BCS Essentials Certificate in Artificial Intelligence

Course Category: AI and UX
Course Duration: 1 Day
Hours: Hours: 7 Contact Hours

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Course Overview

BCS Essentials Certificate in Artificial Intelligence

Artificial intelligence (AI) has boomed in popularity and use in recent years and is now widely used. It’s transforming industry and the future of technology by enabling systems to learn

and mimic human intelligence.

The BCS Essentials Certificate in Artificial Intelligence provides an introduction into key AI

terminology and tools and what they mean for society.

The syllabus covers the following aspects of AI: its history, ethical and sustainable AI challenges, key AI enablers like data, and the future of AI human interaction in the workplace. This certification offers a broad yet straightforward first step into navigating the constantly evolving AI landscape.

Who is it for?

  • The BCS Essentials Certificate in Artifical intelligence is suitable for individuals with an interest in exploring the basic functions and abilities of AI, and how these could impact an organization, especially those working in areas such as science, engineering, knowledge engineering, finance, or IT services.
  • Middle and senior managers running or assembling teams to create AI dependent applications and services, that need to understand the context of AI in an industrial setting.
  • Roles with a particular interest may be; developers, project managers, product managers, chief information officers, chief finance officers, change practitioners, business consultants and leaders of people.

Entry Requirements

  • There are no entry requirements for this certification.

Learning Outcome

  • Upon completion of the certificate, candidates will recognise:
      • Key terminology in AI.
      • Key legal, ethical and regulatory considerations in AI.
      • The use of AI in an organisation.
      • The potential future impact of AI on society and business.

What Will You Learn?

Develop your knowledge and understanding of:

  • Terminology and general principles, including benefits and types of AI
  • Basic process of machine learning (ML)
  • Challenges and risks associated with an AI project
  • Future of AI and humans in work

Training and Exam Duration

Training: 1 Day

The course material shall be issued on the first day of the course during registration.

Exam: 30 minute ‘closed book’ with 20 multiple choice questions. Pass mark is 65% (13/20)

Course Coverage

1. INTRODUCTION TO AI

1.1 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

2. ETHICAL AND LEGAL CONSIDERATIONS

2.1 Ethics in AI

  1. What is ethics?
  2. Differences between ethics and law.

2.2 Key ethical concerns in AI.

  1. Ethical concerns of AI;
  • Potential for bias, unfairness and discrimination.
  • Data privacy and protection.
  • Impact on employment and the economy.

2.3 Principles in the use of ethical AI

  1. UK AI Principles and other relevant legislation
  • Safety, security and robustness.
  • Transparency and explainability.
  • Fairness
  • Accountability and governance.
  • Contestability and redress.

2. AI governance models including ISO42001

3. ENABLERS OF ARTIFICIAL INTELLIGENCE

3.1 Common examples of AI.

  1. Human compatible.
  2. Internet of Things.
  3. Generative AI tools.

3.2 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. Deep learning

3.4 Common machine learning concepts.

  1. Prediction
  2. Object recognition.
  3. Classification
  4. Clustering
  5. Recommendations

4. FINDING AND USING DATA IN ARTIFICIAL INTELLIGENCE

4.1 Key data terms.

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

4.2 Characteristics of data quality.

  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?

4.3 Risks associated with handling data in AI.

  1. Bias
  2. Misinformation
  3. Processing restrictions
  4. Legal restrictions

4.4 Data visualization technique and tools

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

4.5 Key generative AI terms.

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

4.6 Use of data 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. USING AI IN YOUR ORGANISATION

5.1 AI in your organisation.

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

5.2 Project management approaches

  1. Agile
  2. Waterfall
  3. Hybrid

5.3 Governance activities associated with implementing AI

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

6. FUTURE PLANNING AND IMPACT - HUMAN PLUS MACHINE

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

  1. Benefits of AI.
  2. Challenges of AI.
  3. Potential problems of AI.
  4. Societal impact.
  5. Environmental impact
  6. Economic impact

6.4 Future of AI

  1. Human and machine working together
  2. Near and long-term developments in AI
  3. Ethical AI