I like being a mad scientist

I like being a mad scientist. Am I a mad scientist? A tiny bit, yes, ‘cause I do research on things just because I feel like. Mind you, me being that mad scientist I like being happens to be practical. Those rabbit holes I dive into prove to have interesting outcomes in real life.

I feel like writing, and therefore thinking in an articulate way, about two things I do in parallel: science and investment. I have just realized these two realms of activity tend to merge and overlap in me. When I do science, I tend to think like an investor, or a gardener. I invest my personal energy in ideas which I think have potential for growth. On the other hand, I invest in the stock market with a strong dose of curiosity. Those companies, and the investment positions I can open therein, are like animals which I observe, try to figure out how not to get killed by them, or by predators that hunt them, and I try to domesticate those beasts.

The scientific thing I am working on is the application of artificial intelligence to studying collective intelligence in human societies. The thing I am working on sort of at the crest between science and investment is fundraising for scientific projects (my new job at the university).

The project aims at defining theoretical and empirical fundamentals for using intelligent digital clouds, i.e. large datasets combined with artificial neural networks, in the field of remote digital diagnostics and remote digital care, in medical sciences and medical engineering. That general purpose translates into science strictly speaking, and into the prospective development of medical technologies.

There is observable growth in the percentage of population using various forms of digital remote diagnostics and healthcare. Yet, that growth is very uneven across different social groups, which suggests an early, pre-popular stage of development in those technologies (Mahajan et al. 2020[i]). Other research confirms that supposition, as judging by the very disparate results obtained with those technologies, in terms of diagnostic and therapeutic effectiveness (Cheng et al. 2020[ii]; Wong et al. 2020[iii]). There are known solutions where intelligent digital cloud allows transforming the patient’s place of stay (home, apartment) into the local substitute of a hospital bed, which opens interesting possibilities as regards medical care for patients with significantly reduced mobility, e.g. geriatric patients (Ben Hassen et al. 2020[iv]). Already around 2015, creative applications of medical imagery appeared, where the camera of a person’s smartphone served for early detection of skin cancer (Bliznuks et al. 2017[v]). The connection between distance diagnostics with the acquisition and processing of image comes as one of the most interesting and challenging innovations to make in the here-discussed field of technology (Marwan et al. 2018[vi]). The experience of COVID-19 pandemic has already showed the potential of digital intelligent clouds in assisting national healthcare systems, especially in optimising and providing flexibility to the use of resources, both material and human (Alashhab et al. 2020[vii]). Yet, the same pandemic experience has shown the depth of social disparities as regards real actual access to digital technologies supported by intelligent clouds (Whitelaw et al. 2020[viii]). Intelligent digital clouds enter into learning-generative interactions with the professionals of healthcare. There is observable behavioural modification, for example, in students of healthcare who train with such technologies from the very beginning of their education (Brown Wilson et al. 2020[ix]). That phenomenon of behavioural change requires rethinking from scratch, with the development of each individual technology, the ethical and legal issues relative to interactions between users, on the one hand, and system operators, on the other hand (Godding 2019[x]).

Against that general background, the present project focuses on studying the phenomenon of tacit coordination among the users of digital technologies in remote medical diagnostics and remote medical care. Tacit coordination is essential as regards the well-founded application of intelligent digital cloud to support and enhance these technologies. Intelligent digital clouds are intelligent structures, i.e. they learn by producing many alternative versions of themselves and testing those versions for fitness in coping with a vector of external constraints. It is important to explore the extent and way that populations of users behave similarly, i.e. as collectively intelligent structures. The deep theoretical meaning of that exploration is the extent to which the intelligent structure of a digital cloud really maps and represents the collectively intelligent structure of the users’ population.

The scientific method used in the project explores the main working hypothesis that populations of actual and/or prospective patients, in their own health-related behaviour, and in their relations with the healthcare systems, are collectively intelligent structures, with tacit coordination. In practical terms, that hypothesis means that any intelligent digital cloud in the domain of remote medical care should assume collectively intelligent, thus more than just individual, behavioural change on the part of users. Collectively intelligent behavioural change in a population, marked by tacit coordination, is a long-term, evolutionary process of adaptive walk in rugged landscape (Kauffman & Levin 1987[xi]; Nahum et al. 2015[xii]). Therefore, it is something deeper and more durable that fashions and styles. It is the deep, underlying mechanism of social change accompanying the use of digital intelligent clouds in medical engineering.

The scientific method used in this project aims at exploring and checking the above-stated working hypothesis by creating a large and differentiated dataset of health-related data, and processing that dataset in an intelligent digital cloud, in two distinct phases. The first phase consists in processing a first sample of data with a relatively simple, artificial neural network, in order to discover its underlying orientations and its mechanisms of collective learning. The second phase allows an intelligent digital cloud to respond adaptively to users behaviour, i.e to produce intelligent interaction with them. The first phase serves to understand the process of adaptation observable in the second phase. Both phases are explained more in detail below.

The tests of, respectively, orientation and mode of learning, in the first phase of empirical research aim at defining the vector of collectively pursued social outcomes in the population studied. The initially collected empirical dataset is transformed, with the use of an artificial neural network, into as many representations as there are variables in the set, with each representation being oriented on a different variable as its output (with the remaining ones considered as instrumental input). Each such transformation of the initial set can be tested for its mathematical similarity therewith (e.g. for Euclidean distance between the vectors of expected mean values). Transformations displaying relatively the greatest similarity to the source dataset are assumed to be the most representative for the collectively intelligent structure in the population studied, and, consequently, their output variables can be assumed to represent collectively pursued social outcomes in that collective intelligence (see, for example: Wasniewski 2020[xiii]). Modes of learning in that dataset can be discovered by creating a shadow vector of probabilities (representing, for example, a finite set of social roles endorsed with given probabilities by members of the population), and a shadow process that introduces random disturbance, akin to the theory of Black Swans (Taleb 2007[xiv]; Taleb & Blyth 2011[xv]). The so-created shadow structure is subsequently transformed with an artificial neural network in as many alternative versions as there are variables in the source empirical dataset, each version taking a different variable from the set as its pre-set output. Three different modes of learning can be observed, and assigned to particular variables: a) cyclical adjustment without clear end-state b) finite optimisation with defined end-state and c) structural disintegration with growing amplitude of oscillation around central states.

The above-summarised first phase of research involves the use of two basic digital tools, i.e. an online functionality to collect empirical data from and about patients, and an artificial neural network to process it. There comes an important aspect of that first phase in research, i.e. the actual collectability and capacity to process the corresponding data. It can be assumed that comprehensive medical care involves the collection of both strictly health-related data (e.g. blood pressure, blood sugar etc.), and peripheral data of various kinds (environmental, behavioural). The complexity of data collected in that phase can be additionally enhanced by including imagery such as pictures taken with smartphones (e.g. skin, facial symmetry etc.). In that respect, the first phase of research aims at testing the actual possibility and reliability of collection in various types of data. Phenomena such as outliers of fake data can be detected then.

Once the first phase is finished and expressed in the form of theoretical conclusions, the second phase of research is triggered. An intelligent digital cloud is created, with the capacity of intelligent adaptation to users’ behaviour. A very basic example of such adaptation are behavioural reinforcements. The cloud can generate simple messages of praise for health-functional behaviour (positive reinforcements), or, conversely, warning messages in the case of health-dysfunctional behaviour (negative reinforcements). More elaborate form of intelligent adaptation are possible to implement, e.g. a Twitter-like reinforcement to create trending information, or a Tik-Tok-like reinforcement to stay in the loop of communication in the cloud. This phase aims specifically at defining the actually workable scope and strength of possible behavioural reinforcements which a digital functionality in the domain of healthcare could use vis a vis its end users. Legal and ethical implications thereof are studied as one of the theoretical outcomes of that second phase.

I feel like generalizing a bit my last few updates, and to develop on the general hypothesis of collectively intelligent, human social structures. In order to consider any social structure as manifestation of collective intelligence, I need to place intelligence in a specific empirical context. I need an otherwise exogenous environment, which the social structure has to adapt to. Empirical study of collective intelligence, such as I have been doing it, and, as a matter of fact, the only one I know how to do, consists in studying adaptive effort in human social structures. 

[i] Shiwani Mahajan, Yuan Lu, Erica S. Spatz, Khurram Nasir, Harlan M. Krumholz, Trends and Predictors of Use of Digital Health Technology in the United States, The American Journal of Medicine, 2020, ISSN 0002-9343, https://doi.org/10.1016/j.amjmed.2020.06.033 (http://www.sciencedirect.com/science/article/pii/S0002934320306173  )

[ii] Lei Cheng, Mingxia Duan, Xiaorong Mao, Youhong Ge, Yanqing Wang, Haiying Huang, The effect of digital health technologies on managing symptoms across pediatric cancer continuum: A systematic review, International Journal of Nursing Sciences, 2020, ISSN 2352-0132, https://doi.org/10.1016/j.ijnss.2020.10.002 , (http://www.sciencedirect.com/science/article/pii/S2352013220301630 )

[iii] Charlene A. Wong, Farrah Madanay, Elizabeth M. Ozer, Sion K. Harris, Megan Moore, Samuel O. Master, Megan Moreno, Elissa R. Weitzman, Digital Health Technology to Enhance Adolescent and Young Adult Clinical Preventive Services: Affordances and Challenges, Journal of Adolescent Health, Volume 67, Issue 2, Supplement, 2020, Pages S24-S33, ISSN 1054-139X, https://doi.org/10.1016/j.jadohealth.2019.10.018 , (http://www.sciencedirect.com/science/article/pii/S1054139X19308675 )

[iv] Hassen, H. B., Ayari, N., & Hamdi, B. (2020). A home hospitalization system based on the Internet of things, Fog computing and cloud computing. Informatics in Medicine Unlocked, 100368, https://doi.org/10.1016/j.imu.2020.100368

[v] Bliznuks, D., Bolocko, K., Sisojevs, A., & Ayub, K. (2017). Towards the Scalable Cloud Platform for Non-Invasive Skin Cancer Diagnostics. Procedia Computer Science, 104, 468-476

[vi] Marwan, M., Kartit, A., & Ouahmane, H. (2018). Security enhancement in healthcare cloud using machine learning. Procedia Computer Science, 127, 388-397.

[vii] Alashhab, Z. R., Anbar, M., Singh, M. M., Leau, Y. B., Al-Sai, Z. A., & Alhayja’a, S. A. (2020). Impact of Coronavirus Pandemic Crisis on Technologies and Cloud Computing Applications. Journal of Electronic Science and Technology, 100059. https://doi.org/10.1016/j.jnlest.2020.100059

[viii] Whitelaw, S., Mamas, M. A., Topol, E., & Van Spall, H. G. (2020). Applications of digital technology in COVID-19 pandemic planning and response. The Lancet Digital Health. https://doi.org/10.1016/S2589-7500(20)30142-4

[ix] Christine Brown Wilson, Christine Slade, Wai Yee Amy Wong, Ann Peacock, Health care students experience of using digital technology in patient care: A scoping review of the literature, Nurse Education Today, Volume 95, 2020, 104580, ISSN 0260-6917, https://doi.org/10.1016/j.nedt.2020.104580 ,(http://www.sciencedirect.com/science/article/pii/S0260691720314301 )

[x] Piers Gooding, Mapping the rise of digital mental health technologies: Emerging issues for law and society, International Journal of Law and Psychiatry, Volume 67, 2019, 101498, ISSN 0160-2527, https://doi.org/10.1016/j.ijlp.2019.101498 , (http://www.sciencedirect.com/science/article/pii/S0160252719300950 )

[xi] Kauffman, S., & Levin, S. (1987). Towards a general theory of adaptive walks on rugged landscapes. Journal of theoretical Biology, 128(1), 11-45

[xii] Nahum, J. R., Godfrey-Smith, P., Harding, B. N., Marcus, J. H., Carlson-Stevermer, J., & Kerr, B. (2015). A tortoise–hare pattern seen in adapting structured and unstructured populations suggests a rugged fitness landscape in bacteria. Proceedings of the National Academy of Sciences, 112(24), 7530-7535, www.pnas.org/cgi/doi/10.1073/pnas.1410631112 

[xiii] Wasniewski, K. (2020). Energy efficiency as manifestation of collective intelligence in human societies. Energy, 191, 116500. https://doi.org/10.1016/j.energy.2019.116500

[xiv] Taleb, N. N. (2007). The black swan: The impact of the highly improbable (Vol. 2). Random house

[xv] Taleb, N. N., & Blyth, M. (2011). The black swan of Cairo: How suppressing volatility makes the world less predictable and more dangerous. Foreign Affairs, 33-39

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