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1:20:21EN
Lex Fridman· Jan 19, 2019Podcast · 2 speakersEinstein Was the Worst of Five Ph.D. StudentsGuest Tomaso Poggio · Host Lex FridmanMIT neuroscientist Tomaso Poggio on why intelligence is science's deepest problem, and what brains still have to teach machines.
46:25ENLex Fridman· Jan 17, 2019PodcastDeep Learning at the Edge of 2019: BERT, Autopilot, and the Ideas That Broke ThroughHost Lex FridmanA tour of the architectural, applied, and automation breakthroughs that defined deep learning in 2018 and set up 2019.
1:19:59ENLex Fridman· Dec 23, 2018Podcast · 2 speakersSchmidhuber’s bet: true meta-learning rewrites the learner, not the last layerGuest Juergen Schmidhuber · Host Lex FridmanJürgen Schmidhuber tells Lex Fridman how Gödel machines, compression-as-science, Power Play, LSTMs, and world models all descend from a teenage plan to multiply one person’s curiosity into a better physicist than he could ever be.
42:45ENLex Fridman· Dec 16, 2018Podcast · 2 speakersA one-legged robot learned a backflip from "this is better"Guest Pieter Abbeel · Host Lex FridmanPieter Abbeel tells Lex Fridman why preference comparisons, self-play, ten-minute demos, and messy ensembles of simulators are the practical path through deep reinforcement learning.
54:03ENLex Fridman· Nov 16, 2018Podcast · 2 speakersThe Hardest Part of Learning Was Left Out of the TextbooksGuest Vladimir Vapnik · Host Lex FridmanVladimir Vapnik tells Lex Fridman why intelligence is the work of inventing predicates that shrink the admissible set of functions, not of fitting a rule to a mountain of data.
42:18ENLex Fridman· Oct 20, 2018Podcast · 2 speakersAn Adult Can Change Last Year's Mind. A Network Still Cannot.Guest Yoshua Bengio · Host Lex FridmanYoshua Bengio tells Lex Fridman that the missing pieces of deep learning sit in credit assignment, training objectives, and a kind of compositionality borrowed from classical AI — not in ten thousand layers.
1:29:03ENLex Fridman· Sep 27, 2016PodcastFrom Word Vectors to Memory Networks: The Building Blocks of Deep NLPHost Lex FridmanRichard Socher explains how word embeddings, recurrent models, GRUs, pointer mixtures, and dynamic memory networks turn language tasks into trainable end-to-end systems.
1:00:51ENLex FridmanPodcastDeep Learning From the Ground Up: Hugo Larochelle's One-Hour Tour of Neural Network FoundationsHost Lex FridmanA fast, equation-level walk through feed-forward networks, backpropagation, practical training tricks, dropout and batch normalization, from the 2016 Deep Learning School.
1:07:29ENLex FridmanPodcastDeep Reinforcement Learning from First Principles: Rewards, Q-Learning, Policy Gradients and AlphaZeroHost Lex FridmanLex Fridman's MIT 6.S091 opening lecture walks from the basic RL framework through DQN, actor-critic methods and AlphaZero to the unsolved problem of moving from simulation to the real world.
9:59ENLex FridmanPodcastIs Language or Vision Harder? Ilya Sutskever on the Unity of Machine LearningHost Lex FridmanIn a short clip from his 2020 conversation with Lex Fridman, Ilya Sutskever explains why vision, language and reinforcement learning share the same few principles, and why asking which problem is harder is the wrong question.
15:01ENLex FridmanPodcast · 2 speakersDeep Learning Is a System 1 Machine: Daniel Kahneman on What AI Still LacksGuest Daniel Kahneman · Host Lex FridmanThe Nobel-winning psychologist tells Lex Fridman how his fast-and-slow thinking framework maps onto deep learning, and why driving among pedestrians may require understanding people.
1:25:16ENLex FridmanPodcastFrom Hand-Built Features to ResNets: Andrej Karpathy's Crash Course in Convolutional NetworksHost Lex FridmanA 2016 Deep Learning School talk covering how convnets work, how the ImageNet winners evolved, and how to apply them without being a hero.
54:33ENLex FridmanPodcast · 2 speakersVision or Lidar, Human or Machine: Lex Fridman's 2019 Map of the Self-Driving RaceGuest Self-Driving Cars · Host Lex FridmanThe opening lecture of MIT's 6.S094 course surveys 2018's milestones, the industry's optimistic predictions, and the beliefs about driving and humans that will decide who wins.
1:24:43ENLex FridmanPodcastFrom Sparse Coding to GANs: A Tour of Unsupervised Deep Learning in 2016Host Lex FridmanRuslan Salakhutdinov walks through autoencoders, Boltzmann machines, variational autoencoders and adversarial networks at the Deep Learning School, with candid notes on what did and did not work.
58:46ENLex FridmanPodcastCaging the Neural Network: How Aptiv Thinks About Safe Self-DrivingHost Lex FridmanAptiv Autonomous Mobility's Karl Iagnemma and Oscar Beijbom explain why end-to-end learning is hard to trust in cars, and show how PointPillars and nuScenes push lidar perception forward.
1:15:59ENLex FridmanPodcastSteering Through Time: How Recurrent Networks and LSTMs Learn from SequencesHost Lex FridmanLex Fridman's MIT 6.S094 lecture walks from backpropagation basics to LSTMs and the way they are used to predict steering in self-driving cars.
18:46ENLex FridmanPodcastDeep 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.
1:31:52ENLex FridmanPodcastFrom 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.
1:11:50ENLex FridmanPodcastData 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.
10:16ENLex FridmanPodcast · 2 speakersReasoning Without Logic: Yann LeCun on Teaching Neural Networks to ThinkGuest Yann Lecun · Host Lex FridmanYann LeCun explains why he believes neural networks can reason, and what memory, recurrence and continuous representations would need to look like.
1:18:48ENLex FridmanPodcastData 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.
16:41ENLex FridmanPodcastAlphaFold 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.
1:19:52ENLex FridmanPodcastFrom 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.
17:21ENLex FridmanPodcast · 2 speakersHow Alexa Learned to Hear From Across the Room: Rohit Prasad on Far-Field Speech and IntentGuest Rohit Prasad · Host Lex FridmanAlexa's head scientist recalls the early days of the Echo, when far-field speech recognition seemed unsolvable and there were no customers to collect data from.Load moreShowing 24 of 101