This AI Learned Boxing…With Serious Knockout Power! 🥊

❤️ Check out Perceptilabs and sign up for a free demo here: https://ift.tt/2WIdXXn 📝 The paper "Control Strategies for Physically Simulated Characters Performing Two-player Competitive Sports" is available here: https://ift.tt/3mGO1JA ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

[ML News] Standford HAI coins Foundation Models & High-profile case of plagiarism uncovered

#plagiarism #foundationmodels #tesla The best place to keep up to date with the latest and greatest from the ML world! OUTLINE: 0:00 - Intro & Sponsor 3:15 - A high-profile case of plagiarism shocks the ML world 11:55 - Stanford AI releases paper on "Foundation Models" 19:45 - Updates on Apple's NeuralHash 20:45 - RL control for two-player splorts 21:45 - Tesla's AI Day 23:55 - COMMA THREE announced 24:40 - Intel winding down RealSense cameras 25:20 - IBM unveils Telum Processor 25:50 - Lux AI Challenge & Neural MMO Challenge 26:50 - Dribnet's CLIP PixelArt 27:40 - Multi-Agent RL papers are mostly fake 28:50 - I can't even come up with a segment title 29:25 - AI News Questions 31:20 - Frameworks & Libraries Sponsor: Weights & Biases https://wandb.ai Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Fastformer: Additive Attention Can Be All You Need (Machine Learning Research Paper Explained)

#attention #transformer #fastformer Transformers have become the dominant model class in the last few years for large data, but their quadratic complexity in terms of sequence length has plagued them until now. Fastformer claims to be the fastest and most performant linear attention variant, able to consume long contexts at once. This is achieved by a combination of additive attention and elementwise products. While initial results look promising, I have my reservations... OUTLINE: 0:00 - Intro & Outline 2:15 - Fastformer description 5:20 - Baseline: Classic Attention 10:00 - Fastformer architecture 12:50 - Additive Attention 18:05 - Query-Key element-wise multiplication 21:35 - Redundant modules in Fastformer 25:00 - Problems with the architecture 27:30 - Is this even attention? 32:20 - Experimental Results 34:50 - Conclusion & Comments Paper: https://ift.tt/3goWgWT Abstract: Transformer is a powerful model for text understanding. However, it is inefficient due to its quadratic complexity to input sequence length. Although there are many methods on Transformer acceleration, they are still either inefficient on long sequences or not effective enough. In this paper, we propose Fastformer, which is an efficient Transformer model based on additive attention. In Fastformer, instead of modeling the pair-wise interactions between tokens, we first use additive attention mechanism to model global contexts, and then further transform each token representation based on its interaction with global context representations. In this way, Fastformer can achieve effective context modeling with linear complexity. Extensive experiments on five datasets show that Fastformer is much more efficient than many existing Transformer models and can meanwhile achieve comparable or even better long text modeling performance. Authors: Chuhan Wu, Fangzhao Wu, Tao Qi, Yongfeng Huang Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

This Magical AI Cuts People Out Of Your Videos! ✂️

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2S5tXnb ❤️ Their mentioned report is available here: https://ift.tt/3yf8jMn 📝 The paper "Omnimatte: Associating Objects and Their Effects in Video" is available here: https://ift.tt/3yeyVhP Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join Thumbnail background image credit: https://ift.tt/3zkPs3X Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

PonderNet: Learning to Ponder (Machine Learning Research Paper Explained)

#pondernet #deepmind #machinelearning Humans don't spend the same amount of mental effort on all problems equally. Instead, we respond quickly to easy tasks, and we take our time to deliberate hard tasks. DeepMind's PonderNet attempts to achieve the same by dynamically deciding how many computation steps to allocate to any single input sample. This is done via a recurrent architecture and a trainable function that computes a halting probability. The resulting model performs well in dynamic computation tasks and is surprisingly robust to different hyperparameter settings. OUTLINE: 0:00 - Intro & Overview 2:30 - Problem Statement 8:00 - Probabilistic formulation of dynamic halting 14:40 - Training via unrolling 22:30 - Loss function and regularization of the halting distribution 27:35 - Experimental Results 37:10 - Sensitivity to hyperparameter choice 41:15 - Discussion, Conclusion, Broader Impact Paper: https://ift.tt/3yd0VRP Abstract: In standard neural networks the amount of computation used grows with the size of the inputs, but not with the complexity of the problem being learnt. To overcome this limitation we introduce PonderNet, a new algorithm that learns to adapt the amount of computation based on the complexity of the problem at hand. PonderNet learns end-to-end the number of computational steps to achieve an effective compromise between training prediction accuracy, computational cost and generalization. On a complex synthetic problem, PonderNet dramatically improves performance over previous adaptive computation methods and additionally succeeds at extrapolation tests where traditional neural networks fail. Also, our method matched the current state of the art results on a real world question and answering dataset, but using less compute. Finally, PonderNet reached state of the art results on a complex task designed to test the reasoning capabilities of neural networks.1 Authors: Andrea Banino, Jan Balaguer, Charles Blundell Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

DeepMind’s AI Plays Catch…And So Much More! 🤖

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "Open-Ended Learning Leads to Generally Capable Agents" is available here: https://ift.tt/3f0Z5wO https://ift.tt/3x7YT4Z ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m #deepmind

NeuralHash is BROKEN | How to evade Apple's detection and forge hash collisions (w/ Code)

#apple #icloud #neuralhash Send your Apple fanboy friends to prison with this one simple trick ;) We break Apple's NeuralHash algorithm used to detect CSAM for iCloud photos. I show how it's possible to craft arbitrary hash collisions from any source / target image pair using an adversarial example attack. This can be used for many purposes, such as evading detection, or forging false positives, triggering manual reviews. OUTLINE: 0:00 - Intro 1:30 - Forced Hash Collisions via Adversarial Attacks 2:30 - My Successful Attack 5:40 - Results 7:15 - Discussion DISCLAIMER: This is for demonstration and educational purposes only. This is not an endorsement of illegal activity or circumvention of law. Code: https://ift.tt/3sDQllO Extract Model: https://ift.tt/3gchx68 My Video on NeuralHash: https://youtu.be/z15JLtAuwVI Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

[ML News] Nvidia renders CEO | Jurassic-1 larger than GPT-3 | Tortured Phrases reveal Plagiarism

#mlnews #nvidia #openai An in-depth look over what's going on in the world of Machine Learning and Artificial intelligence. Subscribe now and make Monday the best day of the week! OUTLINE: 0:00 - Intro 0:20 - Sponsor: Weights & Biases 3:00 - Nvidia's CEO was rendered during Keynote 5:00 - AI21 Labs releases Jurassic-1 language model 7:00 - Tortured Phrases reveal plagiarism 10:05 - Cortical neurons are computationally complex 11:55 - OpenAI Codex Update & Challenge 13:30 - Automated drug abuse prevention gone wrong 17:55 - Rapid News Questions 18:40 - SoundStream learned neural audio codec 19:40 - RoboMimic framework for robotics research 20:05 - Droidlet framework for agent training 20:40 - Unidentified Video Objects Benchmark 21:45 - Grammatical Error Correction Dataset 22:15 - ColabPro Plus available 23:05 - BigBench Self-Awareness benchmark for language models Sponsor: Weights & Biases https://wandb.ai References: NVIDIA renders CEO during keynote https://ift.tt/3CHfJeU https://ift.tt/3AvVv63 https://www.youtube.com/watch?v=eAn_oiZwUXA&t=3760s AI21 Labs announces Jurassic-1 model https://ift.tt/3lRuaHd https://ift.tt/3yESX50 https://twitter.com/yoavgo/status/1425584087016906752 Tortured Phrases point to plagiarism https://ift.tt/2Vr2Fto Real Neurons are insanely complex https://ift.tt/3iCtWlp OpenAI Codex Challenge & Update https://ift.tt/2XaUSQR https://ift.tt/3k5U1c3 https://ift.tt/3z1ahkH Automated drug abuse prevention goes wrong https://ift.tt/3fSlNYh News Questions https://ift.tt/3jLQO0Y https://ift.tt/3fXs2di https://ift.tt/2VQzEHN https://ift.tt/2XDu781 SoundStream Neural Audio Codec https://ift.tt/3CJouVF RoboMimic Framework https://ift.tt/3iBjACq Droidlet Framework https://ift.tt/3jzRI0r Unidentified Video Objects Benchmark https://ift.tt/37ULXFo Grammatical Error Correction Dataset https://ift.tt/2VG21YY Colab Pro Plus is "even better" https://ift.tt/3iNu69P BIG-Bench Self-Awareness Benchmark for Language Models https://ift.tt/3y4Sxny Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

How to make TensorFlow models run faster on GPUs

XLA compilation on GPU can greatly boost the performance of your models (~1.2x-35x performance improvements recorded). Learn how to use @tf.function(jit_compile=True) in TensorFlow to control what exact scopes are being compiled, and how to debug the performance of the resulting program. We'll cover writing compiled models, debugging them, and exploring the performance characteristics and optimizations the XLA compiler performs, and we'll do a detailed case study on XLA usage for Google’s GPU MLPerf submission. We'll also cover how automatic kernel fusion by XLA reduces memory bandwidth requirements and improves the performance of your models. You should have basic familiarity with TensorFlow and GPU computing in general. Subscribe to TensorFlow → https://goo.gle/TensorFlow product: TensorFlow - General; re_ty: Publish;

How Apple scans your phone (and how to evade it) - NeuralHash CSAM Detection Algorithm Explained

#apple #icloud #privacy Apple recently announced scanning all images uploaded to iCloud for CSAM (child abuse material), and that this scan would happen locally on users' phones. We take a look at the technical report and explore how the system works in detail, how it is designed to preserve user privacy, and what weak points it still has. OUTLINE: 0:00 - Introduction 3:05 - System Requirements 9:15 - System Overview 14:00 - NeuralHash 20:45 - Private Set Intersection 31:15 - Threshold Secret Sharing 35:25 - Synthetic Match Vouchers 38:20 - Problem 1: Who controls the database? 42:40 - Problem 2: Adversarial Attacks 49:40 - Comments & Conclusion Paper: https://ift.tt/3io8MHC ML News Episode about CSAM: https://youtu.be/gFkBqD2hbnU Abstract: CSAM Detection enables Apple to accurately identify and report iCloud users who store known Child Sexual Abuse Material (CSAM) in their iCloud Photos accounts. Apple servers flag accounts exceeding a threshold number of images that match a known database of CSAM image hashes so that Apple can provide relevant information to the National Center for Missing and Exploited Children (NCMEC). This process is secure, and is expressly designed to preserve user privacy. CSAM Detection provides these privacy and security assurances: • Apple does not learn anything about images that do not match the known CSAM database. • Apple can’t access metadata or visual derivatives for matched CSAM images until a threshold of matches is exceeded for an iCloud Photos account. • The risk of the system incorrectly flagging an account is extremely low. In addition, Apple manually reviews all reports made to NCMEC to ensure reporting accuracy. • Users can’t access or view the database of known CSAM images. • Users can’t identify which images were flagged as CSAM by the system. For detailed information about the cryptographic protocol and security proofs that the CSAM Detection process uses, see The Apple PSI System. Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Virtual Bones Make Everything Better! 💪

❤️ Check out Weights & Biases and sign up for a free demo here: https://ift.tt/2S5tXnb ❤️ Their mentioned post is available here: https://ift.tt/3bHQqg9 📝 The paper "Direct Delta Mush Skinning Compression with Continuous Examples" is available here: https://ift.tt/37EGa6O https://ift.tt/3xMKQC3 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Timothy, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m #gamedev

[ML NEWS] Apple scans your phone | Master Faces beat face recognition | WALL-E is real

#mlnews #apple #nolamarck Your update on the latest news in the AI and Machine Learning world. OUTLINE: 0:00 - Intro 0:15 - Sponsor: Weights & Biases 3:30 - Apple to scan iDevices for illegal content 14:10 - EU approves chatcontrol 15:20 - Machine Learning FAQ book 17:40 - TimeDial & Disfl-QA Conversation Datasets 20:30 - VoxPopuli Speech Dataset 21:00 - Google Tensor chip coming to Pixel 6 21:30 - Pentagon uses AI to predict events 23:10 - Sketch your own GAN 24:45 - Can a Fruit Fly learn Word Embeddings? 26:00 - Master Faces beat facial recognition system 27:25 - PyTorch profiler 1.9 27:55 - 0 A.D. gets reinforcement learning interface 28:40 - BeatBot cleans up cigarette butts on the beach Sponsor: Weights & Biases https://wandb.ai References: Apple to scan iDevices for illegal content https://ift.tt/3jxxgh4 https://ift.tt/2xtyIKb EU approves chatcontrol https://ift.tt/3hBnHMM Machine Learning FAQ book https://ift.tt/2Xspa1J TimeDial & Disfl-QA: New datasets for conversational NLP https://ift.tt/3CjCKUS VoxPopuli: Giant partially labeled speech dataset https://ift.tt/2Wjn57C Google's Tensor chip coming to Pixel 6 https://ift.tt/3jeRlbF Pentagon uses AI for predicting relevant events in advance https://ift.tt/3jLFhic Sketch Your Own GAN https://ift.tt/2Vwl47J Can a fruit fly learn word embeddings? https://ift.tt/2VQXsLu Master Faces for attacking facial recognition systems https://ift.tt/3A0uur8 PyTorch Profiler v1.9 https://ift.tt/3fBLk7J 0 A.D. adds Reinforcement Learning interface https://ift.tt/3jT41VZ https://ift.tt/3qEUqUY BeachBot cleans up cigarette butts on the beach https://ift.tt/3s1OlDE Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Efficient serving with ScaNN for retrieval (Building recommendation systems with TensorFlow)

In our earlier videos, we showed you how to use the brute force approach in your retrieval system. In this video, we are going to use a more efficient method - Approximate Nearest Neighbours (ANN) and introduce you to the Google ScaNN library. Efficient serving with ScaNN →https://goo.gle/3s6kE4f ScaNN repository → https://goo.gle/3w5d6iH Google AI blog on ScaNN → https://goo.gle/3iAxdBE Accelerating Large-Scale Inference with Anisotropic Vector Quantization → https://goo.gle/3lLj5r9 Watch more Building recommendation systems with TensorFlow → https://goo.gle/3Bi8NUS Subscribe to TensorFlow → https://goo.gle/TensorFlow product: TensorFlow - TensorFlow Recommenders, TensorFlow - General; re_ty: Publish;

Beautiful Thin Film Simulations Are Now Possible! 🤯

❤️ Check out Perceptilabs and sign up for a free demo here: https://ift.tt/2WIdXXn 📝 The paper "Thin-Film Smoothed Particle Hydrodynamics Fluid" is available here: https://ift.tt/3s6nBSm 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join Thumbnail background image credit: https://ift.tt/3ArThVe Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

New AI Research Work Fixes Your Choppy Videos! 🎬

❤️ Check out Lambda here and sign up for their GPU Cloud: https://ift.tt/35NkCT7 📝 The paper "Time Lens: Event-based Video Frame Interpolation" is available here: https://ift.tt/2RUEp0W ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://ift.tt/2icTBUb - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

[ML News] AI-generated patent approved | Germany gets an analog to OpenAI | ML cheats video games

#mlnews #dabus #alephalpha OUTLINE: 0:00 - Intro 0:20 - Sponsor: Weights & Biases 3:45 - AI legally recognized as patent inventor 8:35 - Alpeh Alpha raises USD 27Mio to build European OpenAI 10:20 - AMP advances AI aided recycling 11:20 - DeepMind builds XLand RL environment 13:15 - Cognitive Behavioral Therapy as an app 16:15 - Wordcraft interactive AI text editor 17:05 - ML used to cheat in console games 18:10 - Google's OpenBuildings Dataset 20:00 - Most ML COVID tools are flawed 21:10 - DALL-E mini released 21:55 - Helpful Libraries 25:20 - FSF funds papers discussing CoPilot SPONSOR: Weights & Biases https://wandb.ai References: AI legally recognized as patent inventor https://ift.tt/3rTejJv https://ift.tt/3A0HQ6P https://ift.tt/3rX8hHD https://ift.tt/3jtSTPc https://ift.tt/3ju2MMT https://ift.tt/3xwvFwN https://ift.tt/2VCtKJX https://ift.tt/2VyYSdl https://ift.tt/3xtN74U https://ift.tt/2VzJeOW Alpeh Alpha raises USD 27Mio to build European OpenAI https://ift.tt/3f1D0hl AMP advances AI aided recycling https://ift.tt/2VnceK1 DeepMind builds XLand RL environment https://ift.tt/3f0Z5wO https://ift.tt/3x7YT4Z Cognitive Behavioral Therapy as an app https://ift.tt/3uAR86d Wordcraft interactive AI text editor https://ift.tt/36PZSft https://ift.tt/3rbHh6I https://www.youtube.com/watch?v=9p4mfA0Fyd8 ML used to cheat in console games https://ift.tt/3fDzryk Google's OpenBuildings Dataset https://ift.tt/3l7p895 https://ift.tt/3BXJzeC Most ML COVID tools are flawed https://ift.tt/3j2qzD9 DALL-E mini released https://ift.tt/3yfwz23 https://ift.tt/3C1CHNI Helpful Libraries https://ift.tt/3yb8kBY https://ift.tt/373qN7y https://ift.tt/3aR0sMA https://ift.tt/3lECoTg https://ift.tt/3rZyIwv https://ift.tt/2VXv2PO https://ift.tt/2rdjxjj https://ift.tt/3yaEqO3 https://ift.tt/3jy19NY FSF funds papers discussing CoPilot https://ift.tt/3xlNFKp https://ift.tt/24giPzW Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

Best practices for ML product decisions (ML Tech Talks)

In this Machine Learning Tech Talks session, Senior UX Designer Di Dang unpacks best practices for ML product decisions using the People + AI Guidebook. Learn how UX considerations can impact your AI/ML-based product as much as the technical feasibility itself. Chapters: 0:00 - Introduction 6:08 - User needs & defining success 13:31 - Mental models 24:32 - Explainability & trust 34:34 - Summary and next steps Resources: For more guidance on following a human-centered approach to ML, check out the People + AI Guidebook 📖→ https://goo.gle/3qRFl3l 🎥 → https://goo.gle/3wjBUnb Catch more ML Tech Talks → http://goo.gle/ml-tech-talks Subscribe to TensorFlow → https://goo.gle/TensorFlow product: TensorFlow - General; fullname: Di Dang; re_ty: Publish;

Rendering Shiny Things: Finally, A Problem No More! 🔮

❤️ Check out Fully Connected by Weights & Biases: https://wandb.me/papers 📝 The paper "NeX: Real-time View Synthesis with Neural Basis Expansion " is available here: https://ift.tt/38pNT9y 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bryan Learn, Christian Ahlin, Eric Haddad, Eric Martel, Gordon Child, Ivo Galic, Jace O'Brien, Javier Bustamante, John Le, Jonas, Kenneth Davis, Klaus Busse, Lorin Atzberger, Lukas Biewald, Matthew Allen Fisher, Mark Oates, Michael Albrecht, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi. If you wish to appear here or pick up other perks, click here: https://ift.tt/2icTBUb Or join us here: https://www.youtube.com/user/keeroyz/join Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://ift.tt/2TnVBd3 Thumbnail background image credit: https://ift.tt/3yBpt7S Károly Zsolnai-Fehér's links: Instagram: https://ift.tt/2KBCNkT Twitter: https://twitter.com/twominutepapers Web: https://ift.tt/1NwkG9m

Deep & Cross Network (Building recommendation systems with TensorFlow)

In this video, we are going to extend our discussion on Building recommendation systems with TensorFlow to Deep & Cross Network. Deep cross network using TensorFlow Recommenders → https://goo.gle/37if0T0 DCN V1: Deep & Cross Network for Ad Click Predictions → https://goo.gle/3ftTuiJ DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems → https://goo.gle/3frVJTr Watch more Building recommendation systems with TensorFlow → https://goo.gle/3Bi8NUS Subscribe to TensorFlow → https://goo.gle/TensorFlow

[ML News] MMO Game destroys GPUs | OpenAI quits Robotics | Today w/ guest host Sanyam Bhutani

#chai #mlnews #nvidia Follow Saynam here: YouTube: https://www.youtube.com/c/ChaiTimeDataScience Twitter: https://twitter.com/bhutanisanyam1 Apple Podcasts: https://ift.tt/3fnbmeR LinkedIn: https://ift.tt/3focNd1 Spotify: https://ift.tt/3lov6CO Anchor.fm RSS: https://ift.tt/2V6emWl Outline: 0:00 - Intro & Overview 1:30 - Amazon's MMO may destroy gaming GPUs 2:40 - OpenAI pivots away from Robotics 3:35 - Google parent Alphabet launches Intrinsic 4:55 - AI learns how vegetables taste 5:55 - NASA uses AI to better understand the sun 6:50 - Man used AI to bring back deceased fiancee 7:45 - Robot collision sparks warehouse fire 8:20 - AI deduces patients' racial identities from medical records 9:40 - AlphaFold protein structure database 10:15 - ICCV BEHAVIOR challenge 11:05 - IBM, MIT, Harvard release Common Sense database 11:35 - High quality image generation using diffusion models 12:50 - Conclusion References: 1 Amazon’s new MMO may be bricking Nvidia 3090s https://ift.tt/3wWL4WP https://www.youtube.com/watch?v=KLyNFrKyG74 2 Open AI pivotes from Robots https://ift.tt/3zx1wPw 3 Google parent Alphabet launches Intrinsic: a new company to build software for industrial robots https://ift.tt/3kKjwSc Introducing Intrinsic https://ift.tt/3zygkgJ https://ift.tt/3iE6K57 https://ift.tt/3lo9BSM 4 Artificial Intelligence Helps Improve NASA’s Eyes on the Sun https://ift.tt/3y4FUJT 5 A man used AI to bring back his deceased fiancé. But the creators of the tech warn it could be dangerous https://ift.tt/3rwGo9d 6 Robot collision at Ocado warehouse near London sparks fire, delaying customer orders https://ift.tt/2TiwX0i 10 Reading Race: AI Recognizes Patient’s Racial Identity In Medical Images https://ift.tt/2Ve6guI 11 AlphaFold Protein Structure Database https://ift.tt/2UCUlGv https://ift.tt/3By2ahd 12 Behavior Challenge https://ift.tt/3f3Yw5m 13 Researchers from IBM, MIT and Harvard Announced The Release Of DARPA “Common Sense AI” Dataset Along With Two Machine Learning Models At ICML 2021 https://ift.tt/3iyMiCK https://ift.tt/3xTV1pd 14 Google uses diffusion model for image generation https://ift.tt/3iOvYxv https://ift.tt/3Bnbdlb Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ift.tt/3dJpBrR BitChute: https://ift.tt/38iX6OV Minds: https://ift.tt/37igBpB Parler: https://ift.tt/38tQU7C LinkedIn: https://ift.tt/2Zo6XRA BiliBili: https://ift.tt/3mfyjkW 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://ift.tt/2DuKOZ3 Patreon: https://ift.tt/390ewRH Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n