In today's rapidly advancing technological landscape, Artificial Intelligence (AI) is transforming how we live and work. However, while AI has the potential to enhance our lives significantly, it is not without its risks. One of the most significant of these risks is the potential for human error in the development and implementation of AI systems. Learning about AI in manufacturing can help companies make more informed decisions about implementing these systems effectively. This knowledge can also lead to new and innovative uses for AI in manufacturing, further improving efficiency and reducing errors. In short, understanding AI is critical to staying competitive in today's manufacturing landscape.

Learning Objectives:-

• Developing a deeper understanding of the nature and sources of human error in AI systems
• Gaining insights into strategies for preventing and mitigating human error in AI development and use
• Understanding the ethical implications of human error in AI and its impact on society
• Learning about real-world case studies of human error in AI systems and their consequences
• Exploring best practices for developers, operators, and users of AI systems to minimize the risks of human error
• Understanding the importance of transparency and explainability in AI systems to prevent human error
• Gaining knowledge about future directions for research into minimizing human error in the development and use of AI systems

Session Highlights:-

• Understanding the nature of human error in AI systems
• Identifying common sources of human error in AI development and use
• The role of biases in AI systems and how they can be minimized
• Strategies for preventing and mitigating human error in AI systems
• The ethical implications of human error in AI and its impact on society
• Best practices for developers, operators, and users of AI systems to minimize the risks of human error
• The importance of transparency and explainability in AI systems to prevent human error
• Future directions for research into minimizing human error in developing and using systems.

Who Should Attend:-

• Training managers and coordinators
• Manufacturing
• Plant engineering
• QA/QC staff
• Process excellence/improvement professionals
• Industrial/process engineers
• Compliance officers
• Regulatory/legislative affairs professionals
• General/corporate counsel
• ? Executive management