AI knowledge Page 4

Abstract Large-scale language models (LLMs), such as OpenAI's GPT-4, Google's PaLM, and Meta's LLaMA, have dramatically transformed Artificial Intelligence (AI) by enabling human-like text generation and natural language understanding. However, their reliance on static training data limits their ability to respond to dynamic, real-time queries...

Artificial Intelligence (AI) is a rapidly growing field. Language models have evolved to enable AI Agents to perform complex tasks and make complex decisions. However, as the skills of these Agents continue to grow, the infrastructure to support them struggles to keep up. LangGraph, a revolutionary library designed to revolutionize...

Introduction Like many others, over the past few days my news tweets have been filled with news, praise, complaints, and speculation about the Chinese-made DeepSeek-R1 large language model, which was released last week. The model itself is being brought up against some of the best inference models from OpenAI, Meta, and other...

  Summary of Key Contributions of CORAG CORAG (Cost-Constrained Retrieval Optimization for Retrieval-Augmented Generation) is an innovative retrieval-augmented generation (RAG) system designed to address key challenges in existing RAG approaches. The following CORAG ...

Knowledge distillation is a machine learning technique that aims to transfer learning from a large pre-trained model (i.e., a "teacher model") to a smaller "student model". Distillation techniques can help us develop lighter weight generative models for intelligent conversations, content creation, and other areas. Recently Distil...

Recently, many people engaged in large model training and inference have been discussing the relationship between the number of model parameters and model size. For example, the famous alpaca series LLaMA large model contains four versions with different parameter sizes, LLaMA-7B, LLaMA-13B, LLaMA-33B and LLaMA-65B. Here "...

Original article: https://arxiv.org/pdf/2412.15479 INTERPRETATION: This article itself is not very innovative and has little application. However, it reminds me of three highly informative articles I read a long, long time ago. Reading this article in conjunction with the three previous articles will hopefully bring you more inspiration. Recommended reading: the...

In the field of artificial intelligence and machine learning, especially when building applications such as RAG (Retrieval Augmented Generation) systems and semantic search, efficiently processing and retrieving massive unstructured data becomes crucial. Vector databases have emerged as a core technology to address this challenge. They are not only for storing high-dimensional ...

Xiaohongshu, a hot social e-commerce platform in China and even in Asia, has long gone beyond a simple shopping app to become a weathervane for young people's lifestyles and a new position for brand marketing. For overseas brands and individuals wishing to enter the Chinese market or reach young consumers, mastering Xiaohongshu...

Unexpectedly, AI has set off a half-changing sky in the programming field. From v0, bolt.new to various Agant programming tools Cursor, Windsurf, AI Coding has a huge potential of idea MVP. From the traditional AI-assisted coding, to today's direct project generation behind, in the end is...

Before we start, let's understand a few "key words": Workflow (Workflow): Simply put, it is "the complete steps to accomplish something". It's like an "instruction manual" that tells you what needs to be done, in what order, and by whom, in order to achieve your goal. Input: Before the workflow begins, you need to...

When building large language modeling (LLM) applications, memory systems are one of the key technologies to enhance conversation context management, long-term information storage, and semantic understanding. An efficient memory system can help the model maintain consistency over long conversations, extract key information, and even have the ability to retrieve historical conversations...

OpenAI Function calling V2 Features The core goal of Function calling V2 is to give OpenAI models the ability to interact with the outside world, which is reflected in the following two core functions: Fetching Data - A function calling implementation of RAG: Essentially RAG (Retrieve Augmented...

Basic Concepts In the field of information technology, retrieval refers to the process of efficiently locating and extracting relevant information from a large dataset (usually documents, Web pages, images, audio, video, or other forms of information) in response to a user's query or need. Its core goal is to find information that is relevant to the use...

Agent AI: Surveying the Horizons of Multimodal Interaction Originally published at https://ar5iv.labs.arxiv.org/html/2401.03568 Abstract Multimodal AI systems are likely to be ubiquitous in our daily lives. Making these systems more interactive a...

GraphReader: a graph-based intelligence that enhances long text processing for large language models Graphic Expert: like a tutor who is good at making mind maps, it transforms lengthy text into a clear knowledge network, so that the AI can easily find each key point needed for an answer as if it were exploring along a map, and effectively gr...

CAG (Cache Augmented Generation) that is 40 times faster than RAG (Retrieval Augmented Generation).CAG revolutionizes knowledge acquisition: instead of retrieving external data in real time, all knowledge is pre-loaded into the model context. It's like condensing a huge library into an on-the-go toolkit that can be used when needed...

By Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic Originally published at https://www.kaggle.com/whitepaper-agents Table of Contents Introduction What is an Intelligent Body? Models Tools Orchestration Layers Intelligent Bodies and Models Cognitive Architecture: How Intelligent Bodies Work Tools ...