BCS - BCS Foundation — AI and the Digital Ecosystem

Curriculum

Module 1: Key components of AI (20%)

• Recall the general definition of human intelligence.
• State a general definition of artificial intelligence and how it relates to human intelligence.
• Describe how the following are used within an application of Artificial intelligence; Machine Learning, Deep Learning, Data Science, Knowledge-Based systems , Robotics and Automation: Machine Learning
• Deep Learning Data Science
• Knowledge-Based Systems
• Robotics and Automation

Module 2: AI and the digital ecosystem (20%)

• Explain what a digital ecosystem is:
• The purpose of an ecosystem Components of a digital ecosystem Digital Transformation Digitisation – e.g. paperless office, credit cards, e-commerce, online banking, apple/google pay, personal assistants
• Fundamental enabler (human plus machine systems)
• The Internet of Things, big data, smartphones/smart offices, working from home

• Describe an ecosystem mindset: Collaboration Mastery
• Recognising your company strengths Trust Openness Integrity
• Identify how AI can facilitate a digital ecosystem that works intuitively:
• Integrated systems Customer experience Efficient processes
• Data architecture – one true source of data Automation Learning from experience User stories

• Discuss why you might make use of AI to create a digital ecosystem as a driver or enabler:
• Personal assistants e.g. Siri, Alexa Autonomous vehicles (5 levels of autonomy)
• Creating media e.g. movies, music Smart energy e.g. smart meters
• Smart infrastructure e.g. homes, offices, roads Healthcare e.g. monitoring machines, smart wearable technology Social care e.g. care assistants, keeping the customer independent Education e.g. learning assistant Engineering e.g. digital twin concept

Module 3: The relationship between AI and Data (30%)

• Explain data infrastructure with specific relevance to Big Data:
• What is Big Data?
• Why is Big Data difficult to work with?
• Challenges and risks surrounding Big Data Benefits and uses - how can we take advantage of Big Data?

• Describe the following concepts within data architecture:
• Labelled or unlabelled data
• Missing data
• Encoding data
• Cleaning data
• Structured or unstructured data

• Suggest ways in which AI can support in the utilisation of Big Data:
• Machine Learning
• Deep Learning
• Business Analytics

• Recognise different approaches used within the testing of AI systems:
• Regulations and standards
• Stakeholder buy-in and ownership
• User acceptance criteria
• Use of AI in testing automation – AI led quality assurance
• Ongoing feedback through continuous testing and deployment (agile approach) i.e. ‘learning from experience’.

Module 4: The potential future of AI (30%)

• Identify industries that can invest in AI
• Local transmission/ interaction (5G)
• Health Financial Engineering Retail Education

• Recognise the role of AI in raising human intelligence:
• Intelligence Augmentation

• Explain the concept of digital singularity:
• Machine Intelligence equal to Human intelligence
• How close are we to singularity?

• Explain the need for individuals and organisations to maintain and update their skills and knowledge in relation to current and emerging technologies:
• Up and coming computing technologies
• Quantum computing and mechanics
• Advancements in coding
• Changes in technology
• Risk of being left behind
• Competitive edge
• Cloud computing

• Identify the role the IT professional plays in supporting a utopian future with AI:
• What is a utopia?
• Importance of an ethics first approach
• Understanding the power IT professionals have Awareness of risks associated with AI Human compatible AI