Lex Fridman Podcast

Latest Episodes:

#73 – Andrew Ng: Deep Learning, Education, and Real-World AI Feb 20, 2020

Show notes

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.

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/

This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on Patreon.

This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.

This episode is also supported by the Techmeme Ride Home podcast. Get it on Apple Podcasts, on its website, or find it by searching “Ride Home” in your podcast app.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

OUTLINE:
00:00 – Introduction
02:23 – First few steps in AI
05:05 – 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

#72 – Scott Aaronson: Quantum Computing Feb 17, 2020

Show notes

Scott Aaronson is a professor at UT Austin, director of its Quantum Information Center, and previously a professor at MIT. His research interests center around the capabilities and limits of quantum computers and computational complexity theory more generally.

This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.

This episode is also supported by the Techmeme Ride Home podcast. Get it on Apple Podcasts, on its website, or find it by searching “Ride Home” in your podcast app.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction
05:07 – Role of philosophy in science
29:27 – What is a quantum computer?
41:12 – Quantum decoherence (noise in quantum information)
49:22 – Quantum computer engineering challenges
51:00 – Moore’s Law
56:33 – Quantum supremacy
1:12:18 – Using quantum computers to break cryptography
1:17:11 – Practical application of quantum computers
1:22:18 – Quantum machine learning, questionable claims, and cautious optimism
1:30:53 – Meaning of life

Vladimir Vapnik: Predicates, Invariants, and the Essence of Intelligence Feb 14, 2020

Show notes

Vladimir Vapnik is the co-inventor of support vector machines, support vector clustering, VC theory, and many foundational ideas in statistical learning. He was born in the Soviet Union, worked at the Institute of Control Sciences in Moscow, then in the US, worked at AT&T, NEC Labs, Facebook AI Research, and now is a professor at Columbia University. His work has been cited over 200,000 times.

This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction
02:55 – Alan Turing: science and engineering of intelligence
09:09 – What is a predicate?
14:22 – Plato’s world of ideas and world of things
21:06 – Strong and weak convergence
28:37 – Deep learning and the essence of intelligence
50:36 – Symbolic AI and logic-based systems
54:31 – How hard is 2D image understanding?
1:00:23 – Data
1:06:39 – Language
1:14:54 – Beautiful idea in statistical theory of learning
1:19:28 – Intelligence and heuristics
1:22:23 – Reasoning
1:25:11 – Role of philosophy in learning theory
1:31:40 – Music (speaking in Russian)
1:35:08 – Mortality

Jim Keller: Moore’s Law, Microprocessors, Abstractions, and First Principles Feb 05, 2020

Show notes

Jim Keller is a legendary microprocessor engineer, having worked at AMD, Apple, Tesla, and now Intel. He’s known for his work on the AMD K7, K8, K12 and Zen microarchitectures, Apple A4, A5 processors, and co-author of the specifications for the x86-64 instruction set and HyperTransport interconnect.

This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction
02:12 – Difference between a computer and a human brain
03:43 – Computer abstraction layers and parallelism
17:53 – If you run a program multiple times, do you always get the same answer?
20:43 – Building computers and teams of people
22:41 – Start from scratch every 5 years
30:05 – Moore’s law is not dead
55:47 – Is superintelligence the next layer of abstraction?
1:00:02 – Is the universe a computer?
1:03:00 – Ray Kurzweil and exponential improvement in technology
1:04:33 – Elon Musk and Tesla Autopilot
1:20:51 – Lessons from working with Elon Musk
1:28:33 – Existential threats from AI
1:32:38 – Happiness and the meaning of life

David Chalmers: The Hard Problem of Consciousness Jan 29, 2020

Show notes

David Chalmers is a philosopher and cognitive scientist specializing in philosophy of mind, philosophy of language, and consciousness. He is perhaps best known for formulating the hard problem of consciousness which could be stated as “why does the feeling which accompanies awareness of sensory information exist at all?”

This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction
02:23 – Nature of reality: Are we living in a simulation?
19:19 – Consciousness in virtual reality
27:46 – Music-color synesthesia
31:40 – What is consciousness?
51:25 – Consciousness and the meaning of life
57:33 – Philosophical zombies
1:01:38 – Creating the illusion of consciousness
1:07:03 – Conversation with a clone
1:11:35 – Free will
1:16:35 – Meta-problem of consciousness
1:18:40 – Is reality an illusion?
1:20:53 – Descartes’ evil demon
1:23:20 – Does AGI need conscioussness?
1:33:47 – Exciting future
1:35:32 – Immortality

Cristos Goodrow: YouTube Algorithm Jan 25, 2020

Show notes

Cristos Goodrow is VP of Engineering at Google and head of Search and Discovery at YouTube (aka YouTube Algorithm).

This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction
03:26 – Life-long trajectory through YouTube
07:30 – Discovering new ideas on YouTube
13:33 – Managing healthy conversation
23:02 – YouTube Algorithm
38:00 – Analyzing the content of video itself
44:38 – Clickbait thumbnails and titles
47:50 – Feeling like I’m helping the YouTube algorithm get smarter
50:14 – Personalization
51:44 – What does success look like for the algorithm?
54:32 – Effect of YouTube on society
57:24 – Creators
59:33 – Burnout
1:03:27 – YouTube algorithm: heuristics, machine learning, human behavior
1:08:36 – How to make a viral video?
1:10:27 – Veritasium: Why Are 96,000,000 Black Balls on This Reservoir?
1:13:20 – Making clips from long-form podcasts
1:18:07 – Moment-by-moment signal of viewer interest
1:20:04 – Why is video understanding such a difficult AI problem?
1:21:54 – Self-supervised learning on video
1:25:44 – What does YouTube look like 10, 20, 30 years from now?

Paul Krugman: Economics of Innovation, Automation, Safety Nets & Universal Basic Income Jan 21, 2020

Show notes

Paul Krugman is a Nobel Prize winner in economics, professor at CUNY, and columnist at the New York Times. His academic work centers around international economics, economic geography, liquidity traps, and currency crises.

This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction
03:44 – Utopia from an economics perspective
04:51 – Competition
06:33 – Well-informed citizen
07:52 – Disagreements in economics
09:57 – Metrics of outcomes
13:00 – Safety nets
15:54 – Invisible hand of the market
21:43 – Regulation of tech sector
22:48 – Automation
25:51 – Metric of productivity
30:35 – Interaction of the economy and politics
33:48 – Universal basic income
36:40 – Divisiveness of political discourse
42:53 – Economic theories
52:25 – Starting a system on Mars from scratch
55:11 – International trade
59:08 – Writing in a time of radicalization and Twitter mobs

Ayanna Howard: Human-Robot Interaction and Ethics of Safety-Critical Systems Jan 17, 2020

Show notes

Ayanna Howard is a roboticist and professor at Georgia Tech, director of Human-Automation Systems lab, with research interests in human-robot interaction, assistive robots in the home, therapy gaming apps, and remote robotic exploration of extreme environments.

This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction
02:09 – Favorite robot
05:05 – Autonomous vehicles
08:43 – Tesla Autopilot
20:03 – Ethical responsibility of safety-critical algorithms
28:11 – Bias in robotics
38:20 – AI in politics and law
40:35 – Solutions to bias in algorithms
47:44 – HAL 9000
49:57 – Memories from working at NASA
51:53 – SpotMini and Bionic Woman
54:27 – Future of robots in space
57:11 – Human-robot interaction
1:02:38 – Trust
1:09:26 – AI in education
1:15:06 – Andrew Yang, automation, and job loss
1:17:17 – Love, AI, and the movie Her
1:25:01 – Why do so many robotics companies fail?
1:32:22 – Fear of robots
1:34:17 – Existential threats of AI
1:35:57 – Matrix
1:37:37 – Hang out for a day with a robot

Daniel Kahneman: Thinking Fast and Slow, Deep Learning, and AI Jan 14, 2020

Show notes

Daniel Kahneman is winner of the Nobel Prize in economics for his integration of economic science with the psychology of human behavior, judgment and decision-making. He is the author of the popular book “Thinking, Fast and Slow” that summarizes in an accessible way his research of several decades, often in collaboration with Amos Tversky, on cognitive biases, prospect theory, and happiness. The central thesis of this work is a dichotomy between two modes of thought: “System 1” is fast, instinctive and emotional; “System 2” is slower, more deliberative, and more logical. The book delineates cognitive biases associated with each type of thinking.

This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction
02:36 – Lessons about human behavior from WWII
08:19 – System 1 and system 2: thinking fast and slow
15:17 – Deep learning
30:01 – How hard is autonomous driving?
35:59 – Explainability in AI and humans
40:08 – Experiencing self and the remembering self
51:58 – Man’s Search for Meaning by Viktor Frankl
54:46 – How much of human behavior can we study in the lab?
57:57 – Collaboration
1:01:09 – Replication crisis in psychology
1:09:28 – Disagreements and controversies in psychology
1:13:01 – Test for AGI
1:16:17 – Meaning of life

Grant Sanderson: 3Blue1Brown and the Beauty of Mathematics Jan 07, 2020

Show notes

Grant Sanderson is a math educator and creator of 3Blue1Brown, a popular YouTube channel that uses programmatically-animated visualizations to explain concepts in linear algebra, calculus, and other fields of mathematics.

This episode is presented by Cash App. Download it (App Store, Google Play), use code “LexPodcast”.

Here’s the outline of the episode. On some podcast players you should be able to click the timestamp to jump to that time.

00:00 – Introduction
01:56 – What kind of math would aliens have?
03:48 – Euler’s identity and the least favorite piece of notation
10:31 – Is math discovered or invented?
14:30 – Difference between physics and math
17:24 – Why is reality compressible into simple equations?
21:44 – Are we living in a simulation?
26:27 – Infinity and abstractions
35:48 – Most beautiful idea in mathematics
41:32 – Favorite video to create
45:04 – Video creation process
50:04 – Euler identity
51:47 – Mortality and meaning
55:16 – How do you know when a video is done?
56:18 – What is the best way to learn math for beginners?
59:17 – Happy moment

Our TOPPODCAST Picks

Follow Us

Stay Connected

Related Podcasts

Links

Stay Connected