When the Verifier Can Be Wrong — Stanford CS329A Part 3
Stanford CS329A Part 3 traces robust verification from outcome verifiers and process reward models to Math-Shepherd, weak-verifier ensembles, and verifier distillation.
Artificial Intelligence
Test-Time Compute Is a Resource Allocation Problem — Stanford CS329A Part 2
Stanford CS329A Part 2 shows why inference scaling is not just about generating more samples, but about allocating compute across search, verification, revision, fusion, and architecture design.
Why AI Progress Is Moving Beyond Bigger Models — Stanford CS329A Part 1
Stanford CS329A Part 1 traces the shift from pretraining scale to chain-of-thought, post-training, inference-time compute, verifiers, and agentic feedback loops.
Self-Improving AI Agents Aren’t One Model. They’re a Feedback Loop.
Stanford’s CS329A shows why self-improving AI is less about one smarter model than a closed loop of search, verification, tools, learning, and long-horizon evaluation.
AI Data Centers Need More Power. The Harder Question Is Who Pays for It
AI data centers are forcing utilities and regulators to solve two problems at once: how to add power quickly, and how to keep the cost and risk of new infrastructure from landing on ordinary ratepayers.
When AI Makes Work Cheaper, the Bottleneck Moves
When AI makes one stage of work dramatically faster, the rest of the system does not automatically keep up. Startups, government services, and AI research show what happens next.