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Inicio / Buscador / Ciencias de la Computación / Machine Learning
University of Washington vía Coursera
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Machine Teaching for Autonomous AI

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  • An Introduction to Autonomous AI & Machine Teaching
    • 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.
  • Analyzing the Problem
    • 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.
  • Learning the Solution
    • 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.
  • Storytelling
    • 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.