BCS AI Foundation Mock Tests

About this course

1. Introduction to AI and Historical DevelopmentThis section establishes the foundational terminology and traces the evolution of AI from academic theory to modern-day ubiquity.Key Definitions: Understanding human intelligence versus artificial intelligence, machine learning, and the scientific method.Historical Milestones: From the Dartmouth Conference (1956) to the "AI Winters," the rise of Big Data, and the 2022 breakthrough of Large Language Models (LLMs).Types of AI: Distinguishing between Narrow (Weak) AI—such as Siri, Alexa, and recommendation engines—and the theoretical concept of General (Strong) AI or AGI.Societal and Environmental Impact: * Evaluating the economic and social shifts caused by AI.Sustainability: Understanding the carbon footprint of data centers, green IT initiatives, and the UN Sustainable Development Goals (SDGs).2. Ethical and Legal ConsiderationsAs AI becomes more integrated into society, the Product Owner and AI practitioner must navigate a complex web of ethics and law.Ethical Concerns: Identifying bias, unfairness, and discrimination in algorithmic decision-making.Privacy and Data Protection: Navigating the UK GDPR, Data Protection Act 2018, and international data standards.Guiding Principles: Implementing transparency, explainability, accountability, and the "Human-in-the-Loop" (HITL) concept.Regulation: Deep dive into the regulatory landscape, including the EU AI Act (2024) and UK AI Principles.Risk Management: Using frameworks like PESTLE, SWOT, and Cynefin to assess and mitigate AI-related risks (e.g., liability in autonomous vehicles).3. Enablers of Artificial IntelligenceThis m

Course objectives

• to familiarize students with the evolution of AI technology
• to educate on the ethical, privacy, and legal aspects of AI
• to develop an understanding of risk management frameworks in AI