ykilcher

#mlnews #ai #mlinpl Your news from the world of Machine Learning! OUTLINE: 0:00 - Introduction 1:25 - Stable Diffusion Multiplayer 2:15 - Huggingface: DOI for Models & Datasets 3:10 - OpenAI asks for more funding 4:25 - The Stack: Source Code Dataset 6:30 - Google Vizier Open-Sourced 7:10 - New Models 11:50 - Helpful Things 20:30 - Prompt Databases 22:15 - Lexicap by Karpathy References: Stable Diffusion Multiplayer https://huggingface.co/spaces/huggingface-projects/stable-diffusion-multiplayer?roomid=room-0 Huggingface: DOI for Models & Datasets https://huggingface.co/blog/introducing-doi OpenAI asks for more funding https://www.theinformation.com/articles/openai-valued-at-nearly-20-billion-in-advanced-talks-with-microsoft-for-more-funding https://www.wsj.com/articles/microsoft-in-advanced-talks-to-increase-investment-in-openai-11666299548 The Stack: Source Code Dataset https://huggingface.co/datasets/bigcode/the-stack?utm_source=pocket_mylist Google Vizier Open-Sourced https://github.com/google/vizier New Models https://imagen.research.google/video/ https://phenaki.github.io/ https://makeavideo.studio/?utm_source=pocket_mylist https://dreamfusion3d.github.io/ https://arxiv.org/pdf/2210.15257.pdf https://huggingface.co/spaces/PaddlePaddle/ERNIE-ViLG https://github.com/PaddlePaddle/PaddleHub Helpful Things https://thecharlieblake.co.uk/visualising-ml-number-formats https://griddly.ai/ https://engineering.fb.com/2022/10/18/open-source/ocp-summit-2022-grand-teton/?utm_source=twitter&utm_medium=organic_social&utm_campaign=eng2022h2 https://twitter.com/psuraj28/status/1580640841583902720?utm_source=pocket_mylist https://huggingface.co/blog/stable_diffusion_jax https://github.com/Lightning-AI/stable-diffusion-deploy https://lightning.ai/docs/stable/ https://github.com/CarperAI/trlx https://github.com/DLR-RM/rl-baselines3-zoo https://github.com/Sea-Snell/JAXSeq https://www.reddit.com/r/MachineLearning/comments/xoitw9/p_albumentations_13_is_released_a_python_library/?utm_source=pocket_mylist https://twitter.com/Warvito/status/1570691960792580096?utm_source=pocket_mylist https://arxiv.org/abs/2209.07162 https://academictorrents.com/details/63aeb864bbe2115ded0aa0d7d36334c026f0660b https://huggingface.co/spaces/THUDM/CodeGeeX https://ai.facebook.com/blog/gpu-inference-engine-nvidia-amd-open-source/?utm_source=twitter&utm_medium=organic_social&utm_campaign=blog https://github.com/nerfstudio-project/nerfstudio https://www.nerfacc.com/en/latest/ https://github.com/dstackai/dstack https://www.reddit.com/r/MachineLearning/comments/yeyxlo/p_openai_whisper_3x_cpu_inference_speedup/?utm_source=pocket_mylist https://github.com/MiscellaneousStuff/openai-whisper-cpu/issues/1 Prompt Databases https://huggingface.co/datasets/poloclub/diffusiondb https://publicprompts.art/ https://visualise.ai/ https://twitter.com/SamuelAlbanie/status/1574111928431026179/photo/1 Lexicap by Karpathy https://karpathy.ai/lexicap/0139-large.html 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

#ai #stablediffusion #license So-called responsible AI licenses are stupid, counterproductive, and have a dangerous legal loophole in them. OpenRAIL++ License here: https://www.ykilcher.com/license OUTLINE: 0:00 - Introduction 0:40 - Responsible AI Licenses (RAIL) of BLOOM and Stable Diffusion 3:35 - Open source software's dilemma of bad usage and restrictions 8:45 - Good applications, bad applications 12:45 - A dangerous legal loophole 15:50 - OpenRAIL++ License 16:50 - This has nothing to do with copyright 26:00 - Final thoughts References: https://huggingface.co/CompVis/stable-diffusion/tree/main https://huggingface.co/spaces/CompVis/stable-diffusion-license https://huggingface.co/bigscience/bloom?text=34%2B10%3D44+%0A54%2B20%3D https://huggingface.co/spaces/bigscience/license https://huggingface.co/runwayml/stable-diffusion-v1-5 https://huggingface.co/spaces/CompVis/stable-diffusion-license/raw/main/license.txt https://www.gnu.org/philosophy/programs-must-not-limit-freedom-to-run.en.html https://www.gnu.org/philosophy/free-sw.html#four-freedoms https://www.licenses.ai/blog/2022/8/26/bigscience-open-rail-m-license https://bigscience.huggingface.co/blog/bigscience-ethical-charter https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses https://en.wikipedia.org/wiki/Copyright#Eligible_works https://en.wikipedia.org/wiki/Creative_work https://www.pearlcohen.com/copyright-office-reiterates-that-works-created-by-ai-cannot-be-copyrighted/ https://jipel.law.nyu.edu/vol-8-no-2-1-hedrick/#II https://www.ykilcher.com/license 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

#ai #language #knowledge Large Language Models have the ability to store vast amounts of facts about the world. But little is known, how these models actually do this. This paper aims at discovering the mechanism and location of storage and recall of factual associations in GPT models, and then proposes a mechanism for the targeted editing of such facts, in form of a simple rank-one update to a single MLP layer. This has wide implications both for how we understand such models' inner workings, and for our ability to gain greater control over such models in the future. OUTLINE: 0:00 - Introduction 1:40 - What are the main questions in this subfield? 6:55 - How causal tracing reveals where facts are stored 18:40 - Clever experiments show the importance of MLPs 24:30 - How do MLPs store information? 29:10 - How to edit language model knowledge with precision? 36:45 - What does it mean to know something? 39:00 - Experimental Evaluation & the CounterFact benchmark 45:40 - How to obtain the required latent representations? 51:15 - Where is the best location in the model to perform edits? 58:00 - What do these models understand about language? 1:02:00 - Questions for the community Paper: https://arxiv.org/abs/2202.05262 Follow-up paper on Mass-Editing Memory in a Transformer: https://arxiv.org/abs/2210.07229 Abstract: We analyze the storage and recall of factual associations in autoregressive transformer language models, finding evidence that these associations correspond to localized, directly-editable computations. We first develop a causal intervention for identifying neuron activations that are decisive in a model's factual predictions. This reveals a distinct set of steps in middle-layer feed-forward modules that mediate factual predictions while processing subject tokens. To test our hypothesis that these computations correspond to factual association recall, we modify feed-forward weights to update specific factual associations using Rank-One Model Editing (ROME). We find that ROME is effective on a standard zero-shot relation extraction (zsRE) model-editing task, comparable to existing methods. To perform a more sensitive evaluation, we also evaluate ROME on a new dataset of counterfactual assertions, on which it simultaneously maintains both specificity and generalization, whereas other methods sacrifice one or another. Our results confirm an important role for mid-layer feed-forward modules in storing factual associations and suggest that direct manipulation of computational mechanisms may be a feasible approach for model editing. The code, dataset, visualizations, and an interactive demo notebook are available at this https URL Authors: Kevin Meng, David Bau, Alex Andonian, Yonatan Belinkov 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

#ai #cicero #diplomacy A team from Meta AI has developed Cicero, an agent that can play the game Diplomacy, in which players have to communicate via chat messages to coordinate and plan into the future. Paper Title: Human-level play in the game of Diplomacy by combining language models with strategic reasoning Commented game by human expert: https://www.youtube.com/watch?v=u5192bvUS7k OUTLINE: 0:00 - Introduction 9:50 - AI in cooperation games 13:50 - Cicero agent overview 25:00 - A controllable dialogue model 36:50 - Dialogue-conditional strategic planning 49:00 - Message filtering 53:45 - Cicero's play against humans 55:15 - More examples & discussion Homepage: https://ai.facebook.com/research/cicero/ Code: https://github.com/facebookresearch/diplomacy_cicero Blog: https://ai.facebook.com/blog/cicero-ai-negotiates-persuades-and-cooperates-with-people/ Paper: https://www.science.org/doi/10.1126/science.ade9097 Abstract: Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. We introduce Cicero, the first AI agent to achieve human-level performance in Diplomacy, a strategy game involving both cooperation and competition that emphasizes natural language negotiation and tactical coordination between seven players. Cicero integrates a language model with planning and reinforcement learning algorithms by inferring players' beliefs and intentions from its conversations and generating dialogue in pursuit of its plans. Across 40 games of an anonymous online Diplomacy league, Cicero achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game. Authors: Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, Athul Paul Jacob, Mojtaba Komeili, Karthik Konath, Minae Kwon, Adam Lerer, Mike Lewis, Alexander H. Miller, Sasha Mitts, Adithya Renduchintala, Stephen Roller, Dirk Rowe, Weiyan Shi, Joe Spisak, Alexander Wei, David Wu, Hugh Zhang, Markus Zijlstra 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

#neuralnetworks #machinelearning #ai Alexander Mattick joins me to discuss the paper "Neural Networks are Decision Trees", which has generated a lot of hype on social media. We ask the question: Has this paper solved one of the large mysteries of deep learning and opened the black-box neural networks up to interpretability? OUTLINE: 0:00 - Introduction 2:20 - Aren't Neural Networks non-linear? 5:20 - What does it all mean? 8:00 - How large do these trees get? 11:50 - Decision Trees vs Neural Networks 17:15 - Is this paper new? 22:20 - Experimental results 27:30 - Can Trees and Networks work together? Paper: https://arxiv.org/abs/2210.05189 Abstract: In this manuscript, we show that any feedforward neural network having piece-wise linear activation functions can be represented as a decision tree. The representation is equivalence and not an approximation, thus keeping the accuracy of the neural network exactly as is. We believe that this work paves the way to tackle the black-box nature of neural networks. We share equivalent trees of some neural networks and show that besides providing interpretability, tree representation can also achieve some computational advantages. The analysis holds both for fully connected and convolutional networks, which may or may not also include skip connections and/or normalizations. Author: Caglar Aytekin 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

#ai #sparsity #gpu Sparsity is awesome, but only recently has it become possible to properly handle sparse models at good performance. Neural Magic does exactly this, using a plain CPU. No specialized hardware needed, just clever algorithms for pruning and forward-propagation of neural networks. Nir Shavit and I talk about how this is possible, what it means in terms of applications, and why sparsity should play a much larger role in the Deep Learning community. Sponsor: AssemblyAI Link: https://www.assemblyai.com/?utm_source=youtube&utm_medium=social&utm_campaign=yannic_autochapters Check out Neural Magic: https://neuralmagic.com/ and DeepSparse: https://github.com/neuralmagic/deepsparse OUTLINE: 0:00 Introduction 1:08 Sponsor: AssemblyAI 2:50 Start of Interview 4:15 How the NIR company was founded? 5:10 What is Sparsity about? 9:30 Link between the human brain and sparsity 12:10 Where should the extra resource that the human brain doesn't have go? 14:40 Analogy for Sparse Architecture 16:48 Possible future for Sparse Architecture as standard architure for Neural Networks 20:08 Pruning & Sparsification 22:57 What keeps us from building sparse models? 25:34 Why are GPUs so unsuited for sparse models? 28:47 CPU and GPU in connection with memory 30:14 What Neural Magic does? 32:54 How do you deal with overlaps in tensor columns? 33:41 The best type of sparsity to execute tons of CPU 37:24 What kind of architecture would make the best use out of a combined system of CPUs and GPUs? 41:04 Graph Neural Networks in connection to sparsity 43:04 Intrinsic connection between the Sparsification of Neural Networks, Non Layer-Wise Computation, Blockchain Technology, Smart Contracts and Distributed Computing 45:23 Neural Magic's target audience 48:16 Is there a type of model where it works particularly well and the type where it doesn't? 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

#stablediffusion #aiart #mlnews Stable Diffusion has been released and is riding a wave of creativity and collaboration. But not everyone is happy about this... Sponsor: NVIDIA GPU Raffle: https://ykilcher.com/gtc OUTLINE: 0:00 - Introduction 0:30 - What is Stable Diffusion? 2:25 - Open-Source Contributions and Creations 7:55 - Textual Inversion 9:30 - OpenAI vs Open AI 14:20 - Journalists be outraged 16:20 - AI Ethics be even more outraged 19:45 - Do we need a new social contract? 21:30 - More applications 22:55 - Helpful Things 23:45 - Sponsor: NVIDIA (& how to enter the GPU raffle) References: https://early-hair-c20.notion.site/Stable-Diffusion-Takes-Over-Referenes-7a2f45b8f7e04ae0ba19dbfcd2b7f7c0 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

#alphatensor #deepmind #ai Matrix multiplication is the most used mathematical operation in all of science and engineering. Speeding this up has massive consequences. Thus, over the years, this operation has become more and more optimized. A fascinating discovery was made when it was shown that one actually needs less than N^3 multiplication operations to multiply to NxN matrices. DeepMind goes a step further and creates AlphaTensor, a Deep Reinforcement Learning algorithm that plays a single-player game, TensorGame, in order to find even more optimized algorithms for matrix multiplication. And it turns out, there exists a plethora of undiscovered matrix multiplication algorithms, which not only will make everything from computers to smart toasters faster, but also bring new insights into fundamental math and complexity theory. Sponsor: Assembly AI Link: https://www.assemblyai.com/?utm_source=youtube&utm_medium=social&utm_campaign=yannic_sentiment OUTLINE: 0:00 - Intro 1:50 - Sponsor: Assembly AI (link in description) 3:25 - What even is Matrix Multiplication? 6:10 - A very astounding fact 8:45 - Trading multiplications for additions 12:35 - Matrix Multiplication as a Tensor 17:30 - Tensor Decompositions 20:30 - A formal way of finding multiplication algorithms 31:00 - How to formulate this as a game? 39:30 - A brief primer on AlphaZero / MCTS 45:40 - The Results 48:15 - Optimizing for different hardware 52:40 - Expanding fundamental math 53:45 - Summary & Final Comments Paper: https://www.nature.com/articles/s41586-022-05172-4 Title: Discovering faster matrix multiplication algorithms with reinforcement learning Abstract: Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many systems—from neural networks to scientific computing routines. The automatic discovery of algorithms using machine learning offers the prospect of reaching beyond human intuition and outperforming the current best human-designed algorithms. However, automating the algorithm discovery procedure is intricate, as the space of possible algorithms is enormous. Here we report a deep reinforcement learning approach based on AlphaZero1 for discovering efficient and provably correct algorithms for the multiplication of arbitrary matrices. Our agent, AlphaTensor, is trained to play a single-player game where the objective is finding tensor decompositions within a finite factor space. AlphaTensor discovered algorithms that outperform the state-of-the-art complexity for many matrix sizes. Particularly relevant is the case of 4 × 4 matrices in a finite field, where AlphaTensor’s algorithm improves on Strassen’s two-level algorithm for the first time, to our knowledge, since its discovery 50 years ago2. We further showcase the flexibility of AlphaTensor through different use-cases: algorithms with state-of-the-art complexity for structured matrix multiplication and improved practical efficiency by optimizing matrix multiplication for runtime on specific hardware. Our results highlight AlphaTensor’s ability to accelerate the process of algorithmic discovery on a range of problems, and to optimize for different criteria. Authors: Alhussein Fawzi, Matej Balog, Aja Huang, Thomas Hubert, Bernardino Romera-Paredes, Mohammadamin Barekatain, Alexander Novikov, Francisco J. R. Ruiz, Julian Schrittwieser, Grzegorz Swirszcz, David Silver, Demis Hassabis & Pushmeet Kohli 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

#ai #interview #research Jacob Steinhardt believes that future AI systems will be qualitatively different than the ones we know currently. We talk about how emergence happens when scaling up, what implications that has on AI Safety, and why thought experiments like the Paperclip Maximizer might be more useful than most people think. OUTLINE: 0:00 Introduction 1:10 Start of Interview 2:10 Blog posts series 3:56 More Is Different for AI (Blog Post) 7:40 Do you think this emergence is mainly a property from the interaction of things? 9:17 How does phase transition or scaling-up play into AI and Machine Learning? 12:10 GPT-3 as an example of qualitative difference in scaling up 14:08 GPT-3 as an emergent phenomenon in context learning 15:58 Brief introduction of different viewpoints on the future of AI and its alignment 18:51 How does the phenomenon of emergence play into this game between the Engineering and the Philosophy viewpoint? 22:41 Paperclip Maximizer on AI safety and alignment 31:37 Thought Experiments 37:34 Imitative Deception 39:30 TruthfulQA: Measuring How Models Mimic Human Falsehoods (Paper) 42:24 ML Systems Will Have Weird Failure Models (Blog Post) 51:10 Is there any work to get a system to be deceptive? 54:37 Empirical Findings Generalize Surprisingly Far (Blog Post) 1:00:18 What would you recommend to guarantee better AI alignment or safety? 1:05:13 Remarks References: https://bounded-regret.ghost.io/more-is-different-for-ai/ https://docs.google.com/document/d/1FbTuRvC4TFWzGYerTKpBU7FJlyvjeOvVYF2uYNFSlOc/edit#heading=h.n1wk9bxo847o Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord BitChute: https://www.bitchute.com/channel/yannic-kilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 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

#huggingface #pickle #exploit Did you know that something as simple as loading a model can execute arbitrary code on your machine? Try the model: https://huggingface.co/ykilcher/totally-harmless-model Get the code: https://github.com/yk/patch-torch-save Sponsor: Weights & Biases Go here: https://wandb.me/yannic OUTLINE: 0:00 - Introduction 1:10 - Sponsor: Weights & Biases 3:20 - How Hugging Face models are loaded 5:30 - From PyTorch to pickle 7:10 - Understanding how pickle saves data 13:00 - Executing arbitrary code 15:05 - The final code 17:25 - How can you protect yourself? 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

#ai #selforganization #emergence Read Sebastian's article here: https://sebastianrisi.com/self_assembling_ai/ OUTLINE: 0:00 - Introduction 2:25 - Start of Interview 4:00 - The intelligence of swarms 9:15 - The game of life & neural cellular automata 14:10 - What's missing from neural CAs? 17:20 - How does local computation compare to centralized computation? 25:40 - Applications beyond games and graphics 33:00 - Can we do away with goals? 35:30 - Where do these methods shine? 43:30 - The paradox of scales & brains 49:45 - Connections to graphical systems & GNNs 51:30 - Could this solve ARC? 57:45 - Where can people get started? References: https://sebastianrisi.com/ https://modl.ai/ https://sebastianrisi.com/self_assembling_ai/ https://twitter.com/risi1979/status/1519053654921293827?cxt=HHwWhsC9hYfQ4ZQqAAAA https://distill.pub/2020/growing-ca/ https://arxiv.org/abs/2201.12360?source=techstories.org https://distill.pub/2020/selforg/mnist/ https://arxiv.org/pdf/2204.11674.pdf https://github.com/fchollet/ARC https://github.com/volotat/ARC-Game http://animalaiolympics.com/AAI/ https://www.deepmind.com/publications/alchemy-a-structured-task-distribution-for-meta-reinforcement-learning-f https://melaniemitchell.me/BooksContent/CAGTReviews.html 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

#stablediffusion #ai #stabilityai An interview with Emad Mostaque, founder of Stability AI. OUTLINE: 0:00 - Intro 1:30 - What is Stability AI? 3:45 - Where does the money come from? 5:20 - Is this the CERN of AI? 6:15 - Who gets access to the resources? 8:00 - What is Stable Diffusion? 11:40 - What if your model produces bad outputs? 14:20 - Do you employ people? 16:35 - Can you prevent the corruption of profit? 19:50 - How can people find you? 22:45 - Final thoughts, let's destroy PowerPoint 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

#mlnews #bloom #ai Today we look at all the recent giant language models in the AI world! OUTLINE: 0:00 - Intro 0:55 - BLOOM: Open-Source 176B Language Model 5:25 - YALM 100B 5:40 - Chinese Brain-Scale Supercomputer 7:25 - Meta AI Translates over 200 Languages 10:05 - Reproducibility Crisis Workshop 10:55 - AI21 Raises $64M 11:50 - Ian Goodfellow leaves Apple 12:20 - Andrej Karpathy leaves Tesla 12:55 - Wordalle References: BLOOM: Open-Source 176B Language Model https://bigscience.huggingface.co/blog/bloom https://huggingface.co/spaces/bigscience/license https://huggingface.co/bigscience/bloom?text=34%2B10%3D44+%0A54%2B20%3D YALM 100B https://github.com/yandex/YaLM-100B Chinese Brain-Scale Supercomputer https://www.scmp.com/news/china/science/article/3182498/china-supercomputer-achieves-global-first-brain-scale-ai-model?utm_source=pocket_mylist https://archive.ph/YaoA6#selection-1237.156-1237.246 Meta AI Translates over 200 Languages https://ai.facebook.com/research/no-language-left-behind/ Reproducibility Crisis Workshop https://reproducible.cs.princeton.edu/ AI21 Raises $64M https://techcrunch.com/2022/07/12/openai-rival-ai21-labs-raises-64m-to-ramp-up-its-ai-powered-language-services/?guccounter=1 Ian Goodfellow leaves Apple https://twitter.com/goodfellow_ian/status/1544638709039091717 Andrey Karpathy leaves Tesla https://mobile.twitter.com/karpathy/status/1547332300186066944 https://www.businessinsider.com/report-tesla-laid-off-about-200-people-in-autopilot-unit-2022-6?r=US&IR=T Wordalle https://huggingface.co/spaces/huggingface-projects/wordalle?utm_source=pocket_mylist 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

#jepa #ai #machinelearning Yann LeCun's position paper on a path towards machine intelligence combines Self-Supervised Learning, Energy-Based Models, and hierarchical predictive embedding models to arrive at a system that can teach itself to learn useful abstractions at multiple levels and use that as a world model to plan ahead in time. OUTLINE: 0:00 - Introduction 2:00 - Main Contributions 5:45 - Mode 1 and Mode 2 actors 15:40 - Self-Supervised Learning and Energy-Based Models 20:15 - Introducing latent variables 25:00 - The problem of collapse 29:50 - Contrastive vs regularized methods 36:00 - The JEPA architecture 47:00 - Hierarchical JEPA (H-JEPA) 53:00 - Broader relevance 56:00 - Summary & Comments Paper: https://openreview.net/forum?id=BZ5a1r-kVsf Abstract: How could machines learn as efficiently as humans and animals? How could machines learn to reason and plan? How could machines learn representations of percepts and action plans at multiple levels of abstraction, enabling them to reason, predict, and plan at multiple time horizons? This position paper proposes an architecture and training paradigms with which to construct autonomous intelligent agents. It combines concepts such as configurable predictive world model, behavior driven through intrinsic motivation, and hierarchical joint embedding architectures trained with self-supervised learning. Author: Yann LeCun 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

#openai #vpt #minecraft Minecraft is one of the harder challenges any RL agent could face. Episodes are long, and the world is procedurally generated, complex, and huge. Further, the action space is a keyboard and a mouse, which has to be operated only given the game's video input. OpenAI tackles this challenge using Video PreTraining, leveraging a small set of contractor data in order to pseudo-label a giant corpus of scraped footage of gameplay. The pre-trained model is highly capable in basic game mechanics and can be fine-tuned much better than a blank slate model. This is the first Minecraft agent that achieves the elusive goal of crafting a diamond pickaxe all by itself. OUTLINE: 0:00 - Intro 3:50 - How to spend money most effectively? 8:20 - Getting a large dataset with labels 14:40 - Model architecture 19:20 - Experimental results and fine-tuning 25:40 - Reinforcement Learning to the Diamond Pickaxe 30:00 - Final comments and hardware Blog: https://openai.com/blog/vpt/ Paper: https://arxiv.org/abs/2206.11795 Code & Model weights: https://github.com/openai/Video-Pre-Training Abstract: Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities. However, for many sequential decision domains such as robotics, video games, and computer use, publicly available data does not contain the labels required to train behavioral priors in the same way. We extend the internet-scale pretraining paradigm to sequential decision domains through semi-supervised imitation learning wherein agents learn to act by watching online unlabeled videos. Specifically, we show that with a small amount of labeled data we can train an inverse dynamics model accurate enough to label a huge unlabeled source of online data -- here, online videos of people playing Minecraft -- from which we can then train a general behavioral prior. Despite using the native human interface (mouse and keyboard at 20Hz), we show that this behavioral prior has nontrivial zero-shot capabilities and that it can be fine-tuned, with both imitation learning and reinforcement learning, to hard-exploration tasks that are impossible to learn from scratch via reinforcement learning. For many tasks our models exhibit human-level performance, and we are the first to report computer agents that can craft diamond tools, which can take proficient humans upwards of 20 minutes (24,000 environment actions) of gameplay to accomplish. Authors: Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune 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

#parti #ai #aiart Parti is a new autoregressive text-to-image model that shows just how much scale can achieve. This model's outputs are crips, accurate, realistic, and can combine arbitrary styles, concepts, and fulfil even challenging requests. OUTLINE: 0:00 - Introduction 2:40 - Example Outputs 6:00 - Model Architecture 17:15 - Datasets (incl. PartiPrompts) 21:45 - Experimental Results 27:00 - Picking a cherry tree 29:30 - Failure cases 33:20 - Final comments Website: https://parti.research.google/ Paper: https://arxiv.org/abs/2206.10789 Github: https://github.com/google-research/parti 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

#lamda #google #ai Google engineer Blake Lemoine was put on leave after releasing proprietary information: An interview with the chatbot LaMDA that he believes demonstrates that this AI is, in fact, sentient. We analyze the claims and the interview in detail and trace how a statistical machine managed to convince at least one human that it is more than just an algorithm. OUTLINE: 0:00 - Whistleblower put on leave 4:30 - What is a language model? 6:40 - The prompt is the key 10:40 - Who are we talking to exactly? 12:50 - LaMDA analyzes stories 15:20 - Fear, pain, and consent 20:25 - How would we recognize sentience? When is a machine conscious? References: https://cajundiscordian.medium.com/is-lamda-sentient-an-interview-ea64d916d917 https://cajundiscordian.medium.com/what-is-lamda-and-what-does-it-want-688632134489 https://www.washingtonpost.com/technology/2022/06/11/google-ai-lamda-blake-lemoine/ https://www.theguardian.com/technology/2022/jun/12/google-engineer-ai-bot-sentient-blake-lemoine https://www.businessinsider.com/transcript-of-sentient-google-ai-chatbot-was-edited-for-readability-2022-6?inline-endstory-related-recommendations=&r=US&IR=T 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 BitChute: https://www.bitchute.com/channel/yannic-kilcher 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

#flamingo #mlnews #tech Your updates directly from the state of the art in Machine Learning! OUTLINE: 0:00 - Intro 0:30 - DeepMind's Flamingo: Unified Vision-Language Model 8:25 - LiT: Locked Image Tuning 10:20 - Jurassic X & MRKL Systems 15:05 - Helpful Things 22:40 - This AI does not exist References: DeepMind's Flamingo: Unified Vision-Language Model https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/tackling-multiple-tasks-with-a-single-visual-language-model/flamingo.pdf https://twitter.com/Inoryy/status/1522621712382234624 LiT: Locked Image Tuning https://ai.googleblog.com/2022/04/locked-image-tuning-adding-language.html https://google-research.github.io/vision_transformer/lit/ Jurassic X & MRKL Systems https://www.ai21.com/blog/jurassic-x-crossing-the-neuro-symbolic-chasm-with-the-mrkl-system#reading https://arxiv.org/pdf/2205.00445.pdf https://arxiv.org/pdf/2204.10019.pdf https://studio.ai21.com/jurassic-x StyleGAN Human https://stylegan-human.github.io/ https://github.com/stylegan-human/StyleGAN-Human?utm_source=pocket_mylist https://huggingface.co/spaces/hysts/StyleGAN-Human Helpful Things https://github.com/rish-16/grafog https://huggingface.co/bertin-project/bertin-gpt-j-6B https://github.com/pytorch/torchdistx https://pytorch.org/torchdistx/latest/fake_tensor.html https://github.com/Netflix/vectorflow?utm_source=pocket_mylist https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/ https://twitter.com/DeepMind/status/1517146462571794433 https://github.com/ai-forever/mgpt https://github.com/cleanlab/cleanlab https://efficientdlbook.com/?utm_source=pocket_mylist https://minihack-editor.github.io/ https://mugen-org.github.io/ https://www.amazon.science/blog/amazon-releases-51-language-dataset-for-language-understanding https://github.com/phuselab/openFACS?utm_source=pocket_mylist https://medium.com/pytorch/avalanche-and-end-to-end-library-for-continual-learning-based-on-pytorch-a99cf5661a0d This AI does not exist https://thisaidoesnotexist.com/ Links: Merch: https://ykilcher.com/merch 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/yannic-kilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 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

#mlnews #dalle #gpt3 An inside look of what's happening in the ML world! Sponsor: Weights & Biases https://wandb.me/yannic OUTLINE: 0:00 - Intro 0:20 - Sponsor: Weights & Biases 1:40 - Meta AI releases OPT-175B 4:55 - CoCa: New CLIP-Competitor 8:15 - DALL-E Mega is training 10:05 - TorToiSe TTS is amazing! 11:50 - Investigating Vision Transformers 12:50 - Hugging Face Deep RL class launched 13:40 - Helpful Things 17:00 - John Deere's driverless tractors References: Meta AI releases OPT-175B https://ai.facebook.com/blog/democratizing-access-to-large-scale-language-models-with-opt-175b/ https://arxiv.org/abs/2205.01068 https://arxiv.org/pdf/2205.01068.pdf https://github.com/facebookresearch/metaseq/tree/main/projects/OPT https://github.com/facebookresearch/metaseq/blob/main/projects/OPT/chronicles/OPT175B_Logbook.pdf https://github.com/facebookresearch/metaseq/tree/main/projects/OPT/chronicles https://twitter.com/yoavgo/status/1522150063815987201 CoCa: New CLIP-Competitor https://arxiv.org/abs/2205.01917 https://arxiv.org/pdf/2205.01917.pdf DALL-E Mega is training https://twitter.com/borisdayma https://twitter.com/borisdayma/status/1521891895001112577 https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-Mega--VmlldzoxODMxMDI2 TorToiSe TTS is amazing! https://github.com/neonbjb/tortoise-tts https://nonint.com/static/tortoise_v2_examples.html https://colab.research.google.com/drive/1wVVqUPqwiDBUVeWWOUNglpGhU3hg_cbR https://github.com/neonbjb Investigating Vision Transformers https://github.com/sayakpaul/probing-vits/?utm_source=pocket_mylist https://twitter.com/RisingSayak/status/1515918406171914240?utm_source=pocket_mylist https://keras.io/examples/vision/probing_vits/ https://github.com/sayakpaul/probing-vits/tree/main/notebooks?utm_source=pocket_mylist Hugging Face Deep RL class launched https://github.com/huggingface/deep-rl-class Helpful Things https://merantix-momentum.com/technology/squirrel/?utm_source=pocket_mylist https://github.com/merantix-momentum/squirrel-core?utm_source=pocket_mylist https://pyscript.net/?utm_source=pocket_mylist https://github.com/google-research/big_vision https://deepsportradar.github.io/challenge.html https://github.com/DeepSportRadar/camera-calibration-challenge https://twitter.com/alekseykorshuk/status/1515989357961920514?utm_source=pocket_mylist https://github.com/AlekseyKorshuk/huggingnft John Deere's driverless tractors https://thenextweb.com/news/john-deere-slowly-becoming-one-worlds-most-important-ai-companies https://tractorhacking.github.io/ Links: Merch: https://ykilcher.com/merch 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/yannic-kilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 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

#nft #gan #ai Today we build our own AI that can create as many bored apes as we want! Fungibility for everyone! Try the model here: https://huggingface.co/spaces/ykilcher/apes or here: https://ykilcher.com/apes Files & Models here: https://huggingface.co/ykilcher/apes/tree/main Code here: https://github.com/yk/apes-public (for the "what's your ape" app, look for the file interface_projector.py) This video is sponsored by BrightData, use this link for free credits: https://brightdata.grsm.io/yannickilcher OUTLINE: 0:00 - Introduction 2:05 - Generative Adversarial Networks 3:40 - Scraping Opensea with BrightData 7:55 - Training the GAN 11:35 - Here are the results! 15:20 - Diving deeper into BrightData References: Stylegan 3 imagery: https://nvlabs.github.io/stylegan3/ Bored Ape Yacht Club NFT Collection: https://opensea.io/collection/boredapeyachtclub Better GANFT model: https://medium.com/@nathancooperjones/these-bored-apes-do-not-exist-6bed2c73f02c Abstract AI-created apes: https://opensea.io/collection/gan-apes-nft https://mobile.twitter.com/gannft Another good model: https://twitter.com/cyrilzakka/status/1463944040878071811 StyleGAN2 versions: https://thispersondoesnotexist.com/ https://thissneakerdoesnotexist.com/ https://thischairdoesnotexist.com/ GANs: https://en.wikipedia.org/wiki/Generative_adversarial_network https://arxiv.org/pdf/1406.2661.pdf StyleGAN3: https://nvlabs.github.io/stylegan3/ StyleGAN2 code: https://github.com/NVlabs/stylegan2-ada-pytorch CLIP: https://openai.com/blog/clip/ DALL-E 2 images: https://twitter.com/search?q=%23dalle&f=image My music video: https://www.youtube.com/watch?v=2iq7WXSw26s BrightData Links: https://brightdata.com/products/data-collector https://brightdata.com/testimonials https://brightdata.com/use-cases/adtech https://brightdata.com/use-cases/social-media-for-marketing https://brightdata.com/use-cases/ecommerce Links: Merch: https://ykilcher.com/merch 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/yannic-kilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 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), find options at https://ykilcher.com

#saycan #robots #ai This is an interview with the authors Brian Ichter, Karol Hausman, and Fei Xia. Original Paper Review Video: https://youtu.be/Ru23eWAQ6_E Large Language Models are excellent at generating plausible plans in response to real-world problems, but without interacting with the environment, they have no abilities to estimate which of these plans are feasible or appropriate. SayCan combines the semantic capabilities of language models with a bank of low-level skills, which are available to the agent as individual policies to execute. SayCan automatically finds the best policy to execute by considering a trade-off between the policy's ability to progress towards the goal, given by the language model, and the policy's probability of executing successfully, given by the respective value function. The result is a system that can generate and execute long-horizon action sequences in the real world to fulfil complex tasks. OUTLINE: 0:00 - Introduction & Setup 3:40 - Acquiring atomic low-level skills 7:45 - How does the language model come in? 11:45 - Why are you scoring instead of generating? 15:20 - How do you deal with ambiguity in language? 20:00 - The whole system is modular 22:15 - Going over the full algorithm 23:20 - What if an action fails? 24:30 - Debunking a marketing video :) 27:25 - Experimental Results 32:50 - The insane scale of data collection 40:15 - How do you go about large-scale projects? 43:20 - Where did things go wrong? 45:15 - Where do we go from here? 52:00 - What is the largest unsolved problem in this? 53:35 - Thoughts on the Tesla Bot 55:00 - Final thoughts Paper: https://arxiv.org/abs/2204.01691 Website: https://say-can.github.io/ Abstract: Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's "hands and eyes," while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment. We evaluate our method on a number of real-world robotic tasks, where we show the need for real-world grounding and that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project's website and the video can be found at this https URL Authors: Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan 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/yannic-kilcher LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 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

#saycan #robots #ai Large Language Models are excellent at generating plausible plans in response to real-world problems, but without interacting with the environment, they have no abilities to estimate which of these plans are feasible or appropriate. SayCan combines the semantic capabilities of language models with a bank of low-level skills, which are available to the agent as individual policies to execute. SayCan automatically finds the best policy to execute by considering a trade-off between the policy's ability to progress towards the goal, given by the language model, and the policy's probability of executing successfully, given by the respective value function. The result is a system that can generate and execute long-horizon action sequences in the real world to fulfil complex tasks. Sponsor: Zeta Alpha https://zeta-alpha.com Use code YANNIC for 20% off! OUTLINE: 0:00 - Introduction & Overview 3:20 - Sponsor: Zeta Alpha 5:00 - Using language models for action planning 8:00 - Combining LLMs with learned atomic skills 16:50 - The full SayCan system 20:30 - Experimental setup and data collection 21:25 - Some weaknesses & strengths of the system 27:00 - Experimental results Paper: https://arxiv.org/abs/2204.01691 Website: https://say-can.github.io/ Abstract: Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, which makes it difficult to leverage them for decision making within a given embodiment. For example, asking a language model to describe how to clean a spill might result in a reasonable narrative, but it may not be applicable to a particular agent, such as a robot, that needs to perform this task in a particular environment. We propose to provide real-world grounding by means of pretrained skills, which are used to constrain the model to propose natural language actions that are both feasible and contextually appropriate. The robot can act as the language model's "hands and eyes," while the language model supplies high-level semantic knowledge about the task. We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment. We evaluate our method on a number of real-world robotic tasks, where we show the need for real-world grounding and that this approach is capable of completing long-horizon, abstract, natural language instructions on a mobile manipulator. The project's website and the video can be found at this https URL Authors: Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan 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... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 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

#ai #accel #evolution Automatic curriculum generation is one of the most promising avenues for Reinforcement Learning today. Multiple approaches have been proposed, each with their own set of advantages and drawbacks. This paper presents ACCEL, which takes the next step into the direction of constructing curricula for multi-capable agents. ACCEL combines the adversarial adaptiveness of regret-based sampling methods with the capabilities of level-editing, usually found in Evolutionary Methods. OUTLINE: 0:00 - Intro & Demonstration 3:50 - Paper overview 5:20 - The ACCEL algorithm 15:25 - Looking at the pseudocode 23:10 - Approximating regret 33:45 - Experimental results 40:00 - Discussion & Comments Website: https://accelagent.github.io Paper: https://arxiv.org/abs/2203.01302 Abstract: It remains a significant challenge to train generally capable agents with reinforcement learning (RL). A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agent's capabilities. These methods benefit from their generality, with theoretical guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces. By contrast, evolutionary approaches seek to incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources. In this paper we propose to harness the power of evolution in a principled, regret-based curriculum. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior regret-based methods, while providing significant empirical gains in a diverse set of environments. An interactive version of the paper is available at this http URL. Authors: Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel 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... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 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

#ai #accel #evolution This is an interview with the authors Jack Parker-Holder and Minqi Jiang. Original Paper Review Video: https://www.youtube.com/watch?v=povBD... Automatic curriculum generation is one of the most promising avenues for Reinforcement Learning today. Multiple approaches have been proposed, each with their own set of advantages and drawbacks. This paper presents ACCEL, which takes the next step into the direction of constructing curricula for multi-capable agents. ACCEL combines the adversarial adaptiveness of regret-based sampling methods with the capabilities of level-editing, usually found in Evolutionary Methods. OUTLINE: 0:00 - Intro 1:00 - Start of interview 4:45 - How did you get into this field? 8:10 - What is minimax regret? 11:45 - What levels does the regret objective select? 14:20 - Positive value loss (correcting my mistakes) 21:05 - Why is the teacher not learned? 24:45 - How much domain-specific knowledge is needed? 29:30 - What problems is this applicable to? 33:15 - Single agent vs population of agents 37:25 - Measuring and balancing level difficulty 40:35 - How does generalization emerge? 42:50 - Diving deeper into the experimental results 47:00 - What are the unsolved challenges in the field? 50:00 - Where do we go from here? Website: https://accelagent.github.io Paper: https://arxiv.org/abs/2203.01302 ICLR Workshop: https://sites.google.com/view/aloe2022 Book on topic: https://www.oreilly.com/radar/open-en... Abstract: It remains a significant challenge to train generally capable agents with reinforcement learning (RL). A promising avenue for improving the robustness of RL agents is through the use of curricula. One such class of methods frames environment design as a game between a student and a teacher, using regret-based objectives to produce environment instantiations (or levels) at the frontier of the student agent's capabilities. These methods benefit from their generality, with theoretical guarantees at equilibrium, yet they often struggle to find effective levels in challenging design spaces. By contrast, evolutionary approaches seek to incrementally alter environment complexity, resulting in potentially open-ended learning, but often rely on domain-specific heuristics and vast amounts of computational resources. In this paper we propose to harness the power of evolution in a principled, regret-based curriculum. Our approach, which we call Adversarially Compounding Complexity by Editing Levels (ACCEL), seeks to constantly produce levels at the frontier of an agent's capabilities, resulting in curricula that start simple but become increasingly complex. ACCEL maintains the theoretical benefits of prior regret-based methods, while providing significant empirical gains in a diverse set of environments. An interactive version of the paper is available at this http URL. Authors: Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob Foerster, Edward Grefenstette, Tim Rocktäschel 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... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 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

#laion #clip #dalle LAION-5B is an open, free dataset consisting of over 5 billion image-text-pairs. Today's video is an interview with three of its creators. We dive into the mechanics and challenges of operating at such large scale, how to keep cost low, what new possibilities are enabled with open datasets like this, and how to best handle safety and legal concerns. OUTLINE: 0:00 - Intro 1:30 - Start of Interview 2:30 - What is LAION? 11:10 - What are the effects of CLIP filtering? 16:40 - How big is this dataset? 19:05 - Does the text always come from the alt-property? 22:45 - What does it take to work at scale? 25:50 -When will we replicate DALL-E? 31:30 - The surprisingly efficient pipeline 35:20 - How do you cover the S3 costs? 40:30 - Addressing safety & legal concerns 55:15 - Where can people get started? References: LAION website: https://laion.ai/ LAION Discord: https://discord.com/invite/mVcgxMPD7e LAION-5B: https://laion.ai/laion-5b-a-new-era-o... img2dataset tool: https://github.com/rom1504/img2dataset LAION-400M: https://paperswithcode.com/dataset/la... 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... LinkedIn: https://www.linkedin.com/in/ykilcher BiliBili: https://space.bilibili.com/2017636191 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