Učni načrt predmeta

Predmet:
Kognitivne znanosti
Course:
Cognitive Sciences
Študijski program in stopnja /
Study programme and level
Študijska smer /
Study field
Letnik /
Academic year
Semester /
Semester
Informacijske in komunikacijske tehnologije, 3. stopnja Inteligentni sistemi in robotika 1 1
Information and Communication Technologies, 3rd cycle Intelligent Systems and Robotics 1 1
Vrsta predmeta / Course type
Izbirni / Elective
Univerzitetna koda predmeta / University course code:
IKT3-630
Predavanja
Lectures
Seminar
Seminar
Vaje
Tutorial
Klinične vaje
work
Druge oblike
študija
Samost. delo
Individ. work
ECTS
15 15 15 105 5

*Navedena porazdelitev ur velja, če je vpisanih vsaj 15 študentov. Drugače se obseg izvedbe kontaktnih ur sorazmerno zmanjša in prenese v samostojno delo. / This distribution of hours is valid if at least 15 students are enrolled. Otherwise the contact hours are linearly reduced and transfered to individual work.

Nosilec predmeta / Course leader:
prof. dr. Matjaž Gams
Sodelavci / Lecturers:
Tine Kolenik , dr. Vedrana Vidulin
Jeziki / Languages:
Predavanja / Lectures:
Slovenščina, angleščina / Slovenian, English
Vaje / Tutorial:
Pogoji za vključitev v delo oz. za opravljanje študijskih obveznosti:
Prerequisites:

Zaključen študij druge stopnje s področja informacijskih ali komunikacijskih tehnologij ali zaključen študij druge stopnje na drugih področjih z znanjem osnov s področja predmeta. Potrebna so tudi osnovna znanja matematike, računalništva in informatike.

Completed second-cycle studies in information or communication technologies or completed second-cycle studies in other fields with knowledge of fundamentals in the field of this course. Basic knowledge of mathematics, computer science and informatics is also requested.

Vsebina:
Content (Syllabus outline):

Znanstvena metoda:
Strukture znanstvenega védenja, znanstvene aktivnosti in procesi.
Uporaba znanstvene metode v kognitivnih znanostih, zlasti pri razumevanju uma in inteligence.
Vpliv umetne inteligence in velikih jezikovnih modelov (LLM), kot je GPT, na znanstveno raziskovanje in kognitivne procese.

Uvod:
Uvod v kognitivne znanosti kot interdisciplinarni študij uma in inteligence, ki združuje psihologijo, nevroznanost, umetno inteligenco, filozofijo in lingvistiko.
Raziskovanje uma, zavesti, čustev, podzavesti, kvalij, ter različnih pristopov k psihologiji in filozofiji uma.
Povezava med kognitivnimi znanostmi, umetno inteligenco ter inteligentnimi sistemi, z vključitvijo GPT in LLM tehnologij za modeliranje kognitivnih funkcij.

Kognitivni paradoksi in koncepti:
Pregled ključnih paradigm v kognitivni znanosti, kot so Turingov test, variacije testa (TT, TTT, TTTT), in razprava o Searlovi kitajski sobi ter Einsteinovi knjigi.
Problem telo-duh in sodobne teorije zavesti. Razprava o LLM, kot je GPT: ali lahko takšni modeli razvijejo zavest, generativno sposobnost in inteligenco?
Raziskovanje lahkega in težkega vprašanja zavesti ter vpliv velikih jezikovnih modelov na te razprave.
Pregled trenutnih trendov in prihodnosti AI ter njihove vloge v kognitivnih znanostih.

Kognitivne arhitekture:
Teoretične osnove kognitivnih arhitektur in vloga LLM pri modeliranju kognitivnih procesov.
Pregled različnih kognitivnih arhitektur, vključno s sistemi tipa 1 in tipa 2.
Arhitekture podsistemov kognicije in integrirane celovite arhitekture.
Nizko in visokonivojske arhitekture ter njihova vloga pri simulaciji kognitivnih funkcij, z uporabo LLM in GPT modelov.

Kognitivne tehnike in metode:
Metode kognitivne nevroznanosti, kot so funkcionalno slikanje možganov, EEG in druge tehnike, ki se uporabljajo za proučevanje kognitivnih procesov.
Modeliranje kognicije: uporaba logike, pravil, konceptov, analogij, asociacij in povezav.
Kognitivni agenti in vloga umetne inteligence ter velikih jezikovnih modelov, kot so GPT, pri modeliranju inteligentnih kognitivnih sistemov.
Praktična uporaba izbranih kognitivnih tehnik in orodij, kot so simulacije in napovedne analize, ki jih omogočajo AI in GPT.

Praktično usposabljanje:
Praktična uporaba izbranih tehnik in orodij kognitivnih znanosti, vključno z uporabo LLM in GPT za reševanje kognitivnih izzivov.
Razvijanje kognitivnih modelov in sistemov ter aplikacije GPT za simulacijo in analizo kognitivnih procesov v realnem času.

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.

Temeljna literatura in viri / Readings:

M. W. Eysenck, M. T. Keane. Cognitive Psychology, A Student's Handbook, 8th Edition, 2020, Psychology Press, DOI: 10.4324/9781351058513.
P. Dutta, S. Pal, A. Kumar, K. Cengiz. Artificial Intelligence for Cognitive Modeling: Theory and Practice, 2023, CRC Press.
D. Poeppel, G. R. Mangun, M. S. Gazzaniga. The Cognitive Neurosciences, 6th Edition, 2020, MIT Press, ISBN 9780262356176.
M. T. Banich, R. J. Compton. Cognitive Neuroscience, 2023, Cambridge University Press, DOI: 10.1017/9781108923361.
H. J. Levesque. Common Sense, the Turing Test, and the Quest for Real AI, 2018, MIT Press, ISBN 9780262535205.
Sifatkaur Dhingra, Manmeet Singh, Vaisakh S.B., Neetiraj Malviya, S. S. Gill. Mind Meets Machine: Unravelling GPT-4's Cognitive Psychology, 2023, ArXiv, DOI: 10.48550/arXiv.2303.11436.
Marcel Binz, Eric Schulz. Using Cognitive Psychology to Understand GPT-3, 2022, Proceedings of the National Academy of Sciences, DOI: 10.1073/pnas.2218523120.
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, et al. Sparks of Artificial General Intelligence: Early Experiments with GPT-4, 2023, ArXiv, DOI: 10.48550/arXiv.2303.12712.

Cilji in kompetence:
Objectives and competences:

Razviti znanje in sposobnost konkretne vpeljave kognitivnih metod in tehnik v računalniške programe, softverske ali podprte z robotskimi sistemi, je osnovni cilj predmeta.

Seznanitev z osnovnimi pristopi in arhitekturami je tudi pomemben cilj. Osnovna znanja s področja so dodatni cilj.

Pomembno je razumevanje interdisciplinarnih pogledov na vrsto kognitivnih konceptov, od nižjenivojskih do visokonivojskih kognitivnih sistemov, arhitektur in modelov.

Tehnike in metode kognitivnih modelov omogočajo poznavanje računalniških metod, še posebej kognitivnih agentov.

Študenti bodo obvladali osnove kognitivnih znanosti in bodo usposobljeni za praktično uporabo izbranih orodij, metod, tehnik in arhitektur kognitivnih sistemov. Spoznali se bodo tudi na uporabo orodij generativne umetne inteligence.

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.

Predvideni študijski rezultati:
Intendeded learning outcomes:

Študenti bodo z uspešno opravljenimi obveznostmi tega predmeta pridobili:
- osnove znanstvenega pristopa v kognitivnih znanostih,
- osnovna znanja o kognitivnih znanostih,
- pregled obstoječih konceptov in metod kognitivnih znanosti,
- obvladana uporaba izbranih metod in tehnik kognitivnih sistemov,
- boljše znanje izdelovanja kognitivih IT in AI sistemov,
- usposobljenost za praktično implementiranje kognitivnih sistemov.

Students successfully completing this course will acquire:
- Basic scientific approach in cognitive sciences
- Basic knowledge about cognitive sciences
- Overview of existing contexts and methods in cognitive sciences
- Mastering selected methods and techniques of cognitive systems
- Improved knowledge about designing AI and AI cognitive systems
- Capability of practical use of selected cognitive architectures and systems

Metode poučevanja in učenja:
Learning and teaching methods:

Predavanja, seminar, konzultacije, individualno delo.

Lectures, seminar, consultations, individual work.

Načini ocenjevanja:
Delež v % / Weight in %
Assesment:
Seminarska naloga
80 %
Seminar work
Ustni zagovor
20 %
Oral defense
Reference nosilca / Lecturer's references:
1. T. Kolenik, M. Gams. Persuasive Technology for Mental Health: One Step Closer to (Mental Health Care) Equality?, IEEE Technology and Society Magazine 2021, 40 (1), 80-86, DOI 10.1109/MTS.2021.3056288.
2. T. Kolenik, M. Gams. Intelligent Cognitive Assistants for Attitude and Behavior Change Support in Mental Health: State-of-the-Art Technical Review, Electronics 2021, 10 (11), 1250, DOI 10.3390/electronics10111250,
3. M. Gams, T. Kolenik. Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules, Electronics 2021, 10(4), MDPI, DOI 10.3390/electronics10040514
4. M. Gjoreski, B. Mahesh, T. Kolenik, J. Uwe-Garbas, D. Seuss, H. Gjoreski, M. Luštrek, M. Gams, V. Pejović. Cognitive Load Monitoring with Wearables—Lessons Learned from a Machine Learning Challenge. IEEE Access 2021 9, 103325-103336, DOI 10.1109/ACCESS.2021.3093216
5. M. Gams, S. Kramar. Evaluating ChatGPT’s consciousness and its capability to pass the Turing test : A comprehensive analysis. Journal of computer and communications. 2024, vol. 12, no. 3, str. 219-237. ISSN 2327-5227. DOI: 10.4236/jcc.2024.123014. [COBISS.SI-ID 192374531]