https://dka.oszk.hu/078300/078357 csabai_istvan_adatintenziv.jpg https://dka.oszk.hu/078300/078357/csabai_istvan_adatintenziv_kiskep.jpg Adatintenzív megközelítés a tudományokban Adatintenzív megközelítés a tudományokban létrehozó Csabai István N felvéve 2021-05-14 2021-05-14 elérhető 2021-04-07 2021-04-07 Az előadás időpontja. prezentáció előadás Prezentáció Könyvtártudomány - prezentáció Networkshop 2021 Videotorium https://kifu.videotorium.hu/hu/recordings/42210 Csabai István Jogvédett Számítástechnika, hálózatok Internetes technológia Számítástechnika, hálózatok Internet használat tudomány tárgyszó/kulcsszó adat tárgyszó/kulcsszó adatfeldolgozás tárgyszó/kulcsszó információáramlás tárgyszó/kulcsszó adatbázis tárgyszó/kulcsszó 2021 időszak Adatintenzív megközelítés a tudományokban Data-intensive approach in sciences ISTVAN CSABAI DEPARTMENT OF PHYSICS OF COMPLEX SYSTEMS ELTE EÖTVÖS LORÁND UNIVERSITY, BUDAPEST Acknowledgement: Ministry of Innovation and Technology NRDI Office, MILAB Artificial Intelligence National Laboratory Program, FIEK_16-1-2016-0005, 2020-4.1.1.-TKP2020, NVKP_16-1-2016-0004, H2020 VEO No. 874735. NETWORKSHOP 2021.04.07 History of (machine) intelligence / data science World Model History of (machine) intelligence / data science World Model History of (machine) intelligence / data science Model Instruments World Natural intelligence Homo Sapiens: Technical Specifications CPU 100 GN (giga-neurons) 7±2 bit Pollack, I. The information of elementary auditory displays. J. Acoust. Soc. Amer., 1952, 24, 745-749. Clock frequency 4-32 Hz CPU cores 1 (male version), 2+ (female v.) CPU speed 0.1 Flops (floating point op. / sec) Memory (short term) 7 +/-2 bits Storage 1TB-2.5PB Power 20 W Camera 576Mpix, 24Hz Touch Yes Display No Speakers Mono GPS No WIFI No Bluetooth No 2G/3G/4G/5G No/No/No/No Latest version update 100 000 BC Main Features : • Find food • Escape predators • Kill enemies • Find mate and reproduce History of (machine) intelligence / data science First "Data Science" Tabulae Rudolphinae (1627), 23 years, History of (machine) intelligence / data science Science - technology - science - technology... Prototype of modern "data science" SLOAN DIGITAL SKY SURVEY: 2.5 terapixel image - 300 million 640 fibers - galaxies - 5 optical bands 1 million spectra 2.5 terapixel image - 300 million 640 fibers- 2.5m 120Mp –> 2.5Tp 5 years:10TB New issue: BIG DATA !!! CfA 1989: 1100 galaxies Huge data tables Scientific goals and researcher’s perspective Queries in data space: e.g. separate stars and galaxies petroMag_i > 17.5 and (petroMag_r > 15.5 or petroR50_r > 2) and(petroMag_r > 0 andg > 0 and r > 0 and i> 0) and ( (petroMag_r extinction_r) < 19.2 and (petroMag_r extinction_r < (13.1 + (7/3) * (dered_g dered_r) + 4 * (dered_r dered_i) 4 * 0.18) ) and ( (dered_r dered_i (dered_g dered_r)/4 0.18) < 0.2) and ( (dered_r dered_i (dered_g dered_r)/4 0.18) > 0.2) and ( (petroMag_r extinction_r + 2.5 * LOG10(2 * 3.1415 * petroR50_r * petroR50_r)) < 24.2) ) or ( (petroMag_r extinction_r < 19.5) and ( (dered_r dered_i (dered_g dered_r)/4 0.18) > (0.45 4 * (dered_g dered_r)) ) and ( (dered_g dered_r) > (1.35 + 0.25 * (dered_r dered_i)) ) ) and ( (petroMag_r extinction_r + 2.5 * LOG10(2 * 3.1415 * petroR50_r * petroR50_r) ) < 23.3 ) ) New skills: Indexing, databases • SDSS data "read through"~1 day • Astronomers should learn: Database programming, computer geometry, search trees,... • Multidimensional-and spherical indexing Modern data science: same trends in biology, environmental sciences, social sciences, ... Not only astronomy: genomics Sanger-sequencing First virus sequence 1977: .X174, 5386nt Nyitray Lászl, Pál Gábor: A biokémia és molekuláris biolgia alapjai (2013) 30 years later: NGS, nanopore Moore's law in genomics Sequencing is getting cheaper. More (public) data available. (HGP) 1990–2003 2020 2030? 13 years / 2,7 billion USD Few days / loss function optimization images -> points in N dim space Loss = number of wrong categorizations (error) Complex systems – complex models To understand complex systems we need complex models Complex models, 2M+ parameters! We need • Huge amount of data to set up, constrain, parametrize the models • Powerful computers and clever algorithms Complex function regression: machine learning! AI Research, Education and Applications @ Ev University Dept. of Physics of Complex Systems • Genetics -> antibiotics resistance Matamoros et al., Pataki et al. 2020. • Mobile sensors -> Parkinson Pataki @DREAM, Laki et al. 2016 • Mosquito images -> vector borne diseases Pataki et al. Sci.Rep. 2021 • Medical imaging -> breast cancer Ribli et al. @DREAM, Sci. Rep. 2018 • Weak lensing map -> cosmology parameters Ribli et al. Nature Astro. 2018, MNRAS 2019 • Explainable AI Ribli et al. in prep. • Control of aging related methylation networks Palla et al. subm. • Pathology images SOTE TKP collab. • Quantum ML • MSc, PhD courses Vector borne diseases: MosquitoAlert image deep learning "Zika, dengue, chikungunya, and yellow fever are all transmitted to humans by Ae. aegypti and Ae. Albopictus." F. Bartumeus et al. http://www.mosquitoalert.com/ False(?) negatives: False(?) positives: Pataki et al. Sci. Rep. 2021. Space weather : whistler detection Language of the genome Pollen monitoring Animal health Deep learning for colorectal cancer pathology Mammography with deep learning (Faster R-CNN ) • Digital Mammography DREAM challenge • 1200 participants • Dezső Ribli, best final result • the only solution with localization • AUC = 0.95 • Publication: Nature Scientific Reports (2018) • 30-th most popular from 17000 articles • New collaborations with hospitals, clinics • more training data • open source plugin • steps towards licensing D. Ribli, A. Horváth, Z. Unger, P. Pollner, and I. Csabai. "Detecting and classifying lesions in mammograms with deep learning." Scientific reports (2018) Explainable AI: automatic classification enhancement Any sufficiently advanced technology is indistinguishable from magic. /Arthur C. Clarke/ Indeed, understanding the laws of mechanics made us able to build pyramids and cathedrals, based on the laws of thermodynamics the invention of the steam engine empowered us to cross oceans and continents and today we all have "seven-league boots" in our garages. Understanding electrodynamics and quantum mechanics brought us the transistor that is at the heart of the Internet and the modern "magic mirrors", the mobile phones. With the advancements of high throughput techniques we may be ready to tackle another frontier: life and intelligence at last, because it is the most sophisticated and complex. End of diseases, much longer healthy life,...? What miracles will the advancements of machine learning bring? And what kind of challenges? NEW PARADIGMS NEED NEW RESEARCHERS EDUCATION: We need new scientist who have professional skills both in their István Csabai ELTE Dept. of Physics of Complex Systems csabai@elte.hu http://complex.elte.hu/~csabai/ angol Dengel Eszter: Az interfész és az információ tárolása https://dka.oszk.hu/060900/060989 PowerPoint prezentáció 38 Microsoft Office PowerPoint 2016 N PDF dokumentum 38 N HTML dokumentum HTML 5 verzió N JPEG képállomány 770x433 72 színes közepesen tömörített Networkshop konferencia 2021 KÉSZ katalogizálás Nagy Zsuzsanna