EXIN BCS Artificial Intelligence Essentials
Artificial Intelligence (AI) is a methodology for using a non-human system to learn from experience and imitate human intelligent behavior. The EXIN BCS Artificial Intelligence Essentials exam tests a candidate’s knowledge and understanding of the terminology and the general principles. This syllabus covers the potential benefits; types of Artificial Intelligence; the basic process of Machine Learning (ML); the challenges and risks associated with an AI project, and the future of AI and Humans in work.
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Who should attend?
The Artificial Intelligence Essentials certificate is focused on individuals with an interest in, (or need to implement) AI in an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, or IT services.
Learning Outcome
You will be able to
- Recall the general definition of human and Artificial Intelligence (AI).
- Describe ‘learning from experience’ and how it relates to Machine Learning (ML) (Tom Mitchell’s explicit definition).
- Understand that ML is a significant contribution to the growth of Artificial Intelligence.
- Describe how AI is part of ‘Universal Design,’ and ‘The Fourth Industrial Revolution’.
- Describe the challenges of Artificial Intelligence, and give general examples of the limitations of AI compared to human systems, general ethical challenges AI raises.
- Demonstrate understanding of the risks of Artificial Intelligence, identify a typical funding source for AI projects and list opportunities for AI.
- 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.
Course Content
1. An introduction to artificial intelligence (AI) and historical development
1.1 State the definitions of key AI terms
- Human intelligence
- Artificial intelligence (AI)
- Machine learning
- Scientific method
1.2 Identify key milestones in the development of AI
- Asilomar principles
- Dartmouth conference of 1956
- AI winters
- Big data and the Internet of Things (IoT)
- Large language models (LLMs)
1.3 Identify different types of AI
- Narrow/weak AI
- General/strong AI
2. Ethical and legal considerations
2.1 Identify the role of ethics in AI
- What is ethics?
- Differences between ethics and law
2.2 State key ethical concerns in AI
- Potential for bias, unfairness and discrimination
- Data privacy and protections
- Impact on employment and the economy
2.3 Identify guiding principles in the use of ethical AI
- Safety, security, and robustness
- Transparency and explainability
- Fairness
- Accountability and governance
- Contestability and redress
3. Enablers of AI
3.1 List common examples of AI
- Human compatible
- Internet of Things (IoT)
- Generative AI tools
3.2 Identify robotics in AI
- Definition of robotics: “A machine that can carry out a complex series of tasks automatically, either with or without intelligence.”
- Intelligent or non-intelligent
- Types of robots: Industrial, Personal, Autonomous, Nanobots, Humanoids
- Robotic process automation (RPA)
3.3 Describe machine learning
- Machine learning: “The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.”
- Deep learning: A multi-layered neural network
3.4 Identify common machine learning concepts
- Prediction
- Object recognition
- Classification
- Clustering
- Recommendations
4. Finding and using data in AI
4.1 State key data terms
- Big data
- Data visualization
- Structured data
- Semi-structured data
- Unstructured data
4.2 Identify the characteristics of data quality
- Accuracy
- Completeness
- Uniqueness
- Consistency
- Timeliness
4.3 State the risks associated with handling data in AI
- Bias
- Misinformation
- Processing restrictions
- Legal restrictions
4.4 Identify data visualization techniques and tools
- Written
- Verbal
- Pictorial
- Sounds
- Dashboards and infographics
- Virtual and augmented reality
4.5 State key generative AI terms
- Generative AI: Refers to deep-learning models that can generate high-quality text, images, and other content based on the data they were trained on
- Large language models (LLMs): Deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets
4.6 Identify the use of data in the machine learning process
- Analyze the problem
- Data selection
- Data pre-processing
- Data visualization
- Select a machine learning model (algorithm)
- Train the model
- Test the model
- Repeat (learning from experience to improve results)
- Review
5. Using AI in your organization
5.1 Identify opportunities for AI in your organization
- Opportunities for automation
- Repetitive tasks
- Content creation – generative AI
5.2 Identify project management approaches
- Agile
- Waterfall
- Hybrid
5.3 Identify governance activities associated with implementing AI
- Compliance
- Risk management
- Lifecycle governance
6. Future planning and impact – human plus machine
6.1 Describe the roles and career opportunities presented by AI
- AI-specific roles: machine learning engineer, data scientist, AI research scientist, computer vision engineer, natural language processing (NLP) engineer, robotics engineer, AI ethics specialist, AI anthropologist
- Opportunities for existing roles (additional training, improved efficiency, automation)
6.2 Identify AI uses in the real world
- Marketing
- Healthcare
- Finance
- Transportation
- Education
- Manufacturing
- Entertainment
- IT
6.3 Identify AI’s impact on society
- Benefits of AI
- Challenges of AI
- Societal impact
- Environmental impact (sustainability, climate change)
- Economic impact (job losses, retraining for AI roles)
6.4 Describe the future of AI
- Human and machine working together – augmented roles
- Near and long-term developments in AI (e.g., business automation, chatbots)
- Ethical AI
Certification
| Duration: | 30 minutes |
| Number of Questions: | 20 (Multiple Choice) |
| Pass mark: | 65% |
| Open book: | No |
| Electronic equipment allowed: | No |
| Level: | Foundation |
| ECTS Credits: | 2 |
| Languages: | English |
