Exam Vouchers: EXIN BCS Artificial Intelligence Essentials (AIE) (AIE-EXAM)

Artificial Intelligence (AI) is a methodology for using a non-human system to learn from experience and imitate human intelligent behavior. The EXIN BCS Artificial Intelligence Essentials exam tests a candidate’s knowledge and understanding of the terminology and the general principles. This syllabus covers the potential benefits; types of Artificial Intelligence; the basic process of Machine Learning (ML); the challenges and risks associated with an AI project, and the future of AI and Humans in work.

The Artificial Intelligence Essentials certificate is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, or IT services.

• Duration: 30 minutes
• Number of questions: 20 (Multiple Choice)
• Pass mark: 65%
• Open book: No
• Electronic equipment allowed: No
• Level: Foundation ECTS credits: 2
• Available languages: English

• Recall the general definition of human and Artificial Intelligence (AI).
• Describe ‘learning from experience’ and how it relates to Machine Learning (ML) (Tom Mitchell’s explicit definition).
• Understand that ML is a significant contribution to the growth of Artificial Intelligence.
• Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’.
• Describe the challenges of Artificial Intelligence, and give general examples of the limitations of AI compared to human systems, general ethical challenges AI raises.
• Demonstrate understanding of the risks of Artificial Intelligence, identify a typical funding source for AI projects and list opportunities for AI.
• Demonstrate an understanding that Artificial Intelligence (in particular, Machine Learning) will drive humans and machines to work together;
• List future directions of humans and machines working together.

1.     Artificial and Human Intelligence: An Introduction and History

• general definition of human and Artificial Intelligence (AI).
• ‘learning from experience’ and how it relates to Machine Learning (ML) (Tom Mitchell’s explicit definition).
• ML is a significant contribution to the growth of Artificial Intelligence.
• AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’.

 2.      Examples of AI: Benefits, Challenges and Risks

• benefits of Artificial Intelligence
• advantages of machine and human and machine systems;
• challenges of Artificial Intelligence
• the limitations of AI compared to human systems
• general ethical challenges AI raises
• understanding of the risks of Artificial Intelligence
• funding source for AI projects
• opportunities for AI

 3.      An introduction to Machine Learning

• AI intelligent agent
• differences with Machine Learning (ML)
• the four rational agent dependencies,
• performance measure, environment, actuators and sensors,
• reflex, model-based reflex, goal-based and utility-based.
• examples of Machine Learning in the following contexts:
  -  business
  -  social (media, entertainment)
  -  science
• which typical, narrow AI capability is useful in ML and AI agents’ functionality;
• forms of ML:
  -  supervised
  -  unsupervised
  -  reinforcement
• the basic schematic of a neutral network.

 4.      The Future of Artificial Intelligence – Human and Machine Together

• Artificial Intelligence (in particular Machine Learning) will drive humans and machines to work together
• list future directions of humans and machines working together