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
This course is part of a specialization called Autonomous AI for Industry.