Yannic Kilcher Videos (Audio Only)

#llm #ai #chatgpt How does one run inference for a generative autoregressive language model that has been trained with a fixed context size? Streaming LLMs combine the performance of windowed attention, but avoid the drop in performance by using attention sinks - an interesting phenomenon where the token at position 0 acts as an absorber of "extra" attention. OUTLINE: 0:00 - Introduction 1:20 - What is the problem? 10:30 - The hypothesis: Attention Sinks 15:10 - Experimental evidence 18:45 - Streaming LLMs 20:45 - Semantics or position? 22:30 - Can attention sinks be learned? 27:45 - More experiments 30:10 - Comparison to Big Bird Paper: https://arxiv.org/abs/2309.17453 Abstract: Deploying Large Language Models (LLMs) in streaming applications such as multi-round dialogue, where long interactions are expected, is urgently needed but poses two major challenges. Firstly, during the decoding stage, caching previous tokens' Key and Value states (KV) consumes extensive memory. Secondly, popular LLMs cannot generalize to longer texts than the training sequence length. Window attention, where only the most recent KVs are cached, is a natural approach -- but we show that it fails when the text length surpasses the cache size. We observe an interesting phenomenon, namely attention sink, that keeping the KV of initial tokens will largely recover the performance of window attention. In this paper, we first demonstrate that the emergence of attention sink is due to the strong attention scores towards initial tokens as a ``sink'' even if they are not semantically important. Based on the above analysis, we introduce StreamingLLM, an efficient framework that enables LLMs trained with a finite length attention window to generalize to infinite sequence lengths without any fine-tuning. We show that StreamingLLM can enable Llama-2, MPT, Falcon, and Pythia to perform stable and efficient language modeling with up to 4 million tokens and more. In addition, we discover that adding a placeholder token as a dedicated attention sink during pre-training can further improve streaming deployment. In streaming settings, StreamingLLM outperforms the sliding window recomputation baseline by up to 22.2x speedup. Code and datasets are provided at this https URL. Authors: Guangxuan Xiao, Yuandong Tian, Beidi Chen, Song Han, Mike Lewis 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 #promptengineering #evolution Promptbreeder is a self-improving self-referential system for automated prompt engineering. Give it a task description and a dataset, and it will automatically come up with appropriate prompts for the task. This is achieved by an evolutionary algorithm where not only the prompts, but also the mutation-prompts are improved over time in a population-based, diversity-focused approach. OUTLINE: 0:00 - Introduction 2:10 - From manual to automated prompt engineering 10:40 - How does Promptbreeder work? 21:30 - Mutation operators 36:00 - Experimental Results 38:05 - A walk through the appendix Paper: https://arxiv.org/abs/2309.16797 Abstract: Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. In this paper, we present Promptbreeder, a general-purpose self-referential self-improvement mechanism that evolves and adapts prompts for a given domain. Driven by an LLM, Promptbreeder mutates a population of task-prompts, and subsequently evaluates them for fitness on a training set. Crucially, the mutation of these task-prompts is governed by mutation-prompts that the LLM generates and improves throughout evolution in a self-referential way. That is, Promptbreeder is not just improving task-prompts, but it is also improving the mutationprompts that improve these task-prompts. Promptbreeder outperforms state-of-the-art prompt strategies such as Chain-of-Thought and Plan-and-Solve Prompting on commonly used arithmetic and commonsense reasoning benchmarks. Furthermore, Promptbreeder is able to evolve intricate task-prompts for the challenging problem of hate speech classification. Authors: Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel 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 #retnet #transformers Retention is an alternative to Attention in Transformers that can both be written in a parallel and in a recurrent fashion. This means the architecture achieves training parallelism while maintaining low-cost inference. Experiments in the paper look very promising. OUTLINE: 0:00 - Intro 2:40 - The impossible triangle 6:55 - Parallel vs sequential 15:35 - Retention mechanism 21:00 - Chunkwise and multi-scale retention 24:10 - Comparison to other architectures 26:30 - Experimental evaluation Paper: https://arxiv.org/abs/2307.08621 Abstract: In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance. We theoretically derive the connection between recurrence and attention. Then we propose the retention mechanism for sequence modeling, which supports three computation paradigms, i.e., parallel, recurrent, and chunkwise recurrent. Specifically, the parallel representation allows for training parallelism. The recurrent representation enables low-cost O(1) inference, which improves decoding throughput, latency, and GPU memory without sacrificing performance. The chunkwise recurrent representation facilitates efficient long-sequence modeling with linear complexity, where each chunk is encoded parallelly while recurrently summarizing the chunks. Experimental results on language modeling show that RetNet achieves favorable scaling results, parallel training, low-cost deployment, and efficient inference. The intriguing properties make RetNet a strong successor to Transformer for large language models. Code will be available at this https URL. Authors: Yutao Sun, Li Dong, Shaohan Huang, Shuming Ma, Yuqing Xia, Jilong Xue, Jianyong Wang, Furu Wei 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 #rlhf #llm ReST uses a bootsrap-like method to produce its own extended dataset and trains on ever higher-quality subsets of it to improve its own reward. The method allows for re-using the same generated data multiple times and thus has an efficiency advantage with respect to Online RL techniques like PPO. Paper: https://arxiv.org/abs/2308.08998 Abstract: Reinforcement learning from human feedback (RLHF) can improve the quality of large language model's (LLM) outputs by aligning them with human preferences. We propose a simple algorithm for aligning LLMs with human preferences inspired by growing batch reinforcement learning (RL), which we call Reinforced Self-Training (ReST). Given an initial LLM policy, ReST produces a dataset by generating samples from the policy, which are then used to improve the LLM policy using offline RL algorithms. ReST is more efficient than typical online RLHF methods because the training dataset is produced offline, which allows data reuse. While ReST is a general approach applicable to all generative learning settings, we focus on its application to machine translation. Our results show that ReST can substantially improve translation quality, as measured by automated metrics and human evaluation on machine translation benchmarks in a compute and sample-efficient manner. Authors: Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, Ksenia Konyushkova, Lotte Weerts, Abhishek Sharma, Aditya Siddhant, Alex Ahern, Miaosen Wang, Chenjie Gu, Wolfgang Macherey, Arnaud Doucet, Orhan Firat, Nando de Freitas 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 #llama2 #openai Your regular irregular update on the world of Machine Learning. References: https://twitter.com/ylecun/status/1681336284453781505 https://ai.meta.com/llama/ https://about.fb.com/news/2023/07/llama-2-statement-of-support/ https://247wallst.com/special-report/2023/08/12/this-is-the-biggest-social-media-platform-ranking-the-worlds-largest-networking-sites/4/ https://github.com/Alpha-VLLM/LLaMA2-Accessory https://together.ai/blog/llama-2-7b-32k?s=09&utm_source=pocket_saves https://github.com/imoneoi/openchat https://twitter.com/lmsysorg/status/1686794639469371393?s=09&t=sS3awkbavmSMSmwp64Ef4A&utm_source=pocket_saves https://huggingface.co/lmsys/vicuna-13b-v1.5-16k https://blog.google/outreach-initiatives/public-policy/google-microsoft-openai-anthropic-frontier-model-forum/ https://www.earthdata.nasa.gov/news/impact-ibm-hls-foundation-model?utm_source=pocket_reader https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M https://ai.meta.com/blog/generative-ai-text-images-cm3leon/ https://www.deepmind.com/blog/rt-2-new-model-translates-vision-and-language-into-action?utm_source=twitter&utm_medium=social&utm_campaign=rt2 https://arxiv.org/abs/2307.14334 https://sites.research.google/med-palm/ https://open-catalyst.metademolab.com/?utm_source=twitter&utm_medium=organic_social&utm_campaign=opencatalyst&utm_content=card https://open-catalyst.metademolab.com/demo https://www.anthropic.com/index/claude-2?utm_source=pocket_reader https://claude.ai/login https://audiocraft.metademolab.com/?utm_source=pocket_saves https://venturebeat.com/programming-development/stability-ai-launches-stablecode-an-llm-for-code-generation/ https://stability.ai/blog/stablecode-llm-generative-ai-coding https://twitter.com/JeffDean/status/1686806525862608896?s=09&t=LG2z9ok9QExHbSy0fvBsxA&utm_source=pocket_saves https://sites.research.google/open-buildings/ https://twitter.com/deliprao/status/1687283117873106946?s=09&t=1NmC-B55Z8IuF_HTuGOo7w&utm_source=pocket_saves https://arxiv.org/pdf/2308.01320.pdf https://twitter.com/javilopen/status/1687795349719547905?utm_source=pocket_saves https://research.nvidia.com/labs/par/Perfusion/ https://ar5iv.labs.arxiv.org/html/2307.14936 https://www.linkedin.com/feed/update/urn:li:activity:7093463974750371840/?utm_source=pocket_saves https://huggingface.co/syzymon/long_llama_3b_instruct https://arxiv.org/abs/2307.03170 https://dynalang.github.io/ https://github.com/mlfoundations/open_flamingo https://twitter.com/akshay_pachaar/status/1687079353937698816?s=09&t=fos8QSCsGEEM6dMflhq0Mg&utm_source=pocket_saves https://github.com/OpenBMB/ToolBench https://llm-attacks.org/ https://arstechnica.com/information-technology/2023/07/openai-discontinues-its-ai-writing-detector-due-to-low-rate-of-accuracy/ https://sites.google.com/view/steve-1 https://github.com/Shalev-Lifshitz/STEVE-1 https://erichartford.com/dolphin https://huggingface.co/ehartford/dolphin-llama-13b https://www.mosaicml.com/blog/long-context-mpt-7b-8k https://twitter.com/camenduru/status/1688045780244848640?s=09&t=ubJ2Qtz-TG6Xo3_GMtt2Cw&utm_source=pocket_saves https://github.com/IDEA-Research/DWPose https://twitter.com/tri_dao/status/1680987577913065472?s=09&t=Q181vFmM6d3nDq-5BwfDeg&utm_source=pocket_saves https://tridao.me/publications/flash2/flash2.pdf https://thehackernews.com/2023/07/wormgpt-new-ai-tool-allows.html https://www.tomshardware.com/news/ai-steals-data-with-keystroke-audio https://arxiv.org/pdf/2308.01074.pdf https://www.foxnews.com/politics/ai-test-flight-air-force-unmanned-wingman-aircraft https://www.theverge.com/2023/8/2/23817406/white-castle-soundhound-ai-sliders https://www.google.com/search?sca_esv=556495916&q=food+delivery+bot+kicked&tbm=vid&source=lnms&sa=X&ved=2ahUKEwjZ6PDPrdmAAxUThf0HHWzrBGgQ0pQJegQIChAB&cshid=1691920142432720&biw=2327&bih=1180&dpr=2.2 https://www.youtube.com/watch?v=--n_NhmXnfc https://www.thesun.co.uk/tech/20793591/coop-delivery-robots-cambridge-kicked-by-workers-tiktok/

#cybercrime #chatgpt #security An interview with Sergey Shykevich, Threat Intelligence Group Manager at Check Point, about how models like ChatGPT have impacted the realm of cyber crime. https://threatmap.checkpoint.com/ Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

#llm #safety #gpt4 A prime example of intellectual dishonesty of journalists and AI critics. Article: https://gizmodo.com/paknsave-ai-savey-recipe-bot-chlorine-gas-1850725057 My Recipe AI: https://github.com/yk/recipe-ai 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 #diffusion #stabilityai An interview with DeepFloyd members Misha Konstantinov and Daria Bakshandaeva on the release of the model IF, an open-source model following Google's implementation of Imagen. References: https://www.deepfloyd.ai/deepfloyd-if https://huggingface.co/DeepFloyd https://twitter.com/_gugutse_ https://twitter.com/_bra_ket 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

#gpt4 #mit #ai A new paper claims to use GPT-4 to solve 100% of a set of MIT university exercises. Some people are skeptic and their investigations reveal more than one problem with this paper... OUTLINE: 0:00 - ChatGPT gives out Windows 10 keys 0:30 - MIT exam paper 2:50 - Prompt engineering 5:30 - Automatic grading 6:45 - Response by other MIT students 8:30 - Unsolvable questions 10:50 - Duplicates 13:30 - Cascading the heuristics 22:40 - Other problems 29:25 - OpenLLaMA 13B published References: https://twitter.com/immasiddtweets/status/1669721470006857729/photo/1https://arxiv.org/abs/2306.08997https://arxiv.org/pdf/2306.08997.pdfhttps://flower-nutria-41d.notion.site/No-GPT4-can-t-ace-MIT-b27e6796ab5a48368127a98216c76864https://github.com/idrori/MITQ/commit/3feee1026318e537c0ad27968001ef76e4a36890https://twitter.com/hardmaru/status/1670246674760077312https://twitter.com/giffmana/status/1670258748286472193https://twitter.com/T3816440886465/status/1670127224131862531https://twitter.com/qrdl/status/1669856336652414977https://www.chegg.com/homework-help/questions-and-answers/consider-mdp-set-possible-states-mathcal-s-0-1-2-3-set-possible-actions-mathcal-b-c--rewar-q111042613https://github.com/openlm-research/open_llamahttps://huggingface.co/openlm-research/open_llama_13b 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 #watermark Watermarking the outputs of generative models is usually done as a post-processing step on the model outputs. Tree-Ring Watermarks are applied in the latent space at the beginning of a diffusion process, which makes them nearly undetectable, robust to strong distortions, and only recoverable by the model author. It is a very promising technique with applications potentially beyond watermarking itself. OUTLINE: 0:00 - Introduction & Overview 1:30 - Why Watermarking? 4:20 - Diffusion Models Recap 13:40 - Inverting Diffusion Models 17:05 - Tree-Ring Watermarking 26:15 - Effects of Tree-Ring Watermarks 30:00 - Experimental Results 32:40 - Limitations 34:40 - Conclusion Paper: https://arxiv.org/abs/2305.20030 Abstract: Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs. Unlike existing methods that perform post-hoc modifications to images after sampling, Tree-Ring Watermarking subtly influences the entire sampling process, resulting in a model fingerprint that is invisible to humans. The watermark embeds a pattern into the initial noise vector used for sampling. These patterns are structured in Fourier space so that they are invariant to convolutions, crops, dilations, flips, and rotations. After image generation, the watermark signal is detected by inverting the diffusion process to retrieve the noise vector, which is then checked for the embedded signal. We demonstrate that this technique can be easily applied to arbitrary diffusion models, including text-conditioned Stable Diffusion, as a plug-in with negligible loss in FID. Our watermark is semantically hidden in the image space and is far more robust than watermarking alternatives that are currently deployed. Code is available at this https URL. Authors: Yuxin Wen, John Kirchenbauer, Jonas Geiping, Tom Goldstein 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

#gpt4 #rwkv #transformer We take a look at RWKV, a highly scalable architecture between Transformers and RNNs. Fully Connected (June 7th in SF) Promo Link: https://www.fullyconnected.com/?promo=ynnc OUTLINE: 0:00 - Introduction 1:50 - Fully Connected In-Person Conference in SF June 7th 3:00 - Transformers vs RNNs 8:00 - RWKV: Best of both worlds 12:30 - LSTMs 17:15 - Evolution of RWKV's Linear Attention 30:40 - RWKV's Layer Structure 49:15 - Time-Parallel vs Sequence Mode 53:55 - Experimental Results & Limitations 58:00 - Visualizations 1:01:40 - Conclusion Paper: https://arxiv.org/abs/2305.13048 Code: https://github.com/BlinkDL/RWKV-LM Abstract: Transformers have revolutionized almost all natural language processing (NLP) tasks but suffer from memory and computational complexity that scales quadratically with sequence length. In contrast, recurrent neural networks (RNNs) exhibit linear scaling in memory and computational requirements but struggle to match the same performance as Transformers due to limitations in parallelization and scalability. We propose a novel model architecture, Receptance Weighted Key Value (RWKV), that combines the efficient parallelizable training of Transformers with the efficient inference of RNNs. Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, which parallelizes computations during training and maintains constant computational and memory complexity during inference, leading to the first non-transformer architecture to be scaled to tens of billions of parameters. Our experiments reveal that RWKV performs on par with similarly sized Transformers, suggesting that future work can leverage this architecture to create more efficient models. This work presents a significant step towards reconciling the trade-offs between computational efficiency and model performance in sequence processing tasks. Authors: Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Huanqi Cao, Xin Cheng, Michael Chung, Matteo Grella, Kranthi Kiran GV, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartlomiej Koptyra, Hayden Lau, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Xiangru Tang, Bolun Wang, Johan S. Wind, Stansilaw Wozniak, Ruichong Zhang, Zhenyuan Zhang, Qihang Zhao, Peng Zhou, Jian Zhu, Rui-Jie Zhu 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

#gpt4 #ai #prompt Tree-of-Thought improves prompting of large language models (LLMs) by generalizing the concept of Chain-of-Thought prompting and introduces a tree search across language model thoughts, including state evaluation and backtracking. Experiments on toy tasks show large improvements over both classic and Chain-of-Thought prompting. OUTLINE: 0:00 - Introduction 1:20 - From Chain-of-Thought to Tree-of-Thought 11:10 - Formalizing the algorithm 16:00 - Game of 24 & Creative writing 18:30 - Crosswords 23:30 - Is this a general problem solver? 26:50 - Ablation studies 28:55 - Conclusion Paper: https://arxiv.org/abs/2305.10601 Abstract: Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of Thought approach to prompting language models, and enables exploration over coherent units of text (thoughts) that serve as intermediate steps toward problem solving. ToT allows LMs to perform deliberate decision making by considering multiple different reasoning paths and self-evaluating choices to decide the next course of action, as well as looking ahead or backtracking when necessary to make global choices. Our experiments show that ToT significantly enhances language models' problem-solving abilities on three novel tasks requiring non-trivial planning or search: Game of 24, Creative Writing, and Mini Crosswords. For instance, in Game of 24, while GPT-4 with chain-of-thought prompting only solved 4% of tasks, our method achieved a success rate of 74%. Code repo with all prompts: this https URL. Authors: Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, Karthik Narasimhan 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 #openai #gpt4 US Senate hearing on AI regulation. MLST video on the hearing: https://www.youtube.com/watch?v=DeSXnESGxr4 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

#google #openai #mlnews Updates from the world of Machine Learning and AI Great AI memes here: https://twitter.com/untitled01ipynb OUTLINE: 0:00 - Google I/O 2023: Generative AI in everything 0:20 - Anthropic announces 100k tokens context 0:35 - Intro 1:20 - Geoff Hinton leaves Google 7:00 - Google memo leaked: we have no moat 11:30 - OpenAI loses 540M 12:30 - Google AI: Product first 15:50 - Ilya Sutskever on safety vs competition 18:00 - AI works cannot be copyrighted 19:40 - OpenAI tries to trademark GPT 20:30 - StarCoder: accessible code model 21:40 - RedPyjama & OpenLlama 22:55 - Mosaic 7B model 23:50 - YoloNAS 24:10 - Mojo programming language 25:30 - Random helpful things 37:40 - DeepMind soccer robots References: https://twitter.com/weirddalle/status/1649908805788893185https://www.nytimes.com/2023/05/01/technology/ai-google-chatbot-engineer-quits-hinton.htmlhttps://www.technologyreview.com/2023/05/01/1072478/deep-learning-pioneer-geoffrey-hinton-quits-google/https://archive.ph/TrPoHhttps://twitter.com/DanHendrycks/status/1654560913939374080https://twitter.com/ylecun/status/1654930029569101824https://twitter.com/homehttps://twitter.com/ylecun/status/1654931495419621376https://twitter.com/pkedrosky/status/1653955254181068801https://www.semianalysis.com/p/google-we-have-no-moat-and-neitherhttps://twitter.com/untitled01ipynb/mediahttps://www.theinformation.com/articles/openais-losses-doubled-to-540-million-as-it-developed-chatgpthttps://archive.ph/bKsdMhttps://www.washingtonpost.com/technology/2023/05/04/google-ai-stop-sharing-research/https://twitter.com/giffmana/status/1654962145707130880https://twitter.com/Ken_Goldberg/status/1651309843804987393https://tsdr.uspto.gov/documentviewer?caseId=sn97733259&docId=PTD20230418160641&s=09#docIndex=1&page=1https://twitter.com/osanseviero/status/1654230764513370112https://huggingface.co/bigcode/starcoderhttps://huggingface.co/spaces/bigcode/bigcode-model-license-agreementhttps://twitter.com/hardmaru/status/1654649036333514753https://www.together.xyz/blog/redpajama-models-v1https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1https://github.com/openlm-research/open_llamahttps://www.mosaicml.com/blog/mpt-7bhttps://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.mdhttps://www.modular.com/mojohttps://www.aicrowd.com/challenges/hackaprompt-2023https://learnprompting.org/https://developer.nvidia.com/blog/nvidia-enables-trustworthy-safe-and-secure-large-language-model-conversational-systems/?ncid=prsy-552511https://blogs.nvidia.com/blog/2023/04/25/ai-chatbot-guardrails-nemo/https://lmql.ai/#distributionhttps://github.com/gventuri/pandas-ai?utm_source=pocket_readerhttps://lamini.ai/blog/introducing-laminihttps://github.com/deep-floyd/IFhttps://huggingface.co/spaces/DeepFloyd/IFhttps://twitter.com/FaramaFound/status/1650952295901720576https://txt.cohere.com/embedding-archives-wikipedia/?hsa_acc=509563538&hsa_ad=242008083&hsa_cam=626636963&hsa_grp=205646033&hsa_net=linkedin&hsa_ver=3&hss_channel=lcp-24024765https://arxiv.org/abs/2304.12210https://github.com/h2oai/h2ogpthttps://huggingface.co/h2oai/h2ogpt-oasst1-512-20bhttps://github.com/h2oai/h2o-llmstudiohttps://ai.facebook.com/blog/ai-dataset-animating-kids-drawings/https://www.camel-ai.org/https://github.com/lightaime/camel?utm_source=pocket_readerhttps://huggingface.co/Writer/camel-5b-hfhttps://laion.ai/blog/paella/https://magazine.sebastianraschka.com/p/finetuning-large-language-modelshttps://pickapic.io/https://github.com/yuvalkirstain/heroku_apphttps://huggingface.co/datasets/yuvalkirstain/PickaPichttps://future.snorkel.ai/poster-contest/https://twitter.com/d_feldman/status/1649466422018318338/photo/4https://twitter.com/DeepMind/status/1651897358894919680https://arxiv.org/abs/2304.13653https://twitter.com/SmokeAwayyy/status/1652712832738422784 If you want to support me, the best thing to do is to share out the content :)

#ai #transformer #gpt4 This paper promises to scale transformers to 1 million tokens and beyond. We take a look at the technique behind it: The Recurrent Memory Transformer, and what its strenghts and weaknesses are. OUTLINE: 0:00 - Intro 2:15 - Transformers on long sequences 4:30 - Tasks considered 8:00 - Recurrent Memory Transformer 19:40 - Experiments on scaling and attention maps 24:00 - Conclusion Paper: https://arxiv.org/abs/2304.11062 Abstract: This technical report presents the application of a recurrent memory to extend the context length of BERT, one of the most effective Transformer-based models in natural language processing. By leveraging the Recurrent Memory Transformer architecture, we have successfully increased the model's effective context length to an unprecedented two million tokens, while maintaining high memory retrieval accuracy. Our method allows for the storage and processing of both local and global information and enables information flow between segments of the input sequence through the use of recurrence. Our experiments demonstrate the effectiveness of our approach, which holds significant potential to enhance long-term dependency handling in natural language understanding and generation tasks as well as enable large-scale context processing for memory-intensive applications. Authors: Aydar Bulatov, Yuri Kuratov, Mikhail S. Burtsev 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

#openassistant #chatgpt #mlnews Try the chat: https://open-assistant.io/chat Homepage: https://open-assistant.io Dataset: https://huggingface.co/datasets/OpenAssistant/oasst1 Code: https://github.com/LAION-AI/Open-Assistant Paper (temporary): https://ykilcher.com/oa-paper 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

#openassistant #chatgpt #gpt4https://open-assistant.io/chathttps://huggingface.co/OpenAssistanthttps://github.com/LAION-AI/Open-Assistant 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 #gpt4 #copilot Your weekly news all around the AI world Check out W&B courses (free): https://wandb.courses/ OUTLINE: 0:00 - Intro 0:20 - GPT-4 announced! 4:30 - GigaGAN: The comeback of Generative Adversarial Networks 7:55 - ChoppedAI: AI Recipes 8:45 - Samsung accused of faking space zoom effect 14:00 - Weights & Biases courses are free 16:55 - Data Portraits 18:50 - Data2Vec 2.0 19:50 - Gated Models on Hugging Face & huggingface.js 22:05 - Visual ChatGPT 23:35 - Bing crosses 100 million daily active users 24:50 - Casual Conversations Dataset 25:50 - Anthropic AI Safety Research 27:30 - Magnushammer & more advances in AI-assisted math 30:30 - LLaMA license change PR 32:00 - Self-Instruct dataset 33:35 - PaLM-E: Multimodal Pathways 35:45 - USM: Universal Speech Model 37:25 - GILGEN: Grounded Text-to-Image 39:55 - Fruit Fly Connectome released References: https://www.heise.de/news/GPT-4-kommt-naechste-Woche-und-es-wird-multimodal-Vorankuendigung-von-Microsoft-7540383.htmlhttps://mingukkang.github.io/GigaGAN/https://www.choppedai.com/https://www.reddit.com/r/Android/comments/11nzrb0/samsung_space_zoom_moon_shots_are_fake_and_here/https://imgur.com/ULVX933https://imgur.com/9XMgt06https://imgur.com/9kichAphttps://imgur.com/RSHAz1lhttps://imgur.com/PIAjVKphttps://imgur.com/xEyLajWhttps://imgur.com/3STX9mZhttps://imgur.com/ifIHr3Shttps://imgur.com/bXJOZgIhttps://dataportraits.org/https://arxiv.org/abs/2303.03919https://arxiv.org/pdf/2303.03919.pdfhttps://ai.facebook.com/blog/ai-self-supervised-learning-data2vec/https://github.com/facebookresearch/fairseq/tree/main/examples/data2vechttps://huggingface.co/docs/hub/models-gatedhttps://huggingface.co/abouthttps://github.com/huggingface/huggingface.js?utm_source=pocket_readerhttps://github.com/microsoft/visual-chatgpthttps://arxiv.org/abs/2303.04671https://github.com/microsoft/visual-chatgpt/blob/main/visual_chatgpt.pyhttps://huggingface.co/spaces/RamAnanth1/visual-chatGPThttps://www.engadget.com/microsoft-bing-crossed-100-million-daily-active-users-080138371.htmlhttps://ai.facebook.com/blog/casual-conversations-v2-dataset-measure-fairness/https://ai.facebook.com/datasets/casual-conversations-v2-dataset/https://www.anthropic.com/index/core-views-on-ai-safetyhttps://arxiv.org/abs/2303.04488https://arxiv.org/pdf/2303.04488.pdfhttps://arxiv.org/abs/2303.04910https://arxiv.org/pdf/2303.04910.pdfhttps://twitter.com/astro_wassim/status/1633645134934949888https://ai.papers.bar/paper/ede58b1ebca911ed8f9c3d8021bca7c8https://arxiv.org/pdf/2303.03192.pdfhttps://www.theverge.com/2023/3/8/23629362/meta-ai-language-model-llama-leak-online-misusehttps://knightcolumbia.org/blog/the-llama-is-out-of-the-bag-should-we-expect-a-tidal-wave-of-disinformationhttps://github.com/facebookresearch/llama/pull/184https://huggingface.co/datasets/yizhongw/self_instructhttps://openai.com/policies/terms-of-usehttps://palm-e.github.io/https://pickapic.io/https://ai.googleblog.com/2023/03/universal-speech-model-usm-state-of-art.htmlhttps://arxiv.org/abs/2303.01037https://github.com/BlinkDL/RWKV-LM?utm_source=pocket_readerhttps://gligen.github.io/https://github.com/microsoft/GLIPhttps://arxiv.org/abs/2301.07093https://huggingface.co/spaces/gligen/demohttps://www.sciencealert.com/the-first-ever-complete-map-of-an-insect-brain-is-truly-mesmerizinghttps://en.wikipedia.org/wiki/Tidal_locking 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 :)

#gpt4 #chatgpt #openai References: https://openai.com/product/gpt-4https://openai.com/research/gpt-4https://cdn.openai.com/papers/gpt-4.pdf Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :) If you want 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 #chatgpt #llama ChatGPT goes around the world and is finally available via API. Stunning mind-reading performed using fMRI and Stable Diffusion. LLaMA weights leak and hilarity ensues. GTC23 is around the corner! ERRATA: It's a 4090, not a 4090 ti 🙃 OUTLINE: 0:00 - Introduction 0:20 - GTC 23 on March 20 1:55 - ChatGPT API is out! 4:50 - OpenAI becomes more business-friendly 7:15 - OpenAI plans for AGI 10:00 - ChatGPT influencers 12:15 - Open-Source Prompting Course 12:35 - Flan UL2 20B 13:30 - LLaMA weights leaked 15:50 - Mind-Reading from fMRI 20:10 - Random News / Helpful Things 25:30 - Interview with Bryan Catanzaro Participate in the GTC Raffle: https://ykilcher.com/gtc References: GTC 23 on March 20 https://www.nvidia.com/gtc/https://ykilcher.com/gtc ChatGPT API is out! https://twitter.com/gdb/status/1630991925984755714https://openai.com/blog/introducing-chatgpt-and-whisper-apishttps://twitter.com/greggyb/status/1631121912679002112https://www.haihai.ai/chatgpt-api/ OpenAI becomes more business-friendly https://twitter.com/sama/status/1631002519311888385https://techcrunch.com/2023/02/21/openai-foundry-will-let-customers-buy-dedicated-capacity-to-run-its-ai-models/?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAFL1O8s22qBsEtytYZWR7O2VlTa9nAGhdZPFfeQfZCDWjkNBIac7WlDikRNLEH1tqSszUN02ouqRyyCsShDa1kQyUbiApD1IUPfgmHXZxgIMFxr8bwr8BuBa7sK55dYqMRFFbE7YILuBn_rmj7aJI1tp7GAXubODfCUaKvOkoOYjhttps://www.bain.com/vector-digital/partnerships-alliance-ecosystem/openai-alliance/ OpenAI plans for AGI https://openai.com/blog/planning-for-agi-and-beyond ChatGPT influencers https://www.youtube.com/watch?v=4kp7oVTu9Ckhttps://www.youtube.com/watch?v=k13v8jp8H5ohttps://www.linkedin.com/posts/eniascailliau_create-an-online-course-100-ai-ugcPost-7036969935796891648-H_uj/https://www.linkedin.com/posts/linasbeliunas_must-know-ai-tools-ugcPost-7035700089947836416-Qri4/https://twitter.com/LinusEkenstam/status/1629879567514238976https://www.linkedin.com/posts/imarpit_50-awesome-chatgpt-prompts-ugcPost-7036905788631646209-2CU-/ Open-Source Prompting Course https://learnprompting.org/ Flan UL2 20B https://www.yitay.net/blog/flan-ul2-20bhttps://huggingface.co/google/flan-ul2 LLaMA weights leaked https://github.com/facebookresearch/llama/pull/73https://github.com/facebookresearch/llama/pull/73/files#diff-b335630551682c19a781afebcf4d07bf978fb1f8ac04c6bf87428ed5106870f5https://github.com/ChristopherKing42https://open-assistant.io/dashboard Mind-Reading from fMRI https://sites.google.com/view/stablediffusion-with-brain/?s=09https://www.nature.com/articles/s41562-022-01516-2?utm_content=animation Random News https://www.wired.com/story/alphabet-layoffs-hit-trash-sorting-robots/https://huggingface.co/blog/fast-mac-diffusershttps://pyribs.org/https://twitter.com/rowancheung/status/1630569844654460928https://pimeyes.com/enhttps://cacti-framework.github.io/https://twitter.com/bhutanisanyam1/status/1630980866775330819https://www.linkedin.com/in/bryancatanzaro/ 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 #meta #languagemodel LLaMA is a series of large language models from 7B to 65B parameters, trained by Meta AI. They train for longer on more data and show that something like gpt-3 can be outperformed by significantly smaller models when trained like this. Meta also releases the trained models to the research community. OUTLINE: 0:00 - Introduction & Paper Overview 4:30 - Rant on Open-Sourcing 8:05 - Training Data 12:40 - Training Hyperparameters 14:50 - Architecture Modifications 17:10 - Optimizer 19:40 - Efficient Implementation 26:15 - Main Results 38:00 - Some more completions 40:00 - Conclusion Paper: https://arxiv.org/abs/2302.13971 Website: https://ai.facebook.com/blog/large-language-model-llama-meta-ai/ Abstract: We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community. Authors: Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample 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 #huggingface #coding Join me as I build streaming inference into the Hugging Face text generation server, going through cuda, python, rust, grpc, websockets, server-sent events, and more... Original repo is here: https://github.com/huggingface/text-generation-inference OpenAssistant repo is here: https://github.com/LAION-AI/Open-Assistant (see inference/) Check out https://www.wandb.courses/ for free MLOps courses! 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

#openassistant #chatgpt #ai Help us collect data for OpenAssistant, the largest and most open alternative to ChatGPT. https://open-assistant.io OUTLINE: 0:00 - Intro 0:30 - The Project 2:05 - Getting to Minimum Viable Prototype 5:30 - First Tasks 10:00 - Leaderboard 11:45 - Playing the Assistant 14:40 - Tricky Facts 16:25 - What if humans had wings? 17:05 - Can foxes be tamed? 23:45 - Can zebras be tamed? 26:15 - Yo (spam) 27:00 - More tasks 29:10 - Entitled Emails 34:35 - Final Words 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

#chatgpt #ai #openai 

ChatGPT, OpenAI's newest model is a GPT-3 variant that has been fine-tuned using Reinforcement Learning from Human Feedback, and it is taking the world by storm!

Sponsor: Weights & Biases

https://wandb.me/yannic

OUTLINE:

0:00 - Intro

0:40 - Sponsor: Weights & Biases

3:20 - ChatGPT: How does it work?

5:20 - Reinforcement Learning from Human Feedback

7:10 - ChatGPT Origins: The GPT-3.5 Series

8:20 - OpenAI's strategy: Iterative Refinement

9:10 - ChatGPT's amazing capabilities

14:10 - Internals: What we know so far

16:10 - Building a virtual machine in ChatGPT's imagination (insane)

20:15 - Jailbreaks: Circumventing the safety mechanisms

29:25 - How OpenAI sees the future

References:

https://openai.com/blog/chatgpt/

https://openai.com/blog/language-model-safety-and-misuse/

https://beta.openai.com/docs/model-index-for-researchers

https://scale.com/blog/gpt-3-davinci-003-comparison#Conclusion

https://twitter.com/johnvmcdonnell/status/1598470129121374209

https://twitter.com/blennon_/status/1597374826305318912

https://twitter.com/TimKietzmann/status/1598230759118376960/photo/1

https://twitter.com/_lewtun/status/1598056075672027137/photo/2

https://twitter.com/raphaelmilliere/status/1598469100535259136

https://twitter.com/CynthiaSavard/status/1598498138658070530/photo/1

https://twitter.com/tylerangert/status/1598389755997290507/photo/1

https://twitter.com/amasad/status/1598042665375105024/photo/1

https://twitter.com/goodside/status/1598129631609380864/photo/1

https://twitter.com/moyix/status/1598081204846489600/photo/2

https://twitter.com/JusticeRage/status/1598959136531546112

https://twitter.com/yoavgo/status/1598594145605636097

https://twitter.com/EladRichardson/status/1598333315764871174

https://twitter.com/charles_irl/status/1598319027327307785/photo/4

https://twitter.com/jasondebolt/status/1598243854343606273

https://twitter.com/mattshumer_/status/1598185710166896641/photo/1

https://twitter.com/i/web/status/1598246145171804161

https://twitter.com/bleedingedgeai/status/1598378564373471232

https://twitter.com/MasterScrat/status/1598830356115124224

https://twitter.com/Sentdex/status/1598803009844256769

https://twitter.com/harrison_ritz/status/1598828017446371329

https://twitter.com/parafactual/status/1598212029479026689

https://www.engraved.blog/building-a-virtual-machine-inside/

https://twitter.com/317070

https://twitter.com/zehavoc/status/1599193444043268096

https://twitter.com/yoavgo/status/1598360581496459265

https://twitter.com/yoavgo/status/1599037412411596800

https://twitter.com/yoavgo/status/1599045344863879168

https://twitter.com/natfriedman/status/1598477452661383168

https://twitter.com/conradev/status/1598487973351362561/photo/1

https://twitter.com/zswitten/status/1598100186605441024

https://twitter.com/CatEmbedded/status/1599141379879600128/photo/2

https://twitter.com/mattshumer_/status/1599175127148949505

https://twitter.com/vaibhavk97/status/1598930958769860608/photo/1

https://twitter.com/dan_abramov/status/1598800508160024588/photo/1

https://twitter.com/MinqiJiang/status/1598832656422432768/photo/2

https://twitter.com/zswitten/status/1598088280066920453

https://twitter.com/m1guelpf/status/1598203861294252033/photo/1

https://twitter.com/SilasAlberti/status/1598257908567117825/photo/1

https://twitter.com/gf_256/status/1598962842861899776/photo/1

https://twitter.com/zswitten/status/1598088267789787136

https://twitter.com/gf_256/status/1598178469955112961/photo/1

#ai #mlnews #gpt4

Your weekly news from the AI & Machine Learning world.

OUTLINE:

0:00 - Introduction

0:25 - AI reads brain signals to predict what you're thinking

3:00 - Closed-form solution for neuron interactions

4:15 - GPT-4 rumors

6:50 - Cerebras supercomputer

7:45 - Meta releases metagenomics atlas

9:15 - AI advances in theorem proving

10:40 - Better diffusion models with expert denoisers

12:00 - BLOOMZ & mT0

13:05 - ICLR reviewers going mad

21:40 - Scaling Transformer inference

22:10 - Infinite nature flythrough generation

23:55 - Blazing fast denoising

24:45 - Large-scale AI training with MultiRay

25:30 - arXiv to include Hugging Face spaces

26:10 - Multilingual Diffusion

26:30 - Music source separation

26:50 - Multilingual CLIP

27:20 - Drug response prediction

27:50 - Helpful Things

ERRATA:

HF did not acquire spaces, they launched spaces themselves and supported Gradio from the start. They later acquired Gradio.

References:

AI reads brain signals to predict what you're thinking

https://mind-vis.github.io/?s=09&utm_source=pocket_saves

https://neurosciencenews.com/bmi-internal-speech-21837/

Closed-form solution for neuron interactions

https://twitter.com/ramin_m_h/status/1592585672606769153/photo/1

https://github.com/raminmh/CfC

https://github.com/raminmh/CfC/blob/main/torch_cfc.py

GPT-4 rumors

https://thealgorithmicbridge.substack.com/p/gpt-4-rumors-from-silicon-valley?utm_source=pocket_reader

Cerebras supercomputer

https://www.cerebras.net/andromeda/

Meta releases metagenomics atlas

https://ai.facebook.com/blog/protein-folding-esmfold-metagenomics/

https://www.genome.gov/genetics-glossary/Metagenomics

AI advances in theorem proving

https://ai.facebook.com/blog/ai-math-theorem-proving/

https://marketplace.visualstudio.com/items?itemName=jroesch.lean

Better diffusion models with expert denoisers

https://deepimagination.cc/eDiffi/

BLOOMZ & mT0

https://arxiv.org/abs/2211.01786?utm_source=pocket_reader

https://huggingface.co/bigscience/bloomz?text=Suggest+at+least+five+related+search+terms+to+%22M%E1%BA%A1ng+neural+nh%C3%A2n+t%E1%BA%A1o%22.

ICLR reviewers going mad

https://twitter.com/XiangruTang/status/1589703605098975237?utm_source=pocket_reader

https://twitter.com/BlancheMinerva/status/1588164585961422849?utm_source=pocket_reader

https://openreview.net/forum?id=pfuqQQCB34

https://twitter.com/peter_richtarik/status/1591408710366408706?utm_source=pocket_reader

Scaling Transformer inference

https://arxiv.org/abs/2211.05102

Infinite nature flythrough generation

https://ai.googleblog.com/2022/11/infinite-nature-generating-3d.html?utm_source=pocket_reader

Blazing fast denoising

https://github.com/dome272/Paella

https://arxiv.org/abs/2211.07292

Large-scale AI training with MultiRay

https://ai.facebook.com/blog/multiray-large-scale-AI-models/

arXiv to include Hugging Face spaces

https://blog.arxiv.org/2022/11/17/discover-state-of-the-art-machine-learning-demos-on-arxiv/

Multilingual Diffusion

https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion

Music source separation

https://github.com/facebookresearch/demucs

https://arxiv.org/abs/2211.08553

#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

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#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

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#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

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#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

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#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

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#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

#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

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#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_sourc...

Check out Neural Magic: https://neuralmagic.com/

and DeepSparse: https://github.com/neuralmagic/deepsp...

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?

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#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

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#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/total...

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?

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Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

 #ai #selforganization #emergence

Read Sebastian's article here: https://sebastianrisi.com/self_assemb...

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_assemb...

https://twitter.com/risi1979/status/1...

https://distill.pub/2020/growing-ca/

https://arxiv.org/abs/2201.12360?sour...

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...

https://melaniemitchell.me/BooksConte... 

Links:

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 #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

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#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/blo...

https://huggingface.co/spaces/bigscie...

https://huggingface.co/bigscience/blo...

YALM 100B

https://github.com/yandex/YaLM-100B

Chinese Brain-Scale Supercomputer

https://www.scmp.com/news/china/scien...

https://archive.ph/YaoA6#selection-12...

Meta AI Translates over 200 Languages

https://ai.facebook.com/research/no-l...

Reproducibility Crisis Workshop

https://reproducible.cs.princeton.edu/

AI21 Raises $64M

https://techcrunch.com/2022/07/12/ope...

Ian Goodfellow leaves Apple

https://twitter.com/goodfellow_ian/st...

Andrey Karpathy leaves Tesla

https://mobile.twitter.com/karpathy/s...

https://www.businessinsider.com/repor...

Wordalle

https://huggingface.co/spaces/hugging...

Links:

Homepage: https://ykilcher.com

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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=BZ5a1...

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

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#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-T...

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:

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 #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

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SubscribeStar: https://www.subscribestar.com/yannick...

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Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2

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#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

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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/tacklin...

https://storage.googleapis.com/deepmi...

https://twitter.com/Inoryy/status/152...

LiT: Locked Image Tuning

https://ai.googleblog.com/2022/04/loc...

https://google-research.github.io/vis...

Jurassic X & MRKL Systems

https://www.ai21.com/blog/jurassic-x-...

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/Sty...

https://huggingface.co/spaces/hysts/S...

Helpful Things

https://github.com/rish-16/grafog

https://huggingface.co/bertin-project...

https://github.com/pytorch/torchdistx

https://pytorch.org/torchdistx/latest...

https://github.com/Netflix/vectorflow...

https://iclr-blog-track.github.io/202...

https://twitter.com/DeepMind/status/1...

https://github.com/ai-forever/mgpt

https://github.com/cleanlab/cleanlab

https://efficientdlbook.com/?utm_sour...

https://minihack-editor.github.io/

https://mugen-org.github.io/

https://www.amazon.science/blog/amazo...

https://github.com/phuselab/openFACS?...

https://medium.com/pytorch/avalanche-...

This AI does not exist

https://thisaidoesnotexist.com/

Links:

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#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/

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#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

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#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.

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

#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

#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:

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#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

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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

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#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:

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Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n