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<p>Dear Alex, Mark and FIS coleagues,</p>
<p>Thanks a lot for your comments and inputs. I am learning a lot from all of you. From my ignorance computers are logic machines. I am not sure if intuition could be considered a logical way of thinking, somehow yes because it is based in our experience/learning, based in our success/failure (binary output of experiences).</p>
<p>All the best,<br />AJ<br /> </p>
<p> </p>
<p>El 13-03-2018 08:38, Alex Hankey escribió:</p>
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<div dir="ltr">Dear Mark and Alberto,
<div> </div>
<div>Let me propose a radical new input. </div>
<div>The Human intuition is far more </div>
<div>powerful than anything anyone </div>
<div>has previously imagined, except </div>
<div>those who use it regularly. </div>
<div> </div>
<div>It can be strengthen by particular </div>
<div>mental practices, well described </div>
<div>in the literature of Yoga. </div>
<div> </div>
<div>Digital Computing machines are </div>
<div>not capable of this, and although </div>
<div>number crunching is a way for </div>
<div>Technology to assist, it is no substitute </div>
<div>for the highest levels of the human mind. </div>
<div> </div>
<div>Alex </div>
<div> </div>
</div>
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<div class="gmail_quote">On 13 March 2018 at 01:10, Mark Johnson <span><<a href="mailto:johnsonmwj1@gmail.com">johnsonmwj1@gmail.com</a>></span> wrote:<br />
<blockquote class="gmail_quote" style="margin: 0 0 0 .8ex; border-left: 1px #ccc solid; padding-left: 1ex;">Dear Alberto,<br /> <br /> Thank you for this topic – it cuts to the heart of why we think the<br /> study of information really matters, and most importantly, brings to<br /> the fore the thorny issue of technology.<br /> <br /> It has become commonplace to say that our digital computers have<br /> changed the world profoundly. Yet at a deep level it has left us very<br /> confused and disorientated, and we struggle to articulate exactly how<br /> the world has been transformed. Norbert Wiener once remarked in the<br /> wake of cybernetics, "We have changed the world. Now we have to change<br /> ourselves to survive in it". Things haven't got any easier in the<br /> intervening decades; quite the reverse.<br /> <br /> The principal manifestation of the effects of technology is confusion<br /> and ambiguity. In this context, it seems that the main human challenge<br /> to which the topic of information has the greatest bearing is not<br /> "information" per se, but decision. That, in a large part, depends of<br /> hypothesis and the judgement of the human intellect.<br /> <br /> The reaction to confusion and ambiguity is that some people and most<br /> institutions acquire misplaced confidence in making decisions about<br /> "the way forwards", usually invoking some new tool or device as a<br /> solution to the problem of dealing with ambiguity (right now, it's<br /> blockchain and big data). We - and particularly our institutions -<br /> remain allergic to uncertainty. To what extent is "data-ism" a<br /> reaction to the confusion produced by technology? Von Foerster sounded<br /> the alarm in the 1970s:<br /> <br /> "we have, hopefully only temporarily, relinquished our responsibility<br /> to ask for a technology that will solve existent problems. Instead we<br /> have allowed existent technology to create problems it can solve." (in<br /> Von Foerster, H (1981) "Observing Systems")<br /> <br /> With every technical advance, there is an institutional reaction. The<br /> Catholic church reacted to printing; Universities reacted to the<br /> microscope and other empirical apparatus; political institutions<br /> reacted to the steam engine, and so on. Today it is the institution of<br /> science itself which reacts to the uncertainty it finds itself in. In<br /> each case, technology introduces new options for doing things, and the<br /> increased uncertainty of choice between an increased number of options<br /> means that an attenuative process must ensue as the institution seeks<br /> to preserve its identity. Technology in modern universities is a<br /> particularly powerful example: what a stupid use of technology to<br /> reproduce the ancient practices of the "classroom" online?! How<br /> ridiculous in an age of self-publishing that academic journals seek to<br /> use technology to maintain the "scarcity" (and cost) of their<br /> publications through paywalls? And what is it about machine learning<br /> and big data (I'm struggling with this in a project I'm doing at the<br /> moment - the machine learning thing is not all it's cracked up to be!)<br /> <br /> Judgement and decision are at the heart of this. Technologies do not<br /> make people redundant: it is the decisions of leaders of companies and<br /> institutions who do that. Technology does not poison the planet;<br /> again, that process results from ineffective global political<br /> decisions. Technology also sits in the context for decision-making,<br /> and as Cohen and March pointed out in 1971, the process of<br /> decision-making about technology is anything but rational (see "The<br /> Garbage Can Model of Organisational Decision-making"<br /> <a href="https://www.jstor.org/stable/2392088" rel="noreferrer">https://www.jstor.org/stable/<wbr />2392088</a>). Today we see "Blockchain" and<br /> "big data" in Cohen and March's Garbage can. It is the reached-for<br /> "existent technology which creates problems it can solve".<br /> <br /> My colleague Peter Rowlands, who some of you know, puts the blame on<br /> our current way of thinking in science: most scientific methodologies<br /> are "synthetic" - they attempt to amalgamate existing theory and<br /> manifest phenomena into a coherent whole. Peter's view is that an<br /> analytic approach is required, which thinks back to originating<br /> mechanisms. Of course, our current institutions of science make such<br /> analytical approaches very difficult, with few journals prepared to<br /> publish the work. That's because they are struggling to manage their<br /> own uncertainty.<br /> <br /> So I want to ask a deeper question: Effective science and effective<br /> decision-making go hand-in-hand. What does an effective society<br /> operating in a highly ambiguous and technologically abundant<br /> environment look like? How does it use its technology for effective<br /> decision-making? My betting is it doesn't look anything like what<br /> we've currently got!<br /> <br /> Best wishes,<br /> <br /> Mark<br />
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<div class="h5"><br /> On 6 March 2018 at 20:23, Alberto J. Schuhmacher <<a href="mailto:ajimenez@iisaragon.es">ajimenez@iisaragon.es</a>> wrote:<br /> > Dear FIS Colleagues,<br /> ><br /> > I very much appreciate this opportunity to discuss with all of you.<br /> ><br /> > My mentors and science teachers taught me that Science had a method, rules<br /> > and procedures that should be followed and pursued rigorously and with<br /> > perseverance. The scientific research needed to be preceded by one or<br /> > several hypotheses that should be subjected to validation or refutation<br /> > through experiments designed and carried out in a laboratory. The Oxford<br /> > Dictionaries Online defines the scientific method as "a method or procedure<br /> > that has characterized natural science since the 17th century, consisting in<br /> > systematic observation, measurement, and experiment, and the formulation,<br /> > testing, and modification of hypotheses". Experiments are a procedure<br /> > designed to test hypotheses. Experiments are an important tool of the<br /> > scientific method.<br /> ><br /> > In our case, molecular, personalized and precision medicine aims to<br /> > anticipate the future development of diseases in a specific individual<br /> > through molecular markers registered in the genome, variome, metagenome,<br /> > metabolome or in any of the multiple "omes" that make up the present "omics"<br /> > language of current Biology.<br /> ><br /> > The possibilities of applying these methodologies to the prevention and<br /> > treatment of diseases have increased exponentially with the rise of a new<br /> > religion, Dataism, whose foundations are inspired by scientific agnosticism,<br /> > a way of thinking that seems classical but applied to research, it hides a<br /> > profound revolution.<br /> ><br /> > Dataism arises from the recent human desire to collect and analyze data,<br /> > data and more data, data of everything and data for everything-from the most<br /> > banal social issues to those that decide the rhythms of life and death.<br /> > "Information flow" is one the "supreme values" of this religion. The next<br /> > floods will be of data as we can see just looking at any electronic window.<br /> ><br /> > The recent development of gigantic clinical and biological databases, and<br /> > the concomitant progress of the computational capacity to handle and analyze<br /> > these growing tides of information represent the best substrate for the<br /> > progress of Dataism, which in turn has managed to provide a solid content<br /> > material to an always-evanescent scientific agnosticism.<br /> ><br /> > On many occasions the establishment of correlative observations seems to be<br /> > sufficient to infer about the relevance of a certain factor in the<br /> > development of some human pathologies. It seems that we are heading towards<br /> > a path in which research, instead of being driven by hypotheses confirmed<br /> > experimentally, in the near future experimental hypotheses themselves will<br /> > arise from the observation of data of previously performed experiments. Are<br /> > we facing the end of the wet lab? Is Dataism the end of classical<br /> > hypothesis-driven research (and the beginning of data-correlation-driven<br /> > research)?<br /> ><br /> > Deep learning is based on learning data representations, as opposed to<br /> > task-specific algorithms. Learning can be supervised, semi-supervised or<br /> > unsupervised. Deep learning models are loosely related to information<br /> > processing and communication patterns in a biological nervous system, such<br /> > as neural coding that attempts to define a relationship between various<br /> > stimuli and associated neuronal responses in the brain. Deep learning<br /> > architectures such as deep neural networks, deep belief networks and<br /> > recurrent neural networks have been applied to fields including computer<br /> > vision, audio recognition, speech recognition, machine translation, natural<br /> > language processing, social network filtering, bioinformatics and drug<br /> > design, where they have produced results comparable to and in some cases<br /> > superior to human experts. Will be data-correlation-driven research the new<br /> > scientific method for unsupervised deep learning machines? Will computers<br /> > became fundamentalists of Dataism?<br /> ><br /> > Best regards,<br /> ><br /> > AJ<br /> ><br /> ><br /> ></div>
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<span class="im HOEnZb">> ---<br /> > Alberto J. Schuhmacher, PhD.<br /> > Head, Molecular Oncology Group<br /> ><br /> > Aragon Health Research Institute (IIS Aragón)<br /> > Biomedical Research Center of Aragon (CIBA)<br /> > Avda. Juan Bosco 13, 50009 Zaragoza (Spain)<br /> </span><span class="im HOEnZb">> email: <a href="mailto:ajimenez@iisaragon.es">ajimenez@iisaragon.es</a><br /> > Phone:(+34) 637939901<br /> ><br /> > ______________________________<wbr />_________________<br /> > Fis mailing list<br /> > <a href="mailto:Fis@listas.unizar.es">Fis@listas.unizar.es</a><br /> > <a href="http://listas.unizar.es/cgi-bin/mailman/listinfo/fis" rel="noreferrer">http://listas.unizar.es/cgi-<wbr />bin/mailman/listinfo/fis</a><br /> ><br /> <br /> <br /> <br /> </span><span class="HOEnZb"><span style="color: #888888;">--<br /> Dr. Mark William Johnson<br /> Institute of Learning and Teaching<br /> Faculty of Health and Life Sciences<br /> University of Liverpool<br /> <br /> Phone: 07786 064505<br /> Email: <a href="mailto:johnsonmwj1@gmail.com">johnsonmwj1@gmail.com</a><br /> Blog: <a href="http://dailyimprovisation.blogspot.com" rel="noreferrer">http://dailyimprovisation.<wbr />blogspot.com</a><br /> </span></span>
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<div><span style="font-family: arial,sans-serif; font-size: 13px; border-collapse: collapse;">Alex Hankey M.A. (Cantab.) PhD</span><span style="font-family: arial,sans-serif; font-size: 13px; border-collapse: collapse;"> (M.I.T.)<br />Distinguished Professor of Yoga and Physical Science,<br /> SVYASA, Eknath Bhavan, 19 Gavipuram Circle<br /> Bangalore 560019, Karnataka, India <br />Mobile (Intn'l): +44 7710 534195 </span><span style="font-family: arial,sans-serif; font-size: 13px; border-collapse: collapse;"></span>
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<div style="font-size: 12.8px;"><span style="font-family: georgia,serif;"><a style="color: #1155cc;" href="http://www.sciencedirect.com/science/journal/00796107/119/3">2015 JPBMB Special Issue on Integral Biomathics: Life Sciences, Mathematics and Phenomenological Philosophy</a></span></div>
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