BCS - Foundation Certificate in Artificial Intelligence

Curriculum

Module 1: Ethical and Sustainable Human and Artificial Intelligence (20%)

• Recall the general definition of Human and Artificial Intelligence (AI).
  • Describe the concept of intelligent agents.
  • Describe a modern approach to Human logical levels of thinking using Robert Dilt’s Model.
• Describe what are Ethics and Trustworthy AI, in particular:
  • Recall the general definition of ethics.
  • Recall that a Human Centric Ethical Purpose respects fundamental rights, principles, and values.
  • Recall that Ethical Purpose AI is delivered using Trustworthy AI that is technically robust.
  • Recall that the Human Centric Ethical Purpose Trustworthy AI is continually assessed and monitored.
• Describe the three fundamental areas of sustainability and the United Nation’s seventeen sustainability goals.
• Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’.
• Understand that ML is a significant contribution to the growth of Artificial Intelligence.
• Describe ‘learning from experience’ and how it relates to Machine Learning (ML)

Module 2: Artificial Intelligence and Robotics (20%)

• Demonstrate understanding of the AI intelligent agent description:
  • list the four rational agent dependencies.
  • describe agents in terms of performance measure, environment, actuators, and sensors.
  • describe four types of agent: reflex, model-based reflex, goal-based and utility-based.
  • identify the relationship of AI agents with Machine Learning (ML).
• Describe what a robot is:
  • Describe robotic paradigms,
• Describe what an intelligent robot is and:
  • Relate intelligent robotics to intelligent agents.

Module 3: Applying the benefits of AI - challenges and risks (15%)

• Describe how sustainability relates to human-centric ethical AI and how our values will drive our use of AI will change humans, society, and organisations.
• Explain the benefits of Artificial Intelligence by.
  • list advantages of machine and human and machine systems.
• Describe the challenges of Artificial Intelligence, and give;
  • general ethical challenges AI raises.
  • general examples of the limitations of AI systems compared to human systems.
• Demonstrate understanding of the risks of AI project, and:
  • give at least one a general example of the risks of AI,
  • describe a typical AI project team in particular,
  • describe a domain expert,
  • describe what is ‘fit-of-purpose’,
  • describe the difference between waterfall and agile projects.
• List opportunities for AI.
• Identify a typical funding source for AI projects and relate to the NASA Technology Readiness Levels (TRLs).

Module 4: Starting AI how to build a Machine Learning Toolbox - Theory and Practice (30%)

• Describe how we learn from data – functionality, software, and hardware,
  • List common open-source machine learning functionality, software, and hardware.
  • Describe introductory theory of Machine Learning.
  • Describe typical tasks in the preparation of data.
  • Describe typical types of Machine Learning Algorithms.
  • Describe the typical methods of visualising data.
• Recall which typical, narrow AI capability is useful in ML and AI agents’ functionality

Module 5: The Management, Roles and Responsibilities of humans and machines (15%)

•  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.
• Describe a ‘learning from experience’ Agile approach to projects
  • Describe the type of team members needed for an Agile project.