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BICSI AIF2.0 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Enablers of Artificial Intelligence | 15% | - Describe machine learning
- 1. Machine learning, neural networks, deep learning and large language models
- Describe supervised and unsupervised learning
- 1. Unsupervised learning using unlabelled data
- 2. Supervised learning using labelled data
- 3. Semi-supervised learning
- Describe the role of robotics in AI
- 1. Industrial, personal, autonomous, nanobot and humanoid robots
- 2. Intelligent and non-intelligent robots
- 3. Robotic process automation
- List common examples of AI
- 1. Human-compatible and wearable AI, edge AI, IoT, personal care, self-driving vehicles and generative AI tools
- Identify common machine learning concepts
- 1. Prediction, object recognition, classification, random decision forests, clustering and recommendations
|
| Topic 2: Using AI in Your Organisation | 20% | - Identify risks, costs and benefits associated with a proposed solution
- 1. Risk acceptance, mitigation, contingency planning, avoidance and transfer
- 2. Risk analysis, assessment, risk owners and risk appetite
- 3. Financial forecasting, costs and benefits, socioeconomic benefits and the triple bottom line
- Identify and categorise stakeholders relevant to an AI project
- 1. Power-interest grids and stakeholder wheels
- 2. Stakeholder definitions and categorisation
- Identify opportunities for AI in your organisation
- 1. Process automation, repetitive tasks and generative AI for content creation
- Describe project management approaches
- 1. Agile, waterfall and hybrid approaches
- List the contents and structure of a business case
- 1. Introduction, executive summary and current-state description
- 2. Recommendations, appendices and supporting information
- 3. Options analysis, costs and benefits, impact assessment and risk assessment
- Describe ongoing governance activities required when implementing AI
- 1. Compliance and risk management
- 2. AI lifecycle governance, management, monitoring and oversight
|
| Topic 3: Ethical and Legal Considerations | 15% | - Explain the role of regulation in AI
- 1. AI regulation, professional standards, UK GDPR and Data Protection Act 2018
- 2. International standards, ISO, NIST and consequences of unregulated AI
- Explain the process of risk management in AI
- 1. Risk analysis, SWOT analysis, PESTLE and Cynefin
- 2. Risk mitigation, ownership, accountability, stakeholder involvement and subject-matter expertise
- Describe the importance of guiding principles in ethical AI development
- 1. UK AI Principles: safety, security, robustness, transparency, explainability, fairness, accountability, governance, contestability and redress
- 2. Ethics, legislation and responsible AI governance
- Describe ethical concerns in AI
- 1. Bias, unfairness, discrimination, data privacy and protection
- 2. Employment, economic impacts, autonomous weapons, autonomous vehicles and liability
- Explain strategies for addressing ethical challenges in AI projects
- 1. Bias management, openness, transparency, trustworthiness and explainability
- 2. Self-interest, self-review, conflicts of interest, intimidation and advocacy
|
| Topic 4: Future Planning and Impact - Human Plus Machine | 15% | - Explain AI's impact on society and the future of AI
- 1. Environmental sustainability, climate change and economic impacts
- 2. Benefits, challenges, potential problems and societal impacts
- 3. Future AI advancements and human-machine collaboration
- Identify AI uses in the real world
- 1. Translation, fraud detection, auditing, credit assessment, autonomous vehicles, chatbots and digital assistants
- Describe AI-related roles and opportunities for existing roles
- 1. Machine learning engineers, data scientists, AI research scientists, computer vision engineers, NLP engineers, robotics engineers and AI ethics specialists
- 2. Additional training, improved efficiency and automation
- Describe consciousness and its impact on ethical AI
- 1. The technological singularity and theories of consciousness
- 2. Functional capabilities versus human consciousness
- 3. Ethical challenges associated with artificial consciousness
- 4. Human consciousness and artificial consciousness
|
| Topic 5: Finding and Using Data in Artificial Intelligence | 20% | - Describe the purpose and use of generative AI, including large language models
- 1. Training data, next-word prediction and human-sounding language generation
- 2. Prompt engineering, natural language processing and image generation
- Describe how data is used to train AI in the machine learning process
- 1. Model and algorithm selection, training, testing, iteration and review
- 2. Problem analysis, data selection, preprocessing and visualisation
- Describe data quality characteristics and their importance in AI
- 1. Accuracy, completeness, uniqueness, consistency and timeliness
- 2. Consequences of poor-quality data, including errors, bias, loss of trust and financial penalties
- Explain data visualisation techniques and tools
- 1. Written, verbal, pictorial and sound-based representations
- 2. Dashboards, infographics, virtual reality and augmented reality
- Describe key generative AI terms
- 1. Generative AI and large language models
- Explain risks associated with handling data in AI and how to minimise them
- 1. Data bias, misinformation, processing restrictions and legal restrictions
- 2. UK GDPR, Data Protection Act 2018, source reliability and the scientific method
- Describe the purpose and use of big data
- 1. Data storage and use, understanding users, process improvement and user experience
- Describe key data terms
- 1. Big data, data visualisation, structured data, semi-structured data and unstructured data
|
| Topic 6: An Introduction to AI and Historical Development | 15% | - Describe key milestones in the development of artificial intelligence
- 1. AI winters, big data, the Internet of Things and large language models
- 2. Asilomar principles and the Dartmouth Conference of 1956
- Describe sustainability measures to reduce the environmental impact of AI
- 1. Algorithm selection, low-code and no-code programming, environmental monitoring and reporting
- 2. Green IT, data-centre energy efficiency and sustainable supply chains
- Describe different types of AI
- 1. Real-world applications, strengths and limitations
- 2. Narrow AI and general AI
- Identify key definitions of artificial intelligence terms
- 1. Human intelligence, artificial intelligence, machine learning and the scientific method
- Explain the impact of AI on society
- 1. Ethical principles and social, economic and environmental impacts
- 2. United Nations Sustainable Development Goals and the EU AI Act
|
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