Prof. Nikola K. Kasabov will give his plenary talk after the conference opening ceremony on 14 May 2026. We are looking forward to hear about Generative-, Predictive and Agentic AI: All they need is neural networks.
Professor Nikola K. Kasabov is a Life Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK. He has Doctor Honoris Causa from Obuda University, Budapest. He is the Founding Director of KEDRI and Professor Emeritus at the School of Engineering, Computing and Mathematical Sciences at Auckland University of Technology, New Zealand. He is also Visiting Professor at the Institute for Information and Communication Technologies of the Bulgarian Academy of Sciences and Dalian University, China. Kasabov is Director of Knowledgeengineering.ai and member of the advisory board of Conscium.com. He is Past President of the Asia Pacific Neural Network Society (APNNS) and the International Neural Network Society (INNS). Kasabov holds MSc in computer engineering and PhD in mathematics from TU Sofia. His main research interests are in the areas of neural networks, intelligent information systems, soft computing, neuroinformatics, spiking neural networks. He has published more than 750 publications, highly cited internationally. He has extensive academic experience at various academic and research organisations in Europe and Asia. Kasabov has received a number of awards, among them: INNS Ada Lovelace Meritorious Service Award; NN journal Best Paper Award; APNNA ‘Outstanding Achievements Award’; INNS Gabor Award for ‘Outstanding contributions to engineering applications of neural networks’; EU Marie Curie Fellowship; Medal “Bacho Kiro” and Honorary Citizen of Pavlikeni, Bulgaria; Honorary Member of the Bulgarian-, the Greek- and the Scottish Societies for Computer Science. More information of Prof. Kasabov can be found on: https://academics.aut.ac.nz/nkasabov and on https://knowledgeengineering.ai.
Generative-, Predictive and Agentic AI: All they need is neural networks
Significant advances in AI, including generative-, predictive and agentic AI, have been achieved due to the use neural networks. Most recent advances are based on a class of neural networks – brain-inspired spiking neural networks (SNN). SNN and their neuromorphic hardware platforms, have proved its efficiency not only in their minimal power consumption and massive parallelism, but in adaptive and predictive modelling, due to their spike-based/event-based information processing [1, 2]. The talk presents how these techniques can be used now to build more efficient Generative, Predictive and Agentic AI. Generative AI, such as LLM, generate new information based on pretrained neural network models. The use of SNN makes them more efficient. Predictive AI predict events in a future time and SNN have been used due to their predictive coding feature. Agentic AI designs AI agents that are autonomous entities, able to evolve itself from data, make decisions, take actions, adapt to the environment, communicate with other agents. SNN are fit for this task too. The talk presents current methods, systems, their applications, along with current EU projects and future directions [3].
- K. Kasabov, N., Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer-Nature (2019) 750p., https://doi.org/10.1007/978-3-662-57715-8
- K. Kasabov, “NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data,” Neural Networks, 52, pp. 62–76, 2014, https://doi.org/10.1016/j.neunet.2014.01.006.
- R K Jha, N Kasabov, et al, A hybrid spiking neural network - quantum framework for spatio-temporal data classification: a case study on EEG data, EPJ Quantum Technologies, (2025) 12:130, 1-23, https://doi.org/10.1140/epjqt/s40507-025-00443-1.