EBCS-AIF: EXIN BCS Artificial Intelligence Foundation

Module 1: Ethical and Sustainable Human and Artificial Intelligence (AI)
1.1 Recall the General Definition of Human and Artificial Intelligence (AI)

• The concept of intelligent agents.
• Modern approach to Human logical levels of thinking using Robert Dilt’s Model.

1.2 Ethics and Trustworthy Artificial Intelligence (AI), in Particular:

• The general definition of Ethics.
• Human Centric Ethical Purpose respects fundamental rights, principles and values.
• Ethical Purpose AI is delivered using Trustworthy Artificial Intelligence (AI) that is technically robust.
• Human Centric Ethical Purpose Trustworthy Artificial Intelligence (AI) is continually assessed and monitored.

1.3 Three Fundamental Areas of Sustainability and the United Nation’s
Seventeen Sustainability Goals

1.4 Artificial Intelligence (AI) is Part of ‘Universal Design,’ and ‘The Fourth
Industrial Revolution’

1.5 Machine Learning (ML) is a Significant Contribution to the Growth of
Artificial Intelligence (AI)

• ‘learning from experience’ and how it relates to Machine Learning (ML) (Tom Mitchell’s explicit definition)

Module 2: Artificial Intelligence (AI) and Robotics
2.1 Demonstrate Understanding of the Artificial Intelligence (AI) Intelligent Agent Description, and:

• Four rational agent dependencies.
• Describe agents in terms of performance measure, environment, actuators and sensors.
• Four types of agent: reflex, model-based reflex, goal-based and utilitybased.
• Identify the relationship of Artificial Intelligence (AI) agents with Machine Learning (ML).

2.2 What a Robot is and:

• Robotic paradigms

2.3 What an Intelligent Robot is and:

• Relate intelligent robotics to intelligent agents.

Module 3: Applying the Benefits of Artificial Intelligence (AI) – Challenges and Risks
3.1 Sustainability Relates to Human-Centric Ethical Artificial Intelligence (AI) and how our Values will Drive our use of Artificial Intelligence (AI) and will Change Humans, Society and Organizations

3.2 Benefits of Artificial Intelligence (AI) by:

• Advantages of machine and human and machine systems.

3.3 Challenges of Artificial Intelligence (AI), and:

• General ethical challenges Artificial Intelligence (AI) raises.
• General examples of the limitations of Artificial Intelligence (AI) systems
  compared to human systems.

3.4 Demonstrate Understanding of the Risks of Artificial Intelligence (AI) Projects, and:

• General example of the risks of Artificial Intelligence (AI).
• Artificial Intelligence (AI) project team in particular.
• A domain expert.
• What is ‘fit-of-purpose’.
• The difference between waterfall and agile projects.

3.5 List Opportunities for Artificial Intelligence (AI)

3.6 Identify a Typical Funding Source for Artificial Intelligence (AI) Projects and Relate to the NASA Technology Readiness Levels (TRLs)
Module 4: Starting Artificial Intelligence (AI): how to Build a Machine Learning (ML) Toolbox – Theory and Practice
4.1 Describe how we Learn from Data – Functionality, Software and Hardware

• List common open source machine learning functionality, software and hardware.
• Introductory theory of Machine Learning (ML).
• Typical tasks in the preparation of data.
• Typical types of Machine Learning (ML) Algorithms.
• Typical methods of visualizing data.
• Typical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine

4.2 Typical, Narrow Artificial Intelligence (AI) Capability is Useful in Machine
Learning (ML) and Artificial Intelligence (AI) Agents’ Functionality
Module 5: The Management, Roles and Responsibilities of Humans and Machines
5.1 Demonstrate an Understanding that Artificial Intelligence (AI) (in Particular, Machine Learning (ML)) will Drive Humans and Machines to Work Together

5.2 List Future Directions of Humans and Machines Working Together

5.3 Describe a ‘Learning from Experience’ Agile Approach to Projects

• Type of team members needed for an Agile project.