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82개정렬
8:02ENMeta’s Segment Anything: Promptable Image Masks and the SA-1B DatasetMatthew Berman · Forward FutureIn April 2023, Matthew Berman reviews Meta’s Segment Anything project and demonstrates selecting image regions with clicks, boxes and automatic mask generation. The model segments pixels; it does not by itself name every object or guarantee accurate results on every image.
15:37ENAI News in March 2023: Firefly, Gen-2, Bard and Job ExposureMatthew Berman · Forward FutureMatthew Berman reviews early generative media launches, a labor-exposure study and single-image view synthesis.
1:21:20ENFreeze the eyes and the brain. Train only the optic nerve.진행자 Nathan LabenzJunnan Li and Dongxu Li tell Nathan Labenz how BLIP cleaned noisy web captions, and how BLIP-2 connected a frozen vision model to a frozen language model in under ten days on one A100.
48:52ENUsers Wanted a Better Brush. Runway Built One-Click Greenscreen진행자 Sarah Guo, Elad GilRunway CEO Cristobal Valenzuela on why a model is not a product, why filmmakers hacked a four-frame-per-second tool, and why 80 percent is enough when nobody dies.
2:36:21ENA Smile Is Not Happiness: Rana el Kaliouby on Emotion AI and the Empathy Crisis게스트 Rana el Kaliouby · 진행자 Lex FridmanThe Affectiva founder and Smart Eye deputy CEO talks with Lex Fridman about teaching machines to read faces, sensing drivers inside the cabin, and why she is proudest of the people her company grew.
2:30:30ENThe Fallacy of the Successful First Step게스트 Ishan Misra · 진행자 Lex FridmanBerkeley's Jitendra Malik explains why computer vision still looks easy, why fully autonomous driving is not near, and what children know that today's networks do not.
2:10:06ENYou Might Have to Understand the World to Understand the Letter A게스트 Dileep George · 진행자 Lex FridmanDileep George on recursive cortical networks, why a five-year-old can read any new CAPTCHA, and what the Wright brothers actually learned from birds.
1:41:37ENThe Fallacy of the Successful First Step: Jitendra Malik on Why Seeing Still Isn't Solved게스트 Jitendra Malik · 진행자 Lex FridmanBerkeley's Jitendra Malik tells Lex Fridman why computer vision keeps underestimating itself, why full self-driving still needs cognition, and why vision is the parent of language.
1:37:31ENThe Robot That Brought the Beer Was Being Teleoperated게스트 Sergey Levine · 진행자 Lex FridmanSergey Levine tells Lex Fridman why hardware is almost closed, why common sense has to be lived, and why offline reinforcement learning is the missing on-ramp from logs to robots that keep getting better.
2:12:36ENAttacks Climb the Stack Until They Land on People게스트 Dawn Song · 진행자 Lex FridmanDawn Song tells Lex Fridman why the next security crisis is human, how a sticker can rewrite a stop sign, and why data still has no property right.
1:17:29ENThe Algorithm Is Already Better. The Clinic Still Uses 1967.게스트 Regina Barzilay · 진행자 Lex FridmanMIT's Regina Barzilay on why deep learning already beats the breast-cancer 'density' score — and why data, liability, and incentives still will not move.
1:15:58ENHuman Intelligence Is a Ridiculously Narrow Sliver게스트 Yann Lecun · 진행자 Lex FridmanYann LeCun tells Lex Fridman why giant nets plus gradient descent broke the textbooks, why "AGI" is the wrong word, and why the next leap is a world model learned like a baby.
1:59:37ENLane Centering Is the Only Self-Driving Feature That Already Helps게스트 George Hotz · 진행자 Lex Fridmancomma.ai founder George Hotz tells Lex Fridman why staying in the lane beats Level 5 promises, and why a robotaxi stack still has no network effect.
24:22ENCraftsmanship, First Principles and the Road to Self-Driving: Jim Keller on Working with Elon Musk게스트 Jim Keller · 진행자 Lex FridmanThe veteran chip architect explains why he thinks autonomous driving is a tractable engineering problem, how Tesla's cost constraints shaped its hardware, and what he took from Musk's way of thinking.
9:59ENIs Language or Vision Harder? Ilya Sutskever on the Unity of Machine Learning진행자 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.
58:46ENCaging the Neural Network: How Aptiv Thinks About Safe Self-Driving진행자 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.
1:23:18ENFrom GPT-2 to GPT-3 Moments in Robotics: Six DeepMind Papers on Bridging Robots and the Internet진행자 Nathan LabenzKeerthana Gopalakrishnan and Ted Xiao walk through RT-2, RT-X, RT-Trajectory, AutoRT, Learning to Learn Faster and PIVOT, and say what is still missing for general-purpose robots.
1:03:24ENWhy Sora Bet on People, Not Just Pixels진행자 Jacob EffronThe Sora team explains how a video model became a social creation network, what cameos revealed about human creativity, and why the next frontier is world simulation.
1:11:50ENData Is Everything: Teaching Self-Driving Cars to Understand the Human Behind the Wheel진행자 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.
58:19ENRunway's Chris Valenzuela on Why Taste Beats Tools in Generative Media진행자 Jacob EffronChris Valenzuela, CEO of Runway, explains why generative media is still early, how taste and iteration shape the work, and what has to change before AI media becomes a new art form.
1:19:52ENFrom Pixels to Steering Wheels: Convolutional Networks and End-to-End Driving진행자 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.
1:19:34ENFei-Fei Li on ImageNet, World Models and Why Everyone Has a Role in AI진행자 Lenny RachitskyThe scientist often called the godmother of AI traces how big data sparked deep learning, explains why spatial intelligence is the next frontier, and shows what World Labs' first product, Marble, can do.
53:13ENHow Machines Learn to See: From Pixel Differences to Convolutions, ResNets and Scene Segmentation진행자 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 Vehicles진행자 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.
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