https://vidert.com/v/artificial-intelligence/video/19699/google-new-turboquant-ai-hype-vs-reality 2026-04-01T14:21:33.000Z weekly 0.5 https://i.ytimg.com/vi/7YVrb3-ABYE/maxresdefault.jpg Google New TurboQuant AI: Hype vs. Reality ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The TurboQuant paper is available here: https://arxiv.org/abs/2504.19874 Reproduction: https://x.com/AlicanKiraz0/status/2038245538865275274 KV-cache source: https://huggingface.co/blog/not-lain/kv-caching Reviews and criticisms of the paper: https://openreview.net/forum?id=tO3ASKZlok https://x.com/gaoj0017/status/2037532673812443214 Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ Thumbnail design: https://felicia.hu https://www.youtube.com/embed/7YVrb3-ABYE 2026-04-01T14:21:33.000Z Two Minute Papers 514 yes no https://vidert.com/v/artificial-intelligence/video/18680/deepmind-s-new-ai-just-changed-science-forever 2026-03-27T16:00:57.000Z weekly 0.5 https://i.ytimg.com/vi/Io_GqmbNBbY/maxresdefault.jpg DeepMind’s New AI Just Changed Science Forever ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://arxiv.org/abs/2602.10177 Source: https://www.youtube.com/watch?v=6evUpgCHtOQ Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ Thumbnail design: https://felicia.hu https://www.youtube.com/embed/Io_GqmbNBbY 2026-03-27T16:00:57.000Z Two Minute Papers 608 yes no https://vidert.com/v/artificial-intelligence/video/18417/the-algorithm-that-made-me-cry 2026-03-26T15:47:12.000Z weekly 0.5 https://i.ytimg.com/vi/XL5bq8DRn64/maxresdefault.jpg The Algorithm That Made Me Cry ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Free course on Ray Tracing: https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/ Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ Thumbnail design: https://felicia.hu https://www.youtube.com/embed/XL5bq8DRn64 2026-03-26T15:47:12.000Z Two Minute Papers 470 yes no https://vidert.com/v/artificial-intelligence/video/17841/deepseek-just-fixed-one-of-the-biggest-problems-with-ai 2026-03-24T15:17:49.000Z weekly 0.5 https://i.ytimg.com/vi/DmtoVnTkQnM/maxresdefault.jpg DeepSeek Just Fixed One Of The Biggest Problems With AI ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The #DeepSeek paper is available here: https://github.com/deepseek-ai/Engram https://arxiv.org/abs/2601.07372 Larry Wheels: https://www.youtube.com/watch?v=7SM816P5G9s&lc=Ugz7yiDrr_8YD7w8gaN4AaABAg Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/DmtoVnTkQnM 2026-03-24T15:17:49.000Z Two Minute Papers 587 yes no https://vidert.com/v/artificial-intelligence/video/14665/how-deepmind-s-new-ai-predicts-what-it-cannot-see 2026-03-17T14:16:57.000Z weekly 0.5 https://i.ytimg.com/vi/ssbHkYB0jYM/maxresdefault.jpg How DeepMind’s New AI Predicts What It Cannot See ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://d4rt-paper.github.io/ Our Gaussian Material Synthesis paper: https://users.cg.tuwien.ac.at/zsolnai/gfx/gaussian-material-synthesis/ Tweet link: https://x.com/GoogleDeepMind/status/2014352808426807527 Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/ssbHkYB0jYM 2026-03-17T14:16:57.000Z Two Minute Papers 642 yes no https://vidert.com/v/artificial-intelligence/video/14664/adobe-nvidia-s-new-tech-shouldn-t-be-real-time-but-it-is 2026-03-17T14:16:42.000Z weekly 0.5 https://i.ytimg.com/vi/j-B8ymGWlIE/maxresdefault.jpg Adobe & NVIDIA’s New Tech Shouldn’t Be Real Time. But It Is. ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://perso.telecom-paristech.fr/boubek/papers/Glinty/ Web demo: https://www.shadertoy.com/view/tcdGDl Sources: https://www.youtube.com/shorts/n07vz6oz78g https://www.youtube.com/watch?v=vPJoP2yzbv0 https://www.youtube.com/watch?v=u6hYj74RhoQ https://www.youtube.com/watch?v=ok1ViHVcXYs https://www.youtube.com/watch?v=jnzhNdWoXMg https://3dstudio.co/uv-unwrapping-software/ https://www.youtube.com/watch?v=jnzhNdWoXMg Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ #nvidia #adobe https://www.youtube.com/embed/j-B8ymGWlIE 2026-03-17T14:16:42.000Z Two Minute Papers 592 yes no https://vidert.com/v/artificial-intelligence/video/14663/the-most-realistic-fire-simulation-ever 2026-03-17T14:16:27.000Z weekly 0.5 https://i.ytimg.com/vi/B6GJjvR6txg/maxresdefault.jpg The Most Realistic Fire Simulation Ever ❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper is available here: https://helgewrede.github.io/firex/ Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/B6GJjvR6txg 2026-03-17T14:16:27.000Z Two Minute Papers 698 yes no https://vidert.com/v/artificial-intelligence/video/14662/nvidia-s-insane-ai-found-the-math-of-reality 2026-03-17T14:16:14.000Z weekly 0.5 https://i.ytimg.com/vi/WNsSzX0L4Es/maxresdefault.jpg NVIDIA’s Insane AI Found The Math Of Reality ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://research.nvidia.com/labs/sil/projects/ppisp/ Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ #nvidia https://www.youtube.com/embed/WNsSzX0L4Es 2026-03-17T14:16:14.000Z Two Minute Papers 550 yes no https://vidert.com/v/artificial-intelligence/video/14661/anthropic-found-out-why-ais-go-insane 2026-03-17T14:16:02.000Z weekly 0.5 https://i.ytimg.com/vi/eGpIXJ0C4ds/maxresdefault.jpg Anthropic Found Out Why AIs Go Insane ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://www.anthropic.com/research/assistant-axis Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ #anthropic https://www.youtube.com/embed/eGpIXJ0C4ds 2026-03-17T14:16:02.000Z Two Minute Papers 572 yes no https://vidert.com/v/artificial-intelligence/video/14660/physics-simulation-just-crossed-a-line 2026-03-17T14:15:51.000Z weekly 0.5 https://i.ytimg.com/vi/vsK4Gb7Eys8/maxresdefault.jpg Physics Simulation Just Crossed A Line ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://sig25ddmpd.github.io/ Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/vsK4Gb7Eys8 2026-03-17T14:15:51.000Z Two Minute Papers 574 yes no https://vidert.com/v/artificial-intelligence/video/14659/nvidia-s-new-ai-erasing-reality 2026-03-17T14:15:39.000Z weekly 0.5 https://i.ytimg.com/vi/RaNay3x0Fmk/maxresdefault.jpg NVIDIA’s New AI: Erasing Reality ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper and the code are now available here: https://dvirsamuel.github.io/omnimattezero.github.io/ https://github.com/dvirsamuel/OmnimatteZero Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ #nvidia https://www.youtube.com/embed/RaNay3x0Fmk 2026-03-17T14:15:39.000Z Two Minute Papers 554 yes no https://vidert.com/v/artificial-intelligence/video/14658/new-deepseek-research-the-future-is-here 2026-03-17T14:15:25.000Z weekly 0.5 https://i.ytimg.com/vi/fFL7la73RO4/maxresdefault.jpg New DeepSeek Research - The Future Is Here! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers I use DeepSeek there by running an instance with enough GPU VRAM and using ollama. 📝 The #DeepSeek paper is available here: https://arxiv.org/abs/2501.12948 Sources: https://x.com/awnihannun/status/1883276535643455790 https://x.com/bcjordan/status/1886825587097878826 https://x.com/izag82161/status/1906347576204640514 Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/fFL7la73RO4 2026-03-17T14:15:25.000Z Two Minute Papers 755 yes no https://vidert.com/v/artificial-intelligence/video/14657/this-broke-my-brain-these-humans-aren-t-real 2026-03-17T14:15:03.000Z weekly 0.5 https://i.ytimg.com/vi/opghSX24clM/maxresdefault.jpg This Broke My Brain - These Humans Aren’t Real ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://neuralbodies.github.io/RFGCA/ Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/opghSX24clM 2026-03-17T14:15:03.000Z Two Minute Papers 501 yes no https://vidert.com/v/artificial-intelligence/video/14656/they-said-it-was-impossible-this-simulation-solved-it 2026-03-17T14:14:51.000Z weekly 0.5 https://i.ytimg.com/vi/9Mcv9vpGW5Q/maxresdefault.jpg They Said It Was Impossible… This Simulation Solved It ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://visualcomputing.ist.ac.at/publications/2025/HomogenizedSand/ Previous Disney grains paper: https://la.disneyresearch.com/publication/multi-scale-modeling-and-rendering-of-granular-materials/ Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/9Mcv9vpGW5Q 2026-03-17T14:14:51.000Z Two Minute Papers 854 yes no https://vidert.com/v/artificial-intelligence/video/14655/this-fluid-simulation-should-not-be-possible 2026-03-17T14:14:39.000Z weekly 0.5 https://i.ytimg.com/vi/O7q52WxYZN8/maxresdefault.jpg This Fluid Simulation Should Not Be Possible ❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper "Fast Octree Neighborhood Search for SPH Simulations" is available here: https://andreaslongva.com/pdf/2022-SA-NeighborhoodSearch-compressed.pdf https://animation.rwth-aachen.de/media/papers/79/2022-SA-NeighborhoodSearch.pdf Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/O7q52WxYZN8 2026-03-17T14:14:39.000Z Two Minute Papers 478 yes no https://vidert.com/v/artificial-intelligence/video/14654/the-secret-equation-behind-hyper-realistic-clothing 2026-03-17T14:14:26.000Z weekly 0.5 https://i.ytimg.com/vi/Wibbnn3hV4U/maxresdefault.jpg The Secret Equation Behind Hyper-Realistic Clothing ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://wanghmin.github.io/publication/zhang-2025-pie/ Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/Wibbnn3hV4U 2026-03-17T14:14:26.000Z Two Minute Papers 452 yes no https://vidert.com/v/artificial-intelligence/video/14653/this-new-physics-engine-is-45x-faster 2026-03-17T14:14:13.000Z weekly 0.5 https://i.ytimg.com/vi/2c8o65JiPQY/maxresdefault.jpg This New Physics Engine Is 45x Faster! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://graphics.cs.utah.edu/research/projects/stable-cosserat-rods/ Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers Note that just watching the series and leaving a kind comment every now and then is as much support as any of us could ever ask for! Sources: https://www.youtube.com/watch?v=kO3NsSX1VTg https://www.youtube.com/watch?v=IQZ_zBX6gQY 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/2c8o65JiPQY 2026-03-17T14:14:13.000Z Two Minute Papers 557 yes no https://vidert.com/v/artificial-intelligence/video/14652/the-bug-that-ruined-game-physics-for-decades 2026-03-17T14:13:49.000Z weekly 0.5 https://i.ytimg.com/vi/wp8d24NkOjI/maxresdefault.jpg The Bug That Ruined Game Physics For Decades ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper "A Stream Function Solver for Liquid Simulations" is available here: https://pub.ista.ac.at/group_wojtan/projects/2015_Ando_ASFSfLS/download/vecpotential.pdf Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers Note that just watching the series and leaving a kind comment every now and then is as much support as any of us could ever ask for! 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/wp8d24NkOjI 2026-03-17T14:13:49.000Z Two Minute Papers 512 yes no https://vidert.com/v/artificial-intelligence/video/14651/nvidia-s-ai-finally-solved-walking-in-games 2026-03-17T14:13:37.000Z weekly 0.5 https://i.ytimg.com/vi/_uo7CXd33Uc/maxresdefault.jpg NVIDIA’s AI Finally Solved Walking In Games ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://research.nvidia.com/labs/toronto-ai/trace-pace/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu #nvidia https://www.youtube.com/embed/_uo7CXd33Uc 2026-03-17T14:13:37.000Z Two Minute Papers 528 yes no https://vidert.com/v/artificial-intelligence/video/14650/game-physics-just-jumped-a-generation 2026-03-17T14:13:21.000Z weekly 0.5 https://i.ytimg.com/vi/oToAGiozQF8/maxresdefault.jpg Game Physics Just Jumped A Generation ❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper is available here: https://wanghmin.github.io/publication/wu-2022-gbm/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/oToAGiozQF8 2026-03-17T14:13:21.000Z Two Minute Papers 411 yes no https://vidert.com/v/artificial-intelligence/video/14649/researchers-built-a-tiny-economy-ais-broke-it-immediately 2026-03-17T14:13:07.000Z weekly 0.5 https://i.ytimg.com/vi/KUekLTqV1ME/maxresdefault.jpg Researchers Built a Tiny Economy. AIs Broke It Immediately ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Using DeepSeek on Lambda: https://lambda.ai/inference-models/deepseek-r1 📝 The paper is available here: https://simworld.org/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/KUekLTqV1ME 2026-03-17T14:13:07.000Z Two Minute Papers 401 yes no https://vidert.com/v/artificial-intelligence/video/14648/deepmind-s-new-game-ai-just-made-history 2026-03-17T14:12:52.000Z weekly 0.5 https://i.ytimg.com/vi/-ZFH4oJzCdU/maxresdefault.jpg DeepMind’s New Game AI Just Made History ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Using DeepSeek on Lambda: https://lambda.ai/inference-models/deepseek-r1 📝 The SIMA 2 paper is available here: https://deepmind.google/blog/sima-2-an-agent-that-plays-reasons-and-learns-with-you-in-virtual-3d-worlds/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/-ZFH4oJzCdU 2026-03-17T14:12:52.000Z Two Minute Papers 521 yes no https://vidert.com/v/artificial-intelligence/video/14647/the-biggest-physics-breakthrough-nobody-noticed 2026-03-17T14:12:35.000Z weekly 0.5 https://i.ytimg.com/vi/rRMlhHDCNr0/maxresdefault.jpg The Biggest Physics Breakthrough Nobody Noticed ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Using DeepSeek on Lambda: https://lambda.ai/inference-models/deepseek-r1 My hobby channel with guitars and labcoats 🥼: https://www.youtube.com/watch?v=GjMMhn4pS38 https://www.youtube.com/watch?v=BxS62W6V48E 📝 The paper is available here: https://arxiv.org/abs/2505.21946 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/rRMlhHDCNr0 2026-03-17T14:12:35.000Z Two Minute Papers 449 yes no https://vidert.com/v/artificial-intelligence/video/14646/alphafold-the-most-important-ai-breakthrough-ever-made 2026-03-17T14:12:20.000Z weekly 0.5 https://i.ytimg.com/vi/Vhcwjzeukts/maxresdefault.jpg AlphaFold - The Most Important AI Breakthrough Ever Made To celebrate the 5th anniversary of #AlphaFold, I was invited by Google DeepMind to interview Nobel Prize Winner and Distinguished Scientist, John Jumper. Note that we have no business ties with them. Thank you so much to John for being so kind and insightful, and to the film crew as well - they all did an incredible job. AlphaFold: https://deepmind.google/science/alphafold/ The full Thinking Game Movie: https://www.youtube.com/watch?v=d95J8yzvjbQ Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers Note that just watching the series and leaving a kind comment every now and then is as much support as any of us could ever ask for! My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/Vhcwjzeukts 2026-03-17T14:12:20.000Z Two Minute Papers 1369 yes no https://vidert.com/v/artificial-intelligence/video/14645/unreal-engine-5-7-billions-of-triangles-in-real-time 2026-03-17T14:12:04.000Z weekly 0.5 https://i.ytimg.com/vi/IyLQyob8W-w/maxresdefault.jpg Unreal Engine 5.7: Billions Of Triangles, In Real Time ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The Unreal Engine 5.7 is available here: https://www.unrealengine.com/en-US/news/unreal-engine-5-7-is-now-available Sources: https://www.youtube.com/watch?v=Mj_-2SdsYLw https://www.youtube.com/watch?v=ngzPTqtZWo4 https://advances.realtimerendering.com/s2023/2023%20Siggraph%20-%20Substrate.pdf 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/IyLQyob8W-w 2026-03-17T14:12:04.000Z Two Minute Papers 479 yes no https://vidert.com/v/artificial-intelligence/video/14644/blender-5-0-is-here-a-revolution-for-free 2026-03-17T14:11:51.000Z weekly 0.5 https://i.ytimg.com/vi/Py1I96F_R4Q/maxresdefault.jpg Blender 5.0 Is Here - A Revolution…For Free! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Get Blender 5.0 here: https://www.blender.org/ Example scenes: https://www.blender.org/download/demo-files/ Multiple scattering paper: https://cg.iit.bme.hu/~szirmay/volreuse_link.htm 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/Py1I96F_R4Q 2026-03-17T14:11:51.000Z Two Minute Papers 385 yes no https://vidert.com/v/artificial-intelligence/video/14643/deepmind-s-new-ai-beats-openai-with-100x-less-data 2026-03-17T14:11:35.000Z weekly 0.5 https://i.ytimg.com/vi/bsrXd0loJFM/maxresdefault.jpg DeepMind’s New AI Beats OpenAI With 100x Less Data ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPUs with over 16GB of VRAM Open a terminal Just get Ollama following the command from here - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here: https://danijar.com/project/dreamer4/ Source: https://www.youtube.com/watch?v=6bnM84xGxbg 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia #minecraft https://www.youtube.com/embed/bsrXd0loJFM 2026-03-17T14:11:35.000Z Two Minute Papers 505 yes no https://vidert.com/v/artificial-intelligence/video/14642/games-have-never-simulated-clothing-like-this-before 2026-03-17T14:11:21.000Z weekly 0.5 https://i.ytimg.com/vi/vYZbwJJk_hc/maxresdefault.jpg Games Have Never Simulated Clothing Like This Before ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPUs with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper "Fast Physics-Based Modeling of Knots and Ties Using Templates" is available here: https://wanghmin.github.io/publication/guo-2025-fpb/ Sources: https://www.youtube.com/watch?v=2RQcoLV_bVk https://www.youtube.com/watch?v=7d158rQ1R3k https://www.youtube.com/watch?v=qirVdKg3qgs https://www.youtube.com/watch?v=TPokJdN2bkw https://www.youtube.com/watch?v=DRzT3c1jk14 https://www.youtube.com/watch?v=er23-Kt-uHE https://www.youtube.com/watch?v=Odg7acl3nIM https://www.youtube.com/watch?v=jo2ppdJ0Jao 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/vYZbwJJk_hc 2026-03-17T14:11:21.000Z Two Minute Papers 430 yes no https://vidert.com/v/artificial-intelligence/video/14641/you-ll-never-look-at-chocolate-tv-ads-the-same-way-again 2026-03-17T14:11:07.000Z weekly 0.5 https://i.ytimg.com/vi/Mh2y2Z6Iy0U/maxresdefault.jpg You’ll Never Look At Chocolate TV Ads The Same Way Again ❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper "A practical octree liquid simulator with adaptive surface resolution" is available here: https://cs.uwaterloo.ca/~c2batty/papers/Ando2020/Ando2020.pdf Sources: https://www.youtube.com/watch?v=kdt5Cs1VYJA https://www.youtube.com/watch?v=YmmSDZ6dBdY https://www.youtube.com/shorts/FVIDRU9-FW8 https://www.youtube.com/watch?v=gNZtx3ijjpo&pp=ygUHb2N0cmVlcw%3D%3D https://www.youtube.com/shorts/1Euba1QvhW0 https://www.youtube.com/shorts/k2P9yWSMaXE https://www.youtube.com/watch?v=Z5qbxQI6dgw https://www.youtube.com/watch?v=laoGmqNtUMI 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/Mh2y2Z6Iy0U 2026-03-17T14:11:07.000Z Two Minute Papers 446 yes no https://vidert.com/v/artificial-intelligence/video/14640/the-physics-glitch-everyone-gave-up-on-finally-fixed 2026-03-17T14:10:51.000Z weekly 0.5 https://i.ytimg.com/vi/cg7k-7QThqU/maxresdefault.jpg The Physics Glitch Everyone Gave Up On… Finally Fixed ❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper "Multi-Material Mesh-Based Surface Tracking with Implicit Topology Changes" is available here under one of these links hopefully: https://pub.ista.ac.at/group_wojtan/projects/2024_MultimatMeshing/SuperDuperTopoFixer.pdf https://dl.acm.org/doi/10.1145/3658223 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 Sources: https://www.youtube.com/watch?v=dtBqv-qIFLo https://www.youtube.com/watch?v=EZul6DR-fHc https://www.youtube.com/watch?v=F6t8LR2mX1I https://www.youtube.com/watch?v=d3a5OquQ4kU 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/cg7k-7QThqU 2026-03-17T14:10:51.000Z Two Minute Papers 467 yes no https://vidert.com/v/artificial-intelligence/video/14639/nvidia-s-new-ai-just-made-real-physics-look-slow 2026-03-17T14:10:38.000Z weekly 0.5 https://i.ytimg.com/vi/M8s_cS-aH5w/maxresdefault.jpg NVIDIA’s New AI Just Made Real Physics Look Slow ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPUs with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper "Neural Robot Dynamics" is available here: https://neural-robot-dynamics.github.io/ https://github.com/NVlabs/neural-robot-dynamics 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu #nvidia https://www.youtube.com/embed/M8s_cS-aH5w 2026-03-17T14:10:38.000Z Two Minute Papers 567 yes no https://vidert.com/v/artificial-intelligence/video/14638/they-said-it-was-impossible-weta-fx-just-solved-it 2026-03-17T14:10:25.000Z weekly 0.5 https://i.ytimg.com/vi/OZz5PonQKu8/maxresdefault.jpg They Said It Was Impossible… Weta FX Just Solved It ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPUs with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper "A unified multi-scale method for simulating immersed bubbles" is available here: https://alexey.stomakhin.com/research/unibubbles.html 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/OZz5PonQKu8 2026-03-17T14:10:25.000Z Two Minute Papers 603 yes no https://vidert.com/v/artificial-intelligence/video/14637/new-ai-just-made-fashion-in-games-real 2026-03-17T14:10:11.000Z weekly 0.5 https://i.ytimg.com/vi/9_ypA131CPc/maxresdefault.jpg New AI Just Made Fashion In Games Real ❤️ Check out the Fully Connected Conference by Weights & Biases - https://wandb.me/fclon2025-2min 20% discount code: FCLON2025-2MIN 📝 The paper is available here: https://dress-1-to-3.github.io/ ❤️ Get cool perks and support The Papers on Patreon! Link: https://www.patreon.com/c/TwoMinutePapers 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/9_ypA131CPc 2026-03-17T14:10:11.000Z Two Minute Papers 600 yes no https://vidert.com/v/artificial-intelligence/video/14636/nvidia-s-new-ai-s-movements-are-so-real-it-s-uncanny 2026-03-17T14:09:55.000Z weekly 0.5 https://i.ytimg.com/vi/FM8yNkWad1w/maxresdefault.jpg NVIDIA’s New AI’s Movements Are So Real It’s Uncanny ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPUs with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here: https://add-moo.github.io/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu #nvidia https://www.youtube.com/embed/FM8yNkWad1w 2026-03-17T14:09:55.000Z Two Minute Papers 631 yes no https://vidert.com/v/artificial-intelligence/video/14635/the-worst-bug-in-games-is-now-gone-forever 2026-03-17T14:09:40.000Z weekly 0.5 https://i.ytimg.com/vi/VOORiyip4_c/maxresdefault.jpg The Worst Bug In Games Is Now Gone Forever ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPUs with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝Paper: https://drive.google.com/file/d/1OrOKJH_im1L4j1cJB18sfvNHEbZVSqjL/view Code and examples are available here: https://github.com/st-tech/ppf-contact-solver Guide on how to try it: https://drive.google.com/file/d/1n068Ai_hlfgapf2xkAutOHo3PkLpJXA4/view Sources: https://www.youtube.com/watch?v=5GDIoshj9Rw https://www.youtube.com/watch?v=X53VuYLP0VY https://www.youtube.com/shorts/x0WjJgotCXU https://www.youtube.com/watch?v=Qu4Of18Kf2M 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/VOORiyip4_c 2026-03-17T14:09:40.000Z Two Minute Papers 702 yes no https://vidert.com/v/artificial-intelligence/video/14634/deepmind-s-ai-just-solved-video-generation-in-a-way-nobody-expected 2026-03-17T14:09:27.000Z weekly 0.5 https://i.ytimg.com/vi/spn_eTODPg8/maxresdefault.jpg DeepMind’s AI Just Solved Video Generation In A Way Nobody Expected ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPUs with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here: https://video-zero-shot.github.io/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/spn_eTODPg8 2026-03-17T14:09:27.000Z Two Minute Papers 468 yes no https://vidert.com/v/artificial-intelligence/video/14633/why-gamers-will-never-see-hair-the-same-way-again 2026-03-17T14:09:11.000Z weekly 0.5 https://i.ytimg.com/vi/WYTOxOhKl3Y/maxresdefault.jpg Why Gamers Will Never See Hair The Same Way Again ❤️ Check out the Fully Connected Conference by Weights & Biases - https://wandb.me/fclon2025-2min 20% discount code: FCLON2025-2MIN 📝 The paper is available here: https://www.cemyuksel.com/research/hairmesh_rendering/ Try the demo and try to break it, it is super fun: https://www.cemyuksel.com/research/hairmesh_rendering/demo/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/WYTOxOhKl3Y 2026-03-17T14:09:11.000Z Two Minute Papers 395 yes no https://vidert.com/v/artificial-intelligence/video/14632/nvidia-just-solved-the-hardest-problem-in-physics-simulation 2026-03-17T14:08:54.000Z weekly 0.5 https://i.ytimg.com/vi/7NF3CdXkm68/maxresdefault.jpg NVIDIA Just Solved The Hardest Problem in Physics Simulation! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPUs with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here: https://graphics.cs.utah.edu/research/projects/ogc/ Sources: https://www.youtube.com/watch?v=CfEg7fucVYg Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers Note that just watching the series and leaving a kind comment every now and then is as much support as any of us could ever ask for! 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu #NVIDIA https://www.youtube.com/embed/7NF3CdXkm68 2026-03-17T14:08:54.000Z Two Minute Papers 469 yes no https://vidert.com/v/artificial-intelligence/video/14631/the-next-level-of-ai-video-games-is-here 2026-03-17T14:08:39.000Z weekly 0.5 https://i.ytimg.com/vi/6Adcl7nXWuU/maxresdefault.jpg The Next Level of AI Video Games Is Here! ❤️ Check out Vast.ai and run DeepSeek or any AI project: https://vast.ai/papers 📝 Magica 2 is available here: https://blog.dynamicslab.ai/ Try it out: https://demo.dynamicslab.ai/chaos 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/6Adcl7nXWuU 2026-03-17T14:08:39.000Z Two Minute Papers 375 yes no https://vidert.com/v/artificial-intelligence/video/14630/no-ai-needed-1-000-000-000-particle-asteroid-crash-simulation-but-how 2026-03-17T14:08:20.000Z weekly 0.5 https://i.ytimg.com/vi/LhzKXjwC8vE/maxresdefault.jpg No AI Needed - 1,000,000,000 Particle Asteroid Crash Simulation! But How? ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPUs with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here: https://ge.in.tum.de/publications/very-large-scale-two-phase-flip/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 Sources: https://www.youtube.com/watch?v=ielqS1hkoLc https://www.youtube.com/watch?v=nDKlrRA_hEA 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/LhzKXjwC8vE 2026-03-17T14:08:20.000Z Two Minute Papers 574 yes no https://vidert.com/v/artificial-intelligence/video/14629/this-free-ai-generates-video-faster-than-real-life 2026-03-17T14:08:03.000Z weekly 0.5 https://i.ytimg.com/vi/gT98Kq-PV8M/maxresdefault.jpg This Free AI Generates Video FASTER Than Real Life 🤯 ❤️ Check out the Fully Connected Conference by Weights & Biases - https://wandb.me/fclon2025-2min 20% discount code: FCLON2025-2MIN 📝 The paper is available here: https://github.com/Lightricks/LTX-Video 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/gT98Kq-PV8M 2026-03-17T14:08:03.000Z Two Minute Papers 349 yes no https://vidert.com/v/artificial-intelligence/video/14628/intel-just-changed-computer-graphics-forever 2026-03-17T14:07:45.000Z weekly 0.5 https://i.ytimg.com/vi/_WjU5d26Cc4/maxresdefault.jpg Intel Just Changed Computer Graphics Forever! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPU's with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here: https://www.sdiolatz.info/publications/00ImageGS.html Genetic algorithm for the Mona Lisa: https://users.cg.tuwien.ac.at/zsolnai/gfx/mona_lisa_parallel_genetic_algorithm/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/_WjU5d26Cc4 2026-03-17T14:07:45.000Z Two Minute Papers 399 yes no https://vidert.com/v/artificial-intelligence/video/14627/google-s-new-ai-fixes-the-1-problem-with-your-photos 2026-03-17T14:07:26.000Z weekly 0.5 https://i.ytimg.com/vi/YzGzCWydMh0/maxresdefault.jpg Google’s New AI Fixes The #1 Problem With Your Photos! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPU's with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here: https://nadmag.github.io/LightLab/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/YzGzCWydMh0 2026-03-17T14:07:26.000Z Two Minute Papers 425 yes no https://vidert.com/v/artificial-intelligence/video/14626/the-future-of-sound-is-not-recorded-it-is-computed 2026-03-17T14:07:10.000Z weekly 0.5 https://i.ytimg.com/vi/1bS7sHyfi58/maxresdefault.jpg The Future Of Sound Is Not Recorded. It is Computed. ❤️ Check out DeepInfra and run DeepSeek or many other AI projects: https://deepinfra.com/papers 📝 The paper is available here: https://graphics.stanford.edu/papers/waveblender/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu #nvidia https://www.youtube.com/embed/1bS7sHyfi58 2026-03-17T14:07:10.000Z Two Minute Papers 450 yes no https://vidert.com/v/artificial-intelligence/video/14625/new-ai-finally-solved-the-hardest-animation-problem 2026-03-17T14:06:52.000Z weekly 0.5 https://i.ytimg.com/vi/nHBgc_oNfQw/maxresdefault.jpg New AI Finally Solved The Hardest Animation Problem! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPUs with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper is available here: https://diffusecloc.github.io/website/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/nHBgc_oNfQw 2026-03-17T14:06:52.000Z Two Minute Papers 314 yes no https://vidert.com/v/artificial-intelligence/video/14624/this-isn-t-ai-it-s-even-wilder-squishy-physics-that-learn-to-move 2026-03-17T14:06:33.000Z weekly 0.5 https://i.ytimg.com/vi/EEvewoxv0TA/maxresdefault.jpg This Isn’t AI - It’s Even Wilder: Squishy Physics That Learn to Move! ❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.me/papers 📝 The paper is available here: https://arxiv.org/abs/2405.14595 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 Sources: https://www.youtube.com/shorts/Mq7zzK-ZiWI https://www.youtube.com/watch?v=A_Cdz-QBlT4 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/EEvewoxv0TA 2026-03-17T14:06:33.000Z Two Minute Papers 305 yes no https://vidert.com/v/artificial-intelligence/video/14623/deepmind-just-made-the-most-powerful-game-ai-engine 2026-03-17T14:06:18.000Z weekly 0.5 https://i.ytimg.com/vi/YvuEKrJhjos/maxresdefault.jpg DeepMind Just Made The Most Powerful Game AI Engine! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPU's with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b Genie 3: https://deepmind.google/discover/blog/genie-3-a-new-frontier-for-world-models/ Sources: https://x.com/amoufarek/status/1955776162447102238 https://x.com/amoufarek/status/1955299375548076382 https://x.com/holynski_/status/1953882726656094622 https://x.com/holynski_/status/1953879983535141043 https://x.com/RuiHuang_art/status/1954716703340048877 https://x.com/mattmcgill_/status/1953827141700772186 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/YvuEKrJhjos 2026-03-17T14:06:18.000Z Two Minute Papers 403 yes no https://vidert.com/v/artificial-intelligence/video/14622/new-ai-research-solved-the-problem-photoshop-never-could 2026-03-17T14:05:59.000Z weekly 0.5 https://i.ytimg.com/vi/Ab9gJv-lrOw/maxresdefault.jpg New AI Research Solved The Problem Photoshop Never Could! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPU's with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b 📝 The paper "Physically Controllable Relighting of Photographs" is available here: https://yaksoy.github.io/PhysicalRelighting/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/Ab9gJv-lrOw 2026-03-17T14:05:59.000Z Two Minute Papers 404 yes no https://vidert.com/v/artificial-intelligence/video/14621/openai-s-new-free-ai-the-good-the-bad-the-unexpected 2026-03-17T14:05:04.000Z weekly 0.5 https://i.ytimg.com/vi/I1_iXwa-7dA/maxresdefault.jpg OpenAI’s New Free AI: The Good, The Bad, The Unexpected! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide: Rent one of their GPU's with over 16GB of VRAM Open a terminal Just get Ollama with this command - https://ollama.com/download/linux Then run ollama run gpt-oss:120b - https://ollama.com/library/gpt-oss:120b Try it online: https://gpt-oss.com/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 Humanity's Last Exam: https://agi.safe.ai/ Sources: https://x.com/flavioad/status/1952792389636198489 https://x.com/kwindla/status/1952947685012717659 https://x.com/productshiv/status/1952793922964734431 https://x.com/philip_kiely/status/1953174333024813340 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/I1_iXwa-7dA 2026-03-17T14:05:04.000Z Two Minute Papers 327 yes no https://vidert.com/v/artificial-intelligence/video/14620/new-game-ai-turns-photos-into-playable-worlds-celebrating-10-years-of-papers 2026-03-17T14:04:49.000Z weekly 0.5 https://i.ytimg.com/vi/ecRFKfNy-Ms/maxresdefault.jpg New Game AI Turns Photos Into Playable Worlds! | Celebrating 10 Years Of Papers! 🎂 ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda: https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 The paper is available here: https://hunyuan-gamecraft.github.io/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/ecRFKfNy-Ms 2026-03-17T14:04:49.000Z Two Minute Papers 365 yes no https://vidert.com/v/artificial-intelligence/video/14619/the-forgotten-research-that-fixed-the-worst-physics-bug 2026-03-17T14:04:35.000Z weekly 0.5 https://i.ytimg.com/vi/4X5T2eeG7iw/maxresdefault.jpg The Forgotten Research That Fixed The Worst Physics Bug! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers Guide for using DeepSeek on Lambda: https://docs.lambdalabs.com/education/large-language-models/deepseek-r1-ollama/?utm_source=two-minute-papers&utm_campaign=relevant-videos&utm_medium=video 📝 The paper is available here: https://graphics.cs.utah.edu/research/projects/merging-and-splitting/ 📝 My paper on simulations that look almost like reality is available for free here: https://rdcu.be/cWPfD Or this is the orig. Nature Physics link with clickable citations: https://www.nature.com/articles/s41567-022-01788-5 Video game glitch: https://www.youtube.com/watch?v=fZgRVatBXTE 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Gordon Child, John Le, Juan Benet, Kyle Davis, Loyal Alchemist, Lukas Biewald, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Sven Pfiffner, Taras Bobrovytsky, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers My research: https://cg.tuwien.ac.at/~zsolnai/ X/Twitter: https://twitter.com/twominutepapers Thumbnail design: Felícia Zsolnai-Fehér - http://felicia.hu https://www.youtube.com/embed/4X5T2eeG7iw 2026-03-17T14:04:35.000Z Two Minute Papers 339 yes no https://vidert.com/v/artificial-intelligence/video/14618/tidar-think-in-diffusion-talk-in-autoregression-paper-analysis 2026-03-17T14:03:38.000Z weekly 0.5 https://i.ytimg.com/vi/taCVT5vDAk0/maxresdefault.jpg TiDAR: Think in Diffusion, Talk in Autoregression (Paper Analysis) Paper: https://arxiv.org/abs/2511.08923 Abstract: Diffusion language models hold the promise of fast parallel generation, while autoregressive (AR) models typically excel in quality due to their causal structure aligning naturally with language modeling. This raises a fundamental question: can we achieve a synergy with high throughput, higher GPU utilization, and AR level quality? Existing methods fail to effectively balance these two aspects, either prioritizing AR using a weaker model for sequential drafting (speculative decoding), leading to lower drafting efficiency, or using some form of left-to-right (AR-like) decoding logic for diffusion, which still suffers from quality degradation and forfeits its potential parallelizability. We introduce TiDAR, a sequence-level hybrid architecture that drafts tokens (Thinking) in Diffusion and samples final outputs (Talking) AutoRegressively - all within a single forward pass using specially designed structured attention masks. This design exploits the free GPU compute density, achieving a strong balance between drafting and verification capacity. Moreover, TiDAR is designed to be serving-friendly (low overhead) as a standalone model. We extensively evaluate TiDAR against AR models, speculative decoding, and diffusion variants across generative and likelihood tasks at 1.5B and 8B scales. Thanks to the parallel drafting and sampling as well as exact KV cache support, TiDAR outperforms speculative decoding in measured throughput and surpasses diffusion models like Dream and Llada in both efficiency and quality. Most notably, TiDAR is the first architecture to close the quality gap with AR models while delivering 4.71x to 5.91x more tokens per second. Authors: Jingyu Liu, Xin Dong, Zhifan Ye, Rishabh Mehta, Yonggan Fu, Vartika Singh, Jan Kautz, Ce Zhang, Pavlo Molchanov Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord Linked https://www.youtube.com/embed/taCVT5vDAk0 2026-03-17T14:03:38.000Z Yannic Kilcher 2822 yes no https://vidert.com/v/artificial-intelligence/video/14617/titans-learning-to-memorize-at-test-time-paper-analysis 2026-03-17T14:03:20.000Z weekly 0.5 https://i.ytimg.com/vi/v67plFw1nMw/maxresdefault.jpg Titans: Learning to Memorize at Test Time (Paper Analysis) Paper: https://arxiv.org/abs/2501.00663 Abstract: Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention. While recurrent models aim to compress the data into a fixed-size memory (called hidden state), attention allows attending to the entire context window, capturing the direct dependencies of all tokens. This more accurate modeling of dependencies, however, comes with a quadratic cost, limiting the model to a fixed-length context. We present a new neural long-term memory module that learns to memorize historical context and helps attention to attend to the current context while utilizing long past information. We show that this neural memory has the advantage of fast parallelizable training while maintaining a fast inference. From a memory perspective, we argue that attention due to its limited context but accurate dependency modeling performs as a short-term memory, while neural memory due to its ability to memorize the data, acts as a long-term, more persistent, memory. Based on these two modules, we introduce a new family of architectures, called Titans, and present three variants to address how one can effectively incorporate memory into this architecture. Our experimental results on language modeling, common-sense reasoning, genomics, and time series tasks show that Titans are more effective than Transformers and recent modern linear recurrent models. They further can effectively scale to larger than 2M context window size with higher accuracy in needle-in-haystack tasks compared to baselines. Authors: Ali Behrouz, Peilin Zhong, Vahab Mirrokni Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and volunt https://www.youtube.com/embed/v67plFw1nMw 2026-03-17T14:03:20.000Z Yannic Kilcher 1951 yes no https://vidert.com/v/artificial-intelligence/video/14616/paper-analysis-the-free-transformer-and-some-variational-autoencoder-stuff 2026-03-17T14:03:00.000Z weekly 0.5 https://i.ytimg.com/vi/Nao16-6l6dQ/maxresdefault.jpg [Paper Analysis] The Free Transformer (and some Variational Autoencoder stuff) https://arxiv.org/abs/2510.17558 Abstract: We propose an extension of the decoder Transformer that conditions its generative process on random latent variables which are learned without supervision thanks to a variational procedure. Experimental evaluations show that allowing such a conditioning translates into substantial improvements on downstream tasks. Author: François Fleuret Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/Nao16-6l6dQ 2026-03-17T14:03:00.000Z Yannic Kilcher 2410 yes no https://vidert.com/v/artificial-intelligence/video/14615/video-response-what-cloudflare-s-code-mode-misses-about-mcp-and-tool-calling 2026-03-17T14:02:38.000Z weekly 0.5 https://i.ytimg.com/vi/0bpYCxv2qhw/maxresdefault.jpg [Video Response] What Cloudflare's code mode misses about MCP and tool calling Theo's Video: https://www.youtube.com/watch?v=bAYZjVAodoo Cloudflare article: https://blog.cloudflare.com/code-mode/ Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/0bpYCxv2qhw 2026-03-17T14:02:38.000Z Yannic Kilcher 799 yes no https://vidert.com/v/artificial-intelligence/video/14614/paper-analysis-on-the-theoretical-limitations-of-embedding-based-retrieval-warni 2026-03-17T14:02:21.000Z weekly 0.5 https://i.ytimg.com/vi/zKohTkN0Fyk/maxresdefault.jpg [Paper Analysis] On the Theoretical Limitations of Embedding-Based Retrieval (Warning: Rant) Paper: https://arxiv.org/abs/2508.21038 Abstract: Vector embeddings have been tasked with an ever-increasing set of retrieval tasks over the years, with a nascent rise in using them for reasoning, instruction-following, coding, and more. These new benchmarks push embeddings to work for any query and any notion of relevance that could be given. While prior works have pointed out theoretical limitations of vector embeddings, there is a common assumption that these difficulties are exclusively due to unrealistic queries, and those that are not can be overcome with better training data and larger models. In this work, we demonstrate that we may encounter these theoretical limitations in realistic settings with extremely simple queries. We connect known results in learning theory, showing that the number of top-k subsets of documents capable of being returned as the result of some query is limited by the dimension of the embedding. We empirically show that this holds true even if we restrict to k=2, and directly optimize on the test set with free parameterized embeddings. We then create a realistic dataset called LIMIT that stress tests models based on these theoretical results, and observe that even state-of-the-art models fail on this dataset despite the simple nature of the task. Our work shows the limits of embedding models under the existing single vector paradigm and calls for future research to develop methods that can resolve this fundamental limitation. Authors: Orion Weller, Michael Boratko, Iftekhar Naim, Jinhyuk Lee Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilc https://www.youtube.com/embed/zKohTkN0Fyk 2026-03-17T14:02:21.000Z Yannic Kilcher 2937 yes no https://vidert.com/v/artificial-intelligence/video/14613/agi-is-not-coming 2026-03-17T14:02:06.000Z weekly 0.5 https://i.ytimg.com/vi/hkAH7-u7t5k/maxresdefault.jpg AGI is not coming! jack Morris's investigation into GPT-OSS training data https://x.com/jxmnop/status/1953899426075816164?t=3YRhVQDwQLk2gouTSACoqA&s=09 https://www.youtube.com/embed/hkAH7-u7t5k 2026-03-17T14:02:06.000Z Yannic Kilcher 429 yes no https://vidert.com/v/artificial-intelligence/video/14612/context-rot-how-increasing-input-tokens-impacts-llm-performance-paper-analysis 2026-03-17T14:01:49.000Z weekly 0.5 https://i.ytimg.com/vi/hpC4qjWu_aY/maxresdefault.jpg Context Rot: How Increasing Input Tokens Impacts LLM Performance (Paper Analysis) Paper: https://research.trychroma.com/context-rot Abstract: Large Language Models (LLMs) are typically presumed to process context uniformly—that is, the model should handle the 10,000th token just as reliably as the 100th. However, in practice, this assumption does not hold. We observe that model performance varies significantly as input length changes, even on simple tasks. In this report, we evaluate 18 LLMs, including the state-of-the-art GPT-4.1, Claude 4, Gemini 2.5, and Qwen3 models. Our results reveal that models do not use their context uniformly; instead, their performance grows increasingly unreliable as input length grows. Authors: Kelly Hong, Anton Troynikov, Jeff Huber Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/hpC4qjWu_aY 2026-03-17T14:01:49.000Z Yannic Kilcher 2269 yes no https://vidert.com/v/artificial-intelligence/video/14611/energy-based-transformers-are-scalable-learners-and-thinkers-paper-review 2026-03-17T14:01:25.000Z weekly 0.5 https://i.ytimg.com/vi/RAEy3JZmIaA/maxresdefault.jpg Energy-Based Transformers are Scalable Learners and Thinkers (Paper Review) Paper: https://arxiv.org/abs/2507.02092 Code: https://github.com/alexiglad/EBT Website: https://energy-based-transformers.github.io/ Abstract: Inference-time computation techniques, analogous to human System 2 Thinking, have recently become popular for improving model performances. However, most existing approaches suffer from several limitations: they are modality-specific (e.g., working only in text), problem-specific (e.g., verifiable domains like math and coding), or require additional supervision/training on top of unsupervised pretraining (e.g., verifiers or verifiable rewards). In this paper, we ask the question "Is it possible to generalize these System 2 Thinking approaches, and develop models that learn to think solely from unsupervised learning?" Interestingly, we find the answer is yes, by learning to explicitly verify the compatibility between inputs and candidate-predictions, and then re-framing prediction problems as optimization with respect to this verifier. Specifically, we train Energy-Based Transformers (EBTs) -- a new class of Energy-Based Models (EBMs) -- to assign an energy value to every input and candidate-prediction pair, enabling predictions through gradient descent-based energy minimization until convergence. Across both discrete (text) and continuous (visual) modalities, we find EBTs scale faster than the dominant Transformer++ approach during training, achieving an up to 35% higher scaling rate with respect to data, batch size, parameters, FLOPs, and depth. During inference, EBTs improve performance with System 2 Thinking by 29% more than the Transformer++ on language tasks, and EBTs outperform Diffusion Transformers on image denoising while using fewer forward passes. Further, we find that EBTs achieve better results than existing models on most downstream tasks given the same or worse pretraining performance, suggesting that EBTs generalize better than existing approaches. Consequently, EBTs are a promising new paradigm for scaling both the learning and thinking capabilities of mo https://www.youtube.com/embed/RAEy3JZmIaA 2026-03-17T14:01:25.000Z Yannic Kilcher 2871 yes no https://vidert.com/v/artificial-intelligence/video/14610/on-the-biology-of-a-large-language-model-part-2 2026-03-17T14:01:05.000Z weekly 0.5 https://i.ytimg.com/vi/V71AJoYAtBQ/maxresdefault.jpg On the Biology of a Large Language Model (Part 2) An in-depth look at Anthropic's Transformer Circuit Blog Post Part 1 here: https://youtu.be/mU3g2YPKlsA Discord here: https;//ykilcher.com/discord https://transformer-circuits.pub/2025/attribution-graphs/biology.html Abstract: We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic's lightweight production model — in a variety of contexts, using our circuit tracing methodology. Authors: Jack Lindsey†, Wes Gurnee*, Emmanuel Ameisen*, Brian Chen*, Adam Pearce*, Nicholas L. Turner*, Craig Citro*, David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton, Trenton Bricken, Callum McDougall◊, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy Jones, Andrew Persic, Zhenyi Qi, T. Ben Thompson, Sam Zimmerman, Kelley Rivoire, Thomas Conerly, Chris Olah, Joshua Batson*‡ Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/V71AJoYAtBQ 2026-03-17T14:01:05.000Z Yannic Kilcher 3386 yes no https://vidert.com/v/artificial-intelligence/video/14609/on-the-biology-of-a-large-language-model-part-1 2026-03-17T14:00:44.000Z weekly 0.5 https://i.ytimg.com/vi/mU3g2YPKlsA/maxresdefault.jpg On the Biology of a Large Language Model (Part 1) An in-depth look at Anthropic's Transformer Circuit Blog Post https://transformer-circuits.pub/2025/attribution-graphs/biology.html Abstract: We investigate the internal mechanisms used by Claude 3.5 Haiku — Anthropic's lightweight production model — in a variety of contexts, using our circuit tracing methodology. Authors: Jack Lindsey†, Wes Gurnee*, Emmanuel Ameisen*, Brian Chen*, Adam Pearce*, Nicholas L. Turner*, Craig Citro*, David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton, Trenton Bricken, Callum McDougall◊, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy Jones, Andrew Persic, Zhenyi Qi, T. Ben Thompson, Sam Zimmerman, Kelley Rivoire, Thomas Conerly, Chris Olah, Joshua Batson*‡ Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/mU3g2YPKlsA 2026-03-17T14:00:44.000Z Yannic Kilcher 3245 yes no https://vidert.com/v/artificial-intelligence/video/14608/grpo-explained-deepseekmath-pushing-the-limits-of-mathematical-reasoning-in-open 2026-03-17T14:00:24.000Z weekly 0.5 https://i.ytimg.com/vi/bAWV_yrqx4w/maxresdefault.jpg [GRPO Explained] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models #deepseek #llm #grpo GRPO is one of the core advancements used in Deepseek-R1, but was introduced already last year in this paper that uses a combination of new RL techniques and iterative data collection to achieve remarkable performance on mathematics benchmarks with just a 7B model. Paper: https://arxiv.org/abs/2402.03300 Abstract: Mathematical reasoning poses a significant challenge for language models due to its complex and structured nature. In this paper, we introduce DeepSeekMath 7B, which continues pre-training DeepSeek-Coder-Base-v1.5 7B with 120B math-related tokens sourced from Common Crawl, together with natural language and code data. DeepSeekMath 7B has achieved an impressive score of 51.7% on the competition-level MATH benchmark without relying on external toolkits and voting techniques, approaching the performance level of Gemini-Ultra and GPT-4. Self-consistency over 64 samples from DeepSeekMath 7B achieves 60.9% on MATH. The mathematical reasoning capability of DeepSeekMath is attributed to two key factors: First, we harness the significant potential of publicly available web data through a meticulously engineered data selection pipeline. Second, we introduce Group Relative Policy Optimization (GRPO), a variant of Proximal Policy Optimization (PPO), that enhances mathematical reasoning abilities while concurrently optimizing the memory usage of PPO. Authors: Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, Y.K. Li, Y. Wu, Daya Guo Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https:/ https://www.youtube.com/embed/bAWV_yrqx4w 2026-03-17T14:00:24.000Z Yannic Kilcher 4140 yes no https://vidert.com/v/artificial-intelligence/video/14607/byte-latent-transformer-patches-scale-better-than-tokens-paper-explained 2026-03-17T13:59:54.000Z weekly 0.5 https://i.ytimg.com/vi/loaTGpqfctI/maxresdefault.jpg Byte Latent Transformer: Patches Scale Better Than Tokens (Paper Explained) #tokenization #llm #meta This paper does away with tokenization and creates an LLM architecture that operates on dynamically sized "patches" instead of tokens. By controlling the patch size, they gain a level of control over the tradeoff between model size and FLOPs and use that to achieve more favorable scaling behavior than classically tokenized LLMs. Paper: https://ai.meta.com/research/publications/byte-latent-transformer-patches-scale-better-than-tokens/ Code: https://github.com/facebookresearch/blt Abstract: We introduce the Byte Latent Transformer (BLT), a new byte-level LLM architecture that, for the first time, matches tokenization-based LLM performance at scale with significant improvements in inference efficiency and robustness. BLT encodes bytes into dynamically sized patches, which serve as the primary units of computation. Patches are segmented dynamically based on the entropy of the next byte, allocating more compute and model capacity where increased data complexity demands it. We present the first flop controlled scaling study of byte-level models up to 8B parameters with 4T training bytes. Our results demonstrate the feasibility of scaling models trained on raw bytes without a fixed-vocabulary. Both training and inference efficiency improve due to dynamically selecting long patches when data is predictable, along with qualitative improvements on reasoning and long tail generalization. Overall, for fixed inference costs, BLT shows significantly better scaling than tokenization-based models, by simultaneously growing both patch and model size. Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: h https://www.youtube.com/embed/loaTGpqfctI 2026-03-17T13:59:54.000Z Yannic Kilcher 2175 yes no https://vidert.com/v/artificial-intelligence/video/14606/safety-alignment-should-be-made-more-than-just-a-few-tokens-deep-paper-explained 2026-03-17T13:59:36.000Z weekly 0.5 https://i.ytimg.com/vi/-r0XPC7TLzY/maxresdefault.jpg Safety Alignment Should be Made More Than Just a Few Tokens Deep (Paper Explained) This paper demonstrates in a series of experiments that current safety alignment techniques of LLMs, as well as corresponding jailbreaking attacks, are in large part focusing on modulating the distribution of the first few tokens of the LLM response. Paper: https://openreview.net/forum?id=6Mxhg9PtDE&s=09 Abstract: The safety alignment of current Large Language Models (LLMs) is vulnerable. Simple attacks, or even benign fine-tuning, can jailbreak aligned models. We note that many of these vulnerabilities are related to a shared underlying issue: safety alignment can take shortcuts, wherein the alignment adapts a model's generative distribution primarily over only its very first few output tokens. We unifiedly refer to this issue as shallow safety alignment. In this paper, we present case studies to explain why shallow safety alignment can exist and show how this issue universally contributes to multiple recently discovered vulnerabilities in LLMs, including the susceptibility to adversarial suffix attacks, prefilling attacks, decoding parameter attacks, and fine-tuning attacks. The key contribution of this work is that we demonstrate how this consolidated notion of shallow safety alignment sheds light on promising research directions for mitigating these vulnerabilities. We show that deepening the safety alignment beyond the first few tokens can meaningfully improve robustness against some common exploits. We also design a regularized fine-tuning objective that makes the safety alignment more persistent against fine-tuning attacks by constraining updates on initial tokens. Overall, we advocate that future safety alignment should be made more than just a few tokens deep. Authors: Anonymous Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If https://www.youtube.com/embed/-r0XPC7TLzY 2026-03-17T13:59:36.000Z Yannic Kilcher 2933 yes no https://vidert.com/v/artificial-intelligence/video/14605/tokenformer-rethinking-transformer-scaling-with-tokenized-model-parameters-paper 2026-03-17T13:59:21.000Z weekly 0.5 https://i.ytimg.com/vi/gfU5y7qCxF0/maxresdefault.jpg TokenFormer: Rethinking Transformer Scaling with Tokenized Model Parameters (Paper Explained) A deep dive into the TokenFormer and an opinion about its impact, novelty, and relation to prior work. Paper: https://arxiv.org/abs/2410.23168 Abstract: Transformers have become the predominant architecture in foundation models due to their excellent performance across various domains. However, the substantial cost of scaling these models remains a significant concern. This problem arises primarily from their dependence on a fixed number of parameters within linear projections. When architectural modifications (e.g., channel dimensions) are introduced, the entire model typically requires retraining from scratch. As model sizes continue growing, this strategy results in increasingly high computational costs and becomes unsustainable. To overcome this problem, we introduce TokenFormer, a natively scalable architecture that leverages the attention mechanism not only for computations among input tokens but also for interactions between tokens and model parameters, thereby enhancing architectural flexibility. By treating model parameters as tokens, we replace all the linear projections in Transformers with our token-parameter attention layer, where input tokens act as queries and model parameters as keys and values. This reformulation allows for progressive and efficient scaling without necessitating retraining from scratch. Our model scales from 124M to 1.4B parameters by incrementally adding new key-value parameter pairs, achieving performance comparable to Transformers trained from scratch while greatly reducing training costs. Code and models are available at \url{this https URL}. Authors: Haiyang Wang, Yue Fan, Muhammad Ferjad Naeem, Yongqin Xian, Jan Eric Lenssen, Liwei Wang, Federico Tombari, Bernt Schiele Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share o https://www.youtube.com/embed/gfU5y7qCxF0 2026-03-17T13:59:21.000Z Yannic Kilcher 1703 yes no https://vidert.com/v/artificial-intelligence/video/14604/gsm-symbolic-understanding-the-limitations-of-mathematical-reasoning-in-large-la 2026-03-17T13:59:07.000Z weekly 0.5 https://i.ytimg.com/vi/Bs6eyNQjGpo/maxresdefault.jpg GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models This paper (by Apple) questions the mathematical reasoning abilities of current LLMs and designs a synthetic template-based dataset distribution to investigate various aspects around LLM performance of high-school level math questions. Paper: https://arxiv.org/abs/2410.05229 Abstract: Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities have genuinely advanced, raising questions about the reliability of the reported metrics. To address these concerns, we conduct a large-scale study on several SOTA open and closed models. To overcome the limitations of existing evaluations, we introduce GSM-Symbolic, an improved benchmark created from symbolic templates that allow for the generation of a diverse set of questions. GSM-Symbolic enables more controllable evaluations, providing key insights and more reliable metrics for measuring the reasoning capabilities of this http URL findings reveal that LLMs exhibit noticeable variance when responding to different instantiations of the same question. Specifically, the performance of all models declines when only the numerical values in the question are altered in the GSM-Symbolic benchmark. Furthermore, we investigate the fragility of mathematical reasoning in these models and show that their performance significantly deteriorates as the number of clauses in a question increases. We hypothesize that this decline is because current LLMs cannot perform genuine logical reasoning; they replicate reasoning steps from their training data. Adding a single clause that seems relevant to the question causes significant performance drops (up to 65%) across all state-of-the-art models, even though the clause doesn't contribute to the rea https://www.youtube.com/embed/Bs6eyNQjGpo 2026-03-17T13:59:07.000Z Yannic Kilcher 2226 yes no https://vidert.com/v/artificial-intelligence/video/14603/were-rnns-all-we-needed-paper-explained 2026-03-17T13:58:51.000Z weekly 0.5 https://i.ytimg.com/vi/jE9jAZC42NE/maxresdefault.jpg Were RNNs All We Needed? (Paper Explained) This paper posits the interesting question: How much of the performance of Mamba, S4, and other state-space-like models is actually just attributable to some very core concepts - rather than their elaborate architectures. The authors construct minimal versions of GRUs and LSTMs and report competitive performance. Paper: https://arxiv.org/abs/2410.01201 Abstract: The scalability limitations of Transformers regarding sequence length have renewed interest in recurrent sequence models that are parallelizable during training. As a result, many novel recurrent architectures, such as S4, Mamba, and Aaren, have been proposed that achieve comparable performance. In this work, we revisit traditional recurrent neural networks (RNNs) from over a decade ago: LSTMs (1997) and GRUs (2014). While these models were slow due to requiring to backpropagate through time (BPTT), we show that by removing their hidden state dependencies from their input, forget, and update gates, LSTMs and GRUs no longer need to BPTT and can be efficiently trained in parallel. Building on this, we introduce minimal versions (minLSTMs and minGRUs) that (1) use significantly fewer parameters than their traditional counterparts and (2) are fully parallelizable during training (175x faster for a sequence of length 512). Lastly, we show that these stripped-down versions of decade-old RNNs match the empirical performance of recent sequence models. Authors: Leo Feng, Frederick Tung, Mohamed Osama Ahmed, Yoshua Bengio, Hossein Hajimirsadegh Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patre https://www.youtube.com/embed/jE9jAZC42NE 2026-03-17T13:58:51.000Z Yannic Kilcher 1668 yes no https://vidert.com/v/artificial-intelligence/video/14602/scaling-llm-test-time-compute-optimally-can-be-more-effective-than-scaling-model 2026-03-17T13:58:29.000Z weekly 0.5 https://i.ytimg.com/vi/AfAmwIP2ntY/maxresdefault.jpg Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters (Paper) How can one best use extra FLOPS at test time? Paper: https://arxiv.org/abs/2408.03314 Abstract: Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study the scaling of inference-time computation in LLMs, with a focus on answering the question: if an LLM is allowed to use a fixed but non-trivial amount of inference-time compute, how much can it improve its performance on a challenging prompt? Answering this question has implications not only on the achievable performance of LLMs, but also on the future of LLM pretraining and how one should tradeoff inference-time and pre-training compute. Despite its importance, little research attempted to understand the scaling behaviors of various test-time inference methods. Moreover, current work largely provides negative results for a number of these strategies. In this work, we analyze two primary mechanisms to scale test-time computation: (1) searching against dense, process-based verifier reward models; and (2) updating the model's distribution over a response adaptively, given the prompt at test time. We find that in both cases, the effectiveness of different approaches to scaling test-time compute critically varies depending on the difficulty of the prompt. This observation motivates applying a "compute-optimal" scaling strategy, which acts to most effectively allocate test-time compute adaptively per prompt. Using this compute-optimal strategy, we can improve the efficiency of test-time compute scaling by more than 4x compared to a best-of-N baseline. Additionally, in a FLOPs-matched evaluation, we find that on problems where a smaller base model attains somewhat non-trivial success rates, test-time compute can be used to outperform a 14x larger model. Authors: Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.yout https://www.youtube.com/embed/AfAmwIP2ntY 2026-03-17T13:58:29.000Z Yannic Kilcher 3182 yes no https://vidert.com/v/artificial-intelligence/video/14601/privacy-backdoors-stealing-data-with-corrupted-pretrained-models-paper-explained 2026-03-17T13:58:12.000Z weekly 0.5 https://i.ytimg.com/vi/WwbukAcMM4k/maxresdefault.jpg Privacy Backdoors: Stealing Data with Corrupted Pretrained Models (Paper Explained) #llm #privacy #finetuning Can you tamper with a base model in such a way that it will exactly remember its fine-tuning data? This paper presents a method of doing exactly that, and implements it in modern transformers. OUTLINE: 0:00 - Intro & Overview 10:50 -Core idea: single-use data traps 44:30 - Backdoors in transformer models 58:00 - Additional numerical tricks 1:00:35 - Experimental results & conclusion Paper: https://arxiv.org/abs/2404.00473 Code: https://github.com/ShanglunFengatETHZ/PrivacyBackdoor Abstract: Practitioners commonly download pretrained machine learning models from open repositories and finetune them to fit specific applications. We show that this practice introduces a new risk of privacy backdoors. By tampering with a pretrained model's weights, an attacker can fully compromise the privacy of the finetuning data. We show how to build privacy backdoors for a variety of models, including transformers, which enable an attacker to reconstruct individual finetuning samples, with a guaranteed success! We further show that backdoored models allow for tight privacy attacks on models trained with differential privacy (DP). The common optimistic practice of training DP models with loose privacy guarantees is thus insecure if the model is not trusted. Overall, our work highlights a crucial and overlooked supply chain attack on machine learning privacy. Authors: Shanglun Feng, Florian Tramèr Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum ( https://www.youtube.com/embed/WwbukAcMM4k 2026-03-17T13:58:12.000Z Yannic Kilcher 3836 yes no https://vidert.com/v/artificial-intelligence/video/14600/scalable-matmul-free-language-modeling-paper-explained 2026-03-17T13:57:53.000Z weekly 0.5 https://i.ytimg.com/vi/B45FlSQ8ITo/maxresdefault.jpg Scalable MatMul-free Language Modeling (Paper Explained) Matrix multiplications (MatMuls) are pervasive throughout modern machine learning architectures. However, they are also very resource intensive and require special accelerators (GPUs). This paper explores architectures that do away with MatMuls and use quantization and recurrence to keep performance up. OUTLINE: 0:00 - Intro 2:30 - MatMul is everywhere 5:55 - Ternary accumulation as a substitute for matrix multiplication 16:35 - Replacing attention layers with recurrent layers 32:40 - Replacing dense layers with ternary channel mixing 38:30 - Language modelling results & scaling laws 45:00 - Other experimental results 48:20 - Conclusion Paper: https://arxiv.org/abs/2406.02528 Code: https://github.com/ridgerchu/matmulfreellm Abstract: Matrix multiplication (MatMul) typically dominates the overall computational cost of large language models (LLMs). This cost only grows as LLMs scale to larger embedding dimensions and context lengths. In this work, we show that MatMul operations can be completely eliminated from LLMs while maintaining strong performance at billion-parameter scales. Our experiments show that our proposed MatMul-free models achieve performance on-par with state-of-the-art Transformers that require far more memory during inference at a scale up to at least 2.7B parameters. We investigate the scaling laws and find that the performance gap between our MatMul-free models and full precision Transformers narrows as the model size increases. We also provide a GPU-efficient implementation of this model which reduces memory usage by up to 61% over an unoptimized baseline during training. By utilizing an optimized kernel during inference, our model's memory consumption can be reduced by more than 10x compared to unoptimized models. To properly quantify the efficiency of our architecture, we build a custom hardware solution on an FPGA which exploits lightweight operations beyond what GPUs are capable of. We processed billion-parameter scale models at 13W beyond human readable throughput, moving LLMs closer to https://www.youtube.com/embed/B45FlSQ8ITo 2026-03-17T13:57:53.000Z Yannic Kilcher 2985 yes no https://vidert.com/v/artificial-intelligence/video/14599/hallucination-free-assessing-the-reliability-of-leading-ai-legal-research-tools- 2026-03-17T13:57:36.000Z weekly 0.5 https://i.ytimg.com/vi/no7EQkOiHQM/maxresdefault.jpg Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools (Paper Explained) #rag #hallucinations #legaltech An in-depth look at a recent Stanford paper examining the degree of hallucinations in various LegalTech tools that incorporate LLMs. OUTLINE: 0:00 - Intro 1:58 - What are legal research tools and how are large language models used by them? 5:30 - Overview and abstract of the paper 9:29 - What is a hallucination and why do they occur? 15:45 - What is retrieval augmented generation (RAG)? 25:00 - Why LLMs are a bad choice when reasoning is involved 29:16 - The products that were tested 32:00 - Some shady practices by the researchers in the back and forth with the legal research companies 37:00 - Legal technology companies’ marketing claims to eliminate or solve hallucination risk 45:27 - Researchers evaluation of RAG for legal and requirement to have specialized education to use the research tools 55:27 - How the researchers propose to measure accuracy and the problems of measuring accuracy 1:09:20 - Researchers conclusion Paper: https://arxiv.org/abs/2405.20362 Abstract: Legal practice has witnessed a sharp rise in products incorporating artificial intelligence (AI). Such tools are designed to assist with a wide range of core legal tasks, from search and summarization of caselaw to document drafting. But the large language models used in these tools are prone to "hallucinate," or make up false information, making their use risky in high-stakes domains. Recently, certain legal research providers have touted methods such as retrieval-augmented generation (RAG) as "eliminating" (Casetext, 2023) or "avoid[ing]" hallucinations (Thomson Reuters, 2023), or guaranteeing "hallucination-free" legal citations (LexisNexis, 2023). Because of the closed nature of these systems, systematically assessing these claims is challenging. In this article, we design and report on the first preregistered empirical evaluation of AI-driven legal research tools. We demonstrate that the providers' claims are overstated. While hallucinations are reduced relative to general-purpose chatbots (GPT-4), we find https://www.youtube.com/embed/no7EQkOiHQM 2026-03-17T13:57:36.000Z Yannic Kilcher 4318 yes no https://vidert.com/v/artificial-intelligence/video/14598/xlstm-extended-long-short-term-memory 2026-03-17T13:57:21.000Z weekly 0.5 https://i.ytimg.com/vi/0OaEv1a5jUM/maxresdefault.jpg xLSTM: Extended Long Short-Term Memory xLSTM is an architecture that combines the recurrency and constant memory requirement of LSTMs with the large-scale training of transformers and achieves impressive results. Paper: https://arxiv.org/abs/2405.04517 Abstract: In the 1990s, the constant error carousel and gating were introduced as the central ideas of the Long Short-Term Memory (LSTM). Since then, LSTMs have stood the test of time and contributed to numerous deep learning success stories, in particular they constituted the first Large Language Models (LLMs). However, the advent of the Transformer technology with parallelizable self-attention at its core marked the dawn of a new era, outpacing LSTMs at scale. We now raise a simple question: How far do we get in language modeling when scaling LSTMs to billions of parameters, leveraging the latest techniques from modern LLMs, but mitigating known limitations of LSTMs? Firstly, we introduce exponential gating with appropriate normalization and stabilization techniques. Secondly, we modify the LSTM memory structure, obtaining: (i) sLSTM with a scalar memory, a scalar update, and new memory mixing, (ii) mLSTM that is fully parallelizable with a matrix memory and a covariance update rule. Integrating these LSTM extensions into residual block backbones yields xLSTM blocks that are then residually stacked into xLSTM architectures. Exponential gating and modified memory structures boost xLSTM capabilities to perform favorably when compared to state-of-the-art Transformers and State Space Models, both in performance and scaling. Authors: Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the cont https://www.youtube.com/embed/0OaEv1a5jUM 2026-03-17T13:57:21.000Z Yannic Kilcher 3420 yes no https://vidert.com/v/artificial-intelligence/video/14597/ml-news-openai-is-in-hot-waters-gpt-4o-ilya-leaving-scarlett-johansson-legal-act 2026-03-17T13:57:05.000Z weekly 0.5 https://i.ytimg.com/vi/5bPBbQyLI7E/maxresdefault.jpg [ML News] OpenAI is in hot waters (GPT-4o, Ilya Leaving, Scarlett Johansson legal action) #gpt4o #sky #scarlettjohansson After the release of their flagship model GPT-4o, OpenAI finds itself in multiple controversies and an exodus of senior personnel - notably Ilya Sutskever References: https://openai.com/index/gpt-4o-and-more-tools-to-chatgpt-free/ https://openai.com/index/hello-gpt-4o/ https://x.com/LiamFedus/status/1790064963966370209?t=rx2YBT9AdDdKPhI6dUH4zA&s=09 https://x.com/lmsysorg/status/1790097588399779991?t=rx2YBT9AdDdKPhI6dUH4zA&s=09 https://x.com/bindureddy/status/1790127425705120149?t=mMUBqFBRphx-bDuZ1j3mjQ&s=09 https://openai.com/index/improvements-to-data-analysis-in-chatgpt/ https://openai.com/index/openai-and-reddit-partnership/ https://archive.ph/jHlMm https://www.vox.com/future-perfect/2024/5/17/24158478/openai-departures-sam-altman-employees-chatgpt-release https://x.com/soumithchintala/status/1791547776804831673?t=pKvy-PHndHFb4QBOpDBHFw&s=09 https://x.com/sama/status/1791936857594581428?t=tM0Bi50VmbiIwCypiHS0Gg&s=09 https://x.com/ilyasut/status/1790517455628198322?t=4Rb4lY401dfJRjQAF_H5Fw&s=09 https://x.com/sama/status/1790518031640347056?t=fgL4bpi2oFwYQHykwIb6Lw&s=09 https://x.com/janleike/status/1791498174659715494 https://x.com/sama/status/1791543264090472660 https://x.com/gdb/status/1791869138132218351?t=87L_tKgBpiFO7o8w_oKS4A&s=09 https://openai.com/index/how-the-voices-for-chatgpt-were-chosen/ https://www.forbes.com/sites/roberthart/2024/05/20/openai-says-its-pulling-chatgpt-voice-sky-that-sounds-like-scarlett-johansson/?sh=593844605725 https://x.com/BobbyAllyn/status/1792679435701014908/photo/1 https://x.com/stclairashley/status/1792710045668630905?t=HR7-U3hsxhL6XYCXnINisw&s=09 Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, https://www.youtube.com/embed/5bPBbQyLI7E 2026-03-17T13:57:05.000Z Yannic Kilcher 1762 yes no https://vidert.com/v/artificial-intelligence/video/14596/orpo-monolithic-preference-optimization-without-reference-model-paper-explained 2026-03-17T13:56:49.000Z weekly 0.5 https://i.ytimg.com/vi/52kMBrAI_IM/maxresdefault.jpg ORPO: Monolithic Preference Optimization without Reference Model (Paper Explained) Paper: https://arxiv.org/abs/2403.07691 Abstract: While recent preference alignment algorithms for language models have demonstrated promising results, supervised fine-tuning (SFT) remains imperative for achieving successful convergence. In this paper, we study the crucial role of SFT within the context of preference alignment, emphasizing that a minor penalty for the disfavored generation style is sufficient for preference-aligned SFT. Building on this foundation, we introduce a straightforward and innovative reference model-free monolithic odds ratio preference optimization algorithm, ORPO, eliminating the necessity for an additional preference alignment phase. We demonstrate, both empirically and theoretically, that the odds ratio is a sensible choice for contrasting favored and disfavored styles during SFT across the diverse sizes from 125M to 7B. Specifically, fine-tuning Phi-2 (2.7B), Llama-2 (7B), and Mistral (7B) with ORPO on the UltraFeedback alone surpasses the performance of state-of-the-art language models with more than 7B and 13B parameters: achieving up to 12.20% on AlpacaEval2.0 (Figure 1), 66.19% on IFEval (instruction-level loose, Table 6), and 7.32 in MT-Bench (Figure 12). We release code and model checkpoints for Mistral-ORPO-α (7B) and Mistral-ORPO-β (7B). Authors: Jiwoo Hong, Noah Lee, James Thorne Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPht https://www.youtube.com/embed/52kMBrAI_IM 2026-03-17T13:56:49.000Z Yannic Kilcher 2006 yes no https://vidert.com/v/artificial-intelligence/video/14595/ml-news-chips-robots-and-models 2026-03-17T13:56:34.000Z weekly 0.5 https://i.ytimg.com/vi/tRavLU8Ih4A/maxresdefault.jpg [ML News] Chips, Robots, and Models OUTLINE: 0:00 - Intro 0:19 - Our next-generation Meta Training and Inference Accelerator 01:39 - ALOHA Unleashed 03:10 - Apple Inks $50M Deal with Shutterstock for AI Training Data 04:28 - OpenAI Researchers, Including Ally of Sutskever, Fired for Alleged Leaking 05:01 - Adobe's Ethical Firefly AI was Trained on Midjourney Images 05:52 - Trudeau announces $2.4billion for AI-related investments 06:48 - RecurrentGemma: Moving Past Transformers for Efficient Open Language Models 07:15 - CodeGemma - an official Google release for code LLMs 07:24 - Mistral AI: Cheaper, Better, Faster, Stronger 08:08 - Vezora/Mistral-22B-v0.1 09:00 - WizardLM-2, next generation state-of-the-art-LLM 09:31 - Idefics2, the strongest Vision-Language-Model (VLM) below 10B! 10:14 - BlinkDL/rwkv-6-world 10:50 - Pile-T5: Trained T5 on the Pile 11:35 - Model Card for Zephyr 141B-A39B 12:42 - Parler TTS 13:11 - RHO-1: Not all tokens are what you need 14:59 - Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs References: https://twitter.com/ayzwah/status/1780263768968273923 https://ai.meta.com/blog/next-generation-meta-training-inference-accelerator-AI-MTIA/?utm_source=twitter https://twitter.com/soumithchintala/status/1778087952964374854?t=Mb-mQvm4YIZ35pVpEijs6g&s=09 https://deepnewz.com/tech/apple-inks-50m-deal-shutterstock-ai-training-data https://twitter.com/TolgaBilge_/status/1778598047821291793?t=zInlPDRZzozcz7-pjFSnyA&s=09 https://twitter.com/javilopen/status/1778821749792034911?t=oGLiMj6GQdKTuM6GbiYrAg&s=09 https://twitter.com/paulg/status/1781329523155357914?t=vCQT2mJf5BbtjdN1BMFYFQ&s=09 https://twitter.com/RichardSocher/status/1776706907295846628 https://www.cbc.ca/news/politics/federal-government-ai-investment-1.7166234 https://arxiv.org/pdf/2404.07839 https://huggingface.co/blog/codegemma https://mistral.ai/news/mixtral-8x22b/ https://twitter.com/MistralAILabs/status/1780606904273702932?t=JlSCcYulpJL74pNJbtSZag&s=09 https://huggingface.co/Vezora/Mistral-22B-v0.1 https://huggingface.co/Vezora/Mistral-22B-v0.2 https://twi https://www.youtube.com/embed/tRavLU8Ih4A 2026-03-17T13:56:34.000Z Yannic Kilcher 2354 yes no https://vidert.com/v/artificial-intelligence/video/14594/transformerfam-feedback-attention-is-working-memory 2026-03-17T13:56:17.000Z weekly 0.5 https://i.ytimg.com/vi/3a0_hAiFKag/maxresdefault.jpg TransformerFAM: Feedback attention is working memory Paper: https://arxiv.org/abs/2404.09173 Abstract: While Transformers have revolutionized deep learning, their quadratic attention complexity hinders their ability to process infinitely long inputs. We propose Feedback Attention Memory (FAM), a novel Transformer architecture that leverages a feedback loop to enable the network to attend to its own latent representations. This design fosters the emergence of working memory within the Transformer, allowing it to process indefinitely long sequences. TransformerFAM requires no additional weights, enabling seamless integration with pre-trained models. Our experiments show that TransformerFAM significantly improves Transformer performance on long-context tasks across various model sizes (1B, 8B, and 24B). These results showcase the potential to empower Large Language Models (LLMs) to process sequences of unlimited length. Authors: Dongseong Hwang, Weiran Wang, Zhuoyuan Huo, Khe Chai Sim, Pedro Moreno Mengibar Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/3a0_hAiFKag 2026-03-17T13:56:17.000Z Yannic Kilcher 2221 yes no https://vidert.com/v/artificial-intelligence/video/14593/ml-news-devin-exposed-neurips-track-for-high-school-students 2026-03-17T13:56:01.000Z weekly 0.5 https://i.ytimg.com/vi/GtveKYXYo_0/maxresdefault.jpg [ML News] Devin exposed | NeurIPS track for high school students OUTLINE: 0:00 - Intro 0:21 - Debunking Devin: "First AI Software Engineer" Upwork lie exposed! 07:24 - NeurIPS 2024 will have a track for papers from high schoolers. 13:29 - Opus can operate as a Turing machine. 13:47 - An AI-Powered, Self-Running Propaganda Machine for $105 14:27 - TechScape: How cheap, outsourced labour in Africa is shaping AI English 16:25 - Is ChatGPT Transforming Academics' Writing Style? References: https://news.ycombinator.com/item?id=40008109&s=09 https://www.youtube.com/watch?v=tNmgmwEtoWE https://www.youtube.com/watch?v=xE2fxcETP5E https://twitter.com/itsandrewgao/status/1779369373737668669?t=omW3DvRNmZyce8oo0Ehf1g&s=09 https://twitter.com/0interestrates/status/1779268441226256500?t=tGwngUpChSD2YZ0VQDJHAA&s=09 https://twitter.com/thegautamkamath/status/1778580754785550819?t=Qq1nLUIOyfRfBbZ6BHdXPw&s=09 https://twitter.com/vipul_1011/status/1778619720964419930?t=225aakPnHb-ojIjveaWkkg&s=09 https://twitter.com/avt_im/status/1778913195408626110?t=UPtduAKTX1uvq8Wa_EQOWg&s=09 https://arxiv.org/pdf/2402.05120.pdf https://twitter.com/ctjlewis/status/1779740038852690393?t=AhIQM4rBUim-IWEkXL7OVQ&s=33 https://www.wsj.com/politics/how-i-built-an-ai-powered-self-running-propaganda-machine-for-105-e9888705 https://twitter.com/ylecun/status/1780728376283521191?t=rbTfUT7IWzXy83fvr-f4hw&s=09 https://www.futureofhumanityinstitute.org/ https://www.google.com/search?q=alex+hern+guardian+delve&oq=alex+hern+guardian+delve&gs_lcrp=EgZjaHJvbWUyBggAEEUYOTIHCAEQIRigATIHCAIQIRigATIHCAMQIRigATIHCAQQIRiPAtIBCDQ5NTVqMGo0qAIAsAIB&sourceid=chrome&ie=UTF-8 https://www.theguardian.com/technology/2024/apr/16/techscape-ai-gadgest-humane-ai-pin-chatgpt https://arxiv.org/pdf/2404.08627.pdf Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want t https://www.youtube.com/embed/GtveKYXYo_0 2026-03-17T13:56:01.000Z Yannic Kilcher 1067 yes no https://vidert.com/v/artificial-intelligence/video/14592/leave-no-context-behind-efficient-infinite-context-transformers-with-infini-atte 2026-03-17T13:55:44.000Z weekly 0.5 https://i.ytimg.com/vi/r_UBBfTPcF0/maxresdefault.jpg Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention Google researchers achieve supposedly infinite context attention via compressive memory. Paper: https://arxiv.org/abs/2404.07143 Abstract: This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs. Authors: Tsendsuren Munkhdalai, Manaal Faruqui, Siddharth Gopal Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/r_UBBfTPcF0 2026-03-17T13:55:44.000Z Yannic Kilcher 2237 yes no https://vidert.com/v/artificial-intelligence/video/14591/ml-news-llama-3-changes-the-game 2026-03-17T13:55:30.000Z weekly 0.5 https://i.ytimg.com/vi/kzB23CoZG30/maxresdefault.jpg [ML News] Llama 3 changes the game Meta's Llama 3 is out. New model, new license, new opportunities. References: https://llama.meta.com/llama3/ https://ai.meta.com/blog/meta-llama-3/ https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md https://llama.meta.com/trust-and-safety/ https://ai.meta.com/research/publications/cyberseceval-2-a-wide-ranging-cybersecurity-evaluation-suite-for-large-language-models/ https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai https://llama.meta.com/llama3/license/ https://about.fb.com/news/2024/04/meta-ai-assistant-built-with-llama-3/?utm_source=twitter&utm_medium=organic_social&utm_content=thread&utm_campaign=imagineflash https://twitter.com/minchoi/status/1782775792298037639?t=6U7Ob9P0SQmYdyLGUGq0Kg&s=09 https://twitter.com/_akhaliq/status/1782607138952499661?t=osENiISXOhJEf89b9QAjSA&s=09 https://twitter.com/_philschmid/status/1782420712105357616?t=vQQt7O9abWazZ-R3k3l9Kg&s=09 https://twitter.com/lmsysorg/status/1782483699449332144?t=h1EdrbrXi0_03gXXbhXskw&s=09 https://twitter.com/SebastienBubeck/status/1782627991874678809?t=QvZngdG1k0TllAyzT0qAsg&s=09 https://twitter.com/_Mira___Mira_/status/1782595759726354485?t=QvZngdG1k0TllAyzT0qAsg&s=09 https://twitter.com/_philschmid/status/1782358903558205556?t=h1EdrbrXi0_03gXXbhXskw&s=09 https://twitter.com/cHHillee/status/1781060345366503527?t=5ONxSzdwnghsKcwq3IPmEQ&s=09 https://www.meta.ai/?icebreaker=imagine https://twitter.com/OpenAI/status/1777772582680301665?t=DKDx-qwUP3Xr4oFvAM9mOQ&s=09 https://twitter.com/OpenAIDevs/status/1780640119890047475?t=YOJFQ6Ysx7JVDfZ6o3TT6A&s=09 https://twitter.com/OpenAIDevs/status/1779922566091522492?t=KhlVzoXh3NjCld1JiobsTw&s=09 https://twitter.com/CodeByPoonam/status/1776902550811525146?t=3cK96YjTWJnY0RmHLwAPsg&s=09 https://twitter.com/hey_madni/status/1776950057801236933?t=P2x2bXrYgMHm8jX7k2CAaQ&s=09 https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-gemini-image-2-and-mlops-updates https://twitter.com/altryne/status/1778522661070475586?t=jdDna4B-45yLez12yuElig&s=09 https://twitter.c https://www.youtube.com/embed/kzB23CoZG30 2026-03-17T13:55:30.000Z Yannic Kilcher 1879 yes no https://vidert.com/v/artificial-intelligence/video/14590/hugging-face-got-hacked 2026-03-17T13:55:15.000Z weekly 0.5 https://i.ytimg.com/vi/ZcoOW8nqVP8/maxresdefault.jpg Hugging Face got hacked Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/ZcoOW8nqVP8 2026-03-17T13:55:15.000Z Yannic Kilcher 1081 yes no https://vidert.com/v/artificial-intelligence/video/14589/ml-news-microsoft-to-spend-100-billion-dollars-on-supercomputer-more-industry-ne 2026-03-17T13:55:01.000Z weekly 0.5 https://i.ytimg.com/vi/DRwwjifoVZU/maxresdefault.jpg [ML News] Microsoft to spend 100 BILLION DOLLARS on supercomputer (& more industry news) Some updates from industry in the Machine Learning world Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/DRwwjifoVZU 2026-03-17T13:55:01.000Z Yannic Kilcher 595 yes no https://vidert.com/v/artificial-intelligence/video/14588/ml-news-jamba-cmd-r-and-other-new-models-yes-i-know-this-is-like-a-week-behind 2026-03-17T13:54:50.000Z weekly 0.5 https://i.ytimg.com/vi/Kk8YhCpo1b8/maxresdefault.jpg [ML News] Jamba, CMD-R+, and other new models (yes, I know this is like a week behind 🙃) A flurry of new models continues to appear. Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/Kk8YhCpo1b8 2026-03-17T13:54:50.000Z Yannic Kilcher 1652 yes no https://vidert.com/v/artificial-intelligence/video/14587/flow-matching-for-generative-modeling-paper-explained 2026-03-17T13:54:37.000Z weekly 0.5 https://i.ytimg.com/vi/7NNxK3CqaDk/maxresdefault.jpg Flow Matching for Generative Modeling (Paper Explained) Flow matching is a more general method than diffusion and serves as the basis for models like Stable Diffusion 3. Paper: https://arxiv.org/abs/2210.02747 Abstract: We introduce a new paradigm for generative modeling built on Continuous Normalizing Flows (CNFs), allowing us to train CNFs at unprecedented scale. Specifically, we present the notion of Flow Matching (FM), a simulation-free approach for training CNFs based on regressing vector fields of fixed conditional probability paths. Flow Matching is compatible with a general family of Gaussian probability paths for transforming between noise and data samples -- which subsumes existing diffusion paths as specific instances. Interestingly, we find that employing FM with diffusion paths results in a more robust and stable alternative for training diffusion models. Furthermore, Flow Matching opens the door to training CNFs with other, non-diffusion probability paths. An instance of particular interest is using Optimal Transport (OT) displacement interpolation to define the conditional probability paths. These paths are more efficient than diffusion paths, provide faster training and sampling, and result in better generalization. Training CNFs using Flow Matching on ImageNet leads to consistently better performance than alternative diffusion-based methods in terms of both likelihood and sample quality, and allows fast and reliable sample generation using off-the-shelf numerical ODE solvers. Authors: Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, Maximilian Nickel, Matt Le Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher https://www.youtube.com/embed/7NNxK3CqaDk 2026-03-17T13:54:37.000Z Yannic Kilcher 3376 yes no https://vidert.com/v/artificial-intelligence/video/14586/beyond-a-better-planning-with-transformers-via-search-dynamics-bootstrapping-sea 2026-03-17T13:54:24.000Z weekly 0.5 https://i.ytimg.com/vi/PW4JiJ-WaY4/maxresdefault.jpg Beyond A*: Better Planning with Transformers via Search Dynamics Bootstrapping (Searchformer) Paper: https://arxiv.org/abs/2402.14083 Abstract: While Transformers have enabled tremendous progress in various application settings, such architectures still lag behind traditional symbolic planners for solving complex decision making tasks. In this work, we demonstrate how to train Transformers to solve complex planning tasks and present Searchformer, a Transformer model that optimally solves previously unseen Sokoban puzzles 93.7% of the time, while using up to 26.8% fewer search steps than standard A∗ search. Searchformer is an encoder-decoder Transformer model trained to predict the search dynamics of A∗. This model is then fine-tuned via expert iterations to perform fewer search steps than A∗ search while still generating an optimal plan. In our training method, A∗'s search dynamics are expressed as a token sequence outlining when task states are added and removed into the search tree during symbolic planning. In our ablation studies on maze navigation, we find that Searchformer significantly outperforms baselines that predict the optimal plan directly with a 5-10× smaller model size and a 10× smaller training dataset. We also demonstrate how Searchformer scales to larger and more complex decision making tasks like Sokoban with improved percentage of solved tasks and shortened search dynamics. Authors: Lucas Lehnert, Sainbayar Sukhbaatar, Paul Mcvay, Michael Rabbat, Yuandong Tian Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f https://www.youtube.com/embed/PW4JiJ-WaY4 2026-03-17T13:54:24.000Z Yannic Kilcher 2645 yes no https://vidert.com/v/artificial-intelligence/video/14585/ml-news-grok-1-open-sourced-nvidia-gtc-openai-leaks-model-names-ai-act 2026-03-17T13:54:10.000Z weekly 0.5 https://i.ytimg.com/vi/dnTGn1EQqtQ/maxresdefault.jpg [ML News] Grok-1 open-sourced | Nvidia GTC | OpenAI leaks model names | AI Act OUTLINE: 0:00 - Intro 0:15 - XAI releases Grok-1 2:00 - Nvidia GTC 4:45 - Comment of the Week 5:35 - Brute-forcing OpenAI model names 7:30 - Inflection AI gets eaten by Microsoft 9:25 - EU AI Act moving forward 11:45 - Advances in Robotics 14:00 - India retracts controversial advisory 14:30 - OpenSora 15:20 - Improved Gemma fine-tuning 16:20 - Decoding encrypted LLM traffic 17:45 - Varia References: https://x.ai/blog/grok-os https://github.com/xai-org/grok-1 https://finance.yahoo.com/news/nvidia-debuts-next-generation-blackwell-ai-chip-at-gtc-2024-205825161.html?guccounter=1&guce_referrer=aHR0cHM6Ly9uZXdzLmdvb2dsZS5jb20v&guce_referrer_sig=AQAAAHYRVePPrDnH3HxPV8smDzUiia_ztWttteAmHKxy-x_Z75lqq2trR4Exwq2sFyjNQojO_95xWvqQFHkV3NI_IKmw9W8XZ7d52qBsdvqaDRkdNzBSzQhnskzUE_E-nDo6OFG0LmrM0ygvjqLgJyhMDnraaGHrUsb98kknjn7-83MJ https://spectrum.ieee.org/nvidia-gr00t-ros https://twitter.com/anshelsag/status/1769989302552031473?t=DYAFhri4cu55LMwJV4V99A&s=09 https://twitter.com/ibab_ml/status/1769770983924142475 https://twitter.com/arthurmensch/status/1769842867621581299?t=sYPy011kN9KxzdnA11M4yQ&s=09 https://twitter.com/arithmoquine/status/1770136393563378082?t=FgH3-TABR73QVUQuP5wq2g&s=09 https://files.catbox.moe/od9pyb.txt https://techcrunch.com/2024/03/19/after-raising-1-3b-inflection-got-eaten-alive-by-its-biggest-investor-microsoft/ https://archive.ph/p4W1N#selection-2463.23-2463.114 https://www.instagram.com/reel/C4df3DZg1wj/?igsh=MWQ1ZGUxMzBkMA%3D%3D https://techcrunch.com/2024/03/15/mercedes-begins-piloting-apptronik-humanoid-robots/ https://www.axios.com/2024/03/14/humanoid-robot-army-agility-digit-amazon-warehouse https://techcrunch.com/2024/03/15/india-drops-plan-to-require-approval-for-ai-model-launches/ https://github.com/hpcaitech/Open-Sora https://www.reddit.com/r/LocalLLaMA/comments/1bd18y8/gemma_finetuning_should_be_much_better_now/ https://twitter.com/felix_red_panda/status/1769363356094230837?t=JMMb3OldqfhhCH8X5e7ljA&s=09 https://twitter.com/imaurer/status/1768386949201408103 https://twitter.com/ollama/status/176 https://www.youtube.com/embed/dnTGn1EQqtQ 2026-03-17T13:54:10.000Z Yannic Kilcher 1620 yes no https://vidert.com/v/artificial-intelligence/video/14584/ml-news-devin-ai-software-engineer-gpt-4-5-turbo-leaked-us-gov-t-report-total-ex 2026-03-17T13:53:53.000Z weekly 0.5 https://i.ytimg.com/vi/q1LrXH5_Oy0/maxresdefault.jpg [ML News] Devin AI Software Engineer | GPT-4.5-Turbo LEAKED | US Gov't Report: Total Extinction Your weekly dose of ML News OUTLINE: 0:00 - Intro 0:15 - Devin: AI software engineer 5:50 - Mira Murati on Sora training data 6:50 - Inflection accused of copying Claude 9:00 - Tools & papers 16:30 - GPT-4.5-turbo mystery 17:30 - US government report: total extinction by AI 19:20 - Various other news References: https://www.cognition-labs.com/introducing-devin https://twitter.com/cognition_labs/status/1767548763134964000?t=ZECIn-uqbguwHtY8X_Gvtw&s=09 https://news.google.com/stories/CAAqNggKIjBDQklTSGpvSmMzUnZjbmt0TXpZd1NoRUtEd2lWMUwyU0N4RnVWM3pSRWhWX01pZ0FQAQ?hl=en-US&gl=US&ceid=US%3Aen https://www.bloomberg.com/news/articles/2024-03-12/cognition-ai-is-a-peter-thiel-backed-coding-assistant?embedded-checkout=true https://www.bloomberg.com/authors/AQWHkoPod9g/ashlee-vance https://www.bloomberg.com/news/articles/2024-03-12/cognition-ai-is-a-peter-thiel-backed-coding-assistant?srnd=undefined&embedded-checkout=true https://www.bloomberg.com/news/newsletters/2024-03-12/cognition-ai-s-devin-assistant-can-build-websites-videos-from-a-prompt?srnd=undefined&embedded-checkout=true https://archive.ph/5LZV9 https://github.com/opendevin/opendevin https://twitter.com/MetaGPT_/status/1767965444579692832?t=dsYKmPfOBVGCFCwvPtZVWQ&s=09 https://docs.deepwisdom.ai/main/en/DataInterpreter/detail.html?id=AppleStockPriceAnalysisAndPrediction https://docs.deepwisdom.ai/main/en/guide/use_cases/agent/interpreter/intro.html https://github.com/geekan/MetaGPT/tree/main/examples/di https://inflection.ai/inflection-2-5 https://twitter.com/seshubon/status/1765870717844050221 https://twitter.com/inflectionAI/status/1766173427441049684 https://www.mlxserver.com/ https://huggingface.co/spaces/mlabonne/AutoMerger https://github.com/microsoft/aici https://github.com/google-research/google-research/tree/master/fax https://github.com/stanfordnlp/pyvene https://arxiv.org/pdf/2403.06634.pdf https://twitter.com/mattshumer_/status/1767606938538295757?t=1dYect5ylg9xrWSS4sL38Q&s=09 https://time.com/6898967/ai-extinction-national-security-risks-report/ http https://www.youtube.com/embed/q1LrXH5_Oy0 2026-03-17T13:53:53.000Z Yannic Kilcher 1610 yes no https://vidert.com/v/artificial-intelligence/video/14583/ml-news-elon-sues-openai-mistral-large-more-gemini-drama 2026-03-17T13:53:37.000Z weekly 0.5 https://i.ytimg.com/vi/YOyr9Bhhaq0/maxresdefault.jpg [ML News] Elon sues OpenAI | Mistral Large | More Gemini Drama #mlnews #ainews #openai OUTLINE: 0:00 - Intro 0:20 - Elon sues OpenAI 14:00 - Mistral Large 16:40 - ML Espionage 18:30 - More Gemini Drama 24:00 - Copilot generates spicy images 26:55 - Gemma bugs 28:45 - Varia References: https://gist.github.com/yk/0c065cdc8e414738abfaae4f8e417e00 Thumbnail pictures: Wikipedia Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/YOyr9Bhhaq0 2026-03-17T13:53:37.000Z Yannic Kilcher 3195 yes no https://vidert.com/v/artificial-intelligence/video/14582/on-claude-3 2026-03-17T13:53:21.000Z weekly 0.5 https://i.ytimg.com/vi/GIgOlQ0kAc8/maxresdefault.jpg On Claude 3 Watch On Claude 3 on Vidert https://www.youtube.com/embed/GIgOlQ0kAc8 2026-03-17T13:53:21.000Z Yannic Kilcher 60 yes no https://vidert.com/v/artificial-intelligence/video/14581/no-anthropic-s-claude-3-is-not-sentient 2026-03-17T13:53:09.000Z weekly 0.5 https://i.ytimg.com/vi/GBOE9fVVVSM/maxresdefault.jpg No, Anthropic's Claude 3 is NOT sentient No, Anthropic's Claude 3 is not conscious or sentient or self-aware. References: https://www.anthropic.com/news/claude-3-family https://twitter.com/_akhaliq/status/1764673955313459560?t=gkBx2uTXfrxLl-5_mL7Btg&s=09 https://twitter.com/idavidrein/status/1764675668175094169?t=pJfbN3LtKaxsU8egz83Mvg&s=09 https://twitter.com/TolgaBilge_/status/1764754012824314102?t=9bakXDnVMC1oAEyZFoKimA&s=09 https://twitter.com/karinanguyen_/status/1764670019743690757?t=gkBx2uTXfrxLl-5_mL7Btg&s=09 https://twitter.com/alexalbert__/status/1764722513014329620 https://www.lesswrong.com/posts/pc8uP4S9rDoNpwJDZ/claude-3-claims-its-conscious Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/GBOE9fVVVSM 2026-03-17T13:53:09.000Z Yannic Kilcher 912 yes no https://vidert.com/v/artificial-intelligence/video/14580/ml-news-groq-gemma-sora-gemini-and-air-canada-s-chatbot-troubles 2026-03-17T13:52:55.000Z weekly 0.5 https://i.ytimg.com/vi/3nF8Z6HgSLQ/maxresdefault.jpg [ML News] Groq, Gemma, Sora, Gemini, and Air Canada's chatbot troubles Your dose of ML News! OUTLINE: 0:00 - Intro 0:20 - Gemma & Gemini 3:40 - Groq 6:30 - Nvidia EOS Supercomputer 7:15 - Gpulist.ai 8:20 - Demis Hassabis on scale 10:10 - Hardware wars 12:05 - Sora 15:10 - Gemini 1.5 Pro & Long Context 18:45 - Air Canada must pay for chatbot mistake 23:30 - Giant Rat Balls 26:25 - Various News References: https://blog.google/technology/developers/gemma-open-models/?utm_source=tw https://twitter.com/altryne/status/1760358916624719938?t=PVZkHQA_p7GxmeUX0hcZ_Q&s=09 https://twitter.com/paulg/status/1760078920135872716?t=PVZkHQA_p7GxmeUX0hcZ_Q&s=09 https://groq.com/ https://twitter.com/mattshumer_/status/1759347920543834117?t=cS5nPvZOsV6iDA1mVabHOg&s=09 https://twitter.com/GroqInc/status/1759483896322781584 https://wow.groq.com/news_press/groq-lpu-inference-engine-leads-in-first-independent-llm-benchmark/ https://twitter.com/tianle_cai/status/1759780363361251828?t=SobcZzLkKufAhKaSK56DoA&s=09 https://twitter.com/DZhang50/status/1759728119005712837 https://twitter.com/felix_red_panda/status/1759720197055791188 https://twitter.com/cHHillee/status/1759704303810519271 https://twitter.com/mascobot/status/1759709223276228825 https://www.techpowerup.com/319172/nvidia-unveils-eos-to-public-a-top-ten-supercomputer https://andromeda.ai/ https://gpulist.ai/ https://archive.ph/G6POi https://www.tomshardware.com/tech-industry/artificial-intelligence/jim-keller-responds-to-sam-altmans-plan-to-raise-dollar7-billion-to-make-ai-chips https://futurism.com/the-byte/ai-destroy-humankind-yudkowsky https://twitter.com/_akhaliq/status/1758197872716026209?t=P6KPJIJ4Xxr82oMkh_Hd3w&s=09 https://twitter.com/_Borriss_/status/1758206358376050822?t=drmW5Qzs7OuEaV_00uSqHQ&s=09 https://twitter.com/billpeeb/status/1758650919430848991 https://twitter.com/tsarnick/status/1758323312483303443?t=SmELRZbMIH_1hfx-T4RNHA&s=09 https://twitter.com/MartinNebelong/status/1758431263193543080?t=do6FAkgZL8qpblevr8uxeQ&s=09 https://twitter.com/OriolVinyalsML/status/1758148444588319020?t=K2RYfqbLuBvP-viCaPyC-Q&s=09 https://twitter.com/ https://www.youtube.com/embed/3nF8Z6HgSLQ 2026-03-17T13:52:55.000Z Yannic Kilcher 2554 yes no https://vidert.com/v/artificial-intelligence/video/14579/gemini-has-a-diversity-problem 2026-03-17T13:52:38.000Z weekly 0.5 https://i.ytimg.com/vi/Fr6Teh_ox-8/maxresdefault.jpg Gemini has a Diversity Problem Google turned the anti-bias dial up to 11 on their new Gemini Pro model. References: https://developers.googleblog.com/2024/02/gemini-15-available-for-private-preview-in-google-ai-studio.html https://blog.google/technology/developers/gemma-open-models/?utm_source=tw https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf https://twitter.com/ClementDelangue/status/1760324815888486668?t=spXd7Oq_cSrRN2A-3r6gnQ&s=09 https://twitter.com/paulg/status/1760078920135872716?t=PVZkHQA_p7GxmeUX0hcZ_Q&s=09 https://twitter.com/yoavgo/status/1760445342691016811/photo/3 https://twitter.com/alex_peys/status/1760327435890135279/photo/2 https://twitter.com/woke8yearold/status/1760310705142558781/photo/1 https://twitter.com/stratejake/status/1760333904857497650?t=Z3BZOBaLI1EYAJ-CBAMNEg&s=09 https://twitter.com/JohnLu0x/status/1760066875583816003?t=Z3BZOBaLI1EYAJ-CBAMNEg&s=09 https://twitter.com/IMAO_/status/1760093853430710557?t=0eNmoTuvYZl9HQRaUBOKNw&s=09 https://twitter.com/WallStreetSilv/status/1760474958151426340?t=6k4VwKFvciw2VoDc70Tl2A&s=09 https://twitter.com/JackK/status/1760334258722250785 https://twitter.com/TRHLofficial/status/1760485063941149100?t=hx48DQd64JbVxZ3OzhD0wg&s=09 https://twitter.com/gordic_aleksa/status/1760266452475494828?t=VZ2lX_v-KrY4Thu4FvDh4w&s=09 https://twitter.com/benthompson/status/1760452419627233610?t=qR9D9KDC1axOx3gDBKKc2Q&s=09 https://twitter.com/altryne/status/1760358916624719938?t=PVZkHQA_p7GxmeUX0hcZ_Q&s=09 https://twitter.com/pmarca/status/1760503344035180601?t=6k4VwKFvciw2VoDc70Tl2A&s=09 Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilche https://www.youtube.com/embed/Fr6Teh_ox-8 2026-03-17T13:52:38.000Z Yannic Kilcher 1056 yes no https://vidert.com/v/artificial-intelligence/video/14578/v-jepa-revisiting-feature-prediction-for-learning-visual-representations-from-vi 2026-03-17T13:52:26.000Z weekly 0.5 https://i.ytimg.com/vi/7UkJPwz_N_0/maxresdefault.jpg V-JEPA: Revisiting Feature Prediction for Learning Visual Representations from Video (Explained) #vjepa #meta #unsupervisedlearning V-JEPA is a method for unsupervised representation learning of video data by using only latent representation prediction as objective function. Weights & Biases course on Structured LLM Outputs: https://wandb.me/course-yannic OUTLINE: 0:00 - Intro 1:45 - Predictive Feature Principle 8:00 - Weights & Biases course on Structured LLM Outputs 9:45 - The original JEPA architecture 27:30 - V-JEPA Concept 33:15 - V-JEPA Architecture 44:30 - Experimental Results 46:30 - Qualitative Evaluation via Decoding Blog: https://ai.meta.com/blog/v-jepa-yann-lecun-ai-model-video-joint-embedding-predictive-architecture/ Paper: https://ai.meta.com/research/publications/revisiting-feature-prediction-for-learning-visual-representations-from-video/ Abstract: This paper explores feature prediction as a stand-alone objective for unsupervised learning from video and introduces V-JEPA, a collection of vision models trained solely using a feature prediction objective, without the use of pretrained image encoders, text, negative examples, reconstruction, or other sources of supervision. The models are trained on 2 million videos collected from public datasets and are evaluated on downstream image and video tasks. Our results show that learning by predicting video features leads to versatile visual representations that perform well on both motion and appearance-based tasks, without adaption of the model’s parameters; e.g., using a frozen backbone, our largest model, a ViT-H/16 trained only on videos, obtains 81.9% on Kinetics-400, 72.2% on Something-Something-v2, and 77.9% on ImageNet1K. Authors: Adrien Bardes Quentin Garrido Xinlei Chen Michael Rabbat Yann LeCun Mido Assran Nicolas Ballas Jean Ponce Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share o https://www.youtube.com/embed/7UkJPwz_N_0 2026-03-17T13:52:26.000Z Yannic Kilcher 3003 yes no https://vidert.com/v/artificial-intelligence/video/14577/what-a-day-in-ai-sora-gemini-1-5-v-jepa-and-lots-of-news 2026-03-17T13:52:12.000Z weekly 0.5 https://i.ytimg.com/vi/2TlIZktYCf4/maxresdefault.jpg What a day in AI! (Sora, Gemini 1.5, V-JEPA, and lots of news) Your regularly irregular dose of Machine Learning News! W&B Course on LLM Structured Outputs: https://wandb.me/course-yannic OUTLINE: 0:00 - OpenAI Sora 3:25 - Gemini 1.5 with 1 Million Tokens context window 4:50 - V-JEPA 6:50 - Sam Altman raises 7 TRILLION dollars for AI chips 9:30 - Sponsor: Weights & Biases course on Structure Output from LLMs 11:30 - Bard becomes Gemini 13:55 - GOODY-2: The world's most responsible model 16:05 - miqu-1-70b leaked from Mistral 18:25 - Zuckerberg on Meta's open approach to AI models 21:40 - 1X advances robotics 23:30 - Questions around Bard's arena leaderboard position 27:00 - Various other news References: https://gist.github.com/yk/65fe3d582a43540a61718b9e4b0706d0 (they were too long for this description) Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/2TlIZktYCf4 2026-03-17T13:52:12.000Z Yannic Kilcher 5039 yes no https://vidert.com/v/artificial-intelligence/video/14576/lumiere-a-space-time-diffusion-model-for-video-generation-paper-explained 2026-03-17T13:51:55.000Z weekly 0.5 https://i.ytimg.com/vi/Pl8BET_K1mc/maxresdefault.jpg Lumiere: A Space-Time Diffusion Model for Video Generation (Paper Explained) #lumiere #texttovideoai #google LUMIERE by Google Research tackles globally consistent text-to-video generation by extending the U-Net downsampling concept to the temporal axis of videos. OUTLINE: 0:00 - Introduction 8:20 - Problems with keyframes 16:55 - Space-Time U-Net (STUNet) 21:20 - Extending U-Nets to video 37:20 - Multidiffusion for SSR prediction fusing 44:00 - Stylized generation by swapping weights 49:15 - Training & Evaluation 53:20 - Societal Impact & Conclusion Paper: https://arxiv.org/abs/2401.12945 Website: https://lumiere-video.github.io/ Abstract: We introduce Lumiere -- a text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion -- a pivotal challenge in video synthesis. To this end, we introduce a Space-Time U-Net architecture that generates the entire temporal duration of the video at once, through a single pass in the model. This is in contrast to existing video models which synthesize distant keyframes followed by temporal super-resolution -- an approach that inherently makes global temporal consistency difficult to achieve. By deploying both spatial and (importantly) temporal down- and up-sampling and leveraging a pre-trained text-to-image diffusion model, our model learns to directly generate a full-frame-rate, low-resolution video by processing it in multiple space-time scales. We demonstrate state-of-the-art text-to-video generation results, and show that our design easily facilitates a wide range of content creation tasks and video editing applications, including image-to-video, video inpainting, and stylized generation. Authors: Omer Bar-Tal, Hila Chefer, Omer Tov, Charles Herrmann, Roni Paiss, Shiran Zada, Ariel Ephrat, Junhwa Hur, Yuanzhen Li, Tomer Michaeli, Oliver Wang, Deqing Sun, Tali Dekel, Inbar Mosseri Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https: https://www.youtube.com/embed/Pl8BET_K1mc 2026-03-17T13:51:55.000Z Yannic Kilcher 3264 yes no https://vidert.com/v/artificial-intelligence/video/14575/alphageometry-solving-olympiad-geometry-without-human-demonstrations-paper-expla 2026-03-17T13:50:56.000Z weekly 0.5 https://i.ytimg.com/vi/ZNK4nfgNQpM/maxresdefault.jpg AlphaGeometry: Solving olympiad geometry without human demonstrations (Paper Explained) #deepmind #alphageometry #llm AlphaGeometry is a combination of a symbolic solver and a large language model by Google DeepMind that tackles IMO geometry questions without any human-generated trainind data. OUTLINE: 0:00 - Introduction 1:30 - Problem Statement 7:30 - Core Contribution: Synthetic Data Generation 9:30 - Sampling Premises 13:00 - Symbolic Deduction 17:00 - Traceback 19:00 - Auxiliary Construction 25:20 - Experimental Results 32:00 - Problem Representation 34:30 - Final Comments Paper: https://www.nature.com/articles/s41586-023-06747-5 Abstract: Proving mathematical theorems at the olympiad level represents a notable milestone in human-level automated reasoning1,2,3,4, owing to their reputed difficulty among the world’s best talents in pre-university mathematics. Current machine-learning approaches, however, are not applicable to most mathematical domains owing to the high cost of translating human proofs into machine-verifiable format. The problem is even worse for geometry because of its unique translation challenges1,5, resulting in severe scarcity of training data. We propose AlphaGeometry, a theorem prover for Euclidean plane geometry that sidesteps the need for human demonstrations by synthesizing millions of theorems and proofs across different levels of complexity. AlphaGeometry is a neuro-symbolic system that uses a neural language model, trained from scratch on our large-scale synthetic data, to guide a symbolic deduction engine through infinite branching points in challenging problems. On a test set of 30 latest olympiad-level problems, AlphaGeometry solves 25, outperforming the previous best method that only solves ten problems and approaching the performance of an average International Mathematical Olympiad (IMO) gold medallist. Notably, AlphaGeometry produces human-readable proofs, solves all geometry problems in the IMO 2000 and 2015 under human expert evaluation and discovers a generalized version of a translated IMO theorem in 2004. Authors: Trieu H. Trinh, Yuhuai Wu, Quoc V. L https://www.youtube.com/embed/ZNK4nfgNQpM 2026-03-17T13:50:56.000Z Yannic Kilcher 2127 yes no https://vidert.com/v/artificial-intelligence/video/14574/mixtral-of-experts-paper-explained 2026-03-17T13:50:42.000Z weekly 0.5 https://i.ytimg.com/vi/mwO6v4BlgZQ/maxresdefault.jpg Mixtral of Experts (Paper Explained) #mixtral #mistral #chatgpt OUTLINE: 0:00 - Introduction 3:00 - Mixture of Experts 6:00 - Classic Transformer Blocks 11:15 - Expert Routing 17:00 - Sparse Expert Routing 22:00 - Expert Parallelism 25:00 - Experimental Results 31:30 - Routing Analysis 33:20 - Conclusion Paper: https://arxiv.org/abs/2401.04088 Abstract: We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts). For every token, at each layer, a router network selects two experts to process the current state and combine their outputs. Even though each token only sees two experts, the selected experts can be different at each timestep. As a result, each token has access to 47B parameters, but only uses 13B active parameters during inference. Mixtral was trained with a context size of 32k tokens and it outperforms or matches Llama 2 70B and GPT-3.5 across all evaluated benchmarks. In particular, Mixtral vastly outperforms Llama 2 70B on mathematics, code generation, and multilingual benchmarks. We also provide a model fine-tuned to follow instructions, Mixtral 8x7B - Instruct, that surpasses GPT-3.5 Turbo, Claude-2.1, Gemini Pro, and Llama 2 70B - chat model on human benchmarks. Both the base and instruct models are released under the Apache 2.0 license. Authors: Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Théophile Gervet, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/disc https://www.youtube.com/embed/mwO6v4BlgZQ 2026-03-17T13:50:42.000Z Yannic Kilcher 2072 yes no https://vidert.com/v/artificial-intelligence/video/12003/the-physics-bug-that-stumped-everyone-is-finally-gone 2026-03-17T02:28:57.000Z weekly 0.5 https://i.ytimg.com/vi/qF_tfIieeE0/maxresdefault.jpg The Physics Bug That Stumped Everyone Is Finally Gone! ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://www.geometry.caltech.edu/pubs/LD23.pdf Source: https://www.youtube.com/watch?v=VIV7GYOBTfM Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/qF_tfIieeE0 2026-03-17T02:28:57.000Z Two Minute Papers 611 yes no https://vidert.com/v/artificial-intelligence/video/11860/nvidia-s-new-ai-just-cracked-the-hardest-part-of-self-driving 2026-03-17T01:52:37.000Z weekly 0.5 https://i.ytimg.com/vi/s9SnEE7JXU4/maxresdefault.jpg NVIDIA’s New AI Just Cracked The Hardest Part Of Self Driving ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://github.com/NVlabs/alpamayo Research panel I will be at GTC: https://www.nvidia.com/gtc/session-catalog/sessions/gtc26-s81810/ Sources: https://www.youtube.com/watch?v=0aq4Wi2rsOk https://www.youtube.com/watch?v=I0yPzZp6dM0 Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ #nvidia https://www.youtube.com/embed/s9SnEE7JXU4 2026-03-17T01:52:37.000Z Two Minute Papers 540 yes no https://vidert.com/v/artificial-intelligence/video/11502/this-physics-breakthrough-looks-impossible 2026-03-17T00:25:11.000Z weekly 0.5 https://i.ytimg.com/vi/RDQ4vHAPNls/maxresdefault.jpg This Physics Breakthrough Looks Impossible ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambda.ai/papers 📝 The paper is available here: https://xuan-li.github.io/pdf/publications/li2024dynamicduo.pdf Sources: https://www.youtube.com/watch?v=CfEg7fucVYg Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Charles Ian Norman Venn, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Shawn Becker, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/RDQ4vHAPNls 2026-03-17T00:25:11.000Z Two Minute Papers 578 yes no https://vidert.com/v/artificial-intelligence/video/6150/i-built-a-fully-automatic-mansplainer 2026-03-06T22:07:33.000Z weekly 0.5 https://i.ytimg.com/vi/xHi8PUIVyoo/maxresdefault.jpg I BUILT A FULLY AUTOMATIC MANSPLAINER All information about GTC and the DGX Spark Raffle is here: https://www.ykilcher.com/gtc Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: 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https://www.youtube.com/embed/R3nQ7pGXJcA 2024-12-27T00:48:00.000Z Yannic Kilcher 5297 yes no https://vidert.com/v/artificial-intelligence/video/6198/until-the-litter-end 2024-01-10T17:53:09.000Z weekly 0.5 https://i.ytimg.com/vi/PtfatBOlHIA/maxresdefault.jpg Until the Litter End https://litter.ykilcher.com Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n https://www.youtube.com/embed/PtfatBOlHIA 2024-01-10T17:53:09.000Z Yannic Kilcher 220 yes no https://vidert.com/v/artificial-intelligence/video/6104/unitree-g1-security-disaster 2025-09-30T20:50:15.000Z weekly 0.5 https://i.ytimg.com/vi/Ah0-l0HZwLA/maxresdefault.jpg Unitree G1 Security Disaster Cybersecurity AI: Humanoid Robots as Attack Vectors: https://arxiv.org/abs/2509.14139 Unipwn repo for simple demo of the RCE/bluetooth vulnerabilities: https://github.com/Bin4ry/UniPwn/tree/main Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/Ah0-l0HZwLA 2025-09-30T20:50:15.000Z sentdex 2651 yes no https://vidert.com/v/artificial-intelligence/video/6105/testing-vlms-and-llms-for-robotics-w-the-jetson-thor-devkit 2025-08-30T15:21:01.000Z weekly 0.5 https://i.ytimg.com/vi/eRPSRSGiAA8/maxresdefault.jpg Testing VLMs and LLMs for robotics w/ the Jetson Thor devkit Exploring the Jetson Thor devkit w/ some local LLMs and VLMs. More info on the Jetson Thor Devkit: https://nvda.ws/45xIU4B Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/eRPSRSGiAA8 2025-08-30T15:21:01.000Z sentdex 1521 yes no https://vidert.com/v/artificial-intelligence/video/6106/reinforcement-learning-with-unitree-g1-humanoid-dev-w-g1-p-5 2025-07-25T15:33:58.000Z weekly 0.5 https://i.ytimg.com/vi/wiIUF9pIDYw/maxresdefault.jpg Reinforcement learning with Unitree G1 humanoid - Dev w/ G1 P.5 Training and testing out an arm Policy for the Unitree G1 using the PPO algorithm. Github repo: https://github.com/Sentdex/unitree_g1_vibes/tree/main/RL-shenanigans Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/wiIUF9pIDYw 2025-07-25T15:33:58.000Z sentdex 1720 yes no https://vidert.com/v/artificial-intelligence/video/6107/a-bigger-brain-for-the-unitree-g1-dev-w-g1-humanoid-p-4 2025-05-30T15:34:05.000Z weekly 0.5 https://i.ytimg.com/vi/cmnJhOWp2z4/maxresdefault.jpg A bigger brain for the Unitree G1- Dev w/ G1 Humanoid P.4 Adding a vision language model and procrastinating a little longer about going into the sim Unitree G1 series playlist: https://www.youtube.com/playlist?list=PLQVvvaa0QuDdNJ7QbjYeDaQd6g5vfR8km Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/cmnJhOWp2z4 2025-05-30T15:34:05.000Z sentdex 1821 yes no https://vidert.com/v/artificial-intelligence/video/6108/unitree-g1-moving-the-arms-hands-dev-w-g1-humanoid-p-3 2025-05-09T15:42:41.000Z weekly 0.5 https://i.ytimg.com/vi/Uc1nhT8beTU/maxresdefault.jpg Unitree G1 - Moving the arms/hands - Dev w/ G1 Humanoid P.3 Figuring out how to move the hands/arms in an abstract way in XYZ space rather than per-joint. Unitree G1 series playlist: https://www.youtube.com/playlist?list=PLQVvvaa0QuDdNJ7QbjYeDaQd6g5vfR8km Github for this project: https://github.com/Sentdex/unitree_g1_vibes Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/Uc1nhT8beTU 2025-05-09T15:42:41.000Z sentdex 1772 yes no https://vidert.com/v/artificial-intelligence/video/6109/unitree-g1-lidar-slam-navigation-and-control-dev-w-g1-humanoid-p-2 2025-04-30T15:56:40.000Z weekly 0.5 https://i.ytimg.com/vi/sJYlJlIEBpg/maxresdefault.jpg Unitree G1 LiDAR, SLAM, navigation and control. Dev w/ G1 Humanoid P.2 Doing SLAM with the LiDAR, occupancy graph, better navigation, and a bunch of improvements. Unitree G1 playlist: https://www.youtube.com/playlist?list=PLQVvvaa0QuDdNJ7QbjYeDaQd6g5vfR8km Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/sJYlJlIEBpg 2025-04-30T15:56:40.000Z sentdex 2451 yes no https://vidert.com/v/artificial-intelligence/video/6110/unboxing-the-unitree-g1-edu-humanoid 2025-04-26T16:10:29.000Z weekly 0.5 https://i.ytimg.com/vi/pPTo62O__CU/maxresdefault.jpg Unboxing the Unitree G1 Edu Humanoid Initial experience with unboxing, setting up, and beginning to program the Unitree G1 Edu Ultimate B humanoid robot! Part 2: Developing better control LiDAR, SLAM, and more: https://www.youtube.com/watch?v=sJYlJlIEBpg Unitree G1 playlist: https://www.youtube.com/playlist?list=PLQVvvaa0QuDdNJ7QbjYeDaQd6g5vfR8km Official Python SDK for Unitree: https://github.com/unitreerobotics/unitree_sdk2_python Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/pPTo62O__CU 2025-04-26T16:10:29.000Z sentdex 3119 yes no https://vidert.com/v/artificial-intelligence/video/6111/vibe-coding-a-robotic-hand-to-crawl-inspire-rh56dfq 2025-04-02T15:36:52.000Z weekly 0.5 https://i.ytimg.com/vi/57cPmzwCqd4/maxresdefault.jpg Vibe Coding a Robotic Hand to Crawl (Inspire RH56DFQ) Continuing with our work with the Inspire RH56DFQ robotic hands, this time trying some more gestures and then seeing if we can get a language model to program the hand to crawl. Previous video: https://www.youtube.com/watch?v=MeHWIXLV3Zo The github package we're using (also written by Cursor and 3.7 Sonnet): https://github.com/Sentdex/inspire_hands Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/57cPmzwCqd4 2025-04-02T15:36:52.000Z sentdex 2167 yes no https://vidert.com/v/artificial-intelligence/video/6112/vibe-coding-robot-hands-w-cursor-inspire-rh56dfq-2l-r 2025-03-31T19:40:55.000Z weekly 0.5 https://i.ytimg.com/vi/MeHWIXLV3Zo/maxresdefault.jpg Vibe Coding Robot Hands w/ Cursor (Inspire RH56DFQ-2L/R) We do a bit of vibe coding for the Inspire RH56 series hands. I've uploaded what I think to be a fairly decent package built from cursor and 3.7 sonnet that you can find here: https://github.com/Sentdex/inspire_hands Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/MeHWIXLV3Zo 2025-03-31T19:40:55.000Z sentdex 2916 yes no https://vidert.com/v/artificial-intelligence/video/6113/programming-with-llm-agents-in-2025 2025-02-16T01:08:55.000Z weekly 0.5 https://i.ytimg.com/vi/WKF__cJTxvg/maxresdefault.jpg Programming with LLM Agents in 2025 Some tips and tricks for using modern LLM agents for building stuff. I am using openhands here, but you're free to take some of my advice from here and apply it to just about any of the web-based UIs or other agents...etc. OpenHands github: https://github.com/All-Hands-AI/OpenHands Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/WKF__cJTxvg 2025-02-16T01:08:55.000Z sentdex 3731 yes no https://vidert.com/v/artificial-intelligence/video/6114/what-s-going-on-everybody 2024-10-13T16:54:00.000Z weekly 0.5 https://i.ytimg.com/vi/VyseRArtl5E/maxresdefault.jpg What's going on everybody? Hello from the ranch. Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/VyseRArtl5E 2024-10-13T16:54:00.000Z sentdex 1449 yes no https://vidert.com/v/artificial-intelligence/video/6115/building-an-llm-fine-tuning-dataset 2024-03-06T19:01:15.000Z weekly 0.5 https://i.ytimg.com/vi/pCX_3p40Efc/maxresdefault.jpg Building an LLM fine-tuning Dataset Going through the building of a QLoRA fine-tuning dataset for a language model. NVIDIA GTC signup: https://nvda.ws/3XTqlB6 Fine-tuning code: https://github.com/Sentdex/LLM-Finetuning 5000-step Walls1337bot adapter: https://huggingface.co/Sentdex/Walls1337bot-Llama2-7B-003.005.5000 WSB Dataset: https://huggingface.co/datasets/Sentdex/WSB-003.005 "I have every reddit comment" original reddit post and torrent info: https://www.reddit.com/r/datasets/comments/3bxlg7/i_have_every_publicly_available_reddit_comment/ 2007-2015 Reddit Archive.org: https://archive.org/download/2015_reddit_comments_corpus/reddit_data/ Reddit BigQuery 2007-2019 (this has other data besides reddit comments too!): https://reddit.com/r/bigquery/comments/3cej2b/17_billion_reddit_comments_loaded_on_bigquery/ Contents: 0:00 - Introduction to Dataset building for fine-tuning. 02:53 - The Reddit dataset options (Torrent, Archive.org, BigQuery) 06:07 - Exporting BigQuery Reddit (and some other data) 14:44 - Decompressing all of the gzip archives 25:13 - Re-combining the archives for target subreddits 28:29 - How to structure the data 40:40 - Building training samples and saving to database 48:49 - Creating customized training json files 54:11 - QLoRA training and results Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/pCX_3p40Efc 2024-03-06T19:01:15.000Z sentdex 3715 yes no https://vidert.com/v/artificial-intelligence/video/6116/visualizing-neural-network-internals 2024-02-14T18:23:24.000Z weekly 0.5 https://i.ytimg.com/vi/ChfEO8l-fas/maxresdefault.jpg Visualizing Neural Network Internals Visualizing some of the internals of a neural network during training and inference. Starting and full code: https://github.com/Sentdex/neural-net-internals-visualized Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/ChfEO8l-fas 2024-02-14T18:23:24.000Z sentdex 3221 yes no https://vidert.com/v/artificial-intelligence/video/6117/getting-back-on-grid 2024-02-07T19:22:33.000Z weekly 0.5 https://i.ytimg.com/vi/mm9IHqgCbZc/maxresdefault.jpg Getting Back on Grid Establishing an internet connection in an internet desert, then figuring out (well, starting to) networking. Combined with Starlink as my internet provider, I ended up going with a wifi bridge implementation with a couple of Ubiquiti nanostation AC locos to network between buildings at 100+ meters of distance. The Ubiquiti units can also do point to point (ptp), but so far the wifi bridge setup is working great for me. Ubiquiti NanoStation 5AC Locos (buy in pairs for ptp/wifi bridge): https://amzn.to/3UqnLnQ Mounting hardware I used, but you can use just about anything, including zip tying to a tree or something: https://amzn.to/42ycS5d PoE Injectors (can use any PoE switch too): https://amzn.to/482oNJO Silicone sealant: https://amzn.to/42vu5w9 For shorter distances, you can also use: TPLink Access Points (AP): https://amzn.to/3OCe6qp I also have enjoyed the 2016 model years of the Google wifi: https://amzn.to/495Ydkm These are half the price of the newer version, the nest variant: https://amzn.to/3HSDdBM If I forgot something, feel free to ask! Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/mm9IHqgCbZc 2024-02-07T19:22:33.000Z sentdex 1269 yes no https://vidert.com/v/artificial-intelligence/video/6118/open-source-ai-inference-api-w-together 2023-12-24T16:38:27.000Z weekly 0.5 https://i.ytimg.com/vi/_GQfj3jhXVM/maxresdefault.jpg Open Source AI Inference API w/ Together Exploring the Together Inference API (https://www.together.ai/) Together API basics jupyter notebook examples: https://github.com/Sentdex/Together-API-Basics Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/_GQfj3jhXVM 2023-12-24T16:38:27.000Z sentdex 1525 yes no https://vidert.com/v/artificial-intelligence/video/6119/infinite-inference-power-for-ai 2023-12-16T16:11:50.000Z weekly 0.5 https://i.ytimg.com/vi/9MigSbQ7AQk/maxresdefault.jpg INFINITE Inference Power for AI Testing and enjoying the Comino Grando Server machine with 6x RTX 4090s from Comino (https://www.comino.com/) Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/9MigSbQ7AQk 2023-12-16T16:11:50.000Z sentdex 1082 yes no https://vidert.com/v/artificial-intelligence/video/6120/pandas-dataframes-on-your-gpu-w-cudf 2023-11-10T15:18:19.000Z weekly 0.5 https://i.ytimg.com/vi/OnYGtKQT-rU/maxresdefault.jpg Pandas Dataframes on your GPU w/ CuDF An overview and some quick examples of using CuDF's Pandas accelerator and how much faster it can be than vanilla Pandas for data analysis. Colab demo of Rapids: https://nvda.ws/3LWggQj AI and Data Science Virtual Summit: https://nvda.ws/3ZR3wjL Notebook in this video: https://gist.github.com/Sentdex/469c30385d06719519af13125db85edc Install CuDF: pip install cudf-cu11 --extra-index-url=https://pypi.nvidia.com (or cu12) Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/OnYGtKQT-rU 2023-11-10T15:18:19.000Z sentdex 724 yes no https://vidert.com/v/artificial-intelligence/video/6121/qlora-is-all-you-need-fast-and-lightweight-model-fine-tuning 2023-09-15T15:20:55.000Z weekly 0.5 https://i.ytimg.com/vi/J_3hDqSvpmg/maxresdefault.jpg QLoRA is all you need (Fast and lightweight model fine-tuning) Learning and sharing my process with QLoRA (quantized low rank adapters) fine-tuning. In this case, I use a custom-made reddit dataset, but you can use anything you want. I referenced a LOT of stuff in this video, I will do my best to link everything, but let me know if I forget anything. Resources: WSB-GPT-7B Model: https://huggingface.co/Sentdex/WSB-GPT-7B WSB-GPT-13B Model: https://huggingface.co/Sentdex/WSB-GPT-13B WSB Training data: https://huggingface.co/datasets/Sentdex/wsb_reddit_v002 Code: QLoRA Repo: https://github.com/artidoro/qlora qlora.py: https://github.com/artidoro/qlora/blob/main/qlora.py Simple qlora training notebook: https://colab.research.google.com/drive/1VoYNfYDKcKRQRor98Zbf2-9VQTtGJ24k?usp=sharing qlora merging/dequantizing code: https://gist.github.com/ChrisHayduk/1a53463331f52dca205e55982baf9930 Referenced Research Papers: Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning: https://arxiv.org/abs/2012.13255 LoRA: Low-Rank Adaptation of Large Language Models: https://arxiv.org/abs/2106.09685 QLoRA: Efficient Finetuning of Quantized LLMs: https://arxiv.org/abs/2305.14314 Yannic's GPT-4chan model: https://huggingface.co/ykilcher/gpt-4chan Condemnation letter: https://docs.google.com/forms/d/e/1FAIpQLSdh3Pgh0sGrYtRihBu-GPN7FSQoODBLvF7dVAFLZk2iuMgoLw/viewform https://www.youtube.com/watch?v=efPrtcLdcdM Contents: 0:00 - Why QLoRA? 0:55 - LoRA/QLoRA Research 4:13 - Fine-tuning dataset 11:10 - QLoRA Training Process 15:02 - QLoRA Adapters 17:10 - Merging, Dequantizing, and Sharing 19:34 - WSB QLoRA fine-tuned model examples Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twit https://www.youtube.com/embed/J_3hDqSvpmg 2023-09-15T15:20:55.000Z sentdex 1436 yes no https://vidert.com/v/artificial-intelligence/video/6122/chat-interface-for-your-local-llama-llms 2023-08-22T16:36:58.000Z weekly 0.5 https://i.ytimg.com/vi/m1feTAvlXxw/maxresdefault.jpg Chat Interface for your Local Llama LLMs A tutorial of sorts covering how to create streaming chat interfaces using Gradio for the various chat/instruct large language models from HuggingFace. Sample code: https://huggingface.co/spaces/Sentdex/StableBeluga-7B-Chat/blob/main/app.py Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/m1feTAvlXxw 2023-08-22T16:36:58.000Z sentdex 956 yes no https://vidert.com/v/artificial-intelligence/video/6123/gzip-is-all-you-need-this-should-not-work 2023-07-28T15:21:08.000Z weekly 0.5 https://i.ytimg.com/vi/jkdWzvMOPuo/maxresdefault.jpg Gzip is all You Need! (This SHOULD NOT work) Github code: https://github.com/Sentdex/Simple-kNN-Gzip Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/jkdWzvMOPuo 2023-07-28T15:21:08.000Z sentdex 1187 yes no https://vidert.com/v/artificial-intelligence/video/6124/better-attention-is-all-you-need 2023-07-11T19:52:22.000Z weekly 0.5 https://i.ytimg.com/vi/MNSmOih_pmg/maxresdefault.jpg Better Attention is All You Need Addressing the current state of attention for artificial intelligence and why it's currently holding back maximum context lengths. Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/MNSmOih_pmg 2023-07-11T19:52:22.000Z sentdex 869 yes no https://vidert.com/v/artificial-intelligence/video/6125/the-best-open-source-llm-falcon-40b 2023-07-05T21:14:13.000Z weekly 0.5 https://i.ytimg.com/vi/-IV1NTGy6Mg/maxresdefault.jpg The BEST Open Source LLM? (Falcon 40B) TII Call for Proposals with Falcon 40B: https://falconllm.tii.ae/proposal.php Falcon Github samples: https://github.com/Sentdex/Falcon-LLM TermGPT: https://www.youtube.com/watch?v=O4EmRi0_CI4 GPT-4 Overview: https://www.youtube.com/watch?v=lJNblY3Madg Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/-IV1NTGy6Mg 2023-07-05T21:14:13.000Z sentdex 1436 yes no https://vidert.com/v/artificial-intelligence/video/6126/openai-gpt-4-function-calling-unlimited-potential 2023-06-15T00:14:31.000Z weekly 0.5 https://i.ytimg.com/vi/0lOSvOoF2to/maxresdefault.jpg OpenAI GPT-4 Function Calling: Unlimited Potential Function calling is a new capability for OpenAI's GPT-4 and GPT-3.5 via the API. Function-calling allows you to extract structured outputs from the GPT model. Github notebook: https://github.com/Sentdex/ChatGPT-API-Basics/blob/main/function_calling.ipynb Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/0lOSvOoF2to 2023-06-15T00:14:31.000Z sentdex 1429 yes no https://vidert.com/v/artificial-intelligence/video/6127/letting-gpt-4-control-my-terminal-termgpt 2023-06-03T16:44:02.000Z weekly 0.5 https://i.ytimg.com/vi/O4EmRi0_CI4/maxresdefault.jpg Letting GPT-4 Control My Terminal (TermGPT) Giving LLMs like GPT-4 the ability to plan and execute terminal commands. TermGPT github: https://github.com/Sentdex/TermGPT/ OpenAI Chat API tutorial: https://github.com/Sentdex/ChatGPT-API-Basics Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/O4EmRi0_CI4 2023-06-03T16:44:02.000Z sentdex 1392 yes no https://vidert.com/v/artificial-intelligence/video/6128/building-an-open-assistant-api 2023-05-12T14:07:38.000Z weekly 0.5 https://i.ytimg.com/vi/kkTNg_UOCNE/maxresdefault.jpg Building an Open Assistant API Working with one of the Open Assistant models, a 12B parameter Pythia model (https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5) Github: https://github.com/Sentdex/OpenAssistant_API_Pythia_12B Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex Video Contents: 00:00 - Basics of using OpenAssistant Pythia 12B locally 16:37 - Creating an OpenAssistant API 22:22 - Interfacing with our new OpenAssistant API 31:32 - Handling for long contexts https://www.youtube.com/embed/kkTNg_UOCNE 2023-05-12T14:07:38.000Z sentdex 2471 yes no https://vidert.com/v/artificial-intelligence/video/6129/sparks-of-agi-analyzing-gpt-4-and-the-latest-gpt-llm-models 2023-04-28T14:13:51.000Z weekly 0.5 https://i.ytimg.com/vi/lJNblY3Madg/maxresdefault.jpg Sparks of AGI? - Analyzing GPT-4 and the latest GPT/LLM Models An in-depth look into the current state of the art of Generative Pre-trained Transformer (GPT) language models, with a specific focus on the advancements and examples provided by OpenAI in their GPT4 Technical Report (https://arxiv.org/abs/2303.08774) as well as the Microsoft "Sparks of AGI" Paper (https://arxiv.org/abs/2303.12712). Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex Contents: 00:00 - Introduction 01:31 - Multi-Modal/imagery input 05:44 - Predictable scaling 08:15 - Performance on exams 15:07 - Rule-Based Reward Models (RBRMs) 17:53 - Spatial Awareness of non-vision GPT-4 20:38 - Non-multimodel vision ability 21:27 - Programming 25:07 - Theory of Mind 29:34 - Music and Math 30:44 - Challenges w/ Planning 33:25 - Hallucinations 35:04 - Risks 38:01 - Biases 44:55 - Privacy 48:23 - Generative Models used in Training/Evals 51:36 - Acceleration 57:07 - AGI https://www.youtube.com/embed/lJNblY3Madg 2023-04-28T14:13:51.000Z sentdex 3639 yes no https://vidert.com/v/artificial-intelligence/video/6130/chatglm-the-chatgpt-killer-checking-out-chatglm6b 2023-04-08T13:40:00.000Z weekly 0.5 https://i.ytimg.com/vi/fGpXj4bl5LI/maxresdefault.jpg ChatGLM: The ChatGPT killer? Checking out ChatGLM6B Exploring the concept of a GLM (General Language Model) and working with ChatGLM6B. Original GLM paper: https://arxiv.org/abs/2103.10360 GLM130B paper: https://arxiv.org/abs/2210.02414 ChatGLM6B demo: https://huggingface.co/spaces/multimodalart/ChatGLM-6B Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/fGpXj4bl5LI 2023-04-08T13:40:00.000Z sentdex 997 yes no https://vidert.com/v/artificial-intelligence/video/6131/gpt-journey-a-text-and-image-game-with-chatgpt 2023-03-24T14:53:17.000Z weekly 0.5 https://i.ytimg.com/vi/YY7LIEHiAfg/maxresdefault.jpg GPT Journey - A text and image game with ChatGPT Building a text and image-based game with ChatGPT as the backend via the api... plus a little help from ChatGPT to build it. Github: https://github.com/Sentdex/GPT-Journey Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/YY7LIEHiAfg 2023-03-24T14:53:17.000Z sentdex 2817 yes no https://vidert.com/v/artificial-intelligence/video/6132/chatgpt-api-in-python 2023-03-10T16:27:31.000Z weekly 0.5 https://i.ytimg.com/vi/c-g6epk3fFE/maxresdefault.jpg ChatGPT API in Python Exploring the ChatGPT (GPT3.5) API from OpenAI and building some simple chat applications with it. Github code: https://github.com/Sentdex/ChatGPT-API-Basics Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex Contents: 0:00 - Why use the ChatGPT API 2:10 - How to query the ChatGPT API 7:45 - History and dynamic input w/ ChatGPT API 15:30 - Comining everything so far 18:25 - Building a Gradio chat application with ChatGPT https://www.youtube.com/embed/c-g6epk3fFE 2023-03-10T16:27:31.000Z sentdex 2118 yes no https://vidert.com/v/artificial-intelligence/video/6133/image-editing-a-i 2023-03-03T14:51:47.000Z weekly 0.5 https://i.ytimg.com/vi/zHS3K4T0gAI/maxresdefault.jpg Image Editing A.I. Checking out some of the latest A.I. Photo and Video editing software available on HuggingFace. NVIDIA GTC Signup (Digital event March 20-23): https://nvda.ws/3XTqlB6 Neural Networks from Scratch book: https://nnfs.io ControlNet (doodle to image, edge detection, pose...etc): https://huggingface.co/spaces/hysts/ControlNet Instruct Pix2Pix: https://huggingface.co/spaces/timbrooks/instruct-pix2pix Pix2Pix video: https://huggingface.co/spaces/fffiloni/Pix2Pix-Video Photoguard: https://huggingface.co/spaces/RamAnanth1/photoguard Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/zHS3K4T0gAI 2023-03-03T14:51:47.000Z sentdex 639 yes no https://vidert.com/v/artificial-intelligence/video/6134/the-ai-wars-google-vs-bing-chatgpt 2023-02-11T18:39:11.000Z weekly 0.5 https://i.ytimg.com/vi/G8oyOeOCl0s/maxresdefault.jpg The AI wars: Google vs Bing (ChatGPT) Discussing the latest events surrounding large language models, chatbots, and search engines with respect to Microsoft and Google. Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/G8oyOeOCl0s 2023-02-11T18:39:11.000Z sentdex 1121 yes no https://vidert.com/v/artificial-intelligence/video/6135/chatgpt-writes-a-chatbot-ai 2023-01-25T18:00:41.000Z weekly 0.5 https://i.ytimg.com/vi/QumfkMQr47M/maxresdefault.jpg ChatGPT Writes a Chatbot AI Creating a large language model (LLM)-backed chat bot application entirely with ChatGPT. Github repo for the end result: https://github.com/Sentdex/ChatGPT-at-Home Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/QumfkMQr47M 2023-01-25T18:00:41.000Z sentdex 1335 yes no https://vidert.com/v/artificial-intelligence/video/6136/openai-s-chatgpt-is-a-massive-step-forward-in-generative-ai 2022-12-10T16:37:38.000Z weekly 0.5 https://i.ytimg.com/vi/HTWfA7KFzoA/maxresdefault.jpg OpenAI's ChatGPT is a MASSIVE step forward in Generative AI ChatGPT is the latest GPT style generative AI model from OpenAI, which, at it's most basic level acts as a chatbot, but this back and forth structure allows for many complex capabilities. Chat with ChatGPT here: https://chat.openai.com/chat Operating system example with the script writing to a file: https://twitter.com/Sentdex/status/1600609223548739585?t=3KJw_yb6hPjDNbQoI27ScA&s=19 Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/HTWfA7KFzoA 2022-12-10T16:37:38.000Z sentdex 1392 yes no https://vidert.com/v/artificial-intelligence/video/6137/google-a-i-diffusion-image-editing-w-prompt-to-prompt 2022-11-13T16:20:05.000Z weekly 0.5 https://i.ytimg.com/vi/RtIRE4Kf5SU/maxresdefault.jpg Google A.I. Diffusion Image Editing w/ Prompt to Prompt Prompt to prompt allows you to make natural language edits to your prompt to edit the image. Prompt-to-prompt github: https://github.com/google/prompt-to-prompt Prompt-to-prompt w/ stable diffusion notebook: https://github.com/google/prompt-to-prompt/blob/main/prompt-to-prompt_stable.ipynb Stable DreamFusion video: https://www.youtube.com/watch?v=zWD5ZR5GtJM GAN Theft Auto: https://www.youtube.com/watch?v=udPY5rQVoW0 Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/RtIRE4Kf5SU 2022-11-13T16:20:05.000Z sentdex 951 yes no https://vidert.com/v/artificial-intelligence/video/6138/google-s-dreamfusion-ai-text-to-3d 2022-10-21T13:21:22.000Z weekly 0.5 https://i.ytimg.com/vi/zWD5ZR5GtJM/maxresdefault.jpg Google's DreamFusion AI: Text to 3D DreamFusion is Google research (https://arxiv.org/pdf/2209.14988.pdf) into generating 3D objects from text prompts, and is based off 2D diffusion models. Stable DreamFusion github: https://github.com/ashawkey/stable-dreamfusion?s=03 NeRF (Neural Radiance Fields): https://www.matthewtancik.com/nerf Meta AI make-a-video: https://makeavideo.studio/ Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/zWD5ZR5GtJM 2022-10-21T13:21:22.000Z sentdex 826 yes no https://vidert.com/v/artificial-intelligence/video/6139/open-ai-s-whisper-is-amazing 2022-10-06T12:12:36.000Z weekly 0.5 https://i.ytimg.com/vi/OCBZtgQGt1I/maxresdefault.jpg Open AI’s Whisper is Amazing! OpenAI's Whisper is a speech to text, or automatic speech recognition model. It is a "weakly supervised" encoder-decoder transformer trained on 680,000 hours of audio. Not only can it transcribe English, it can transcribe 96 other languages along with also being able to translate from those languages to English. Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/OCBZtgQGt1I 2022-10-06T12:12:36.000Z sentdex 1551 yes no https://vidert.com/v/artificial-intelligence/video/6140/the-future-of-user-interfaces-with-a-i 2022-09-29T20:26:35.000Z weekly 0.5 https://i.ytimg.com/vi/F0VvtOj6QjQ/maxresdefault.jpg The Future of User Interfaces with A.I. Pondering the future of user interfaces with advancements in natural language processing and artificial intelligence Tweet in reference: https://twitter.com/c_valenzuelab/status/1574448455463862274 Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/F0VvtOj6QjQ 2022-09-29T20:26:35.000Z sentdex 1057 yes no https://vidert.com/v/artificial-intelligence/video/6141/creating-stable-diffusion-interpolation-videos 2022-09-16T15:19:34.000Z weekly 0.5 https://i.ytimg.com/vi/275_oeBw3vY/maxresdefault.jpg Creating Stable Diffusion Interpolation Videos GTC signup: https://nvda.ws/3BQvmCP Stable Diffusion Videos Github: https://github.com/nateraw/stable-diffusion-videos Gist for hunting for images: https://gist.github.com/Sentdex/130c225d90acec7c808b8ba5aba0eda1 Gist for creating stable diffusion video: https://gist.github.com/Sentdex/f9519adf3b0ac79370d2c0e31b00593b Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/275_oeBw3vY 2022-09-16T15:19:34.000Z sentdex 363 yes no https://vidert.com/v/artificial-intelligence/video/6142/exploring-an-ai-s-imagination-stable-diffusion-and-midjourney 2022-09-03T13:54:26.000Z weekly 0.5 https://i.ytimg.com/vi/2R0kGTuYmVI/maxresdefault.jpg Exploring an AI’s Imagination (Stable Diffusion and MidJourney) Exploring a couple of the latest text to image generators that you can begin using right now, Stable Diffusion and MidJourney. Stable Diffusion model download: https://huggingface.co/CompVis/stable-diffusion-v1-4 MidJourney website: https://www.midjourney.com/home/ BLOOM AI model video: https://www.youtube.com/watch?v=3EjtHs_lXnk DreamStudio: https://beta.dreamstudio.ai Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/2R0kGTuYmVI 2022-09-03T13:54:26.000Z sentdex 859 yes no https://vidert.com/v/artificial-intelligence/video/6143/5-million-ai-for-free 2022-08-12T15:18:07.000Z weekly 0.5 https://i.ytimg.com/vi/3EjtHs_lXnk/maxresdefault.jpg $5 MILLION AI for FREE Imagine an AI where, all in the same model you could Translate languages, Write code, solve crossword puzzles, Be a chatbot and do a whole bunch of other crazy things. In this video, we check out the BLOOM large language model. A free and totally open source 176B parameter LLM. BLOOM model: https://huggingface.co/bigscience/bloom Quick examples of running BLOOM locally and/or via API: https://github.com/Sentdex/BLOOM_Examples Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex Contents: 0:00 - BLOOM model basics 3:05 - What's a Large Language Model (LLM)? 4:06 - What's Prompting? 6:40 - BLOOM Training Data & Model Behavior 9:09 - Tokens & Tokenization 12:03 - Using your $5M AI (How to prompt) 16:49 - Advanced Prompt examples 21:16 - What's Next? #deeplearning #artificialintelligence https://www.youtube.com/embed/3EjtHs_lXnk 2022-08-12T15:18:07.000Z sentdex 1645 yes no https://vidert.com/v/artificial-intelligence/video/6144/does-a-deep-learning-laptop-exist-tensorbook-review 2022-07-19T15:12:12.000Z weekly 0.5 https://i.ytimg.com/vi/sMy94CgAMrk/maxresdefault.jpg Does a Deep Learning Laptop Exist? - Tensorbook Review Reviewing Lambda and Razer's Tensorbook, a laptop aimed at deep learning, with 16GB of VRAM (GPU memory), 64GB of RAM, 2TB of NVMe storage and an 8-core intel i7 11800H CPU. https://lambdalabs.com/deep-learning/laptops/tensorbook Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/sMy94CgAMrk 2022-07-19T15:12:12.000Z sentdex 1415 yes no https://vidert.com/v/artificial-intelligence/video/6145/home-lab-build-p-2-rack-has-evolved 2022-07-01T14:40:12.000Z weekly 0.5 https://i.ytimg.com/vi/BVWmstt0AWM/maxresdefault.jpg Home Lab Build - P.2 - Rack has evolved! Quite a few changes to the server rack, starting with a patch panel and another ethernet switch. From here, I've mounted a KVM switch, mouse, keyboard, and monitor. Part 1: https://youtu.be/CIQ20FWs478 Hardware purchase links: Patch Panel: https://amzn.to/3nrAODB KVM switch: https://amzn.to/3bFuTbE KVM switch VGA & USB cables: https://amzn.to/3yxXXuo Boxx rack mount machines: https://www.boxx.com/ 1U drawer: https://amzn.to/3ycRP9U 2U drawer: https://amzn.to/3y6BXWf Rack monitor mount: https://amzn.to/3y9uu8M Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex Contents: 0:00 - Networking 1:23 - KVM Switch (TRENDnet 2-in-1 USB VGA KVM) 3:11 - rack mounting the Boxx machine 5:49 - Rack mounting a monitor 8:24 - Server rack flooring :) 8:55 - Blanking panel, 2U drawer, outro #server #homelab https://www.youtube.com/embed/BVWmstt0AWM 2022-07-01T14:40:12.000Z sentdex 681 yes no https://vidert.com/v/artificial-intelligence/video/6146/home-lab-build-p-1-building-a-nas 2022-06-15T13:27:20.000Z weekly 0.5 https://i.ytimg.com/vi/CIQ20FWs478/maxresdefault.jpg Home Lab Build - P.1 - Building a NAS My home lab journey. Part 2: https://youtu.be/BVWmstt0AWM Server rack: StarTech.com 42U 19" Open Frame Server Rack - 4 Post Adjustable Depth 22-40" https://amzn.to/3NSeQ8B Rails: 1U 19 inch Server Rack Rails: https://amzn.to/3OhHHD9 Switch: 10G 8 Port TP-Link switch: https://amzn.to/3NTRRKn NAS Build: Case: Rosewill 4U 12-bay hot swap server rack casing: https://amzn.to/3xks2fm Motherboard: GIGABYTE Z590 AORUS Master: https://amzn.to/3tzCHlj CPU: Intel® Core™ i5-11600K: https://amzn.to/3Of9W5f RAM: Corsair Vengeance LPX 32GB: https://amzn.to/3NUJM80 Power Supply: Corsair RM850x: https://amzn.to/3xLYqJh 4x 18TB: Seagate Exos X18 18TB Enterprise HDD: https://amzn.to/3MKwM3H 3x 120mm Noctua fans: https://amzn.to/3QlXi6j 2x 80mm Noctua fans: https://amzn.to/3xOFb1w Super quiet Puget Workstation build: https://hubs.ly/H0-By8Q0 PCIe SATA Expansion: https://amzn.to/3Odm9Yi TrueNAS Core 12.0 install tutorial video I used: https://www.youtube.com/watch?v=nVRWpV2xyds Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex Contents: 00:00 - Server rack types 00:30 - StarTech 42U 4 Post Open Frame Server Rack 01:15 - Network Area Storage (NAS) 02:42 - Prebuilt NAS Options (QNAP vs Synology) 03:36 - Custom-built rack-mount NAS 07:52 - Server rack Power Distribution Unit (PDU) (Tripplite PDU1230) 09:48 - Server Rack/Homelab networking (TP-Link TL-SX1008) 8x 10G 11:35 - StarTech 1U 19" Server Rack Rails 12:21 - TrueNAS 13 w/ RAIDZ1 14:03 - Why HomeLab? #homelab #server https://www.youtube.com/embed/CIQ20FWs478 2022-06-15T13:27:20.000Z sentdex 1027 yes no https://vidert.com/v/artificial-intelligence/video/6147/python-plays-gta-v-reboot-announcement 2022-05-03T15:09:23.000Z weekly 0.5 https://i.ytimg.com/vi/P-yxB3muUmM/maxresdefault.jpg Python Plays GTA V: Reboot - Announcement Live self-driving car model training: https://twitch.tv/sentdex Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/P-yxB3muUmM 2022-05-03T15:09:23.000Z sentdex 468 yes no https://vidert.com/v/artificial-intelligence/video/6148/a-i-learns-to-play-starcraft-2-reinforcement-learning 2022-04-23T15:05:05.000Z weekly 0.5 https://i.ytimg.com/vi/q59wap1ELQ4/maxresdefault.jpg A. I. Learns to Play Starcraft 2 (Reinforcement Learning) Tinkering with reinforcement learning via Stable Baselines 3 and Starcraft 2. Code and model: https://github.com/Sentdex/SC2RL Stable Baselines 3 tutorial: https://pythonprogramming.net/introduction-reinforcement-learning-stable-baselines-3-tutorial/ Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex #artificialintelligence #machinelearning #python https://www.youtube.com/embed/q59wap1ELQ4 2022-04-23T15:05:05.000Z sentdex 1062 yes no https://vidert.com/v/artificial-intelligence/video/6149/better-tracking-for-your-deep-learning-training-wandb-ai-weights-biases 2022-03-23T15:50:54.000Z weekly 0.5 https://i.ytimg.com/vi/LQvRhQwDOm0/maxresdefault.jpg Better tracking for your deep learning training - Wandb.ai (Weights & Biases) Introduction and overview of Weights and Biases: https://wandb.ai text-based writeup: https://pythonprogramming.net/wandb-deep-learning-tracking/ Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/LQvRhQwDOm0 2022-03-23T15:50:54.000Z sentdex 1275 yes no https://vidert.com/v/artificial-intelligence/video/6062/surprise-video-what-a-time-to-be-alive 2026-01-31T13:45:42.000Z weekly 0.5 https://i.ytimg.com/vi/4RtUJkjrKMI/maxresdefault.jpg Surprise Video - What A Time To Be Alive! Thank you so much everyone, this was amazing fun! Vocals: Carolina Padrón Drums: Federico Gucciardo Bass: Lagos Bfingerz Guitar tab is available here for free, no paywall nonsense: https://www.dropbox.com/scl/fi/oo1ny6i7mtgu3l006roa5/what-a-time-to-be-alive.gp?rlkey=n0c4xryfip7m15derrudnb8zl&st=fu51yljh&dl=1 Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Adam Bridges, Benji Rabhan, B Shang, Cameron Navor, Christian Ahlin, Eric T, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Ryan Stankye, Steef, Taras Bobrovytsky, Tazaur Sagenclaw, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/4RtUJkjrKMI 2026-01-31T13:45:42.000Z Two Minute Papers 305 yes no https://vidert.com/v/artificial-intelligence/video/6068/we-just-turned-down-millions-of-dollars-here-is-why 2026-01-01T17:03:18.000Z weekly 0.5 https://i.ytimg.com/vi/SWGBN1KvG6c/maxresdefault.jpg We Just Turned Down Millions of Dollars. Here Is Why. Yup. My free course on how to write a light simulation program (ray tracing): https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/ As a thank you for being with us for 1,000 episodes, here is my first ever interview with a Nobel Prize winning chemist: https://www.youtube.com/watch?v=Vhcwjzeukts Our Patreon if you wish to support us: https://www.patreon.com/TwoMinutePapers Note that just watching the series and leaving a kind comment every now and then is as much support as any of us could ever ask for! 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Benji Rabhan, B Shang, Christian Ahlin, Fred R, Gordon Child, Juan Benet, Michael Tedder, Owen Skarpness, Richard Sundvall, Steef, Taras Bobrovytsky, Tybie Fitzhugh, Ueli Gallizzi My research: https://cg.tuwien.ac.at/~zsolnai/ https://www.youtube.com/embed/SWGBN1KvG6c 2026-01-01T17:03:18.000Z Two Minute Papers 633 yes no https://vidert.com/v/artificial-intelligence/video/6103/training-a-unitree-g1-to-walk-w-reinforcement-learning 2025-12-19T14:40:46.000Z weekly 0.5 https://i.ytimg.com/vi/FGnAeUXRZ4E/maxresdefault.jpg Training a Unitree G1 to Walk w/ Reinforcement Learning Using mjlab and PPO to train the Unitree G1 humanoid to walk inside and outside Neural Networks from Scratch book: https://nnfs.io Channel membership: https://www.youtube.com/channel/UCfzlCWGWYyIQ0aLC5w48gBQ/join Discord: https://discord.gg/sentdex Reddit: https://www.reddit.com/r/sentdex/ Support the content: https://pythonprogramming.net/support-donate/ Twitter: https://twitter.com/sentdex Instagram: https://instagram.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex https://www.youtube.com/embed/FGnAeUXRZ4E 2025-12-19T14:40:46.000Z sentdex 2609 yes no