Who Should Attend?
Those with an interest in, or need to implement, AI in an organisation, especially those working in areas such as science, engineering, knowledge engineering, finance, or IT services.
The following broad set of roles would be interested:
Engineers; Scientists; Professional research managers; Chief Technology Officers; Chief Information Officers; Organisational change practitioners and managers; Business change practitioners and managers; Service architects and managers; Programme and planning managers; and Service providers.
Exam Details
The exam will consist of a one-hour, closed book exam consisting of 40 multiple choice questions. The pass mark is 26/40.
Objectives
Over three days, the BCS Foundation in Artificial Intelligence course will take you from a basic understanding of AI to the ability to create your own AI product. It incorporates and builds on the essentials certification to develop a portfolio of AI examples using the basic process of Machine Learning.
Course Content and Agenda
The course consists of five modules, spread over two days.
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).
Candidates will be able to:
- 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.
Candidates will be able to:
- 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, 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).
Candidates will be able to:
- Describe how we learn from data – functionality, software, and hardware.
- List common open-source machine learning functionality, software, and hardware.
- Describe the 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.
Candidates will be able to:
- 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.
