Autonomous AI Fundamentals

Overview

Syllabus

Courses

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  11 hours 41 minutes

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  (This program was formerly part of a three-course specialization called Autonomous AI for Industry. Because the software program Bonsai was discontinued, references to Bonsai have been removed. You can still learn about autonomous AI and machine teaching through our two individual courses "Designing Autonomous AI" and "Machine Teaching for Autonomous AI.") 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
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  12 hours 37 minutes

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  (This program was formerly part of a three-course specialization called Autonomous AI for Industry. Because the software program Bonsai was discontinued, references to Bonsai have been removed. You can still learn about autonomous AI and machine teaching through our two individual courses "Designing Autonomous AI" and "Machine Teaching for Autonomous AI.") 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
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  5 weeks

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  Practice makes perfect. It’s true for people learning to master a new skill, and it’s also true for your AI brain. Just as you need the right environment to practice, get feedback and try again, so does your AI brain.
  In this course, you’ll solve industrial engineering problems inspired by real problems your instructors have worked on in industry. You’ll learn how to build, test and deploy an AI brain using Microsoft Bonsai, a cloud-based, low-code platform. We’ll walk through the entire Bonsai platform from setup to deployment. Along the way, you’ll use Bonsai to conduct machine teaching experimentation to train a brain and assess its progress. Because you’ll be teaching the brain a relatively complex task, you’ll run multiple simulations until you’re satisfied with the results. You’ll then prep the brain for graduation into the real world — deploying it into a machinery control system or other live environment.
  
  At the end of this course, you’ll be able to:
  • Build an autonomous AI that combines reinforcement learning with machine learning, expert rules and other methods that you’ve used in the first two courses of the specialization
  •  Establish requirements for a simulated environment for your brain to practice a task
  •  Validate and assess your brain’s performance of a task and make improvements to your brain design
  • Evaluate whether a simulator is a good practice environment
  • Deploy a brain on a real piece of hardware
  
  This course requires an Azure subscription.
  This course is part of a specialization called Autonomous AI for Industry.

Taught by

John Alexander, Juan Vergara and Kence Anderson

Tags

• united states
• united states