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.
Future of Technology
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.
Elon Musk’s 2022 Future Vision, Revisited in 2026
In 2022, Elon Musk linked autonomy, humanoid robots, brain-computer interfaces and Starship into one optimistic future. Four years later, their uneven progress shows why vision, technical direction and timelines should be judged separately.
What Elon Musk’s 2017 TED Talk Reveals About a Future Worth Building
A 2017 TED conversation connects tunnels, electric cars, solar energy, reusable rockets and Mars through one larger idea: progress should solve real problems and make the future feel worth anticipating.