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1:50:08EN
Cognitive Revolution "How AI Changes Everything"· 2024년 8월 7일팟캐스트Waymo's Injury Rate Is Five Times Lower. It Still Loses to a Lyft.진행자 Nathan LabenzHuman drivers average one fatal crash per 100 million miles — and that bar still defines the self-driving debate.에이전트Autonomous Driving안전과 거버넌스+3
3:04:52EN
Cognitive Revolution "How AI Changes Everything"· 2024년 5월 1일팟캐스트Doctors Chose AI on Eight of Nine Dimensions: A GPT-4 Red-Teamer's Scouting Report진행자 Nathan LabenzNathan Labenz tells 80,000 Hours what late-2023 systems can already do, which thresholds still matter, and why a polarized policy fight is a gift to heavy-handed regulation.기업과 전략안전과 거버넌스AI research+3
1:36:39EN
Cognitive Revolution "How AI Changes Everything"· 2023년 11월 9일팟캐스트OpenAI’s $5 Million Bet: A 60-Person Team Wants GPT-4V to Give Cars Common Sense진행자 Nathan LabenzGhost Autonomy cofounder John Hayes tells Cognitive Revolution why a multimodal model on the driving path could reset self-driving after two decades of DARPA-era stacks.에이전트Autonomous DrivingMultimodal AI+3
1:00:38EN
Lex Fridman· 2017년 12월 6일팟캐스트The Race Car and the Rulebook: Chris Gerdes on Automated Vehicle Safety진행자 Lex FridmanA Stanford engineer and former DOT innovation chief explains how autonomous racing, voluntary safety guidance, and data sharing could shape safer self-driving cars.보안과 안전Autonomous Driving안전과 거버넌스+3
58:46EN
Lex Fridman팟캐스트Caging 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.모델과 학습Autonomous DrivingDeep Learning+3
1:15:59EN
Lex Fridman팟캐스트Steering Through Time: How Recurrent Networks and LSTMs Learn from Sequences진행자 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.모델과 학습Deep Learning머신러닝 기초+2
1:07:22EN
Lex Fridman팟캐스트Cars That Drive Empty: Emilio Frazzoli on Why Self-Driving Needs Rules, Not Just Data진행자 Lex FridmanThe nuTonomy CTO explains why full autonomy and shared fleets are where the value lies, and why the hardest open problem is deciding how vehicles should behave.Autonomous DrivingRoboticsAI startups & small teams+3
1:24:24EN
Lenny's Podcast팟캐스트Intelligence for Heavy Machines: Qasar Younis on Why Physical AI Will Change Farms, Mines and Roads First진행자 Lenny RachitskyThe Applied Intuition co-founder explains why the biggest near-term AI impact is in vehicles and industrial machines, and shares the values, reading habits and management philosophy behind a quiet $15 billion company.기업과 전략Autonomous SystemsRobotics+3
1:13:18EN
Lex Fridman팟캐스트Human-Centered or Fully Autonomous? Lex Fridman's Case for Collaboration Between Driver and Machine진행자 Lex FridmanLecture 2 of MIT's 2018 course on deep learning for self-driving cars covers the two paths to autonomy, how sensors compare, who is building what, and where deep learning can help.Autonomous Driving안전과 거버넌스Robotics+3
1:11:50EN
Lex Fridman팟캐스트Data 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.멀티모달과 미디어Deep Learning비전과 영상+3
42:16EN
Lenny's Podcast팟캐스트The Product Discipline Behind a Driverless Ride진행자 Lenny RachitskyShweta Shrivastava of Waymo explains how autonomous-driving products earn trust, measure safety, and sustain long-term investment.제품과 UXProduct ManagementAutonomous Driving+3
1:19:52EN
Lex Fridman팟캐스트From 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.모델과 학습Autonomous DrivingDeep Learning+3
1:27:30EN
Lex Fridman팟캐스트From NAND Gates to Self-Driving Traffic: Deep Reinforcement Learning in One Lecture진행자 Lex FridmanLex Fridman's second MIT 6.S094 lecture walks from the humble perceptron to deep Q-learning and the browser-based DeepTraffic competition.모델과 학습Reinforcement LearningDeep Learning+2
1:05:15EN
Lex Fridman팟캐스트Taming the Long Tail: How Waymo Builds a Self-Driving Factory진행자 Lex FridmanWaymo principal scientist Drago Anguelov explains how machine learning infrastructure, hybrid design and smart simulated agents tackle the rare events of autonomous driving.모델과 학습Autonomous DrivingRobotics+3
1:21:57EN
Lenny's Podcast팟캐스트Behind the Wheel: How Uber's Product Chief Dogfoods His Way to Better Products진행자 Lenny RachitskySachin Kansal on extreme dogfooding, shipping fast, Uber's hybrid autonomy strategy and making gut calls against the data.제품과 UXProduct ManagementProduct Leadership+3
17:35EN
Lex Fridman팟캐스트Hands Off the Wheel in San Diego: A Comma Two Demo Drive and a Tour of comma.ai's Hardware Shop진행자 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.제품과 UXAutonomous DrivingAI startups & small teams+3
1:13:22EN
Lex Fridman팟캐스트The Second 90%: What It Really Takes to Put Deep Learning on a Driverless Car진행자 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.모델과 학습Autonomous DrivingRobotics+3
37:18EN
Lex Fridman팟캐스트From Intelligent Copilots to Aurora: Sterling Anderson on Shared Control and Building a Self-Driving Company진행자 Lex FridmanA guest lecture in MIT's 6.S094 course tracing Anderson's path from shared human-machine control research, through Tesla Autopilot, to co-founding Aurora.Autonomous DrivingRoboticsHuman-AI Collaboration+2
1:13:40EN
Unsupervised Learning: With Jacob Effron팟캐스트The Long Tail, the World Model, and the 1995 Problem: Inside Waymo's Scaling Phase and Robotics' Missing Moment진행자 Jacob EffronVincent, a distinguished engineer at Waymo, explains how foundation models became teachers for self-driving, why the long tail and causality are the real frontier, and where robotics still lacks its generalist proof point.Autonomous DrivingRoboticsMultimodal AI+3
53:13EN
Lex Fridman팟캐스트How 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.모델과 학습Computer VisionDeep Learning+3
34:33EN
Lex Fridman팟캐스트Teaching 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.모델과 학습Autonomous Driving비전과 영상+3
17:32EN
Lex Fridman팟캐스트Over-Automating, Googling Doctors and Neural Nets: George Hotz Reviews Programming Memes진행자 Lex FridmanLex Fridman and George Hotz rate printed programming memes, and the jokes turn into candid talk about testing, hiring, social media and the power of neural networks.Software engineering개발 워크플로와 테스트Workflow automation+3
11:29EN
Lex Fridman팟캐스트Why Tesla AI Day Was the Most Impressive Real-World AI Effort Lex Fridman Has Seen진행자 Lex FridmanLex Fridman walks through the neural network, data annotation, Dojo and Tesla Bot announcements and what they say about solving real-world robotics.모델과 학습Autonomous DrivingRobotics+3
1:31:29EN
Lex Fridman팟캐스트Is Driving More Like Chess or Conversation? Lex Fridman's Opening Lecture on Deep Learning for Self-Driving Cars진행자 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.모델과 학습Deep LearningAutonomous Driving+3
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