Artificial Intelligence (AI) is a methodology for using a non-human system to learn from experience and imitate human intelligent behavior. The APMG Foundation Certificate in Artificial Intelligence tests a candidate’s knowledge and understanding of AI terminology and general principles. This syllabus covers the potential benefits and challenges of Ethical and Sustainable Robust Artificial Intelligence; the basic process of Machine Learning (ML) – Building a Machine Learning Toolkit; the challenges and risks associated with an AI project; and the future of AI and Humans in work. APMG Foundation in Artificial Intelligence Certification Benefits Course Benefits: Human-centric ethical and sustainable human and artificial intelligence. Artificial intelligence and robotics. Apply the benefits of AI projects – challenges and risks. Machine learning theory and practice – building a machine learning toolbox. The management, roles and responsibilities of humans and machines – the future of AI. Training Prerequisites Recommended to have basic IT literacy and awareness of business processes. Certification Exam Information The APMG Foundation Certificate in Artificial Intelligence is a foundation-level certification focused on core AI concepts, technologies, and applications intended for professionals getting started in AI. Duration: 60-minute, closed-book examNumber of Questions: 40 multiple-choice questionsPassing Score: Answer 26 out of 40 questions (achieve 65% or above) Artificial Intelligence Certification Training Outline Module 1: Ethical and Sustainable Human and Artificial Intelligence 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 is continually assessed and monitored. Describe the three fundamental areas of sustainability and the United Nations’s seventeen sustainability goals. Describe how AI is part of “Universal Design” and “The Fourth Industrial Revolution.” Understanding 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). Module 2: Artificial Intelligence and Robotics Demonstrate understanding of the AI Intelligent agent description. List the four rational agent dependencies. Describe agents in terms of performance measures, environment, actuators and sensors. Describe four types of agents: reflex, model-based reflex, goal-based and utility-based. Identify the relationship of AI agents with Machine Learning (ML) Describe what a robot is and Describe robotic paradigms. Describe an intelligent robot. Relate intelligent robotics to intelligent agents. Module 3: Applying the benefits of AI – challenges and risks Describe how sustainability relates to human-centric ethical AI and how our values will drive our use of AI to change humans, society and organizations. Explain the benefits of Artificial Intelligence. List advantages of machine and human intelligence. Describe the challenges of Artificial Intelligence. General ethical challenges AI raises. General examples of the limitations of AI systems compared to human systems. Demonstrate understanding of the risks of AI projects. Give at least one general example of the risks of AI. Describe a typical AI project team. 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 it to the NASA Technology Readiness Levels (TRLs). Module 4: Starting AI and how to build a Machine Learning Toolbox – Theory and Practice Describe how we learn more 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 preparation of data. Describe typical types of Machine Learning Algorithms. Describe the typical methods of visualizing 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 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.
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