Lex Fridman Podcast

Andrew Ng: Deep Learning, Education, and Real-World AI | Lex Fridman Podcast #73

Andrew Ng is one of the most impactful educators, researchers, innovators, and leaders in artificial intelligence and technology space in general. He co-founded Coursera and Google Brain, launched deeplearning.ai, Landing.ai, and the AI fund, and was the Chief Scientist at Baidu. As a Stanford professor, and with Coursera and deeplearning.ai, he has helped educate and inspire millions of students including me. This episode is presented by Cash App. Download it & use code "LexPodcast": Cash App (App Store): https://apple.co/2sPrUHe Cash App (Google Play): https://bit.ly/2MlvP5w PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ Full episodes playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOdP_8GztsuKi9nrraNbKKp4 Clips playlist: https://www.youtube.com/playlist?list=PLrAXtmErZgOeciFP3CBCIEElOJeitOr41 EPISODE LINKS: Andrew Twitter: https://twitter.com/AndrewYNg Andrew Facebook: https://www.facebook.com/andrew.ng.96 Andrew LinkedIn: https://www.linkedin.com/in/andrewyng/ deeplearning.ai: https://www.deeplearning.ai landing.ai: https://landing.ai AI Fund: https://aifund.ai/ AI for Everyone: https://www.coursera.org/learn/ai-for-everyone The Batch newsletter: https://www.deeplearning.ai/thebatch/ OUTLINE: 0:00 - Introduction 2:23 - First few steps in AI 5:05 - Early days of online education 16:07 - Teaching on a whiteboard 17:46 - Pieter Abbeel and early research at Stanford 23:17 - Early days of deep learning 32:55 - Quick preview: deeplearning.ai, landing.ai, and AI fund 33:23 - deeplearning.ai: how to get started in deep learning 45:55 - Unsupervised learning 49:40 - deeplearning.ai (continued) 56:12 - Career in deep learning 58:56 - Should you get a PhD? 1:03:28 - AI fund - building startups 1:11:14 - Landing.ai - growing AI efforts in established companies 1:20:44 - Artificial general intelligence CONNECT: - Subscribe to this YouTube channel - Twitter: https://twitter.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/LexFridmanPage - Instagram: https://www.instagram.com/lexfridman - Medium: https://medium.com/@lexfridman - Support on Patreon: https://www.patreon.com/lexfridman

Lex FridmanhostAndrew Ngguest

Feb 19, 20201h 29mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

Andrew Ng on Scaling AI, Democratizing Education, and Real-World Impact

Andrew Ng discusses his personal journey into computer science and AI, from childhood coding and early fascination with expert systems to founding Google Brain, Coursera, DeepLearning.AI, Landing AI, and the AI Fund.
He explains how online education and MOOCs revealed massive global demand for AI knowledge, and shares detailed thoughts on how people should learn machine learning, build careers, and choose between academia, industry, and startups.
Ng emphasizes practical, real-world AI: small-data challenges, deployment gaps between notebooks and production, the importance of data-centric thinking, and transforming traditional industries like manufacturing and agriculture.
Throughout, he contrasts long-horizon AGI debates with more urgent issues such as bias, inequality, and job displacement, arguing that meaningful work is defined by whether it truly helps people at scale.

IDEAS WORTH REMEMBERING

5 ideas

Start small but be consistent when learning AI.

Ng stresses that regular, habit-based study (even short daily sessions) and small projects like MNIST are far more powerful than sporadic all‑nighters, gradually compounding into deep expertise.

Master fundamentals and debugging, not just architectures.

Understanding core ideas like gradient descent, overfitting, data quality, and systematic debugging strategies can make you 10–100× more effective than simply stacking trendy models.

Data quality and tooling are as crucial as model design.

From inconsistent human labels in factories to shifting real-world conditions, Ng argues that managing, cleaning, and versioning data is an underdeveloped but central challenge for impactful AI.

Real-world AI success demands robust deployment, not just good test accuracy.

He highlights the huge gap between a Jupyter notebook result and a production system: changing environments, maintenance, integration into workflows, and MLOps often dominate the actual work.

Choose careers and jobs based primarily on the people you’ll work with.

Ng advises that the logo on the building matters less than the manager and peers you interact with daily; great teammates dramatically accelerate learning and long-term growth.

WORDS WORTH SAVING

5 quotes

The number one priority is to do what’s best for learners, do what’s best for students.

— Andrew Ng

I like stuff that works.

— Andrew Ng

If you ask yourself, ‘Why doesn’t it work yet?’ that’s the core question of debugging machine learning systems.

— Andrew Ng

What matters most is not the logo above the door, but who are the ten or thirty people you interact with every day.

— Andrew Ng

Ask yourself, if what you’re working on succeeds beyond your wildest dreams, would you have significantly helped other people? If not, then keep searching for something else to work on.

— Andrew Ng

Andrew Ng’s early inspirations, education philosophy, and creation of MOOCs/CourseraHow to learn deep learning: core concepts, study habits, and project workCareer paths in AI: PhD vs industry, startups, and the importance of teammatesScaling deep learning, the role of data, and supervised vs unsupervised learningReal-world AI deployment challenges, especially in manufacturing and other non-internet industriesAI entrepreneurship: AI Fund, Landing AI, and building customer-focused startupsEthics and societal impact: bias, inequality, job displacement, and AGI discourse

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