COURSES

Goals

The basic goal is to foster knowledge and capability of applying cognitive methods and techniques into computer and robotic systems. The second goal is to improve knowledge of cognitive approaches and architectures. One of the course objectives is to improve knowledge of interdisciplinary viewpoints on selected cognitive concepts from lower-level to higher-level systems, architectures and modules. Various cognitive techniques and methods including cognitive agents enable constructing computer methods simulating cognitive functions. The students will master the basics of cognitive sciences and will be capable of using selected tools, methods, techniques and architectures of cognitive systems. They will also become advanced users of generative AI.

Curriculum

Scientific Method: Structures of scientific knowledge, scientific activities, and processes. Application of the scientific method in cognitive science, particularly in understanding the mind and intelligence. The impact of artificial intelligence and large language models (LLMs), such as GPT, on scientific research and cognitive processes. Introduction: Introduction to cognitive science as an interdisciplinary study of the mind and intelligence, combining psychology, neuroscience, artificial intelligence, philosophy, and linguistics. Exploration of the mind, consciousness, emotions, the subconscious, qualia, and various psychological and philosophical approaches to the mind. The connection between cognitive science, artificial intelligence, and intelligent systems, with the inclusion of GPT and LLM technologies for modeling cognitive functions. Cognitive Paradoxes and Concepts: Review of key paradigms in cognitive science, such as the Turing Test and its variations (TT, TTT, TTTT), and discussions of Searle’s Chinese Room and Einstein’s book. The mind-body problem and contemporary theories of consciousness. Discussions on LLMs like GPT: Can such models develop consciousness, generative ability, and intelligence? Exploration of the easy and hard problems of consciousness and the impact of large language models on these debates. Review of current trends and the future of AI and their role in cognitive sciences. Cognitive Architectures: Theoretical foundations of cognitive architectures and the role of LLMs in modeling cognitive processes. Overview of different cognitive architectures, including Type 1 and Type 2 systems. Subsystem architectures of cognition and integrated comprehensive architectures. Low- and high-level architectures and their role in simulating cognitive functions using LLMs and GPT models. Cognitive Techniques and Methods: Methods of cognitive neuroscience, such as functional brain imaging, EEG, and other techniques used to study cognitive processes. Cognitive modeling: Using logic, rules, concepts, analogies, associations, and connections. Cognitive agents and the role of artificial intelligence and large language models, such as GPT, in modeling intelligent cognitive systems. Practical application of selected cognitive techniques and tools, such as simulations and predictive analyses enabled by AI and GPT . Practical Training: Practical application of selected cognitive science techniques and tools, including the use of LLMs and GPT to solve cognitive challenges. Developing cognitive models and systems and applying GPT to simulate and analyze cognitive processes in real-time.