How to understand the AI included in business processes in digital era?
Digitalization brings a large amount of various data that should be useful for manager decisions. It is not easy to handle data by conventional Information Systems and we need to follow the rapid development of AI. The course aims to understand AI tools, why knowledge plays a crucial role in many AI applications and how to capture knowledge in Expert systems. It is important not only for IT developers but also for managers, business analytics, and all AI users.
Course title | Artificial Intelligence for Economists | |
| Lecturer | RNDr. Eva Rakovská, PhD. | |
Load of the Course | Seminar participation: 26 hours + Preparation for seminars: 26 hours + Project preparation: 16 hours + Preparation for the final exam: 10 hours | |
Number of credits | 3 | |
Requirements to complete the course: | Final exam - written form, 70% (passing the exam means obtaining 51% from the evaluation of exam). The exam consists of two parts: verification of theoretical knowledge (test with different types of questions). The theoretical part verifies the achieved level of educational results A., D, E, F, G. Second part verified the practical skill how to apply the theoretical knowledge in short exercise. | |
| Seminar: | - individual work and continous test during the course 15%, - working in small teams: elaboration and seminar topic presentation 15%, Together: 30% By evaluating individual work and evaluating work in teams, the following educational results are developed and evaluated: B., C., D., G., H. | |
| Total study load (in hours) | 3 credits x 26 hours= 78 hours | |
| Study load distribution | Seminar participation: 26 hours Preparation for seminars: 26 hours Project preparation: 16 hours Preparation for the final exam: 10 hours | |
Learning outcomes: | Students will gain a deeper insight into AI technologies, how they work in practice, and what task types they are suitable for. They will learn to distinguish data, information, and knowledge, as well as the knowledge properties and representations used in AI. Students will be able to recognize the knowledge-oriented tasks in the business processes, understand the process of knowledge licitation and prepare it for implementation in the Knowledge System. Finally, students will be introduced to the principles of knowledge engineering and the importance of Expert Systems in modern Artificial and Cognitive Intelligence. | |
| 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 12. CASE STUDIES AND ETHICAL CONSIDERATIONS IN AI | |
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