“ Hello everyone, this is Goodnote. The knowledge notes on large models RAG & Agent have been updated. The total word count is over 50,000. Due to space limitations, this article will only provide a summary. For detailed notes, please enter our public account and reply with ‘RAG’ and ‘Agent’ to obtain them. RAG Notes … Read more
In the field of artificial intelligence, large language models (LLMs) have achieved significant accomplishments. However, due to their reliance on static training data, they often struggle to respond effectively to dynamic real-time queries. Retrieval-Augmented Generation (RAG) technology has emerged, bringing new hope to address this issue. Agentic RAG further breaks through the limitations of traditional … Read more
Reply ‘data’ to receive a collection of algorithm interview questions (large models, deep learning, machine learning). 1. What is Retrieval-Augmented Generation (RAG)? RAG is a hybrid approach that combines retrieval systems and generative language models. It consists of two steps: Retrieval Component: Searches for relevant information in large external corpora or datasets based on the … Read more
Click the “blue text” to follow us In today’s rapidly changing artificial intelligence (AI) technology landscape, major tech companies are launching innovative products aimed at providing smarter and more efficient solutions for enterprises and individual developers. Recently, Google Cloud announced the full launch of its Vertex AI RAG Engine (Retrieval-Augmented Generation Engine), which has garnered … Read more
Hello, I am the Fisherman. Today, I am sharing a 35-page overview of the latest Agentic RAG. The core problem this paper aims to address is the outdated, inaccurate outputs, and hallucinations that arise when today’s large language models (LLMs) rely on static training data to handle dynamic, real-time queries. It starts from the fundamental … Read more
Click the blue text to follow us To prevent getting lost, please make sure to ‘star’ us after following! 🌟 Hello everyone, I am Shelly, an AI application coach focused on AI tools and cutting-edge technology content, having experienced over 300 AI application tools. I have been observing the impact of technology and large models … Read more
Click Follow us for updates in blue above Cover image: A recent cognitive class on intelligence by the author, demystifying large language models from a comparative perspective “ 𝕀²·ℙarad𝕚g𝕞 Intelligent Square Paradigm Research: Writing to Deconstruct Intelligence。 After all, deep learning LLMs are not the entirety of AI, and the path to AGI is not … Read more
Click the card below to follow the “AI Frontier Express” public account Various important resources delivered promptly Reprinted from Zhihu: Yao Yuan Link: https://zhuanlan.zhihu.com/p/17311602488 1. Introduction The attention mechanism in Transformer [1] can effectively model the correlations between tokens, achieving significant performance improvements in many tasks. However, the attention mechanism itself does not have the … Read more
Machine Heart reports Machine Heart Editorial Department With a theoretical foundation, we can perform deep optimization. Why is the performance of transformers so good? Where does the context learning (In-Context Learning) ability it brings to many large language models come from? In the field of artificial intelligence, transformers have become the dominant model in deep … Read more
Machine Heart reports Editors: Danjiang, Xiaozhou SVM is all you need; Support Vector Machines are never out of date. The Transformer is a new theoretical model of Support Vector Machines (SVM) that has sparked discussion in academia. Last weekend, a paper from the University of Pennsylvania and the University of California, Riverside, sought to explore … Read more