AI Wiki
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  • 👏Welcome
  • AI Wiki 知识百科
    • 🔎什么是人工智能 (AI)-Google
    • 🔎人工智能-百度百科
    • 🔎人工智能-Wikipedia
    • 📖Artificial Intelligence Wiki (English)
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    • 📖机器学习课程术语表
  • Prompt Engineering 教程
    • 🔔Prompt Engineering 是什么?​
    • 📘Learn Prompting (多语言)
    • 📒Learning Prompt (中文)
    • 📗Learn Prompt (English)
    • 📕Deep Learning (English)
  • ChatGPT 教程
    • 🚩基础篇
      • 如何注册ChatGPT账号
      • Prompt简介
      • 基础用法
      • 基本原则 & 建议
      • 基本使用场景 & 使用技巧
        • 场景1:问答问题
        • 场景2:基于示例回答
        • 场景3:推理
        • 场景4:无中生有——写代码
        • 场景5:锦上添花——改写内容
        • 场景6:锦上添花——信息解释
        • 场景7:化繁为简——信息总结
        • 场景8:化繁为简——信息提取
    • 🏳️‍🌈高级篇
      • ChatGPT Prompt Framework
      • Zero-Shot Prompts
      • Few-Shot Prompting
      • Self-Consistency
      • PAL Models
      • OpenAI Playground 使用方法
      • 搭建基于知识库内容的机器人
    • 🏴‍☠️技巧篇
      • 技巧1:To Do and Not To Do
      • 技巧2:增加示例
      • 技巧3:使用引导词,引导模型输出特定内容
      • 技巧4:增加 Role(角色)或人物
      • 技巧5:使用特殊符号指令和需要处理的文本分开
      • 技巧6:通过格式词阐述需要输出的格式
      • 技巧7:Zero-Shot Chain of Thought
      • 技巧8:Few-Shot Chain of Thought
      • 技巧9:其他
    • 🪧Awesome ChatGPT Prompts (English)
  • Midjourney 教程
    • 🚩基础篇
      • 如何使用 Midjourney?
      • Midjourney Prompt 基本结构
      • Midjourney Prompt 常用参数
      • Midjourney 基础设置
      • 订阅 Midjourney 会员
    • 🏳️‍🌈高级篇
      • Midjourney Prompt 高级参数
      • Midjourney 各版本差异
      • Midjourney 官方 FAQ
    • 🏴‍☠️技巧篇
      • 技巧一:临摹
      • 技巧二:多实验
      • 技巧三:善用 Image2Image 功能
      • 技巧四:增加风格——艺术运动
      • 技巧五:增加风格——艺术家
      • 技巧六:善用 no 参数,去掉不想要的元素
      • 技巧七:多参数同时使用
      • 技巧八:使用 Seed 参数对图进行二次修改
      • 技巧九:神秘的 blend 功能
      • 技巧十:控制变量法渐进优化
      • 技巧十一:增加风格——国家
      • 技巧十二:增加权重
      • 技巧十三:善用灯光
      • 技巧十四:增加风格——年份
      • 技巧十五:如何让 Midjourney 生成的人更具有多样性?
      • 技巧十六:改变相机与镜头
      • 技巧十七:看到别人的图,想知道它的 prompt 是啥
    • 📋Text Prompt 篇
      • 撰写 Text Prompt 注意事项
      • 场景1:Stock Photo
      • 场景2:品牌 Logo
      • 场景3:App & 徽章 Logo
      • 场景4:插画
      • 场景5:头像
      • 场景6:游戏
      • 场景7:实物
      • 场景8:人物
      • 场景9:风景
      • 场景10:动漫
      • 场景11:其他
      • 框架总结
    • 🧮Big List
      • Midjourney 完整参数列表
      • Artist List
      • Photographers List
      • Lighting List
      • Anime List
      • Camera and Lens List
  • Sora 教程
    • 🚩基础篇
      • Sora 基础介绍(中文)
      • Sora 官网介绍(English)
      • 如何申请使用 Sora
      • Sora Prompt提示词合集
      • Sora 学习手册汇总
      • 💰Sora 赚钱方法
    • 🏳️‍🌈高级篇
    • 🏴‍☠️技巧篇
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  • What is Artificial Intelligence (AI)?
  • Barriers to AI Adoption
  • Artificial Intelligence + Gradient
  1. AI Wiki 知识百科

Artificial Intelligence Wiki (English)

A repository of machine learning, data science, and artificial intelligence (AI) terms for individuals and businesses.

上一页人工智能-Wikipedia下一页机器之心人工智能专业词汇库

最后更新于1年前

Whether you're looking to explore new concepts or brush up on your terminology, this wiki offers up-to-date information on key topics in data science, machine learning, and deep learning.

Not sure where to start? Check out our definition of MLOps to discover a modern approach to model training and deployment.

What is Artificial Intelligence (AI)?

Artificial Intelligence is an umbrella term for a range of concepts and technologies that allow machines to exhibit human-like capabilities. Some common implementations include self-driving cars, human-impersonating chatbots, and facial recognition apps. A few recent breakthroughs have led to applications that don't just mimic human intelligence but go well above and beyond, performing tasks that are otherwise impossible for humans.

AI dates back to the 1950s and has been through several boom and bust cycles. Over the past few years, we've seen tremendous resurgence in investment and excitement in AI due to the culmination of three key ingredients:

  1. Abundant and cheap parallel computation with GPUs

  2. Growing data sets and collection techniques

  3. Advancements in underlying algorithms -- especially the advent of a neural network-based approach called Deep Learning

AI powers applications used by hundreds of millions of people every day. Businesses are using AI to perform an almost infinite number of tasks, from implementing recommender systems for e-commerce apps to diagnosing cancer.

Barriers to AI Adoption

AI is in its infancy and as an early-stage technology it is rapidly changing and challenging to implement. To gain more widespread adoption, AI needs to overcome a number of hurdles. These obstacles generally fall into two primary areas:

  1. A lack of best practices (MLOps) across the entire model lifecycle

  2. Infrastructure complexity inherent in developing and productionizing models

Today, Data Scientists only spend around 25% of their time developing models -- the other 75% of their time is spent managing tooling and infrastructure.

"The biggest barrier to AI adoption is an infrastructure and tooling problem, not an algorithm problem." -- Dillon Erb, Paperspace CEO

These are the challenges that end-to-end AI platforms like Paperspace Gradient were built to solve.

Artificial Intelligence + Gradient

Gradient enables teams to quickly develop, track, and deploy machine learning models from concept to production. The platform provides infrastructure automation and model lifecycle management with organization-wide visibility, reproducibility, and governance as first-class citizens.

For data scientists, Gradient provides the freedom to use familiar tools. Since Gradient provides DevOps support and resource orchestration, teams can focus on training algorithms and creating business value.

For organizations, Gradient reduces project costs by streamlining hardware resources and data science team productivity. The Kubernetes-native platform provides a unified ML hub that maximizes speed to deployment and time-to-value.

Gradient pioneered the concept of CI/CD for machine learning which is helping transform data driven organizations into model driven organizations. This is also known as agile ML.

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