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18:46ENDeep Learning Plus One Idea: Ilya Sutskever on Building AGIHost Lex FridmanIn a May 2020 conversation, Sutskever discusses self-play, simulation, bodies and consciousness, tests of intelligence, and how an AGI might be governed and aligned.
1:31:52ENFrom Phonemes to CTC: How Deep Learning Rebuilt Speech RecognitionHost Lex FridmanBaidu's Adam Coates walks through the traditional speech pipeline, the CTC-based end-to-end engine that replaced much of it, and what it takes to scale and deploy it.
1:11:50ENData Is Everything: Teaching Self-Driving Cars to Understand the Human Behind the WheelHost Lex FridmanIn an MIT lecture, Lex Fridman shows how deep learning can sense pedestrians, driver posture, gaze, emotion and cognitive load, and why that matters for human-centered autonomy.
1:18:48ENData Movement, Not Math: How to Make Deep Learning Fit on Robots and PhonesHost Lex FridmanMIT professor Vivienne Sze explains why moving data dominates the energy cost of neural networks and how co-designing hardware and algorithms can cut it by orders of magnitude.
16:41ENAlphaFold 2 and the Protein-Folding Breakthrough: Why Lex Fridman Calls It a Landmark for AI and BiologyHost Lex FridmanA quick explainer on DeepMind's CASP14 result, how protein folding works, how AlphaFold 1 and 2 likely differ, and what the advance could unlock.
1:19:52ENFrom Pixels to Steering Wheels: Convolutional Networks and End-to-End DrivingHost Lex FridmanLex Fridman's MIT 6.S094 lecture explains how convolutional neural networks work, where deep learning helps a self-driving car, and how to train a network to steer from video.
1:27:30ENFrom NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One LectureHost Lex FridmanLex Fridman's second MIT 6.S094 lecture walks from the humble perceptron to deep Q-learning and the browser-based DeepTraffic competition.
1:05:15ENTaming the Long Tail: How Waymo Builds a Self-Driving FactoryHost Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.
1:27:15ENPolicy Gradients vs. Q-Learning: John Schulman's Field Guide to Deep Reinforcement LearningHost Lex FridmanOpenAI's John Schulman walks through the core methods of deep RL, from the score function estimator to TRPO and DQN, and says when each one is worth using.
1:19:21ENVapnik's Case for Learning by Predicates, Not Just by More DataHost Lex FridmanIn an MIT lecture, Vladimir Vapnik argues that complete statistical learning needs two mechanisms, data and invariants, and that choosing smart predicates is where intelligence lives.
1:03:25ENTheano in an Hour: Symbolic Graphs, Automatic Gradients and a Compiler That Rewrites Your MathHost Lex FridmanPascal Lamblin of MILA walks through how Theano builds, differentiates, optimizes and runs computation graphs, then trains logistic regression, a convnet and an LSTM.
13:58ENDeep Reinforcement Learning, Explained by the Researcher Behind AlphaGoHost Lex FridmanDavid Silver of DeepMind walks through what reinforcement learning is, how its building blocks fit together, and why deep neural networks keep surprising even the experts.
57:55ENFrom Q-Tables to AlphaGo Zero: A Self-Driving Car Lecture on Deep Reinforcement LearningHost Lex FridmanLex Fridman's MIT 6.S094 lecture walks from the AI pipeline and Q-learning to deep Q-networks, AlphaGo Zero and the DeepTraffic competition.
17:35ENHands Off the Wheel in San Diego: A Comma Two Demo Drive and a Tour of comma.ai's Hardware ShopHost Lex FridmanAfter their podcast conversation, George Hotz takes Lex Fridman on an openpilot drive and then through the comma.ai offices, dashboards, assembly line and compute cluster.
1:13:22ENThe Second 90%: What It Really Takes to Put Deep Learning on a Driverless CarHost Lex FridmanWaymo's Sacha Arnoud walks MIT students through Waymo's history, its perception stack, and the industrial-scale labeling, compute and testing behind it.
57:15ENInside Torch and Autograd: How Deep Learning Libraries Compute GradientsHost Lex FridmanAlex Wiltschko of Twitter walks through Lua Torch, then explains automatic differentiation and what separates modern deep learning frameworks.
1:20:58ENFrom Bag of Words to Attention: Quoc Le Teaches Sequence-to-Sequence Learning Through Email RepliesHost Lex FridmanA Deep Learning School talk that builds sequence-to-sequence models step by step around one running example, automatic email reply, and then surveys memory and operation-augmented networks.
1:19:48ENBigger Models, Cleaner Splits and Human Baselines: Andrew Ng's Practical Recipe for Applied Deep LearningHost Lex FridmanIn a 2016 Deep Learning School talk, Andrew Ng distills lessons from leading Baidu's AI team into a workflow for diagnosing models, handling data and building a career.
1:02:06ENLearning TensorFlow Hands-On: From a Mystery Line to Handwritten DigitsHost Lex FridmanGoogle Brain's Sherry Moore walks a Deep Learning School audience through how TensorFlow works and two core labs, linear regression and MNIST digit recognition.
53:13ENHow Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene SegmentationHost Lex FridmanLex Fridman's MIT 6.S094 lecture walks from the basics of image classification through landmark network architectures to pixel-level segmentation, optical flow and the SegFuse competition.
34:33ENTeaching Cars to Watch the Driver: Deep Learning for Semi-Autonomous VehiclesHost Lex FridmanLex Fridman's MIT 6.S094 lecture on using driver-facing cameras and neural networks to understand body pose, gaze, emotion and cognitive load.
20:50ENGlitches as Features: What John Hopfield Says Artificial Neural Networks Are MissingHost Lex FridmanIn a short conversation with Lex Fridman, physicist and neural-network pioneer John Hopfield compares evolved brains with today's artificial networks and explains why he expects AI to borrow from biology generation after generation.
1:31:29ENIs Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving CarsHost Lex FridmanThe first lecture of MIT 6.S094 introduces neural networks, reinforcement learning and computer vision through the question of how hard driving really is.
1:12:00ENWhy Deep Learning Works at All: Yoshua Bengio on Composition, Saddle Points and the Case for Unsupervised LearningHost Lex FridmanIn a 2016 Deep Learning School talk, Yoshua Bengio explains the assumptions that let neural nets beat the curse of dimensionality and lays out the open challenges ahead.
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