berkeley

Berkeley's Deep RL class is now fully available online on YouTube for free, as announced by Sergey Levine.

The lectures for the Deep Reinforcement Learning course CS185/285 from UC Berkeley are now available online for public viewing.

The tweet highlights the potential of agent harnesses to capture and distill experts' tacit knowledge as new training data, referencing a Berkeley AI Summit talk by Jianfeng Gao on agentic modeling as an emerging AI paradigm.

NYU philosopher David Chalmers argues that modern LLMs act as transient 'quasi-agents' that generate short-lived conversational selves, challenging traditional views of identity and ethics.

Berkeley CS162 offers a free comprehensive online operating systems course with lectures, projects, and assignments covering key OS concepts like processes, virtual memory, scheduling, and distributed systems.

Berkeley 189 lecture provides a clear explanation of the attention mechanism, tracing the evolution from RNN+attention to Transformer and contrasting MLP/CNN parameter efficiency.

A tweet sharing a chapter from a UC Berkeley EECS technical report from 2016, expressing excitement about its content.

This paper introduces Residual Context Diffusion (RCD), a module that recycles discarded token representations in diffusion language models to improve efficiency and accuracy, achieving 5–10% better accuracy and up to 4–5x fewer denoising steps on challenging reasoning tasks.

Sharing the advanced course Advanced LLM Agents from UC Berkeley, focusing on the latest advancements in large language model agents, taught by Professor Dawn Song with guest lecturers from Google, Meta, etc., covering reasoning, planning, code generation, and more.

A Twitter thread shares a curated list of 10 free, legally downloadable textbooks from MIT, Stanford, Berkeley, and Harvard, covering topics like linear algebra, machine learning, probability, and data science.

LOTUSPlan is a new API and optimizer for LLM-based data processing that reduces cost by up to 2.4× and improves accuracy by 4.6× through lazy execution and global planning. Developed at Berkeley and Stanford, it supports tasks like agent trace analysis, RAG, and document extraction.

Machine Learning @ Berkeley has launched a new high school workshop initiative called GREP to make machine learning education more accessible and inclusive for students of all backgrounds.

The article explains 'Vokenization,' a multimodal learning technique that bridges computer vision and natural language processing by using weak supervision to link visual data with language tokens. It contrasts this approach with text-only models like GPT-3 and BERT, highlighting how visual grounding can improve language understanding.