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lexfridman


Sophia is not AGI (Ben Goertzel)

#artificialintelligence

Ben Goertzel is one of the most interesting minds in the artificial intelligence community. He is the founder of SingularityNET, designer of OpenCog AI framework, formerly a director of the Machine Intelligence Research Institute, Chief Scientist of Hanson Robotics, the company that created the Sophia Robot. He has been a central figure in the AGI community for many years, including in the Conference on Artificial General Intelligence. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman


Dawn Song: Adversarial Machine Learning and Computer Security AI Podcast #95 with Lex Fridman

UC Berkeley EECS

Dawn Song is a professor of computer science at UC Berkeley with research interests in security, most recently with a focus on the intersection between computer security and machine learning. This conversation is part of the Artificial Intelligence podcast. Full episodes playlist: https://www.youtube.com/playlist?list... Clips playlist: https://www.youtube.com/playlist?list... OUTLINE: 0:00 - Introduction 1:53 - Will software always have security vulnerabilities?


Nick Bostrom: Simulation and Superintelligence AI Podcast #83 with Lex Fridman

#artificialintelligence

Nick Bostrom is a philosopher at University of Oxford and the director of the Future of Humanity Institute. He has worked on fascinating and important ideas in existential risks, simulation hypothesis, human enhancement ethics, and the risks of superintelligent AI systems, including in his book Superintelligence. I can see talking to Nick multiple times on this podcast, many hours each time, but we have to start somewhere. This conversation is part of the Artificial Intelligence podcast.


Vladimir Vapnik: Deep Learning and the Essence of Intelligence AI Podcast Clips

#artificialintelligence

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. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman



Efficient Computing for Deep Learning, Robotics, and AI (Vivienne Sze) MIT Deep Learning Series

#artificialintelligence

OUTLINE: 0:00 - Introduction 0:43 - Talk overview 1:18 - Compute for deep learning 5:48 - Power consumption for deep learning, robotics, and AI 9:23 - Deep learning in the context of resource use 12:29 - Deep learning basics 20:28 - Hardware acceleration for deep learning 57:54 - Looking beyond the DNN accelerator for acceleration 1:03:45 - Beyond deep neural networks CONNECT: - If you enjoyed this video, please subscribe to this channel.


Daniel Kahneman: Deep Learning (System 1 and System 2) AI Podcast Clips

#artificialintelligence

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. Subscribe to this YouTube channel or connect on: - Twitter: https://twitter.com/lexfridman


Daniel Kahneman: Thinking Fast and Slow, Deep Learning, and AI Artificial Intelligence Podcast

#artificialintelligence

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.



Judea Pearl: Counterfactuals AI Podcast Clips

#artificialintelligence

Judea Pearl is a professor at UCLA and a winner of the Turing Award, that's generally recognized as the Nobel Prize of computing. He is one of the seminal figures in the field of artificial intelligence, computer science, and statistics. He has developed and championed probabilistic approaches to AI, including Bayesian Networks and profound ideas in causality in general. These ideas are important not just for AI, but to our understanding and practice of science. But in the field of AI, the idea of causality, cause and effect, to many, lies at the core of what is currently missing and what must be developed in order to build truly intelligent systems.