AI, Quant 투자 공부

1.지금은 챗봇 전성시대! source: https://modulabs.co.kr/blog/chatbot_era/ openai: Chatgpt google: Bard meta : LLaMA google deepmind: Sparrow 2. Prompts for communicators using the new AI-powered Bing source: https://blogs.microsoft.com/blog/2023/03/16/prompts-for-communicators-using-the-new-ai-powered-bing/ 3. Alpaca: A Strong, Replicable Instruction-Following Model source: https://crfm.stanford.edu/2023/..

1. A Path Towards Autonomous Machine Intelligence source: https://openreview.net/pdf?id=BZ5a1r-kVsf source: https://www.shaped.ai/blog/yann-lecun-a-path-towards-autonomous-machine-intelligence

1. Chat GPT Business Ideas for 2023 SaaS, Services & More source: https://medium.com/cloud-believers/chat-gpt-business-ideas-for-2023-saas-services-more-9645247b3809 2. Large Transformer Model Inference Optimization source: https://lilianweng.github.io/posts/2023-01-10-inference-optimization 3. Differences between autoregressive, autoencoding and sequence-to-sequence-models in machine-learning s..

1. GPT-4 is OpenAI’s most advanced system, producing safer and more useful responses source: https://openai.com/product/gpt-4 papaer: https://cdn.openai.com/papers/gpt-4.pdf 드디어 GPT-4가 발표되었다!!!!!!!! 2. Accelerate Your AI Progress with Synthetic Data: 10 Reasons to Start Now source : https://paralleldomain.com/accelerate-your-ai-progress-with-synthetic-data?utm_source=towardsai&utm_medium=blog&ut..

1. The fundamental limitations of current AI systems exposed via Go source: https://medium.com/geekculture/the-fundamental-limitations-of-current-ai-systems-87dbdc8ae679 2. How ChatGPT really works, explained for non-technical people source: https://bootcamp.uxdesign.cc/how-chatgpt-really-works-explained-for-non-technical-people-71efb078a5c9

1. Building a Multi-Turn Chatbot with GPT and SageMaker: A Step-by-Step Guide source: https://itnext.io/building-a-multi-turn-chatbot-with-gpt-and-sagemaker-a-step-by-step-guide-7d75f33ccea1![]() 2. Quild Memo #3: AI Foundation Models source: https://medium.com/the-quild/quild-memo-3-ai-foundation-models-5a0dd16fd8a5 3. Feature Platforms — A New Paradigm in Machine Learning Operations (MLOps) so..

1. Revolutionizing Natural Language Understanding: GPT-3’s In-Context Learning source: https://medium.com/@yu-joshua/revolutionizing-natural-language-understanding-gpt-3s-in-context-learning-f49c8460ea20 2. Don’t Blame the AI: It’s the Training Data source: https://medium.com/ravenpack/dont-blame-the-ai-it-s-the-training-data-9ddd97df1d9a 다음에 검색 엔진이나 챗봇이 잘못되거나 이상한 답변을 제공할 때 당신을 실망시키는 것은 학습 데이터입니..

1. What Does ChatML Mean For Prompt Chaining Applications source: https://medium.com/@cobusgreyling/what-does-chatml-mean-for-prompt-chaining-applications-e551d53f883 2. Using ChatGPT to build System Diagrams — Part I source: https://aruva.medium.com/using-chatgpt-to-build-system-diagrams-part-i-69efc7603926 3. Using ChatGPT to build system diagrams — Part II source: https://aruva.medium.com/usi..

1. The 2023 edition of the Machine Learning, AI and Data Landscape — a quick analysis source: https://olivermolander.medium.com/the-2023-edition-of-the-mad-machine-learning-ai-and-data-landscape-a-quick-analysis-67208f16125b 2. MAD 2023, PART IV: TRENDS IN ML/AI source: https://mattturck.com/mad2023-part-iv/ 3. Foundation Models: How Startups Win With Generative AI source: https://www.madrona.co..

O.R.E.O 공식 1단계: Opinion (의견 주장하기) - 논리 정연한 메시지를 만들기 위해 핵심 내용을 다듬는 과정 2단계: Reason (이유 대기) - 1단계에서 주장한 의견에 타당한 근거를 증명하는 과정 - 이 단계에서 객관적 수치로 의견이 증명되면 독자가 빠르게 설득됨 3단계: Example (사례 들기) - 예시와 사례로 증명에 쐐기를 받는 단계 - 이유와 근거가 이성에 호소하는 증명 방식이라면 예시와 사례는 독자의 마음에 어필하는 방식 4단계: Opinion (의견 강조하기) - 독자에게 끌어내고자 한 반응은 촉구하는 내용을 담음 - 이로써 핵심을 빠르게 전달하는 메시지 만들기가 완성됨 O.R.E.O 글쓰기 - 각 1 줄씩 만듬 - 각 1 줄을 핵심 주제로 두고 여기에 세부내용을 보태서..