Introduction
Autonomous AI uses Machine Teaching to empower industry subject matter experts to improve decision making. AI-powered solutions allow companies to increase productivity, improve quality, reduce waste, and drive performance. An important challenge for companies is to identify what part of the control process would help them meet their operational objectives. They might have questions such as:
- With dozens of controls and processes across the factory floor, what stations warrant autonomy?
- What process improvements would most dramatically affect efficiency and safety?
- What implementation areas show the highest return?
- How do we anticipate applications of AI impacting a product's full lifecycle?
- How will we prioritize competing objectives? For example, if gains in business efficiency came with the trade-off of a slightly lower-quality customer experience, would it be acceptable?
- How could we start with an autonomous system?
- What use cases make sense for us?
In this module, you'll learn about three main factors that allow you to distinguish if a use case is a good candidate for Machine Teaching.
Learning objectives
After completing this module, you’ll be able to:
- Select use cases for Autonomous AI.
- Evaluate scenarios where you can use Autonomous AI.
Prerequisites
- Basic knowledge of Automated Intelligence
- Basic knowledge of Autonomous Intelligence
- Basic knowledge of Machine Teaching
- Basic knowledge of Brain Design Patterns