BCS Foundation in Artifical Intelligence Training in Republic of the Philippines

There are no entry requirements for this training

1 Ethical and Sustainable Human and Artificial Intelligence (20%). Candidates will be able to:

• 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
• 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) (Tom Mitchell’s explicit definition).
• 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.
• Relate intelligent robotics to intelligent agents.
• 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
• List advantages of machine and human and machine systems.
• Describe the challenges of Artificial Intelligence, and give the general ethical challenges AI raises, along with examples of the limitations of AI systems compared to human systems.
• Demonstrate understanding of the risks of AI project
• Give at least one a general example of the risks of AI
• Describe a typical AI project
• 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).
• 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.
• 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.

Each major subject heading in this syllabus is assigned and allocated a percentage of study time. The purpose of this is:

1. Guidance on the proportion of time allocated to each section of an accredited course.
2. Guidance on the proportion of questions in the exam.

The course will cover the follow topics:

• Ethical and Sustainable Human and Artificial Intelligence ( 25% )
• Applying the benefits, challenges and risks of a Machine Learning project ( 30% )
• An introduction to Machine Learning Theory and Practice (35%)
• The Management, Roles and Responsibilities of humans and machines (10%)