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1:47:38EN19 Neurons Can Drive a Car. Liquid AI Wants Foundation Models on Every ChipHost Nathan LabenzRamin Hasani traces liquid neural networks from a worm's 300-cell brain to a 600-megabyte model that will talk from inside a Mercedes, and explains why architecture search now beats architecture avengers.
2:59:44ENTwo Endangered Languages Broke Transformers. Nested Learning Gave Models a Sleep Button.Host Nathan LabenzGoogle researcher Ali Behrouz tells Nathan Labenz how stacking update frequencies — not just layers — and an offline sleep phase could give language models genuine continual learning.
1:21:56ENYann LeCun Left Meta Because LLMs Will Not Get You to IntelligenceHost Jacob EffronThe Turing Award winner tells Jacob Effron why he spun AMI Labs out of Meta, why JEPA-style world models beat pixel generators, and why he thinks Hinton changed his mind in 2023.
2:02:17ENSAEs Find the Nouns. Parameter Decomposition Looks for the VerbsHost Nathan LabenzGoodfire's Lee Sharkey argues that sparse autoencoders recover a network's variables, not the algorithms those variables run, and shows how stochastic parameter decomposition tries to recover the computations themselves.
1:54:08ENWhy Anthropic's first check went to a company building microscopes for neural netsHost Nathan LabenzGoodfire's Dan Balsam and Tom McGrath argue that mechanistic interpretability now has a proto-paradigm, two stubborn gaps, and three products worth shipping today.
1:43:54ENA Year to Grow a Brain Organoid, an Hour to Simulate ItHost Nathan LabenzStanford postdoc Siyu He tells Nathan Labenz how Squidiff and CORAL move single-cell and spatial experiments into the computer — and where the models still cannot go.
1:40:22ENTwenty-atom crystals taught an AI the physics of a heartbeatHost Nathan LabenzOrbital Materials' Jonathan Godwin and Tim Duignan tell Nathan Labenz how a neural network trained on tiny crystals simulated a potassium ion channel on a single 2017 GPU.
1:42:16EN267 Papers in Seven Months: Albert Gu Explains the State Space Model RevolutionHost Nathan LabenzThe Mamba co-author walks through five years of recurrent-model work, why selective compression beats a growing KV cache, and what Mamba 2 trades away to train faster.
1:54:01ENA Tiny Network Grew a Salt Crystal the Training Data Never ShowedHost Nathan LabenzTim Duignan trained tens of thousands of parameters on salt water. The model invented a crystal — and a continuum solvent theory he had spent a PhD failing to write by hand.
1:52:18ENTwo Weeks, Thirty Compiles: How Adam Majmudar Wrote a GPU From ScratchHost Nathan LabenzA Cognitive Revolution walkthrough of TinyGPU, from SIMD kernels and memory latency to an open-source chip flow that runs on a laptop.
1:19:24ENA Gibberish Prompt Unlocks What GPT-J Plans Three Tokens AheadHost Nathan LabenzNortheastern’s Bau Lab shows that a 40,000-parameter soft prompt can read a model’s future words, and that about ten attention heads carry the task itself.
2:30:06ENTrained on a Thirtieth of GPT-4's Tokens, Mamba Beat Transformers on Home TurfHost Nathan LabenzIn a solo emergency episode, Nathan Labenz argues that Albert Gu and Tri Dao's selective state space model is the first architecture since 2017 that deserves to share the Transformer’s throne.
1:40:07ENThirty thousand voltage updates a second: inside Skydio’s autonomy stackHost Nathan LabenzHayk Martiros walks Nathan Labenz through a two-pound drone that sees in every direction, runs more than ten neural nets on board, and will not be weaponized.
1:52:46ENCharge Neural Nets by Connection Length, and the Black Box Splits Into Modules You Can SeeHost Nathan LabenzMIT physicist Ziming Liu walks Nathan Labenz through BIMT, a brain-inspired training trick that sparsifies networks until you can read them by looking.
1:21:20ENFreeze the eyes and the brain. Train only the optic nerve.Host 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.
46:25ENDeep 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.
1:29:03ENFrom 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.
1:00:51ENDeep 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.
1:07:29ENDeep 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.
1:25:16ENFrom 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.
1:24:43ENFrom 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.
58:46ENCaging 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.
1:15:59ENSteering 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.
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.
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