Founder of DeepLearning.AI and AI Fund managing general partner
Andrew Ng
Andrew Ng is an AI researcher, educator, and entrepreneur who founded DeepLearning.AI, co-founded Coursera, and teaches at Stanford University.
About Andrew Ng
Andrew Ng is an AI researcher, educator, and entrepreneur. He founded DeepLearning.AI, serves as managing general partner at AI Fund, and is an adjunct professor at Stanford University. His current work combines AI training with building and supporting AI businesses.
He co-founded Coursera, led the founding Google Brain team, and previously served as chief scientist at Baidu and director of the Stanford AI Lab. His research and teaching cover machine learning and practical AI applications. He earned his PhD at the University of California, Berkeley.
Mentioned in our coverage
40 articles name Andrew Ng, excluding author credits.
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- Top 10 AI Publications in the World (2026) →
From peer-reviewed journals to daily reported news, the global AI media landscape spans wildly different formats and audiences. Here are the 10 publications actually worth your attention in 2026, and where a fast-growing education-first outlet like explainx.ai fits into the mix.
- Vibe Hardware: Astra Designs a DJ Controller From Prompt to Parts →
On September 10, 2026, nim (@eminimnim) posted that GPT-6 Astra designed a Teenage Engineering-style mini DJ controller end to end — concept art, part sourcing, datasheet reading, CAD, ordering, and a Blender assembly animation. explainx.ai maps the workflow, the unverified parts, and what "vibe hardware" means for builders.
- Anthropic AI GDP Scenarios for 2030 — and What Astra Painting a Bridge Means →
Anthropic's Economics team launched an interactive model on September 9, 2026 that maps AI capability and adoption assumptions to US GDP, unemployment, and wage paths through 2030. The typical American respondent lands near the "substantial" scenario — about 10% higher GDP and ~5% unemployment. The same week, a Stanford robotics demo showed GPT-6 Astra learning to paint with a real arm — work the model explicitly leaves out.
- AI Resolves Your Incidents. Are You Still Able To? →
A 204-point Hacker News thread debates Sylvain Kalache's essay on AI-assisted incident response: as AI SREs resolve routine incidents automatically, engineers get less practice and are left facing only the hardest, rarest failures with less intuition than ever. The aviation-training analogy is compelling — and also happens to describe the product Kalache's own company sells. explainx.ai on the real 1983 research behind the argument, where the comment section's rebuttals land, and what's a genuine insight versus a pitch.
- Rethinking Skills and AGENTS.md for GPT-6 Astra: A Practical Guide →
Eric Provencher of OpenAI's Codex DX team argues that most Skills, AGENTS.md files, and task prompts written for older models actively hurt GPT-6 Astra — bloated descriptions, unnecessary permission-seeking, and unclear stopping points. explainx.ai breaks his guidance into four actionable checklists, with copy-paste before/after examples, and maps each one to the Claude Code equivalent.