AI Basics exam

This certification focuses on giving learners a clear understanding of what AI is, how it functions, and its practical applications—along with the benefits and potential risks.

Key Topics Covered in the exam:

• What is AI?
  Explains the differences between human and artificial intelligence, examines Robert Dilt’s logical levels, and connects these concepts to various types of AI. It also discusses the impact of the 4th industrial revolution.
  Weight: 10%
• Managing Data for AI
  Introduces basic data literacy, explains different data types, and covers the importance of data semantics and syntax. It also addresses privacy concerns and the potential insights that can be derived from data.
  Weight: 15%
• Ethics, Risks, and Trustworthiness
  Explores the ethical challenges and risks associated with AI, including the importance of explainable AI. It introduces EU ethical guidelines and legislation, emphasizing the need to maintain public trust in AI technologies.
  Weight: 20%
• Predictions, Algorithms, Machine, and Deep Learning
  Provides an overview of various levels of prediction, key algorithms, common problem types, and generative AI models. It highlights which algorithms are best suited for specific types of problems.
  Weight: 30%
• Building and Assessing an AI Application
  Outlines a basic approach to developing a simple AI application using the CRISP-DM methodology. It emphasizes the importance of understanding the business context and assessing reliability at each step of the process.
  Weight: 25%

The AI for Business Certification focuses on raising awareness and understanding of AI's components while providing guidelines to help businesses, governments, and society adopt and apply AI responsibly. It covers both the advantages of AI and the ethical considerations and risks that come with it.

Upon completing the certification exam, participants will:

• Understand what AI is, why it matters today, and the benefits it offers.
• Be familiar with fundamental concepts like robotics, algorithms, and machine learning.
• Know how to organize a team for AI projects, including necessary approaches and tools.
• Recognize the crucial role of data in AI and how to manage it effectively.
• Be able to assess risks and ethical dilemmas associated with AI to ensure trustworthiness.
• Be aware of the future directions and developments in AI.

About the AI Coalition (AIC NL):

The AI Coalition is a collaborative effort in the Netherlands that promotes the responsible use of AI. It brings together government, businesses, educational institutions, and communities to accelerate AI development and foster collaboration across different initiatives.