Source: https://www.linkedin.com/feed/update/urn%3Ali%3Ashare%3A6532692178056073217
Non-Equivalence of #Definitions Across #Psychology, IS/IT, #ComputerScience, and, #AI #MachineLearning: The problems of non-equivalence of CONSTRUCTS and MEASURES related to even most fundamental concepts such as Information, Intelligence, Learning, and, Knowledge are pervasive across the SILOS of most of above disciplines. This is the current state of what #ThomasKuhn called #NormalScience as evident across R&D and practices over last two decades. These issues are all the more critical given that many scientists — across academia, policy, and, practice — have yet to span and bridge the schisms such as between SOCIAL SCIENCES and NATURAL SCIENCES and diverse METHODOLOGIES such as Constructivism, Positivism, and, Interpretivism. Having published scientific research ranked for impact among Nobel Laureates by both Business and IT citation impact reports and refereed hundreds of mathematical models for dozens of different academic publishers and scientific journals in and across above disciplines, one finds most DISCIPLINES of #academic and #scientific #research complacently comfortable in their SILOS.
#RESEARCH #DISCIPLINES #METHODS #MODELS
Princeton Presentation: https://lnkd.in/dJ-Gnxx .
R&D: https://lnkd.in/eR6439j .
Thanks to Cassie Kozyrkov for sharing.
