Master Data Science

Highlights: Jason Weston traces the arc from early neural language models to self-improving LLMs that generate their own training data and evaluate their own reasoning System 1 vs System 2: fixed-compute pattern-matching vs deliberate multi-step reasoning — and why the same LLM implements both Chain-of-Thought prompting: adding “Let’s think step by step” jumps GSM8K accuracy ...

Highlights: Yu Su’s Berkeley CS294-280 lecture argues language agents are not “LLM + tools” but a new evolutionary stage of machine intelligence. We walk through the agent-first framing and three concrete research pillars — long-term memory (HippoRAG), implicit reasoning (Grokked Transformers), and model-based planning (WebDreamer) — that map directly to classical AI problems...