Category: 3-SometimeTechTalk

Gary Marcus is a professor emeritus at NYU, founder of Robust.AI and Geometric Intelligence, the latter is a machine learning company acquired by Uber in 2016. He is the author of several books on natural and artificial intelligence, including his new book Rebooting AI: Building Machines We Can Trust. Gary has been a critical voice highlighting the limits of deep learning and discussing the challenges before the AI community that must be solved in order to achieve artificial general intelligence. 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 iTunes or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode):

00:00 – Introduction
01:37 – Singularity
05:48 – Physical and psychological knowledge
10:52 – Chess
14:32 – Language vs physical world
17:37 – What does AI look like 100 years from now
21:28 – Flaws of the human mind
25:27 – General intelligence
28:25 – Limits of deep learning
44:41 – Expert systems and symbol manipulation
48:37 – Knowledge representation
52:52 – Increasing compute power
56:27 – How human children learn
57:23 – Innate knowledge and learned knowledge
1:06:43 – Good test of intelligence
1:12:32 – Deep learning and symbol manipulation
1:23:35 – Guitar

Peter Norvig is a research director at Google and the co-author with Stuart Russell of the book Artificial Intelligence: A Modern Approach that educated and inspired a whole generation of researchers including myself to get into the field. 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 iTunes or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode):

00:00 – Introduction
00:37 – Artificial Intelligence: A Modern Approach
09:11 – Covering the entire field of AI
15:42 – Expert systems and knowledge representation
18:31 – Explainable AI
23:15 – Trust
25:47 – Education – Intro to AI – MOOC
32:43 – Learning to program in 10 years
37:12 – Changing nature of mastery
40:01 – Code review
41:17 – How have you changed as a programmer
43:05 – LISP
47:41 – Python
48:32 – Early days of Google Search
53:24 – What does it take to build human-level intelligence
55:14 – Her
57:00 – Test of intelligence
58:41 – Future threats from AI
1:00:58 – Exciting open problems in AI

Leonard Susskind is a professor of theoretical physics at Stanford University, and founding director of the Stanford Institute for Theoretical Physics. He is widely regarded as one of the fathers of string theory and in general as one of the greatest physicists of our time both as a researcher and an educator. 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 iTunes or support it on Patreon. Here’s the outline with timestamps for this episode (on some players you can click on the timestamp to jump to that point in the episode):

00:00 – Introduction
01:02 – Richard Feynman
02:09 – Visualization and intuition
06:45 – Ego in Science
09:27 – Academia
11:18 – Developing ideas
12:12 – Quantum computers
21:37 – Universe as an information processing system
26:35 – Machine learning
29:47 – Predicting the future
30:48 – String theory
37:03 – Free will
39:26 – Arrow of time
46:39 – Universe as a computer
49:45 – Big bang
50:50 – Infinity
51:35 – First image of a black hole
54:08 – Questions within the reach of science
55:55 – Questions out of reach of science

Regina Barzilay is a professor at MIT and a world-class researcher in natural language processing and applications of deep learning to chemistry and oncology, or the use of deep learning for early diagnosis, prevention and treatment of cancer. She has also been recognized for her teaching of several successful AI-related courses at MIT, including the popular Introduction to Machine Learning course. 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 iTunes or support it on Patreon.