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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-teaching-ai
课程评论：没有评论
课程名称：自主人工智能的机器教学 课程概述：就像教师帮助学生获得新技能一样，人工智能（AI）也可以通过机器教学的范式进行学习。机器学习算法能够适应和变化，使其与学习过程相似。本课程将教您如何构建一个能够超越现有能力的AI系统，了解自动化系统如何做出决策，并分析现有系统以判断其是否适合机器教学方法。您的课程项目将包括选择一个合适的用例，采访专业领域专家（SME）关于某个过程，并制定出构建自主AI系统的故事。 课程结束后，您将能够： - 描述机器教学的概念 - 解释SME在训练先进AI中的角色 - 评估在AI系统设计中利用人类专业知识的利弊 - 区分自动化与自主决策系统 - 描述自动化系统和人类在实时决策中的局限性 - 选择自主AI能够超越人类和自动系统的用例 - 提出针对现实问题的自主AI解决方案 - 根据现有专业知识和解决问题的技术验证您的设计 课程大纲： 1. 自主AI与机器教学简介：了解自主AI与其他人工智能形式的区别，探索一些已经运用自主AI的组织及其所带来的好处，研究不同思维模式在构建AI系统中的作用。 2. 问题分析：并非所有问题都适合自主AI解决方案，本模块将探讨各种自动化系统的优势和局限性，帮助您判断问题是否需要超越自动化系统的AI解决方案。 3. 学习解决方案：本模块聚焦于自主系统，如机器学习（ML）、强化学习（RL）、神经网络（NN）和深度强化学习（DRL），评估各自主系统的优缺点，并讨论机器教学如何发挥所有自动化和自主系统的优势。 4. 讲故事：学习如何通过讲述有说服力的故事来支持您的自主AI设计，因为干燥的事实和数据往往不如有说服力的论证来得令人信服。 通过这个课程，您将获得构建自主AI系统所需的理论知识和实用技能，帮助您在这一领域取得成功。
Name:An Introduction to Autonomous AI & Machine Teaching
Description:This module lays the foundation for this course and the entire specialization. You'll learn what makes autonomous AI different from other forms of artificial intelligence. You're invited to take a behind the scenes look at some organizations using autonomous AI and hear from operators and managers about the benefits they're realizing by harnessing autonomous AI. The focus will then transition to you! You'll explore five different mindset profiles that describe different approaches to building AI systems.
Name:Analyzing the Problem
Description:Not all problems are right for an autonomous AI solution. In this module, we explore types of automated systems and their strengths and limitations for various issues. You'll learn how to determine whether a problem needs a solution that goes beyond automated systems and into useful AI.
Name:Learning the Solution
Description:In the last module we looked at "automated" systems (math, menus, and manuals); examining situations where they excel and understanding their limitations. In this module we'll focus on "autonomous" systems such as: machine learning (ML), reinforcement learning (RL), neural networks (NN) and deep reinforcement learning (DRL); assessing both the strengths and weaknesses of each autonomous system. Lastly you'll see how "machine teaching" can tap into the strengths of all the automated and autonomous systems.
Name:Storytelling
Description:Wondering what has storytelling has got to do with AI? Good storytelling is a tool of persuasion. Dry facts and data are not as compelling as persuasion arguments. In the real world someone has to fund the development of your autonomous AI design, and you need to tell that person a persuasive story.
Just as teachers help students gain new skills, the same is true of artificial intelligence (AI). Machine learning algorithms can adapt and change, much like the learning process itself. Using the machine teaching paradigm, a subject matter expert (SME) can teach AI to improve and optimize a variety of systems and processes. The result is an autonomous AI system. In this course, you’ll learn how automated systems make decisions and how to approach building an AI system that will outperform current capabilities. Since 87% of machine learning systems fail in the proof-concept phase, it’s important you understand how to analyze an existing system and determine whether it’d be a good fit for machine teaching approaches. For your course project, you’ll select an appropriate use case, interview a SME about a process, and then flesh out a story for why and how you might go about building an autonomous AI system. At the end of this course, you’ll be able to: • Describe the concept of machine teaching • Explain the role that SMEs play in training advanced AI • Evaluate the pros and cons of leveraging human expertise in the design of AI systems • Differentiate between automated and autonomous decision-making systems • Describe the limitations of automated systems and humans in real-time decision-making • Select use cases where autonomous AI will outperform both humans and automated systems • Propose an autonomous AI solution to a real-world problem • Validate your design against existing expertise and techniques for solving problems
