Machine Teaching for Autonomous AI
In this path, you'll learn to design and implement a new AI paradigm called Machine Teaching for Autonomous AI. Machine Teaching uses the knowledge of subject matter experts to teach AI. Machine Teaching integrates known control methods, for stable control, Machine Learning, for advance perception, and Deep Reinforcement Learning, for learning strategies and human-like decision making. The deployment in industrial processes without disruption and providing real business value is considered from its design. Machine Teaching for Autonomous AI stores expert operators' skill set, and it helps companies achieve new levels of optimization for competitiveness, profitability, and sustainability. The hands-on development of Autonomous solutions will be taught using the Microsoft Project Bonsai Platform, a low-code platform where Machine Teaching is used to the implementation, training, and validation of Bonsai brains.
At the end of this learning path, you'll be able to:
- Select use cases where autonomous AI will outperform both humans and automated systems.
- Leverage human expertise to design and teach AI solutions.
- Use brain design patterns to quickly design brains for any use case.
- Fill up AI specification documents that accurately describe the problem and the proposed solution.
- Visually understanding brain performance using embedded graph interfaces.
Produced in partnership with the University of Oxford – Ajit Jaokar Artificial Intelligence: Cloud and Edge Implementations course
Prerequisites
None
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Modules in this learning path
In this module, you'll learn about several automated methods such as math (control theory), menus (optimization algorithms) and manuals (expert systems and expert rules), which are components of Machine Teaching. In addition, you’ll learn automated intelligence strengths and limitations, when to best use these technologies and when Autonomous Intelligence methods will be the best choice.
In this module, you'll learn about several AI technologies such as machine learning, deep learning and deep reinforcement learning that are components of Machine Teaching. In addition, you’ll learn Autonomous Intelligence strengths, when to best use these technologies and their limitations, and when Automated Intelligence methods can support them.
In this module, you'll learn about capabilities and essential concepts of Machine Teaching, the steps to add intelligence into Autonomous Intelligence systems and the types of business problems that you can solve using Machine Teaching for Autonomous AI.
In this module, you'll learn about the main Autonomous AI brain design patterns that solve most of the Machine Teaching challenges. You'll also learn when to best use them individually or combined.
In this module, you'll learn how to identify use cases that are a good fit to be solved using Machine Teaching for Autonomous AI.
In this module, you'll learn how to fill out an AI specification document for an Autonomous AI use case.