernel Evolution (CAKE) 🍰, an adaptive kernel design method that leverages LLMs as genetic operators to dynamically evolve Gaussian process (GP) kernels during Bayesian optimization (BO)

We’re training AI on everything that we know, but what about things that we don’t know? At #ICML2025, the "Exploration in AI Today (EXAIT)" Workshop sparked a crucial conversation: as AI systems grow more powerful, they're relying less on genuine exploration and more on curated human data.

Most AI systems today follow the same predictable pattern: they're built for specific tasks and optimized for objectives rather than exploration. Meanwhile, humans are an open-ended species—driven by curiosity and constantly questioning the unknown.

2 years ago, Ilya Sutskever made a bold prediction that large neural networks are learning world models through text 🌏 Recently, a new paper by Google DeepMind provided a compelling insight to this idea.

AI that can improve itself: A deep dive into self-improving AI and the Darwin-Gödel Machine. richardcsuwandi.github.io/blog/2025/dgm/ Excellent blog post by Richard Suwandi reviewing the Darwin Gödel Machine (DGM) and future implications.

A deep dive into self-improving AI and the Darwin-Gödel Machine https://richardcsuwandi.github.io/blog/2025/dgm/ https://news.ycombinator.com/item?id=44174856

Most AI systems today are stuck in a "cage" designed by humans. They rely on fixed architectures crafted by engineers and lack the ability to evolve autonomously over time.

Excited to share that our latest work on grid spectral mixture product (GSMP) kernel has been featured in Prof. Sergios Theodoridis' latest book "Machine Learning: From the Classics to Deep Networks, Transformers and Diffusion Models"! 🎉

What an unforgettable experience at #ICLR2025! 🎉 Still soaking in everything I learned, the inspiring conversations I had, and the amazing connections I made over the past few days.

Honored to attend a talk by Prof. Stephen Boyd this morning on "Embedded Convex Optimization for Control". I have been a huge fan of his work for years and his book, Convex Optimization, was the very first book I picked up on #optimization.

I made a starter pack for machine learning researchers working on Bayesian optimization and Gaussian processes! It's *very* sparse ATM since the migration is still in progress. Please reply to make me aware of people - potentially yourself - who should be added to it! go.bsky.app/QYMEQ52