Artificial Intelligence for Economists

Indicative content

1. AI HISTORY AND THE DEVELOPMENT OF BRANCHES IN AI
Introduction to artificial intelligence, history, Alan Turing is an important person in computer science and artificial intelligence. Definition and concepts of AI, the importance of AI in practice, AI future and some ethical aspects of using AI.
2. AI TECHNOLOGIES AND THEIR PRACTICAL USAGE
AI technologies: what is behind the terms like machine learning, natural language processing, virtual reality, computer vision, evolutionary algorithm, knowledge and expert systems, etc. Classification of the technologies and their usage
3. WHAT IS KNOWLEDGE AND ITS IMPORTANCE IN AI
Definition of the terms intelligence and knowledge. Definition of the terms data, information, knowledge, competencies in the company and their connection with the structuring in informatics (Beckmann`s hierarchy). Necessity of knowledge in AI algorithms. Searching solutions to problems by using heuristics.
4. VARIOUS VIEWS ON KNOWLEDGE CLASSIFICATION
Knowledge classification from various points of view. Explicit vs. Tacit knowledge (Nonaka, Takeuchi spiral), How to externalize the tacit knowledge. Knowledge life cycle within the enterprise.
5. HOW WE CAN IMPLEMENT THE KNOWLEDGE INTO THE COMPUTER
Computer knowledge representation (from logic to rule-based representation; from semantic nets to frame-based representation; procedural representation). Students' work on assignments.
6. KNOWLEDGE SYSTEM AND EXPERT SYSTEM – ARCHITECTURE AND WORKING
Agent in AI, types of agents, and knowledge-based agent architecture. Importance of declarative programming in AI. The definition and features of expert systems and a short description of historical expert systems.
7. BASIC PRINCIPLES OF KNOWLEDGE ENGINEERING – KNOWLEDGE CAPTURING
Knowledge engineering. Importance of knowledge acquistion. The persons involved in the development process (the role of knowledge engineer, expert, software engineer, etc.) How recognize the knowledge tasks within the business processes. Students' work on assignments.
8. PROCESS OF KNOWLEDGE ENGINEERING AND THE DIFFERENCE BETWEEN INFORMATION SYSTEM AND EXPERT SYSTEM
The main phases of IT development and specific features of knowledge and expert systems development. Two approaches to expert system development (linear vs. incremental life cycle of expert system). How to cancel the communication gap between managers, users and IT developers.
9. EXPERT SYSTEMS -PAST AND FUTURE IN PRACTICE
Examples of expert systems, Business rule engines and other applications of knowledge-based systems in current AI (explainable AI)
10. COGNITIVE COMPUTING AND ARTIFICIAL INTELLIGENCE
Definition of cognitive intelligent systems, their importance as tools for handling complex information, enhancing decision-making, and adapting to dynamic environments. Students' work on assignments concerning generative AI and Synthesia
11. PRACTICAL EXERCISES IN KNOWLEDGE REPRESENTATION
Students will work on practical exercises focused on different approaches to representing knowledge in computers. They will design simple business rules, domain ontologies. Students will analyze real-world case studies of artificial intelligence implementation across various fields (medicine, finance, industry, security).
12. CASE STUDIES AND ETHICAL CONSIDERATIONS IN AI The session will include discussions on ethical aspects of AI, such as responsibility for AI-driven decisions, bias in algorithms, and regulatory frameworks in practice. Students will engage in critical analysis and debate ethical dilemmas based on specific examples.
13. AI IN BUSINESS AND INDUSTRY – APPLICATIONS AND CHALLENGES This session will focus on the real-world applications of AI in business and industry, exploring how companies leverage AI for automation, decision-making, and innovation. Key topics will include AI-driven marketing, predictive analytics, supply chain optimization, and customer service automation.