ykilcher

#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

#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 #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

#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 #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

#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

#gpt4 #chatgpt #openai References: https://openai.com/product/gpt-4 https://openai.com/research/gpt-4 https://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/1630991925984755714 https://openai.com/blog/introducing-chatgpt-and-whisper-apis https://twitter.com/greggyb/status/1631121912679002112 https://www.haihai.ai/chatgpt-api/ OpenAI becomes more business-friendly https://twitter.com/sama/status/1631002519311888385 https://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_rmj7aJI1tp7GAXubODfCUaKvOkoOYj https://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=4kp7oVTu9Ck https://www.youtube.com/watch?v=k13v8jp8H5o https://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/1629879567514238976 https://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-ul2-20b https://huggingface.co/google/flan-ul2 LLaMA weights leaked https://github.com/facebookresearch/llama/pull/73 https://github.com/facebookresearch/llama/pull/73/files#diff-b335630551682c19a781afebcf4d07bf978fb1f8ac04c6bf87428ed5106870f5 https://github.com/ChristopherKing42 https://open-assistant.io/dashboard Mind-Reading from fMRI https://sites.google.com/view/stablediffusion-with-brain/?s=09 https://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-diffusers https://pyribs.org/ https://twitter.com/rowancheung/status/1630569844654460928 https://pimeyes.com/en https://cacti-framework.github.io/ https://twitter.com/bhutanisanyam1/status/1630980866775330819 https://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

#openassistant #chatgpt #gpt4 https://open-assistant.io/chat https://huggingface.co/OpenAssistant https://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.html https://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/ULVX933 https://imgur.com/9XMgt06 https://imgur.com/9kichAp https://imgur.com/RSHAz1l https://imgur.com/PIAjVKp https://imgur.com/xEyLajW https://imgur.com/3STX9mZ https://imgur.com/ifIHr3S https://imgur.com/bXJOZgI https://dataportraits.org/ https://arxiv.org/abs/2303.03919 https://arxiv.org/pdf/2303.03919.pdf https://ai.facebook.com/blog/ai-self-supervised-learning-data2vec/ https://github.com/facebookresearch/fairseq/tree/main/examples/data2vec https://huggingface.co/docs/hub/models-gated https://huggingface.co/about https://github.com/huggingface/huggingface.js?utm_source=pocket_reader https://github.com/microsoft/visual-chatgpt https://arxiv.org/abs/2303.04671 https://github.com/microsoft/visual-chatgpt/blob/main/visual_chatgpt.py https://huggingface.co/spaces/RamAnanth1/visual-chatGPT https://www.engadget.com/microsoft-bing-crossed-100-million-daily-active-users-080138371.html https://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-safety https://arxiv.org/abs/2303.04488 https://arxiv.org/pdf/2303.04488.pdf https://arxiv.org/abs/2303.04910 https://arxiv.org/pdf/2303.04910.pdf https://twitter.com/astro_wassim/status/1633645134934949888 https://ai.papers.bar/paper/ede58b1ebca911ed8f9c3d8021bca7c8 https://arxiv.org/pdf/2303.03192.pdf https://www.theverge.com/2023/3/8/23629362/meta-ai-language-model-llama-leak-online-misuse https://knightcolumbia.org/blog/the-llama-is-out-of-the-bag-should-we-expect-a-tidal-wave-of-disinformation https://github.com/facebookresearch/llama/pull/184 https://huggingface.co/datasets/yizhongw/self_instruct https://openai.com/policies/terms-of-use https://palm-e.github.io/ https://pickapic.io/ https://ai.googleblog.com/2023/03/universal-speech-model-usm-state-of-art.html https://arxiv.org/abs/2303.01037 https://github.com/BlinkDL/RWKV-LM?utm_source=pocket_reader https://gligen.github.io/ https://github.com/microsoft/GLIP https://arxiv.org/abs/2301.07093 https://huggingface.co/spaces/gligen/demo https://www.sciencealert.com/the-first-ever-complete-map-of-an-insect-brain-is-truly-mesmerizing https://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 :) 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

#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/1649908805788893185 https://www.nytimes.com/2023/05/01/technology/ai-google-chatbot-engineer-quits-hinton.html https://www.technologyreview.com/2023/05/01/1072478/deep-learning-pioneer-geoffrey-hinton-quits-google/ https://archive.ph/TrPoH https://twitter.com/DanHendrycks/status/1654560913939374080 https://twitter.com/ylecun/status/1654930029569101824 https://twitter.com/home https://twitter.com/ylecun/status/1654931495419621376 https://twitter.com/pkedrosky/status/1653955254181068801 https://www.semianalysis.com/p/google-we-have-no-moat-and-neither https://twitter.com/untitled01ipynb/media https://www.theinformation.com/articles/openais-losses-doubled-to-540-million-as-it-developed-chatgpt https://archive.ph/bKsdM https://www.washingtonpost.com/technology/2023/05/04/google-ai-stop-sharing-research/ https://twitter.com/giffmana/status/1654962145707130880 https://twitter.com/Ken_Goldberg/status/1651309843804987393 https://tsdr.uspto.gov/documentviewer?caseId=sn97733259&docId=PTD20230418160641&s=09#docIndex=1&page=1 https://twitter.com/osanseviero/status/1654230764513370112 https://huggingface.co/bigcode/starcoder https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement https://twitter.com/hardmaru/status/1654649036333514753 https://www.together.xyz/blog/redpajama-models-v1 https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1 https://github.com/openlm-research/open_llama https://www.mosaicml.com/blog/mpt-7b https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md https://www.modular.com/mojo https://www.aicrowd.com/challenges/hackaprompt-2023 https://learnprompting.org/ https://developer.nvidia.com/blog/nvidia-enables-trustworthy-safe-and-secure-large-language-model-conversational-systems/?ncid=prsy-552511 https://blogs.nvidia.com/blog/2023/04/25/ai-chatbot-guardrails-nemo/ https://lmql.ai/#distribution https://github.com/gventuri/pandas-ai?utm_source=pocket_reader https://lamini.ai/blog/introducing-lamini https://github.com/deep-floyd/IF https://huggingface.co/spaces/DeepFloyd/IF https://twitter.com/FaramaFound/status/1650952295901720576 https://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-24024765 https://arxiv.org/abs/2304.12210 https://github.com/h2oai/h2ogpt https://huggingface.co/h2oai/h2ogpt-oasst1-512-20b https://github.com/h2oai/h2o-llmstudio https://ai.facebook.com/blog/ai-dataset-animating-kids-drawings/ https://www.camel-ai.org/ https://github.com/lightaime/camel?utm_source=pocket_reader https://huggingface.co/Writer/camel-5b-hf https://laion.ai/blog/paella/ https://magazine.sebastianraschka.com/p/finetuning-large-language-models https://pickapic.io/ https://github.com/yuvalkirstain/heroku_app https://huggingface.co/datasets/yuvalkirstain/PickaPic https://future.snorkel.ai/poster-contest/ https://twitter.com/d_feldman/status/1649466422018318338/photo/4 https://twitter.com/DeepMind/status/1651897358894919680 https://arxiv.org/abs/2304.13653 https://twitter.com/SmokeAwayyy/status/1652712832738422784 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 #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

#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

#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

#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/1 https://arxiv.org/abs/2306.08997 https://arxiv.org/pdf/2306.08997.pdf https://flower-nutria-41d.notion.site/No-GPT4-can-t-ace-MIT-b27e6796ab5a48368127a98216c76864 https://github.com/idrori/MITQ/commit/3feee1026318e537c0ad27968001ef76e4a36890 https://twitter.com/hardmaru/status/1670246674760077312 https://twitter.com/giffmana/status/1670258748286472193 https://twitter.com/T3816440886465/status/1670127224131862531 https://twitter.com/qrdl/status/1669856336652414977 https://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-q111042613 https://github.com/openlm-research/open_llama https://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

#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

#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

#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/ https://ktla.com/news/local-news/food-delivery-robots-under-attack-from-vandals-thieves-local-businesses-starting-to-be-affected/ https://www.youtube.com/watch?v=xxzS9qaARv0 https://www.psypost.org/2023/08/chatgpt-is-much-better-than-humans-at-accurately-identifying-emotions-in-fictional-textual-scenarios-167380 https://www.theverge.com/2023/8/1/23815287/meta-ai-persona-generative-llama-instagram-facebook https://www.cnbc.com/2023/07/28/microsoft-annual-report-highlights-importance-of-gpus.html Links: Homepage: https://ykilcher.com Merch: https://ykilcher.com/merch YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://ykilcher.com/discord LinkedIn: https://www.linkedin.com/in/ykilcher If you want to support me, the best thing to do is to share out the content :)

#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 https://twitter.com/samczsun/status/1598564871653789696/photo/1 https://twitter.com/haus_cole/status/1598541468058390534/photo/3 https://twitter.com/tailcalled/status/1599181030065246208/photo/1 https://twitter.com/pensharpiero/status/1598731292278865920 https://twitter.com/sleepdensity/status/1598233414683197441 https://twitter.com/goodside/status/1598253337400717313 https://twitter.com/Carnage4Life/status/1598332648723976193/photo/2 https://github.com/sw-yx/ai-notes/blob/main/TEXT.md#jailbreaks https://twitter.com/dannypostmaa/status/1599352584963170309/photo/4 https://twitter.com/sama/status/1599112749833125888 https://twitter.com/sama/status/1599114807474810884 https://twitter.com/sama/status/1599461195005587456 https://twitter.com/deliprao/status/1599451192215887872 https://twitter.com/michlbrmly/status/1599168681711656961 https://twitter.com/zoink/status/1599281052115034113 Links: 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 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): Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2

#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 Multilingual CLIP https://twitter.com/rom1504/status/1593719037808320513 Drug response prediction https://phys.org/news/2022-10-ai-accurately-human-response-drug.html https://huggingface.co/Onodofthenorth/SD_PixelArt_SpriteSheet_Generator https://huggingface.co/spaces/ronvolutional/sd-spritesheets https://github.com/daspartho/prompt-extend https://huggingface.co/blog/fine-tune-whisper https://twitter.com/CarsonKatri/status/1585412662724272128 https://github.com/carson-katri/dream-textures/ https://www.youtube.com/playlist?list=PLzvYlJMoZ02Dxtwe-MmH4nOB5jYlMGBjr https://github.com/xl0/lovely-tensors https://github.com/jerryjliu/gpt_index https://colab.research.google.com/drive/1o1qYJcFeywzCIdkfKJy7cTpgZTCM2EI4 https://dagshub.com/blog/launching-data-streaming-and-upload/ https://dagshub.com/blog/build-an-end-2-end-active-learning-pipeline-part-1/ https://github.com/run-ai/genv https://arxiv.org/abs/2210.14868 https://github.com/timeseriesAI/tsai https://medium.com/@yangyou_berkeley/diffusion-pretraining-and-hardware-fine-tuning-can-be-almost-7x-cheaper-85e970fe207b https://medium.com/@hpcaitech/accelerating-structure-prediction-of-protein-monomers-and-multimer-by-11-times-769715dcb5b5 https://github.com/hpcaitech/ColossalAI/tree/main/examples/images/diffusion https://arxiv.org/abs/2211.03726 https://github.com/Deci-AI/super-gradients https://github.com/facebookresearch/shumai https://github.com/huggingface/safetensors https://github.com/google/learned_optimization/tree/main/learned_optimization/research/general_lopt https://github.com/NVIDIA-Merlin/dataloader https://loda-lang.org/ https://loda-lang.org/edit/ https://github.com/EelcoHoogendoorn/numga https://arxiv.org/abs/2210.07316v1 https://huggingface.co/spaces/mteb/leaderboard https://twitter.com/natfriedman/status/1575631194032549888 https://github.com/nat/natbot