DiffusionGemma Explained
by Timothy Gao
ML@B Blog
Machine Learning at Berkeley is a student organization at UC Berkeley
Subscribe:.rss.atom.json.md.m3u.pls
Live Last read · last published · next check
Latest episodes
Benchmarking Biology’s AI Agent: ML@B's Collaboration with LatchBio
By: Lucas Gu, Daniel Grant, Rishi Athavale, Maggie Dong
The Prose of Proteins - A Lesson in Taste and Vision through the Work of Brian Hie
Cade Gordon, ML@Berkeley Researcher Highlight
Empowering the Next Generation: Machine Learning at Berkeley's High School Workshop Initiative
In the fast-paced world of technology, the realm of machine learning (ML) can seem intimidating, and for students who lack coding experience, the journey into this niche field is especially daunting.
Large Language Models of Babel
ChatGPT and Bard have caught the public's attention by storm, but my own feeling is one of déjà vu. Large language models (LLMs) like these are a recent development, but the 1940s marked the first forays into the technology, when writer Jorge Luis Borges interrogated the nature of generated text and mathematician-engineer Claude Shannon experimented with the simplest language models. Seeing LLMs…
Measuring AI Freedom
By Evan Ellis
MuZero: Checkmate For Software 1.0?
By Ashwin Reddy
How Maximum Entropy makes Reinforcement Learning Robust
Does information theory have a role to play in reinforcement learning? Find out how adding entropy to the reinforcement learning formulation can help robots deal with unexpected obstacles and more.
Alien Dreams: An Emerging Art Scene
In this blog post I document the evolution of this new art scene and share a bunch of cool artwork along the way.
A Case for Robot Learning
What makes programming robots difficult? We’ll look at the promising idea of end-to-end learning and why it is (or maybe isn’t) the future of robotics.