TY - JOUR AU - Végh, János TI - Mi is valójában a mesterséges intelligencia? JF - RENDVÉDELEM TUDOMÁNYOS FOLYÓIRAT (ON-LINE) J2 - RENDVÉDELEM VL - 13 PY - 2024 IS - 1 SP - 25 EP - 40 PG - 16 SN - 2560-2349 DO - 10.53793/RV.2024.1.2 UR - https://m2.mtmt.hu/api/publication/34760960 ID - 34760960 AB - A mesterséges intelligencia (MI) az emberek által végzett, nem rutinszerű tevékenységek ellátására készített számítógépes rendszerek neve, de ismertté szöveggenerálási képessége (LLM) révén vált. Meg kell értenünk alapfogalmait és működési elveit, valamint használatának következményeit energiafelhasználási, fenntarthatósági és környezetszennyezési szempontból is. Az MI lehetőségei messze állnak attól, amit feltételeznek, de veszélyei sem akkorák; feltéve, hogy megértjük, helyesen használjuk és szabályozzuk használatát. LA - Hungarian DB - MTMT ER - TY - JOUR AU - Végh, János AU - Berki, Ádám József TI - On the Role of Speed in Technological and Biological Information Transfer for Computations JF - ACTA BIOTHEORETICA J2 - ACTA BIOTHEOR VL - 70 PY - 2022 IS - 4 PG - 25 SN - 0001-5342 DO - 10.1007/s10441-022-09450-6 UR - https://m2.mtmt.hu/api/publication/33199928 ID - 33199928 AB - In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so in real-world implementations, the propagation speed of information cannot exceed the speed of its carrier. Because of this limitation, one must also consider the transfer time between computing units for any implementation. We need a different mathematical method to consider this limitation: classic mathematics can only describe infinitely fast and small computing system implementations. The difference between mathematical handling methods leads to different descriptions of the computing features of the systems. The proposed handling also explains why biological implementations can have lifelong learning and technological ones cannot. Our conclusion about learning matches published experimental evidence, both in biological and technological computing. LA - English DB - MTMT ER - TY - JOUR AU - Végh, János AU - Berki, Ádám József TI - Towards Generalizing the Information Theory for Neural Communication JF - ENTROPY J2 - ENTROPY-SWITZ VL - 24 PY - 2022 IS - 8 PG - 23 SN - 1099-4300 DO - 10.3390/e24081086 UR - https://m2.mtmt.hu/api/publication/33095696 ID - 33095696 AB - Neuroscience extensively uses the information theory to describe neural communication, among others, to calculate the amount of information transferred in neural communication and to attempt the cracking of its coding. There are fierce debates on how information is represented in the brain and during transmission inside the brain. The neural information theory attempts to use the assumptions of electronic communication; despite the experimental evidence that the neural spikes carry information on non-discrete states, they have shallow communication speed, and the spikes' timing precision matters. Furthermore, in biology, the communication channel is active, which enforces an additional power bandwidth limitation to the neural information transfer. The paper revises the notions needed to describe information transfer in technical and biological communication systems. It argues that biology uses Shannon's idea outside of its range of validity and introduces an adequate interpretation of information. In addition, the presented time-aware approach to the information theory reveals pieces of evidence for the role of processes (as opposed to states) in neural operations. The generalized information theory describes both kinds of communication, and the classic theory is the particular case of the generalized theory. LA - English DB - MTMT ER - TY - JOUR AU - Végh, János AU - Berki, Ádám József TI - Why Learning and Machine Learning Are Different JF - Advances in Artificial Intelligence and Machine Learning J2 - AAIML VL - 1 PY - 2021 IS - 2 SP - 136 EP - 154 PG - 19 SN - 2582-9793 DO - 10.54364/AAIML.2021.1109 UR - https://m2.mtmt.hu/api/publication/32649571 ID - 32649571 LA - English DB - MTMT ER - TY - JOUR AU - Végh, János TI - Revising the Classic Computing Paradigm and Its Technological Implementations JF - INFORMATICS (BASEL) J2 - INFORMATICS-BASEL VL - 8 PY - 2021 IS - 4 PG - 26 SN - 2227-9709 DO - 10.3390/informatics8040071 UR - https://m2.mtmt.hu/api/publication/32649565 ID - 32649565 LA - English DB - MTMT ER - TY - JOUR AU - Végh, János TI - Which scaling rule applies to large artificial neural networks. Technological limitations for biology-imitating computing TS - Technological limitations for biology-imitating computing JF - NEURAL COMPUTING & APPLICATIONS J2 - NEURAL COMPUT APPL VL - 33 PY - 2021 SP - 16847 EP - 16864 PG - 18 SN - 0941-0643 DO - 10.1007/s00521-021-06456-y UR - https://m2.mtmt.hu/api/publication/32234513 ID - 32234513 N1 - Funding Agency and Grant Number: National Research, Development and Innovation Fund of Hungary [136496] Funding text: Project No. 136496 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the K funding scheme. AB - Experience shows that cooperating and communicating computing systems, comprising segregated single processors, have severe performance limitations, which cannot be explained using von Neumann’s classic computing paradigm. In his classic “First Draft,” he warned that using a “too fast processor” vitiates his simple “procedure” (but not his computing model!); furthermore, that using the classic computing paradigm for imitating neuronal operations is unsound. Amdahl added that large machines, comprising many processors, have an inherent disadvantage. Given that artificial neural network’s (ANN’s) components are heavily communicating with each other, they are built from a large number of components designed/fabricated for use in conventional computing, furthermore they attempt to mimic biological operation using improper technological solutions, and their achievable payload computing performance is conceptually modest. The type of workload that artificial intelligence-based systems generate leads to an exceptionally low payload computational performance, and their design/technology limits their size to just above the “toy” level systems: The scaling of processor-based ANN systems is strongly nonlinear. Given the proliferation and growing size of ANN systems, we suggest ideas to estimate in advance the efficiency of the device or application. The wealth of ANN implementations and the proprietary technical data do not enable more. Through analyzing published measurements, we provide evidence that the role of data transfer time drastically influences both ANNs performance and feasibility. It is discussed how some major theoretical limiting factors, ANN’s layer structure and their methods of technical implementation of communication affect their efficiency. The paper starts from von Neumann’s original model, without neglecting the transfer time apart from processing time, and derives an appropriate interpretation and handling for Amdahl’s law. It shows that, in that interpretation, Amdahl’s law correctly describes ANNs. LA - English DB - MTMT ER - TY - JOUR AU - Végh, János TI - Why do we need to Introduce Temporal Behavior in Both Modern Science and Modern Computing, with an Outlook to Researching Modern Effects/Materials and Technologies JF - GLOBAL JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY J2 - GLOBAL J COMP SCI TECHN VL - 20 PY - 2021 IS - 1 SP - 13 EP - 29 PG - 17 SN - 0975-4350 DO - 10.34257/GJCSTAVOL20IS1PG13 UR - https://m2.mtmt.hu/api/publication/31862730 ID - 31862730 LA - English DB - MTMT ER - TY - CHAP AU - Végh, János AU - Berki, Ádám József ED - Hamid, R. Arabnia TI - Do we know the operating principles of our computers better than those of our brain? T2 - 2020 International Conference on Computational Science and Computational Intelligence (CSCI) PB - IEEE CY - Piscataway (NJ) SN - 9781728176246 PY - 2020 SP - 668 EP - 674 PG - 7 DO - 10.1109/CSCI51800.2020.00120 UR - https://m2.mtmt.hu/api/publication/32677831 ID - 32677831 LA - English DB - MTMT ER - TY - CHAP AU - Végh, János ED - Hamid, R. Arabnia TI - von Neumann’s missing "Second Draft": what it should contain T2 - 2020 International Conference on Computational Science and Computational Intelligence (CSCI) PB - IEEE CY - Piscataway (NJ) SN - 9781728176246 PY - 2020 SP - 1260 EP - 1264 PG - 5 DO - 10.1109/CSCI51800.2020.00235 UR - https://m2.mtmt.hu/api/publication/32649572 ID - 32649572 LA - English DB - MTMT ER - TY - BOOK AU - Végh, János TI - How deep the machine learning can be PB - Nova Science Publishers CY - New York, New York PY - 2020 SP - 29 UR - https://m2.mtmt.hu/api/publication/31186668 ID - 31186668 LA - English DB - MTMT ER -