<div dir="ltr">Dear Mark and Alberto, <div><br></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><br></div><div>It can be strengthen by particular </div><div>mental practices, well described </div><div>in the literature of Yoga. </div><div><br></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><br></div><div>Alex </div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On 13 March 2018 at 01:10, Mark Johnson <span dir="ltr"><<a href="mailto:johnsonmwj1@gmail.com" target="_blank">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" target="_blank">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>
<div class="HOEnZb"><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>
><br>
</div></div><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>
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><br>
<br>
<br>
<br>
</span><span class="HOEnZb"><font 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" target="_blank">http://dailyimprovisation.<wbr>blogspot.com</a><br>
</font></span><div class="HOEnZb"><div class="h5"><br>
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</div></div></blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><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><div><span style="font-family:arial,sans-serif;font-size:13px;border-collapse:collapse">Mobile (India) +91 900 800 8789</span></div><div><span style="font-family:arial,sans-serif;font-size:13px;border-collapse:collapse"><div style="font-size:12.8px">____________________________________________________________</div><div style="font-size:12.8px"><span style="font-family:georgia,serif"><br></span></div><div style="font-size:12.8px"><span style="font-family:georgia,serif"><a href="http://www.sciencedirect.com/science/journal/00796107/119/3" style="color:rgb(17,85,204)" target="_blank">2015 JPBMB Special Issue on Integral Biomathics: Life Sciences, Mathematics and Phenomenological Philosophy</a></span></div></span></div></div></div></div>
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