Autonomous AI Fundamentals

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 designing 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 designing 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 Fundamentals.

To design an autonomous AI system, you must figure out how to distill a business challenge into its component parts. When children learn how to hit a baseball, they don’t start with fastballs. Their coaches begin with the basics: how to grip the handle of the bat, where to put their feet and how to keep their eyes on the ball. Similarly, an autonomous AI system needs a subject matter expert (SME) to break a complex process or problem into easier tasks that give the AI important clues about how to find a solution faster. In this course, you’ll learn how to create an autonomous AI design plan. By setting goals, identifying trainable skills, and employing those skills in goal-oriented strategies, you’ll incorporate your SME’s knowledge directly into your AI’s “brain,” the agent that powers your autonomous system. You'll learn when and how to combine various AI architecture design patterns, as well as how to design an advanced AI at the architectural level without worrying about the implementation of neural networks or machine learning algorithms. At the end of this course, you’ll be able to: • Interview SMEs to extract their unique knowledge about a system or process • Combine reinforcement learning with expert rules, optimization and mathematical calculations in an AI brain • Design an autonomous AI brain from modular components to guide the learning process for a particular task • Validate your brain design against existing expertise and techniques for solving problems • Produce a detailed specifications document so that someone else can build your AI brain This course is part of a specialization called Autonomous AI Fundamentals.