Category Archives: ai

#77 – Alex Garland: Ex Machina, Devs, Annihilation, and the Poetry of Science

Alex Garland is a writer and director of many imaginative and philosophical films from the dreamlike exploration of human self-destruction in the movie Annihilation to the deep questions of consciousness and intelligence raised in the movie Ex Machina, which to me is one of the greatest movies on artificial intelligence ever made. I’m releasing this podcast to coincide with the release of his new series called Devs that will premiere this Thursday, March 5, on Hulu.

EPISODE LINKS:
Devs: https://hulu.tv/2x35HaH
Annihilation: https://hulu.tv/3ai9Eqk
Ex Machina: https://www.netflix.com/title/80023689
Alex IMDb: https://www.imdb.com/name/nm0307497/
Alex Wiki: https://en.wikipedia.org/wiki/Alex_Garland

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”.

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
03:42 – Are we living in a dream?
07:15 – Aliens
12:34 – Science fiction: imagination becoming reality
17:29 – Artificial intelligence
22:40 – The new “Devs” series and the veneer of virtue in Silicon Valley
31:50 – Ex Machina and 2001: A Space Odyssey
44:58 – Lone genius
49:34 – Drawing inpiration from Elon Musk
51:24 – Space travel
54:03 – Free will
57:35 – Devs and the poetry of science
1:06:38 – What will you be remembered for?

#76 – John Hopfield: Physics View of the Mind and Neurobiology

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John Hopfield is professor at Princeton, whose life’s work weaved beautifully through biology, chemistry, neuroscience, and physics. Most crucially, he saw the messy world of biology through the piercing eyes of a physicist. He is perhaps best known for his work on associate neural networks, now known as Hopfield networks that were one of the early ideas that catalyzed the development of the modern field of deep learning.

EPISODE LINKS:
Now What? article: http://bit.ly/3843LeU
John wikipedia: https://en.wikipedia.org/wiki/John_Hopfield
Books mentioned:
– Einstein’s Dreams: https://amzn.to/2PBa96X
– Mind is Flat: https://amzn.to/2I3YB84

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.

OUTLINE:
00:00 – Introduction
02:35 – Difference between biological and artificial neural networks
08:49 – Adaptation
13:45 – Physics view of the mind
23:03 – Hopfield networks and associative memory
35:22 – Boltzmann machines
37:29 – Learning
39:53 – Consciousness
48:45 – Attractor networks and dynamical systems
53:14 – How do we build intelligent systems?
57:11 – Deep thinking as the way to arrive at breakthroughs
59:12 – Brain-computer interfaces
1:06:10 – Mortality
1:08:12 – Meaning of life

#75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI

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Marcus Hutter is a senior research scientist at DeepMind and professor at Australian National University. Throughout his career of research, including with Jürgen Schmidhuber and Shane Legg, he has proposed a lot of interesting ideas in and around the field of artificial general intelligence, including the development of the AIXI model which is a mathematical approach to AGI that incorporates ideas of Kolmogorov complexity, Solomonoff induction, and reinforcement learning.

EPISODE LINKS:
Hutter Prize: http://prize.hutter1.net
Marcus web: http://www.hutter1.net
Books mentioned:
– Universal AI: https://amzn.to/2waIAuw
– AI: A Modern Approach: https://amzn.to/3camxnY
– Reinforcement Learning: https://amzn.to/2PoANj9
– Theory of Knowledge: https://amzn.to/3a6Vp7x

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.

OUTLINE:
00:00 – Introduction
03:32 – Universe as a computer
05:48 – Occam’s razor
09:26 – Solomonoff induction
15:05 – Kolmogorov complexity
20:06 – Cellular automata
26:03 – What is intelligence?
35:26 – AIXI – Universal Artificial Intelligence
1:05:24 – Where do rewards come from?
1:12:14 – Reward function for human existence
1:13:32 – Bounded rationality
1:16:07 – Approximation in AIXI
1:18:01 – Godel machines
1:21:51 – Consciousness
1:27:15 – AGI community
1:32:36 – Book recommendations
1:36:07 – Two moments to relive (past and future)

#74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI

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Michael I. Jordan is a professor at Berkeley, and one of the most influential people in the history of machine learning, statistics, and artificial intelligence. He has been cited over 170,000 times and has mentored many of the world-class researchers defining the field of AI today, including Andrew Ng, Zoubin Ghahramani, Ben Taskar, and Yoshua Bengio.

EPISODE LINKS:
(Blog post) Artificial Intelligence—The Revolution Hasn’t Happened Yet

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.

OUTLINE:
00:00 – Introduction
03:02 – How far are we in development of AI?
08:25 – Neuralink and brain-computer interfaces
14:49 – The term “artificial intelligence”
19:00 – Does science progress by ideas or personalities?
19:55 – Disagreement with Yann LeCun
23:53 – Recommender systems and distributed decision-making at scale
43:34 – Facebook, privacy, and trust
1:01:11 – Are human beings fundamentally good?
1:02:32 – Can a human life and society be modeled as an optimization problem?
1:04:27 – Is the world deterministic?
1:04:59 – Role of optimization in multi-agent systems
1:09:52 – Optimization of neural networks
1:16:08 – Beautiful idea in optimization: Nesterov acceleration
1:19:02 – What is statistics?
1:29:21 – What is intelligence?
1:37:01 – Advice for students
1:39:57 – Which language is more beautiful: English or French?

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

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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 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

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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

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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

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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

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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

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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?

Lex Fridman

Research Scientist at MIT. Host of Lex Fridman Podcast.

Category Archives: ai

#77 – Alex Garland: Ex Machina, Devs, Annihilation, and the Poetry of Science

#76 – John Hopfield: Physics View of the Mind and Neurobiology

#75 – Marcus Hutter: Universal Artificial Intelligence, AIXI, and AGI

#74 – Michael I. Jordan: Machine Learning, Recommender Systems, and the Future of AI

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

#72 – Scott Aaronson: Quantum Computing

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

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

David Chalmers: The Hard Problem of Consciousness

Cristos Goodrow: YouTube Algorithm