Come What May, Digital Health Technologies Will Never Be Able to Predict the Emergence of Unknown Viruses and Microorgan
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MOBILE & WIRELESS HEALTH
Come What May, Digital Health Technologies Will Never Be Able to Predict the Emergence of Unknown Viruses and Microorganisms with any Degree of Certainty Arni S. R. Srinivasa Rao 1,2,3 & Steven G. Krantz 4 Received: 31 August 2020 / Accepted: 15 October 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
When there are a finite number of options to play at any stage of a particular game of standard chess played between two players, a deep-learning algorithm can be easily programmed to act like a digital machine to predict the set of all next possible moves by a player. Similar kinds of prediction rules could also be built for other types of online games played between two or multiple players. Deep-learning (DL) combined with an artificial intelligence (AI) framework becomes a powerful tool in several other useful situations in the realworld [1, 2], and we do not minimize such a potential by machines in the future. But predicting and identifying accurately new and unknown microorganisms such as bacteria, viruses, etc., before they emerge with the help of digital technologies equipped with DL algorithms or pre-programmed steps seems to be impossible. This is due to an enormously large number of species possibilities and also very large bounds of uncertainties for emergence of newer species that are involved. Naturally we are interested in the meaning and significance This article is part of the Topical Collection on Mobile & Wireless Health. * Arni S. R. Srinivasa Rao [email protected] Steven G. Krantz [email protected] 1
Health Economics and Modeling, Medical College of Georgia, Augusta, GA, USA
2
Laboratory for Theory and Mathematical Modeling, Division of Infectious Disease Methodology, Medical College of Georgia, Augusta, GA, USA
3
Department of Mathematics, Augusta University, Augusta, GA 30912, USA
4
Department of Mathematics, Washington University in St. Louis, Campus Box 1146, One Brookings Drive, MO 63130 St. Louis, USA
of these ideas in the context of the COVID-19 virus. People seem to be overly optimistic about the efficacy of DL/AI in predicting new viruses. Accurate identification with certainty for the presence of a known microorganism through a digital machine is only possible when the information fed into the machine, before it was employed to identify such a microorganism, is complete. Besides, there should not exist any possibility of emergence of new information to identify that microorganism in addition to the previously noted rules of identification for which a digital machine was developed. Digital technologies should use quantification of the data at a precise level. That facilitates establishing deep mathematical structures and relations between the data points within an AI framework. The power of predictability of machines for the occurrence of events of interest increases (in this case the event of interest will be ‘predicting the emergence of a new microorganism’) with the power of the accurate and complete data that the machine h
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