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Gary Marcus: Toward a Hybrid of Deep Learning and Symbolic AI

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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

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 <a href="https://lexfridman.com/ai">https://lexfridman.com/ai</a> or connect with @lexfridman on <a href="https://twitter.com/lexfridman">Twitter</a>, <a href="https://www.linkedin.com/in/lexfridman/">LinkedIn</a>, <a href="https://www.facebook.com/lexfridman">Facebook</a>, <a href="https://medium.com/@lexfridman">Medium</a>, or <a href="https://www.youtube.com/lexfridman">YouTube</a> 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 <a href="https://www.patreon.com/lexfridman">Patreon</a>. 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

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