Andrew Ng on Deep Learning, Education, and Real-World AI

Andrew Ng on Deep Learning, Education, and Real-World AI

Lex Fridman lands an interview with the one and only Andrew Ng.

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 conversation is part of the Artificial Intelligence podcast.

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

Scott Aaronson on Quantum Computing

Scott Aaronson on Quantum Computing

Lex Fridman  interviews Scott Aaronson,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 conversation is part of the Artificial Intelligence podcast.

OUTLINE:
0:00 – Introduction
5:07 – 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, questinable claims, and cautious optimism
1:30:53 – Meaning of life

Vladimir Vapnik on Predicates, Invariants, and the Essence of Intelligence

Vladimir Vapnik on Predicates, Invariants, and the Essence of Intelligence

Lex Fridman just uploaded the second part of his interview with Vladimir Vapnik.

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 conversation is part of the Artificial Intelligence podcast.

Jim Keller on Moore’s Law, Microprocessors, Abstractions, and First Principles

Jim Keller on Moore’s Law, Microprocessors, Abstractions, and First Principles

Lex Fridman interviews Jim Keller as part of his AI Podcast series.

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 conversation is part of the Artificial Intelligence podcast.

OUTLINE:
0:00 – Introduction
2:12 – Difference between a computer and a human brain
3:43 – 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 on the Hard Problem of Consciousness

David Chalmers on the Hard Problem of Consciousness

Lex Fridman interviews David Chalmers in this thought provoking interview on consciousness.

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 conversation is part of the Artificial Intelligence podcast.

OUTLINE:
0:00 – Introduction
2:23 – 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 on the YouTube Algorithm

Cristos Goodrow on the YouTube Algorithm

Lex Fridman inteviews Cristos Goodrow, VP of Engineering at Google and head of Search and Discovery at YouTube (aka YouTube Algorithm).

This conversation is part of the Artificial Intelligence podcast.

OUTLINE:
0:00 – Introduction
3:26 – Life-long trajectory through YouTube
7:30 – 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?

Economics of Innovation, Automation, Safety Nets & Universal Basic Income

Economics of Innovation, Automation, Safety Nets & Universal Basic Income

Lex Fridman interviews Paul Krugman in the latest episode of his podcast.

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 conversation is part of the Artificial Intelligence podcast.

OUTLINE:
0:00 – Introduction
3:44 – Utopia from an economics perspective
4:51 – Competition
6:33 – Well-informed citizen
7:52 – Disagreements in economics
9:57 – 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

Daniel Kahneman on Thinking Fast and Slow, Deep Learning, and AI

Daniel Kahneman on Thinking Fast and Slow, Deep Learning, and AI

Lex Fridman interview Daniel Kahneman in this thought provoking interview.

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 conversation is part of the Artificial Intelligence podcast.

OUTLINE:
0:00 – Introduction
2:36 – Lessons about human behavior from WWII
8:19 – 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 lifeOUTLINE:
0:00 – Introduction
2:36 – Lessons about human behavior from WWII
8:19 – 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

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Best Way to Understand the Mind is to Build It

Best Way to Understand the Mind is to Build It

Lex Fridman explains that the best way to understand the mind is to build it in the clip from the opening lecture of the MIT Deep Learning lecture series.

Full video: https://www.youtube.com/watch?v=0VH1Lim8gL8

Website: https://deeplearning.mit.edu

This is a clip from the opening lecture of the MIT Deep Learning lecture series.
Full video: https://www.youtube.com/watch?v=0VH1Lim8gL8
Website: https://deeplearning.mit.edu

Grant Sanderson on the Beauty of Mathematics

Grant Sanderson on the Beauty of Mathematics

Lex Fridman interviews 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.

OUTLINE:

0:00 – Introduction
1:56 – What kind of math would aliens have?
3:48 – 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