Artificial intelligence and the future of scientific thinking
7405-AC-AI-2
1. Key problems in AI; definition and status of AI as a branch of science; great AI projects.
2. Key AI methods: search in state problems spaces; heuristics, search methods and human thinking.
3. Agents and multi-agent systems, general ideas.
4. Knowledge representation: types of knowledge, role and various forms of logic, semantic networks, frames.
5. Natural language processing, bots, intelligent information search, WATSON question/answer technologies, personal assistant technologies (Apple, Google, Samsung, Amazon etc).
6. Most ambitious AI projects: CyC, SOAR, unified theories of cognition and Brain Inspired Cognitive Architectures, Large Language Models, Large Multimodal Models, chatGPT and Agents
7. Machine learning and knowledge discovery, applications in science. .
Total student workload
The course presents the current state of AI, focusing on methods that may be of use for work in different fields, such as ChatGPT or other large language models. It should give you an idea how AI tools may help in you're work. This will require reading and exploration of Internet resources.
Learning outcomes - knowledge
Students understand:
* artificial intelligence goals, relations to other fields of science;
* sources of inspirations for the development of the field;
* understands basic concepts in this area;
* understands possible applications and social consequences of AI;
* is able to critically assess AI news in media,
* knows basic heuristic methods used in AI for problem solving;
* knows basic methods of knowledge representations and inference schemes;
* understands challenges and methods applied to natural language processing;
*knows basic principles of expert systems and their possible use;
* understands the potential of AI methods for scientific research in various domains.
The course should lead to development of the following outcomes:
K_W01, K_W02, K_W03, K_W04, K_W05, K_W06, K_W07.
Learning outcomes - skills
Students are able to:
• Find and critically assess information in the Internet and in scientific and poular science journals related to the AI;
• summarize main points of the lectures,
• collect and interpret infromation from differnt sources,
• formulate important questions and think about implications of learned material,
• recognize false assumptions about AI that are quite common;
• find interesting AI tools that may be used to help in scientific research.
The course should lead to development of the following outcomes:
K_U01, K_U02, K_U04, K_U06
Learning outcomes - social competencies
Students are:
• aware of the complexity of issues related to artificial intelligence;
• understand the needs to include cognitive and brain sciences in studying AI;
• analyze and assess available information to understand possible implications of the knowledge gained;
• understand the need to follow scientific literature to catch up with rapid progress in this field;
• understand ethical and social problems created by the widespread introduction of AI.
The course should lead to development of the following outcomes:
K_K01, K_K02, K_K03, K_U08, K_U09, K_U11.
Teaching methods
* Lectures,
* reference to the web pages of important projects,
* demonstration of some AI tools.
Expository teaching methods
- participatory lecture
- informative (conventional) lecture
Exploratory teaching methods
- project work
Online teaching methods
- exchange and discussion methods
- content-presentation-oriented methods
Type of course
compulsory course
Prerequisites
None
Course coordinators
Assessment criteria
Short presentation of AI relevance to student's work. All students have to show how they will be able to use various AI tools.
Practical placement
Additional information
Additional information (registration calendar, class conductors,
localization and schedules of classes), might be available in the USOSweb system: