搜索结果：人工智能

Epsilon is an AI-powered search engine designed for scientific research that scans over 200 million papers to provide answers with citations, search publications and patents, extract information from multiple papers simultaneously, and synthesize research by summarizing and organizing papers into libraries. It's trusted by over 30,000 researchers worldwide but will shut down on April 30, 2026. 原文翻译： Epsilon是一款专为科学研究设计的AI搜索引擎，可扫描超过2亿篇论文，提供带引用的答案、搜索出版物和专利、同时从多篇论文中提取信息，并通过总结和组织论文到库中来综合研究。它被全球超过3万名研究人员信赖，但将于2026年4月30日关闭。

Databricks has acquired Lilac, a scalable tool for exploring and analyzing text datasets, to enhance generative AI application development by simplifying data evaluation and preparation for LLMs. 原文翻译： Databricks收购了Lilac，这是一个用于探索和分析文本数据集的可扩展工具，通过简化LLM的数据评估和准备，增强生成式AI应用程序的开发。

This comprehensive 2026 ranking analyzes major LLMs across reasoning, coding, math, agentic, software engineering, and chat benchmarks, providing detailed performance scores and pricing comparisons for technical professionals. 原文翻译： 这份全面的2026年排名分析了主要大语言模型在推理、编码、数学、代理、软件工程和聊天基准测试中的表现，为技术专业人士提供详细的性能得分和定价对比。

This article provides a comprehensive comparison and ranking of over 100 AI models (LLMs) across key metrics including intelligence, price, performance, speed (tokens per second & latency), and context window size. It identifies top performers in each category, such as Gemini 3.1 Pro Preview and GPT-5.4 (xhigh) for intelligence, Mercury 2 and Granite 3.3 8B for speed, and Qwen3.5 0.8B for affordability. 原文翻译： 本文对100多款AI大模型（LLMs）在智能、价格、性能、速度（每秒令牌数及延迟）和上下文窗口大小等关键指标上进行了全面比较和排名。文章识别了各分类中的顶级模型，例如Gemini 3.1 Pro Preview和GPT-5.4 (xhigh)在智能方面表现最佳，Mercury 2和Granite 3.3 8B速度最快，Qwen3.5 0.8B最具性价比。

NVIDIA H100 Tensor Core GPUs set new records across all eight MLPerf training benchmarks, delivering exceptional performance for generative AI and large language models at both per-accelerator and massive scale configurations. 原文翻译： NVIDIA H100 Tensor Core GPU在MLPerf训练基准测试的所有八项测试中均创下新纪录，在单加速器和大规模配置下均能为生成式AI和大语言模型提供卓越性能。

This article provides a comprehensive analysis of RAG (Retrieval-Augmented Generation) technology, covering its core architecture, implementation methods, application scenarios, and future trends. It explains how RAG enhances AI model capabilities by integrating retrieval systems with generative models to reduce hallucinations and improve answer accuracy, offering practical code examples and optimization strategies for developers. 原文翻译： 本文全面解析了RAG（检索增强生成）技术，涵盖其核心架构、实现路径、应用场景及未来趋势。文章阐述了RAG如何通过整合检索系统与生成模型来增强AI模型的认知能力，减少“幻觉”并提高回答准确性，为开发者提供了实用的代码示例和优化策略。

DeepSeek V4 预览版于2026年4月发布，提供Pro（1.6T参数）和Flash（284B参数）两个版本，均支持1M tokens上下文和领先的Agent能力。其核心Agent能力包括Function Calling（支持并行/嵌套工具调用）、长上下文记忆、思考模式（推理增强）、结构化输出、多模态理解及主流Agent框架适配。API兼容OpenAI接口，支持流式/非流式调用。典型应用覆盖

This paper introduces Generative Engine Optimization (GEO), a black-box optimization framework to help content creators improve their visibility in generative engine responses. It proposes GEO-bench f

WeChat has begun testing an AI-powered search feature integrating the DeepSeek-R1 model, offering a more diverse and intelligent search experience. The feature is currently in limited testing, pulling

This article provides a comprehensive guide on DeepSeek SEO and AI GEO optimization, covering 16 steps for AI reasoning optimization, 15 steps for GEO optimization, prompt types, and keyword strategie