Yannic Kilcher Videos (Audio Only)

#gpt3 #knowledge #symbolic Symbolic knowledge models are usually trained on human-generated corpora that are cumbersome and expensive to create. Such corpora consist of structured triples of symbolic knowledge. This paper takes a different approach and attempts to generate such a corpus by prompting GPT-3. Results show that clever prompting, combined with targeted small critic models trained on human ratings can outperform both human-generated data, as well as the teacher model (GPT-3) itself. The results of this paper give a general recipe for automatically building corpora for various NLP tasks by extracting samples from large language models. OUTLINE: 0:00 - Intro & Overview 2:30 - Sponsor: Weights & Biases 4:15 - Commonsense Knowledge Graphs 7:50 - ATOMIC dataset 10:00 - Generating the corpus from a model 13:00 - Prompting GPT-3 15:30 - Generating Events 18:40 - Generating Inferences 23:00 - Evaluating the created dataset 26:45 - Introducing the critic 31:25 - Using the critic to filter the data 36:30 - Training a student on the generated data 41:00 - Key Findings 44:45 - Comments & Conclusion Paper: https://arxiv.org/abs/2110.07178 Code & Corpus: https://github.com/peterwestai2/symbo... Sponsor: Weights & Biases https://wandb.com https://community.wandb.ai/ Abstract: The common practice for training commonsense models has gone from-human-to-corpus-to-machine: humans author commonsense knowledge graphs in order to train commonsense models. In this work, we investigate an alternative, from-machine-to-corpus-to-machine: general language models author these commonsense knowledge graphs to train commonsense models. Our study leads to a new framework, Symbolic Knowledge Distillation. As with prior art in Knowledge Distillation (Hinton et al., 2015), our approach uses larger models to teach smaller models. A key difference is that we distill knowledge symbolically-as text-in addition to the neural model. We also distill only one aspect-the commonsense of a general language model teacher, allowing the student to be a different type, a commonsense model. Altogether, we show that careful prompt engineering and a separately trained critic model allow us to selectively distill high-quality causal commonsense from GPT-3, a general language model. Empirical results demonstrate that, for the first time, a human-authored commonsense knowledge graph is surpassed by our automatically distilled variant in all three criteria: quantity, quality, and diversity. In addition, it results in a neural commonsense model that surpasses the teacher model's commonsense capabilities despite its 100x smaller size. We apply this to the ATOMIC resource, and share our new symbolic knowledge graph and commonsense models. Authors: Peter West, Chandra Bhagavatula, Jack Hessel, Jena D. Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 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/yannick...

#sbb #seatreview #travel A friendly parody of Travel Vloggers and Airplane Seat Reviews :) No, SBB did not pay me for this (but they should ;) ) Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 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/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#mlnews #turingnlg #convmixer Your latest upates on what's happening in the Machine Learning world. OUTLINE: 0:00 - Intro 0:16 - Weights & Biases raises on 1B valuation (sponsored) 2:30 - Microsoft trains 530 billion parameter model 5:15 - StyleGAN v3 released 6:45 - A few more examples may be worth billions of parameters 8:30 - ConvMixer fits into a tweet 9:45 - Improved VQGAN 11:25 - William Shatner AI chats about his life 12:35 - Google AI pushes material science 14:10 - Gretel AI raises 50M for privacy protection 16:05 - DeepMind's push into ML for biology 19:00 - Schmidhuber laudates Kunihiko Fukushima for Bower Award 21:30 - Helpful Things 22:25 - Mosaic ML out of stealth mode 23:55 - First German self-driving train 24:45 - Ex-Pentagon Chief: China has already won 26:25 - DeepMind becomes profitable Sponsor: Weights & Biases https://wandb.com References: Microsoft Trains 530B Parameter Model https://www.microsoft.com/en-us/resea... StyleGAN 3 Code Released https://nvlabs.github.io/stylegan3/ https://github.com/NVlabs/stylegan3 https://colab.research.google.com/git... When do labels help? https://arxiv.org/pdf/2110.04374.pdf ml_paper.bruh https://openreview.net/pdf?id=TVHS5Y4... Improved VQGAN https://openreview.net/pdf?id=pfNyExj7z2 William Shatner "AI" & Storyfile https://www.livescience.com/william-s... https://www.storyfile.com/ GoogleAI Finds Complex Metal Oxides https://ai.googleblog.com/2021/10/fin... GretelAI raises 50M Series B https://techcrunch.com/2021/10/07/gre... https://gretel.ai/ https://gretel.ai/blog/why-privacy-by... DeepMind's Push in ML for Bio https://www.biorxiv.org/content/10.11... https://deepmind.com/blog/article/enf... Kunihiko Fukushima wins Bower Award: Schmidhuber Congratulates https://www.fi.edu/laureates/kunihiko... https://www.youtube.com/watch?v=ysOw6... Helpful Things https://github.com/UKPLab/beir#beers-... https://arxiv.org/pdf/2104.08663.pdf https://bayesoptbook.com/ https://github.com/nvlabs/imaginaire/ https://github.com/NVlabs/imaginaire/... MosaicML out of Stealth Mode https://www.mosaicml.com/ https://www.mosaicml.com/blog/founder... https://app.mosaicml.com/library/imag... https://github.com/mosaicml/composer https://mosaicml-composer.readthedocs... Germany's first self-driving train https://techxplore.com/news/2021-10-g... Ex-Pentagon Chief: China has already won tech war https://nypost.com/2021/10/11/pentago... DeepMind becomes profitable https://bdtechtalks.com/2021/10/07/go... Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 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/yannick...

#deepmind #nowcasting #machinelearning Your holy update on what's new in the Machine Learning world. OUTLINE: 0:00 - Intro 0:30 - DeepMind tackles Nowcasting 3:30 - The Guardian's shady reporting on TruthfulQA 6:15 - Stochastic training not necessary for generalization 7:35 - Google AI's efficient partitioning of road networks 9:15 - MiniHack Reinforcement Learning Environment 10:45 - Plato XL 11B dialog model 11:35 - AI finishes Beethoven's 10th Symphony 13:10 - AI casts doubt on painting authenticity 15:55 - ShadowDragon social media surveillance 18:45 - Helpful Libraries 25:20 - Samsung to copy-paste brains onto chips References: DeepMind improves Nowcasting https://deepmind.com/blog/article/now... https://www.nature.com/articles/s4158... https://github.com/deepmind/deepmind-... https://colab.research.google.com/git... The Guardian's shady reporting on TruthfulQA https://www.theguardian.com/commentis... Stochastic Training is Not Necessary for Generalization https://arxiv.org/pdf/2109.14119.pdf Google AI - Efficient Partitioning of Road Networks https://ai.googleblog.com/2021/09/eff... MiniHack Reinforcement Learning Environment https://ai.facebook.com/blog/minihack... Baidu PLATO-XL 11B Dialog Model http://research.baidu.com/Blog/index-... AI finishes Beethoven's 10th Symphony https://thenextweb.com/news/computer-... AI casts doubt on paining authenticity https://www.smithsonianmag.com/smart-... https://art-recognition.com/ https://art-recognition.com/case-stud... https://art-recognition.com/faq/ ShadowDragon Social Media Surveillance https://www.rt.com/usa/535630-ai-surv... https://theintercept.com/2021/09/21/s... Helpful Libraries / Datasets https://huggingface.co/infinity https://yanaiela.github.io/TNE/?s=09&... https://arxiv.org/abs/2109.10282 https://github.com/microsoft/unilm/tr... https://medium.com/people-ai-research... https://raft.elicit.org/ https://huggingface.co/spaces/ought/r... https://huggingface.co/spaces/ought/r... https://arxiv.org/pdf/2109.14076.pdf https://arxiv.org/pdf/2109.14394.pdf https://www.robots.ox.ac.uk/~vgg/rese... https://zenodo.org/record/5528345#.YV... https://github.com/yukimasano/PASS/ https://openreview.net/pdf?id=BwzYI-K... https://github.com/pytorch/data?utm_s... Samsung Method to copy paste brain onto chip https://www.engadget.com/samsung-copy... Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 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/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m

#grokking #openai #deeplearning Grokking is a phenomenon when a neural network suddenly learns a pattern in the dataset and jumps from random chance generalization to perfect generalization very suddenly. This paper demonstrates grokking on small algorithmic datasets where a network has to fill in binary tables. Interestingly, the learned latent spaces show an emergence of the underlying binary operations that the data were created with. OUTLINE: 0:00 - Intro & Overview 1:40 - The Grokking Phenomenon 3:50 - Related: Double Descent 7:50 - Binary Operations Datasets 11:45 - What quantities influence grokking? 15:40 - Learned Emerging Structure 17:35 - The role of smoothness 21:30 - Simple explanations win 24:30 - Why does weight decay encourage simplicity? 26:40 - Appendix 28:55 - Conclusion & Comments Paper: https://mathai-iclr.github.io/papers/... Abstract: In this paper we propose to study generalization of neural networks on small algorithmically generated datasets. In this setting, questions about data efficiency, memorization, generalization, and speed of learning can be studied in great detail. In some situations we show that neural networks learn through a process of “grokking” a pattern in the data, improving generalization performance from random chance level to perfect generalization, and that this improvement in generalization can happen well past the point of overfitting. We also study generalization as a function of dataset size and find that smaller datasets require increasing amounts of optimization for generalization. We argue that these datasets provide a fertile ground for studying a poorly understood aspect of deep learning: generalization of overparametrized neural networks beyond memorization of the finite training dataset. Authors: Alethea Power, Yuri Burda, Harri Edwards, Igor Babuschkin & Vedant Misra Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 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/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#deeplearning #co2 #cost Deep Learning has achieved impressive results in the last years, not least due to the massive increases in computational power and data that has gone into these models. Scaling up currently promises to be a reliable way to create more performant systems, but how far can we go? This article explores the limits of exponential scaling in AI, and what people are doing to get around this problem OUTLINE: 0:00 - Intro & Overview 1:00 - Deep Learning at its limits 3:10 - The cost of overparameterization 5:40 - Extrapolating power usage and CO2 emissions 10:45 - We cannot just continue scaling up 13:25 - Current solution attempts 15:25 - Aside: ImageNet V2 17:50 - Are symbolic methods the way out? Paper: https://spectrum.ieee.org/deep-learni... Image by Ralf Vetterle from Pixabay: https://pixabay.com/images/id-1752876/ Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 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/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#plagiarism #surveillance #schmidhuber Your Mondaily updates of what's going in the world of Machine Learning. OUTLINE: 0:00 - Intro 0:20 - New plagiarism case has plot twist 7:25 - CLIP for video surveillance 9:40 - DARPA SubTerranean Challenge 11:00 - Schmidhuber criticizing Turing Lecture 15:00 - OpenAI summarizes books 17:55 - UnBiasIt monitors employees' communications for bias 20:00 - iOS plans to detect depression 21:30 - UK 10 year plan to become AI superpower 23:30 - Helpful Libraries 29:00 - WIT: Wikipedia Image-Text dataset References: New plagiarism case with plot twist https://www.reddit.com/r/MachineLearn... https://zhuanlan.zhihu.com/p/411800486 https://github.com/cybercore-co-ltd/C... CLIP used for video surveillance https://www.reddit.com/r/MachineLearn... https://github.com/johanmodin/clifs DARPA SubTerranean Challenge https://twitter.com/BotJunkie/status/... https://twitter.com/BotJunkie https://www.subtchallenge.com/index.html https://www.subtchallenge.com/resourc... https://twitter.com/dynamicrobots/sta... Schmidhuber Blog: Turing Lecture Errors https://people.idsia.ch/~juergen/scie... OpenAI on Summarizing Books https://openai.com/blog/summarizing-b... https://arxiv.org/pdf/2109.10862.pdf UnBiasIt to monitor employee language https://edition.cnn.com/2021/09/20/te... https://www.unbiasit.com/ iPhone to detect depression https://www.wsj.com/articles/apple-wa... https://archive.ph/hRTnw UK 10-year plan to become AI-superpower https://www.cnbc.com/2021/09/22/uk-pu... https://archive.ph/4gkKK Helpful Libraries https://twitter.com/scikit_learn/stat... https://scikit-learn.org/stable/auto_... https://twitter.com/pcastr/status/144... https://github.com/google/dopamine https://github.com/microsoft/muzic https://ai-muzic.github.io/muzic_logo/ https://ai.facebook.com/blog/dynatask... https://github.com/tum-pbs/PhiFlow https://github.com/facebookresearch/dora Habitat and Matterport 3D Dataset https://github.com/facebookresearch/h... https://aihabitat.org/ https://arxiv.org/pdf/2109.08238.pdf WIT: Wikipedia-Based Image-Text Dataset https://ai.googleblog.com/2021/09/ann... Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 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/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#neurips #peerreview #nips The peer-review system at Machine Learning conferences has come under much criticism over the last years. One major driver was the infamous 2014 NeurIPS experiment, where a subset of papers were given to two different sets of reviewers. This experiment showed that only about half of all accepted papers were consistently accepted by both committees and demonstrated significant influence of subjectivity. This paper revisits the data from the 2014 experiment and traces the fate of accepted and rejected papers during the 7 years since, and analyzes how well reviewers can assess future impact, among other things. OUTLINE: 0:00 - Intro & Overview 1:20 - Recap: The 2014 NeurIPS Experiment 5:40 - How much of reviewing is subjective? 11:00 - Validation via simulation 15:45 - Can reviewers predict future impact? 23:10 - Discussion & Comments Paper: https://arxiv.org/abs/2109.09774 Code: https://github.com/lawrennd/neurips2014/ Abstract: In this paper we revisit the 2014 NeurIPS experiment that examined inconsistency in conference peer review. We determine that 50% of the variation in reviewer quality scores was subjective in origin. Further, with seven years passing since the experiment we find that for accepted papers, there is no correlation between quality scores and impact of the paper as measured as a function of citation count. We trace the fate of rejected papers, recovering where these papers were eventually published. For these papers we find a correlation between quality scores and impact. We conclude that the reviewing process for the 2014 conference was good for identifying poor papers, but poor for identifying good papers. We give some suggestions for improving the reviewing process but also warn against removing the subjective element. Finally, we suggest that the real conclusion of the experiment is that the community should place less onus on the notion of top-tier conference publications when assessing the quality of individual researchers. For NeurIPS 2021, the PCs are repeating the experiment, as well as conducting new ones. Authors: Corinna Cortes, Neil D. Lawrence Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 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/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#truthfulqa #efficientnet #laion400M Your regularly irregular updates on what's happening in the Machine Learning world. OUTLINE: 0:00 - Intro 0:20 - TruthfulQA benchmark shines new light on GPT-3 2:00 - LAION-400M image-text-pair dataset 4:10 - GoogleAI's EfficientNetV2 and CoAtNet 6:15 - Uber's H3: A hexagonal coordinate system 7:40 - AWS NeurIPS 2021 DeepRacer Challenge 8:15 - Helpful Libraries 9:20 - State of PyTorch in September 2021 10:05 - Physics-Based Deep Learning Book 10:35 - Music-conditioned 3D dance generation 11:40 - Stallman's take on legal issues with Codex 12:20 - Tensorflow DirectML on AMD GPUs 13:00 - Schmidhuber Blog: Turing Oversold ERRATA: Uber's H3 is actually not new, but from 2018 References: TruthfulQA - A benchmark assessing truthfulness of language models https://owainevans.github.io/pdfs/tru... LAION-400M image-text-pair dataset https://laion.ai/laion-400-open-dataset/ https://laion.ai/#top https://gogetfunding.com/help-us-buil... https://rom1504.github.io/clip-retrie... GooleAI releases EfficientNetV2 and CoAtNet https://ai.googleblog.com/2021/09/tow... Uber's H3 hexagonal coordinate systems https://eng.uber.com/h3/?utm_source=p... NeurIPS 2021 DeepRacer Challenge https://www.aicrowd.com/challenges/ne... https://aws.amazon.com/deepracer/ https://gitlab.aicrowd.com/deepracer/... Helpful Libraries https://github.com/rom1504/img2dataset https://github.com/facebookresearch/v... https://github.com/pyg-team/pytorch_g... https://aws.amazon.com/blogs/machine-... State of PyTorch in September 2021 https://dev-discuss.pytorch.org/t/sta... Physics-Based Deep Learning Book http://physicsbaseddeeplearning.org/i... https://arxiv.org/pdf/2109.05237.pdf Music Conditioned 3D dance generation https://ai.googleblog.com/2021/09/mus... Richard Stallman on Codex legal issues https://news.slashdot.org/story/21/09... Tensorflow DirectML on AMD https://wccftech.com/amd-microsoft-br... Schmidhuber: Turing Oversold https://people.idsia.ch//~juergen/tur... Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 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/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#gpt-3 #truth #conspiracy A new benchmark paper has created quite an uproar in the community. TruthfulQA is a dataset of 817 questions probing for imitative falsehoods where language models become less truthful, the larger they get. This surprising counter-intuitive finding validates many people's criticisms of large language models, but is it really the correct conclusion? OUTLINE: 0:00 - Intro 0:30 - Twitter Paper Announcement 4:10 - Large Language Models are to blame! 5:50 - How was the dataset constructed? 9:25 - The questions are adversarial 12:30 - Are you surprised?! Paper: https://arxiv.org/abs/2109.07958 Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yann... Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/1824646584 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/yannick... Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n