AI, Quant 투자 공부

1. Landscape of AI/ML in 2023 source: https://www.slideshare.net/HyunJoonJung/landscape-of-aiml-in-2023?fbclid=IwAR1UUpsfWOhbKap4R7FPtKf090hmQXrYsyXZ7jCSufqclNxjStsNlttjaTg 2. A brief history of LLMA models - AGI Sphere source: https://agi-sphere.com/llama-models/?fbclid=IwAR3TY21xWdFl9CiMU-ayTS2kXYE67xLw75b-zq1MJInFfhExpQUG96uBp5I 3. A fascinating tree of GPTs & LLMs reveals what’s been going o..

1. AI 모델의 데이터를 다룰 때 발생하는 세 가지 문제 (Data! Data! Data!) source: https://www.upstage.ai/blog/business/why-is-data-labeling-important-for-ai 2. StableLM: Stability AI Language Models source: https://github.com/Stability-AI/StableLM?fbclid=IwAR3jtwsESYvHx5C5Q9Ru1VpU1lPG-akoZlxkTdtIhmvLkEgJrbKIV-XANZw 3. ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual..

1. Generative Agents: Interactive Simulacra of Human Behavior source: https://arxiv.org/pdf/2304.03442.pdf

1. Integrating ChatGPT with internal knowledge base and question-answer platform source: https://medium.com/singapore-gds/integrating-chatgpt-with-internal-knowledge-base-and-question-answer-platform-36a3283d6334 2. Transformer Taxonomy (the last lit reveiw) source: https://kipp.ly/blog/transformer-taxonomy 3. Generative Agents: Interactive Simulacra of Human Behavior source: https://arxiv.org/p..

FlashAttention: Fast Transformer Training with Long Sequences https://hazyresearch.stanford.edu/blog/2023-01-12-flashattention-long-sequences FlashAttention: Fast Transformer Training with Long Sequences https://arxiv.org/pdf/2205.14135.pdf

1.Deploying Large NLP Models: Infrastructure Cost Optimization source: https://neptune.ai/blog/nlp-models-infrastructure-cost-optimization 2.Large Language Models and Two Modes of Human Thinking source: https://towardsdatascience.com/large-language-models-and-two-modes-of-human-thinking-1322160755e8 3.This is How to Stop ChatGPT, Bing, Poe, and You from Hallucinating source: https://pub.towardsa..

1. A Recipe for Training Large Models source: https://wandb.ai/craiyon/report/reports/Recipe-Training-Large-Models--VmlldzozNjc4MzQz? 2. 5 Lessons Learned from Adding ChatGPT to a Mature Product source: https://blog.trymito.io/5-lessons-learned-from-adding-chatgpt-to-a-mature-product/? 3. Training 175B Parameter Language Models at 1K GPU scale with Alpa and Ray source: https://www.anyscale.com/b..

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 2. Getting started with Generative AI: 4 ways to customize your models source: https://georgian-io.medium.com/getting-started-with-generative-ai-4-ways-to-customize-your-models-f6871b44cb6a 3. B..

1. LLaMA-GPT4All: Simplified Local ChatGPT source: https://medium.com/towards-artificial-intelligence/llama-gpt4all-simplified-local-chatgpt-ab7d28d34923 2. Neural Graph Databases source: https://towardsdatascience.com/neural-graph-databases-cc35c9e1d04f

1. Getting started with Generative AI: 4 ways to customize your models source: https://medium.com/@georgian-io/getting-started-with-generative-ai-4-ways-to-customize-your-models-f6871b44cb6a 2. GPT-4 Might Just Be a Bloated, Pointless Mess source: https://www.theatlantic.com/technology/archive/2023/03/openai-gpt-4-parameters-power-debate/673290/