EXIN BCS Artificial Intelligence Essentials (AIE) Exam Syllabus

• Human intelligence - “The mental quality that consists of the abilities to learn from experience, adapt to new situations, understand and handle abstract concepts, and use knowledge to manipulate one’s environment.”
• Artificial intelligence (AI) - “Intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals.”
• Machine learning - “The study of computer algorithms that allow computer programs to automatically improve through experience.”
• Scientific method - “An empirical method for acquiring knowledge that has characterized the development of science.”

Guidance

• To build their understanding of AI, it is essential for candidates to recognize the definitions of the key AI terms listed.

• Asilomar principles
• Dartmouth conference of 1956
• AI winters
• Big data and the Internet of Things (IoT)
• Large language models (LLMs)

Guidance

• Candidates will be able to identify these key milestones in the evolution of AI. Asilomar principles are a set of guidelines for responsible AI development. The Dartmouth conference, which took place in 1956, is considered to be the starting point of AI as a field of practice. Candidates should understand the concept of an AI winter, which began in the 1980s through to the rise of big data and the development of generative AI.
• Big data refers to the access to enormous amounts of data from a wide variety of sources, including social media, sensors, and other connected devices. Candidates should understand the widespread use of LLMs in 2022, which made AI a matter of public interest like never before.

• Narrow/weak AI
• General/strong AI

Guidance

• Candidates will be able to identify examples of narrow AI (weak AI) and general AI (strong AI).
• Narrow AI (ANI) also known as weak AI, is task- specific and operates within well-defined domains. Examples include: image recognition, speech recognition, language translation and virtual assistants.
• General AI (AGI) also known as strong AI aims to replicate human intelligence. It is the hypothetical intelligence of a machine that has the capacity to understand or learn any intellectual task that a human being can understand or learn.

• What is ethics?
• Differences between ethics and law

Guidance

• AI offers huge opportunities, however there are also commonly held ethical concerns about its increasingly widespread use.
• Ethics are the moral principles that govern a person’s behavior or the conducting of an activity.
• Candidates will be able to state the general definition of ethics and recognize the differences between ethics and law.

• Ethical concerns of AI:
  - Potential for bias, unfairness and discrimination
  - Data privacy and protections
  - Impact on employment and the economy

Guidance

• Candidates will be able to state and identify common ethical concerns in the use of AI, such as the potential for bias in training data leading to biased output, data protection and privacy concerns, and the long-term impact on jobs.

• UK AI Principles and other relevant legislation
  - Safety, security and robustness
  - Transparency and explainability
  - Fairness
  - Accountability and governance
  - Contestability and redress
• AI governance models including ISO 42001

Guidance

• Candidates will be able to identify the key principles and models as listed.

• Human compatible
• Internet of Things (IoT)
• Generative AI tools

Guidance

• There are countless examples of AI in everyday life, and candidates should be able to list examples of those outlined.

• 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)

Guidance

• Candidates should be able to state the definition of robots as outlined.
• They should know that RPA refers to a machine that can carry out a complex series of tasks automatically, either with or without intelligence, usually with a goal of improving processes.
• Various types of robots exist, and candidates should be familiar with each of these.

• Machine learning - “The field of machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.” (Tom Mitchell)
• Deep learning - a multi-layered neural network.

Guidance

• Candidates should understand that machine learning is a subset of AI, and that deep learning is a type of machine learning.
• AI itself is not a new concept; machine learning is another step in the evolution of AI. Machine learning is used within data science and is the application of algorithms to derive insight from data and big data.

• Prediction
• Object recognition
• Classification
• Clustering
• Recommendations

Guidance

• Machine learning can be used in several contexts to complete different types of tasks. Candidates should be encouraged to explore different examples and applications of machine learning.

• Big data - “extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.” (Dialogic.com)
• Data visualization - “the representation of data through use of common graphics, such as charts, plots, infographics and even animations.” (IBM)
• Structured data is data files organized sequentially or organized serially in a tabular format.
• Semi-structured data is data that does not follow the tabular structure of a relational database but does have some defining or organizational properties which allow it to be analyzed.
• Unstructured data is data that does not follow any pre-defined order or structure.

Guidance

• Candidates should be able to identify the key terminologies listed and recognize them in context.

• Five 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?

Guidance

• Candidates should be able to list the five characteristics of good quality data and the importance of each. Good quality data, which demonstrates all five of these characteristics, provides accurate information about its subject, and in turn, this helps to inform good decision making and reliable business intelligence.

• Bias
• Misinformation
• Processing restrictions
• Legal restrictions

Guidance

• Throughout the data lifecycle, there are various risks to consider, including how data is legally gathered and stored, to ensuring it is processed in line with its intended use, and is free from bias or misinformation.
• Candidates should be aware of these risks and recognize examples of them in context.

• Written
• Verbal
• Pictoral
• Sounds
• Dashboards and infographics
• Virtual and augmented reality

Guidance

• Data visualization is required to format data in a manner which is meaningful and digestible to the intended audience. Good data visualization means that data can be consumed, analyzed, summarized, and used easily, which supports decision making.

• Generative AI - “Refers to deep-learning models that can generate highquality text, images, and other content based on the data they were trained on.” (IBM)
• Large language models (LLMs) - “Deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets.” (IBM)

Guidance

• Candidates should be able to state the definitions of generative AI and LLM and identify them in use.

• Stages of the machine learning process:
  1. Analyze the problem
  2. Data selection
  3. Data pre-processing
  4. Data visualization
  5. Select a machine learning model (algorithm)
  - Train the model
  - Test the model
  - Repeat (learning from experience to improve results)
  6. Review

Guidance

• The machine learning process allows us to define the solution based on the problem that has been identified through the process of data selection, preprocessing, visualization and testing of data with specific algorithms.

• Opportunities for automation
• Repetitive tasks
• Content creation – generative AI

Guidance

• Candidates should be able to identify simple opportunities for AI in an organization, such as an opportunity to automate a process, or minimize the human input into a repetitive task.

• Agile
• Waterfall
• Hybrid

Guidance

• Candidates should be able to identify the key characteristics of these project management approaches and their suitability for a given project.

• Compliance
• Risk management
• Lifecycle governance

Guidance

• The three areas that governance must address are: compliance to satisfy regulations; risk management to proactively detect and mitigate risk; and lifecycle governance to manage, monitor and govern AI models. (10 things governments should know about responsible AI, IBM 2024)

• AI-specific roles including: 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 and knowledge
  - Improved efficiency
  - Automation

Guidance

• AI is a rapidly evolving field, and new roles emerge regularly.
• Candidates will be able to describe the various career opportunities evolving in this field – they will not be assessed on the names or duties of specific job roles.

• Marketing
• Healthcare
• Finance
• Transportation
• Education
• Manufacturing
• Entertainment
• IT

Guidance

• AI tools and services are now part of the real world.
• Candidates will be able to describe practical examples of AI applications in different sectors.

• Benefits of AI
• Challenges of AI
• Potential problems with AI
• Societal impact
• Environmental impact – sustainability, climate change and environmental issues
• Economic impact – Job losses, retraining for new AI roles

Guidance

• AI is evolving rapidly. This rapid technological advancement comes with benefits and challenges at societal level. Candidates should be able to identify these benefits and challenges and their impact on society.
• Benefits include: reducing human error through task automation, processing and analyzing vast amounts of data for informed decisions (AI algorithms) and AI-powered tools in assistance in medical diagnosis.
• Challenges include ethical concerns about algorithm bias and privacy, job loss, lack of creativity and empathy, security risks from hacking, socio-economic inequality, market volatility because of AI-driven trading algorithms and AI systems rapid self-improvement.

• Human and machine working together – augmented roles
• Near and long-term developments in AI e.g., increased business automation, chatbots and digital assistants
• Ethical AI

Guidance

• The future of AI will continue to be shaped by technological advancements e.g., increase in data availability, better algorithms, higher computing power.
• Candidates should be able to identify examples of potential future advancement and direction of AI.