BCS Foundation Certificate in Artificial Intelligence

1.1 Key definitions of key artificial intelligence terms.

1. Human intelligence
2. Artificial intelligence
3. Machine learning
4. Scientific method

1.2 Key milestones in the development of artificial intelligence

1. Asilomar principles.
2. Dartmouth conference of 1956.
3. AI winters.
4. Big data and the internet of things (IoT).
5. Large language models (LLMs).

1.3 Different types of AI.

1. Narrow/weak AI.
2. General/strong AI.

1.4 Impact of AI on society

1. Ethical principles.
2. Social impact .
3. Economic impact.
4. Environmental impact.
5. UN 17 Sustainable Development Goals (SDGs). f. EU AI Act (2024)

1.5 Sustainability measures to help reduce the environmental impact of AI

1. Green IT initiatives.
2. Data centre energy and efficiency.
3. Sustainable supply chain.
4. Choice of algorithm.
5. Low-code/no-code programming.
6. Monitoring and reporting environmental impact.

2.1 Ethical concerns, including bias and privacy, in AI

1. What is ethics?
2. Differences between ethics and law.
3. Ethical concerns:

• potential for bias, unfairness, and discrimination.
• data privacy and protection.
• impact on employment and the economy.
• autonomous weapons.
• autonomous vehicles and liability framework.

2.2  The Importance of guiding principles in ethical AI development.

1. UK AI Principles and other relevant legislation.

• Safety, security and robustness.
• Transparency and explainability.
• Fairness.
• Accountability and governance.
• Contestability and redress.

2. What is ethics?

2.3 Strategies for addressing ethical challenges in AI projects.

1. Challenges:

• Self-interest.
• Self-review.
• Conflict of interest.
• Intimidation.
• Advocacy.

2. Strategies:

• Dealing with bias.
• Openness.
• Transparency.
• Trustworthiness.
• Explainability.

2.4 The role of regulation in AI.

1. The need for regulation.
2. The AI regulation landscape, e.g. WCAG.
3. Data Protection Act 2018 and UK GDPR.
4. International Standards Organisation (ISO, NIST).
5. The consequences of unregulated AI.

2.5 The process of risk management in AI.

1. Risk:

• Risk definition
• Risk management

2. Techniques:

• Risk analysis.
• SWOT analysis.
• PESTLE.
• Cynefin.

3. Navigate AI-related regulations and standards:

• UK AI Principles.

4. Risk mitigation strategies:

• Ownership and accountability.
• Stakeholder involvement.
• Subject matter experts

3.1 Common examples of AI.

1. Human compatible.
2. Wearable.
3. Edge.
4. Internet of Things.
5. Personal care.
6. Self-driving vehicles.
7. Generative AI tools.

3.2 Role of robotics in AI.

1. Definition of robotics
2. Intelligent or non-intelligent.
3. Types of robots:

• Industrial.
• Personal.
• Autonomous.
• Nanobots.
• Humanoids.

4. Robotic process automation (RPA).

3.3 Machine learning.

1. Machine learning
2. Neural networks
3. Deep learning
4. Large language models

3.4 Common machine learning concepts.

1. Prediction.
2. Object recognition.
3. Classification including random decision forests.
4. Clustering.
5. Recommendations (e.g. Netflix, Spotify)

3.5 Supervised and unsupervised learning.

1. Supervised learning.
2. Unsupervised learning.
3. Semi-supervised learning

4.1 Key data terms.

1. Big data
2. Data visualisation
3. Structured data

4.2 Characteristics of data quality and why it is important in AI.

1. 5 data quality characteristics:

• Accuracy – is it correct?
• Completeness – is it all there?
• Uniqueness – is it free from duplication?
• Consistency – is it free from conflict?
• Timeliness – is it current and available?

2. Data is money.

3. Data provides insight and supports decision making.

4. Implications of poor quality data can be:

• Errors and inaccuracies.
• Bias.

4.3 Risks associated with handling data in AI and how to minimise them.

1. Bias:

• Multiple sources.
• Diversity in people handling data and training AI.
• Fairness metrics.

2. Misinformation:

• Checking the reliability of sources.
• SME checks.

3. Processing restrictions:

• Organisational requirements.
• Frameworks and regulations.

4. Legal restrictions:

• UK GDPR.
• DPA 2018.
• Staying abreast of new requirements.

5. The scientific method

4.4 Purpose and use of big data.

1. Storage and use.
2. Understanding the user.
3. Improving prcoess.
4. Improving experience

4.5 Data visualisation techniques and tools.

1. Written.
2. Verbal.
3. Pictoral.
4. Sounds.
5. Dashboards and infographics.
6. Virtual and augmented reality.

4.6 Key generative AI terms.

1. Generative AI
2. Large languge models (LLMs)

4.7 Purpose and use of generative AI including large language models.

1. Trained on huge volumes of data.
2. Uses training to predict next word in text.
3. Generates coherent and human-sounding language.
4. Prompt engineering.
5. Natural language processing.
6. Image generation.

4.8 How data is used to train AI in the Machine Learning process.

1. Stages of the Machine Learning process:

• Analyse the problem.
• Data Selection.
• Data Pre-processing.
• Data Visualisation.
• Select a Machine Learning model (algorithm).

– Train the model.

– Test the model.

– Repeat (Learning from experience to improve results).

• Review.

5.1 Opportunities for AI in your organisation.

1. Opportunities for automation.
2. Repetitive tasks.
3. Content creation – generative AI.

5.2 Contents and structure of a business case.

1. Introduction.
2. Management or executive summary.
3. Description of current state.
4. Options considered.

• Option described.
• Analysis of costs and benefits.
• Impact assessment.
• Risk assessment.

1. Recommendations.
2. Appendices/supporting information.

5.3 Categorise stakeholders relevant to an AI project.

1. Stakeholder definition.
2. Stakeholder categorisation.

• Power/interest grid.
• Stakeholder wheel.

5.4 Project management approaches.

1. Agile.
2. Waterfall.
3. Hybrid

5.5 Risks, costs and benefits associated with a proposed solution.

1. Risk analysis.

• Risk assessment.
• Risk owners.

1. Risk appetite.
2. Risk management strategies.

• Accept.
• Mitigate (including sharing, contingency planning).
• Avoid.
• Transfer.

1. Financial costs and benefits.

• Forecasting.
• Margin for error.

1. Socio-economic benefits.
2. Triple bottom line

5.6 Governance activities required when implementing AI.

1. Compliance.
2. Risk management.
3. Lifecycle governance.

• Manage.
• Monitor.
• Govern.

6.1 Roles and career opportunities presented by AI.

1. AI specific roles
2. Opportunities for existing roles.

• Additional training and knowledge.
• Improved efficiency.
• Automation

6.2 AI uses in the real world.

1. Marketing.
2. Healthcare.
3. Finance.
4. Transportation.
5. Education.
6. Manufacturing.
7. Entertainment.
8. IT

6.3 AI’s impact on society, and the future of AI.

1. Benefits of AI.
2. Challenges of AI.
3. Potential problems of AI.
4. Societal impact.
5. Environmental impact – sustainability, climate change and environmental issues.
6. Economic impact – job losses, retraining for new AI roles.
7. Potential future advancements and direction of AI.
8. Human plus machine

6.4 Consciousness and its impact on ethical AI.

1. What is human consciousness?
2. What is AI consciousness?
3. Kurzwell Singularity
4. Functional capabilities v human consciousness.
5. AI projects in light of ethical considerations and consciousness.
6. Ethical challenges associated with artificial consciousness