AI, Quant 투자 공부

1. Five years of GPT progress source: https://finbarr.ca/five-years-of-gpt-progress/? 2. Large language models aren't trained enough. source: https://finbarr.ca/llms-not-trained-enough/ 3. Transformer Inference Arithmetic source: https://kipp.ly/blog/transformer-inference-arithmetic/ 4. Transformer Taxonomy (the last lit review) source: https://kipp.ly/blog/transformer-taxonomy/ 5. What can ..

1. Chinese creators use Midjourney's AI to generate retro urban "photography" source: https://www.technologyreview.com/2023/03/29/1070432/china-ai-retro-photo-midjourney/ 2. The inside story of how ChatGPT was built from the people who made it source: https://www.technologyreview.com/2023/03/03/1069311/inside-story-oral-history-how-chatgpt-built-openai/

1. TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs source : https://arxiv.org/pdf/2303.16434.pdf\ 2. HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in HuggingFace source : https://arxiv.org/pdf/2303.17580.pdf 3. 그림으로 이해하는 스테이블 디퓨전 source : https://medium.com/@aldente0630/%EA%B7%B8%EB%A6%BC%EC%9C%BC%EB%A1%9C-%EC%9D%B4%ED%95%B4%ED%95%98%EB%8A%94-%EC..

1. How to estimate and reduce the carbon footprint of machine learning models source: https://towardsdatascience.com/how-to-estimate-and-reduce-the-carbon-footprint-of-machine-learning-models-49f24510880 The environmental impact of data science and machine learning How to estimate the carbon footprint of a machine learning model How to reduce the carbon footprint of your machine learning models ..

1. A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT source: https://arxiv.org/pdf/2303.04226.pdf 2. Brdging the Gap: AI Research and Real-World Deployment in AI companies source: https://speakerdeck.com/shurain/bridging-the-gap-ai-research-and-real-world-deployment-in-ai-companies 3. Building Large Language Model at Scale source: https://speak..

1. The ChatGPT Debate: Are We Intelligent Enough To Understand ‘Intelligence’ source: https://gabriel-silva.medium.com/the-chatgpt-debate-are-we-intelligent-enough-to-understand-intelligence-1f826e46d174 2. Deep Dive Comparison Between Microsoft’s GPT-4 Copilot And Google’s PaLM source: https://medium.com/mlearning-ai/a-deep-dive-comparison-between-microsofts-gpt-4-copilot-and-google-s-palm-c307..

1. Observations on Microsoft’s Experiments with GPT-4 source: https://machine-learning-made-simple.medium.com/observations-on-microsofts-experiments-with-gpt-4-3d189647556d 2. 얀 르쿤의 auto-regression 모델에 대한 견해 source: https://www.linkedin.com/posts/yann-lecun_i-have-claimed-that-auto-regressive-llms-activity-7045908925660950528-hJGk/?utm_source=share&utm_medium=member_desktop 3. Sparks of Artifici..

1. A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT source: https://arxiv.org/pdf/2303.04226.pdf

1. RL Widens the ChatGPT Moat source: https://dmccreary.medium.com/rl-widens-the-chatgpt-moat-7543041cda54 2. ChatGPT는 어떻게 학습할까요_ChatGPT 대화형 언어모델 소개 (feat, 챗봇) source: https://youtu.be/vziygFrRlZ4 3. How to Run Stable Diffusion Locally to Generate Images source: https://www.assemblyai.com/blog/how-to-run-stable-diffusion-locally-to-generate-images/#prompt-engineering 4. ExplAIning AI: The fundam..

1. Who Will Make Money from the Generative AI Gold Rush? Part I source: https://medium.com/@sgreenman/who-will-make-money-from-the-generative-ai-gold-rush-9e8a291d2af6 2. Review — Flamingo: A Visual Language Model for Few-Shot Learning source: https://sh-tsang.medium.com/review-flamingo-a-visual-language-model-for-few-shot-learning-ec477d47e7bf 3. What you need to know about GPT-4 and its busine..