Ishan Misra

Ishan Misra is a research scientist at FAIR working on self-supervised visual learning. Please support this podcast by checking out our sponsors: – Onnit: https://lexfridman.com/onnit to get up to 10% off – The Information: https://theinformation.com/lex to get 75% off first month – Grammarly: https://grammarly.com/lex to get 20% off premium – Athletic Greens: https://athleticgreens.com/lex and use code LEX to get 1 month of fish oil EPISODE LINKS: Ishan’s twitter: https://twitter.com/imisra_ Ishan’s website: https://imisra.github.io Ishan’s FAIR page: https://ai.facebook.com/people/ishan-misra/ PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/lexfridman – Twitter: https://twitter.com/lexfridman – Instagram: https://www.instagram.com/lexfridman – LinkedIn: https://www.linkedin.com/in/lexfridman – Facebook: https://www.facebook.com/lexfridman – Medium: https://medium.com/@lexfridman OUTLINE: Here’s the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) – Introduction (07:49) – Self-supervised learning (16:24) – Self-supervised learning is the dark matter of intelligence (20:17) – Categorization (28:50) – Is computer vision still really hard? (32:35) – Understanding Language (42:14) – Harder to solve: vision or language (48:59) – Contrastive learning & energy-based models (52:59) – Data augmentation (57:19) – Fixed audio spike by lowering sound with pen tool (1:05:33) – Real data vs. augmented data (1:09:16) – Non-contrastive learning energy based self supervised learning methods (1:12:54) – Unsupervised learning (SwAV) (1:15:37) – Self-supervised Pretraining (SEER) (1:20:44) – Self-supervised learning (SSL) architectures (1:26:43) – VISSL pytorch-based SSL library (1:29:38) – Multi-modal (1:37:06) – Active learning (1:42:45) – Autonomous driving (1:54:12) – Limits of deep learning (1:58:19) – Difference between learning and reasoning (2:03:26) – Building super-human AI (2:11:14) – Most beautiful idea in self-supervised learning (2:15:02) – Simulation for training AI (2:18:27) – Video games replacing reality (2:19:40) – How to write a good research paper (2:24:08) – Best programming language for beginners (2:25:01) – PyTorch vs TensorFlow (2:28:26) – Advice for getting into machine learning (2:30:31) – Advice for young people (2:32:58) – Meaning of life

Ishan Misra

Research Scientist

Meta AI

Dr. Ishan Misra is a Research Scientist at Facebook AI Research where he works on Computer Vision and Machine Learning. His main research interest is reducing the need for human supervision, and indeed, human knowledge in visual learning systems. He finished his PhD at the Robotics Institute at Carnegie Mellon. He has done stints at Microsoft Research, INRIA and Yale. His bachelors is in computer science where he achieved the highest GPA in his cohort. Ishan is fast becoming a prolific scientist, already with more than 3000 citations under his belt and co-authoring with Yann LeCun; the godfather of deep learning. Today though we will be focusing an exciting cluster of recent papers around unsupervised representation learning for computer vision released from FAIR. These are; DINO: Emerging Properties in Self-Supervised Vision Transformers, BARLOW TWINS: Self-Supervised Learning via Redundancy Reduction and PAWS: Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples. All of these papers are hot off the press, just being officially released in the last month or so. Many of you will remember PIRL: Self-Supervised Learning of Pretext-Invariant Representations which Ishan was the primary author of in 2019. References; Shuffle and Learn - https://arxiv.org/abs/1603.08561 DepthContrast - https://arxiv.org/abs/2101.02691 DINO - https://arxiv.org/abs/2104.14294 Barlow Twins - https://arxiv.org/abs/2103.03230 SwAV - https://arxiv.org/abs/2006.09882 PIRL - https://arxiv.org/abs/1912.01991 AVID - https://arxiv.org/abs/2004.12943 (best paper candidate at CVPR'21 (just announced over the weekend) - http://cvpr2021.thecvf.com/node/290) Alexei (Alyosha) Efros http://people.eecs.berkeley.edu/~efros/ http://www.cs.cmu.edu/~tmalisie/projects/nips09/ Exemplar networks https://arxiv.org/abs/1406.6909 The bitter lesson - Rich Sutton http://www.incompleteideas.net/IncIdeas/BitterLesson.html Machine Teaching: A New Paradigm for Building Machine Learning Systems https://arxiv.org/abs/1707.06742 POET https://arxiv.org/pdf/1901.01753.pdf

Ishan Misra

Research Scientist

Meta AI