Autonomous AI design architect
This learning path is a brief introduction to a new AI paradigm called Machine Teaching for Autonomous AI. Machine Teaching uses the knowledge of subject matter experts to teach AI. It integrates known control methods for stable control, Machine Learning for advance perception, and Deep Reinforcement Learning for learning strategies and human-like decision making. It's AI integrated in industrial processes without disruption and providing real business value. It's validated in simulation, explainable that your experts can validate. Machine Teaching for Autonomous AI stores expert operators' skill set, enhancing and homogenizing expert operators' best performance, and/or efficiently training novices on the job when deployed as a Decision Support tool. It helps your company achieve new levels of optimization for competitiveness, profitability, and sustainability. It provides a robust innovation path for all areas of industrial processes and business with real ROI. At the end of this learning path, you'll be able to:
- Know the difference between automated and autonomous decision-making systems.
- 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.
- Use AI specification documents to talk about use cases and brain designs to diverse stakeholders.
Produced in partnership with the University of Oxford – Ajit Jaokar Artificial Intelligence: Cloud and Edge Implementations course
Prerequisites
None
Achievement Code
Would you like to request an achievement code?
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.