[Fis] Is Dataism the end of classical hypothesis-driven research and the beginning of data-correlation-driven research?

Mon Mar 19 07:47:10 CET 2018

Dear Alex, Mark and FIS coleagues, 

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). 

All the best,
AJ

El 13-03-2018 08:38, Alex Hankey escribió:

> Dear Mark and Alberto,  
> 
> Let me propose a radical new input.  
> The Human intuition is far more  
> powerful than anything anyone  
> has previously imagined, except  
> those who use it regularly.  
> 
> It can be strengthen by particular  
> mental practices, well described  
> in the literature of Yoga.  
> 
> Digital Computing machines are  
> not capable of this, and although  
> number crunching is a way for  
> Technology to assist, it is no substitute  
> for the highest levels of the human mind.  
> 
> Alex  
> 
> On 13 March 2018 at 01:10, Mark Johnson <johnsonmwj1 at gmail.com> wrote:
> 
>> Dear Alberto,
>> 
>> Thank you for this topic - it cuts to the heart of why we think the
>> study of information really matters, and most importantly, brings to
>> the fore the thorny issue of technology.
>> 
>> It has become commonplace to say that our digital computers have
>> changed the world profoundly. Yet at a deep level it has left us very
>> confused and disorientated, and we struggle to articulate exactly how
>> the world has been transformed. Norbert Wiener once remarked in the
>> wake of cybernetics, "We have changed the world. Now we have to change
>> ourselves to survive in it". Things haven't got any easier in the
>> intervening decades; quite the reverse.
>> 
>> The principal manifestation of the effects of technology is confusion
>> and ambiguity. In this context, it seems that the main human challenge
>> to which the topic of information has the greatest bearing is not
>> "information" per se, but decision. That, in a large part, depends of
>> hypothesis and the judgement of the human intellect.
>> 
>> The reaction to confusion and ambiguity is that some people and most
>> institutions acquire misplaced confidence in making decisions about
>> "the way forwards", usually invoking some new tool or device as a
>> solution to the problem of dealing with ambiguity (right now, it's
>> blockchain and big data). We - and particularly our institutions -
>> remain allergic to uncertainty. To what extent is "data-ism" a
>> reaction to the confusion produced by technology? Von Foerster sounded
>> the alarm in the 1970s:
>> 
>> "we have, hopefully only temporarily, relinquished our responsibility
>> to ask for a technology that will solve existent problems. Instead we
>> have allowed existent technology to create problems it can solve." (in
>> Von Foerster, H (1981) "Observing Systems")
>> 
>> With every technical advance, there is an institutional reaction. The
>> Catholic church reacted to printing; Universities reacted to the
>> microscope and other empirical apparatus; political institutions
>> reacted to the steam engine, and so on. Today it is the institution of
>> science itself which reacts to the uncertainty it finds itself in. In
>> each case, technology introduces new options for doing things, and the
>> increased uncertainty of choice between an increased number of options
>> means that an attenuative process must ensue as the institution seeks
>> to preserve its identity. Technology in modern universities is a
>> particularly powerful example: what a stupid use of technology to
>> reproduce the ancient practices of the "classroom" online?! How
>> ridiculous in an age of self-publishing that academic journals seek to
>> use technology to maintain the "scarcity" (and cost) of their
>> publications through paywalls? And what is it about machine learning
>> and big data (I'm struggling with this in a project I'm doing at the
>> moment - the machine learning thing is not all it's cracked up to be!)
>> 
>> Judgement and decision are at the heart of this. Technologies do not
>> make people redundant: it is the decisions of leaders of companies and
>> institutions who do that. Technology does not poison the planet;
>> again, that process results from ineffective global political
>> decisions. Technology also sits in the context for decision-making,
>> and as Cohen and March pointed out in 1971, the process of
>> decision-making about technology is anything but rational (see "The
>> Garbage Can Model of Organisational Decision-making"
>> https://www.jstor.org/stable/2392088 [1]). Today we see "Blockchain" and
>> "big data" in Cohen and March's Garbage can. It is the reached-for
>> "existent technology which creates problems it can solve".
>> 
>> My colleague Peter Rowlands, who some of you know, puts the blame on
>> our current way of thinking in science: most scientific methodologies
>> are "synthetic" - they attempt to amalgamate existing theory and
>> manifest phenomena into a coherent whole. Peter's view is that an
>> analytic approach is required, which thinks back to originating
>> mechanisms. Of course, our current institutions of science make such
>> analytical approaches very difficult, with few journals prepared to
>> publish the work. That's because they are struggling to manage their
>> own uncertainty.
>> 
>> So I want to ask a deeper question: Effective science and effective
>> decision-making go hand-in-hand. What does an effective society
>> operating in a highly ambiguous and technologically abundant
>> environment look like? How does it use its technology for effective
>> decision-making? My betting is it doesn't look anything like what
>> we've currently got!
>> 
>> Best wishes,
>> 
>> Mark
>> 
>> On 6 March 2018 at 20:23, Alberto J. Schuhmacher <ajimenez at iisaragon.es> wrote:
>>> Dear FIS Colleagues,
>>> 
>>> I very much appreciate this opportunity to discuss with all of you.
>>> 
>>> My mentors and science teachers taught me that Science had a method, rules
>>> and procedures that should be followed and pursued rigorously and with
>>> perseverance. The scientific research needed to be preceded by one or
>>> several hypotheses that should be subjected to validation or refutation
>>> through experiments designed and carried out in a laboratory. The Oxford
>>> Dictionaries Online defines the scientific method as "a method or procedure
>>> that has characterized natural science since the 17th century, consisting in
>>> systematic observation, measurement, and experiment, and the formulation,
>>> testing, and modification of hypotheses". Experiments are a procedure
>>> designed to test hypotheses. Experiments are an important tool of the
>>> scientific method.
>>> 
>>> In our case, molecular, personalized and precision medicine aims to
>>> anticipate the future development of diseases in a specific individual
>>> through molecular markers registered in the genome, variome, metagenome,
>>> metabolome or in any of the multiple "omes" that make up the present "omics"
>>> language of current Biology.
>>> 
>>> The possibilities of applying these methodologies to the prevention and
>>> treatment of diseases have increased exponentially with the rise of a new
>>> religion, Dataism, whose foundations are inspired by scientific agnosticism,
>>> a way of thinking that seems classical but applied to research, it hides a
>>> profound revolution.
>>> 
>>> Dataism arises from the recent human desire to collect and analyze data,
>>> data and more data, data of everything and data for everything-from the most
>>> banal social issues to those that decide the rhythms of life and death.
>>> "Information flow" is one the "supreme values" of this religion. The next
>>> floods will be of data as we can see just looking at any electronic window.
>>> 
>>> The recent development of gigantic clinical and biological databases, and
>>> the concomitant progress of the computational capacity to handle and analyze
>>> these growing tides of information represent the best substrate for the
>>> progress of Dataism, which in turn has managed to provide a solid content
>>> material to an always-evanescent scientific agnosticism.
>>> 
>>> On many occasions the establishment of correlative observations seems to be
>>> sufficient to infer about the relevance of a certain factor in the
>>> development of some human pathologies. It seems that we are heading towards
>>> a path in which research, instead of being driven by hypotheses confirmed
>>> experimentally, in the near future experimental hypotheses themselves will
>>> arise from the observation of data of previously performed experiments. Are
>>> we facing the end of the wet lab? Is Dataism the end of classical
>>> hypothesis-driven research (and the beginning of data-correlation-driven
>>> research)?
>>> 
>>> Deep learning is based on learning data representations, as opposed to
>>> task-specific algorithms. Learning can be supervised, semi-supervised or
>>> unsupervised. Deep learning models are loosely related to information
>>> processing and communication patterns in a biological nervous system, such
>>> as neural coding that attempts to define a relationship between various
>>> stimuli and associated neuronal responses in the brain. Deep learning
>>> architectures such as deep neural networks, deep belief networks and
>>> recurrent neural networks have been applied to fields including computer
>>> vision, audio recognition, speech recognition, machine translation, natural
>>> language processing, social network filtering, bioinformatics and drug
>>> design, where they have produced results comparable to and in some cases
>>> superior to human experts. Will be data-correlation-driven research the new
>>> scientific method for unsupervised deep learning machines? Will computers
>>> became fundamentalists of Dataism?
>>> 
>>> Best regards,
>>> 
>>> AJ
>>> 
>>> 
>>>> ---
>>> Alberto J. Schuhmacher, PhD.
>>> Head, Molecular Oncology Group
>>> 
>>> Aragon Health Research Institute (IIS Aragón)
>>> Biomedical Research Center of Aragon (CIBA)
>>> Avda. Juan Bosco 13, 50009 Zaragoza (Spain)
>>> email: ajimenez at iisaragon.es
>>> Phone:(+34) 637939901
>>> 
>>> _______________________________________________
>>> Fis mailing list
>>> Fis at listas.unizar.es
>>> http://listas.unizar.es/cgi-bin/mailman/listinfo/fis [2]
>>> 
>> 
>> --
>> Dr. Mark William Johnson
>> Institute of Learning and Teaching
>> Faculty of Health and Life Sciences
>> University of Liverpool
>> 
>> Phone: 07786 064505
>> Email: johnsonmwj1 at gmail.com
>> Blog: http://dailyimprovisation.blogspot.com [3]
>> 
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> 
> -- 
> 
> Alex Hankey M.A. (Cantab.) PhD (M.I.T.)
> Distinguished Professor of Yoga and Physical Science,
> SVYASA, Eknath Bhavan, 19 Gavipuram Circle
> Bangalore 560019, Karnataka, India  
> Mobile (Intn'l): +44 7710 534195  
> Mobile (India) +91 900 800 8789 
> 
> ____________________________________________________________ 
> 
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Links:
------
[1] https://www.jstor.org/stable/2392088
[2] http://listas.unizar.es/cgi-bin/mailman/listinfo/fis
[3] http://dailyimprovisation.blogspot.com
[4] http://www.sciencedirect.com/science/journal/00796107/119/3
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