[Fis] Is Dataism the end of classical hypothesis-driven research and the beginning of data-correlation-driven research?
Pedro C. Marijuan
pcmarijuan.iacs at aragon.es
Wed Mar 28 00:06:59 CEST 2018
Thanks Plamen, very interesting references and comments. There are many
new avenues opening around data, from nasty ones (recent politics) to
the economic, biomedical and scientific in general. It is a very
important "information" theme of our time. Perhaps I disagree that deep
learning could not develop similar processes to what we call intuition
and analogy. If we situate ourselves within one particular neuron of our
nervous system, those intuitions and analogies passing by are but more
of the same: electro-molecular mechanisms and topology. Plus "something"
else, of course... Let us continue the discussion after Easter vacations.
Best--Pedro
El 27/03/2018 a las 14:25, Dr. Plamen L. Simeonov escribió:
>
> Dear Alberto, Pedro and All,
>
> I could not follow this discussion in the past 3 weeks since I was
> engaged in other activities, but again ß with respect to my other
> question regarding the value of the FIS exchange as a forum and
> virtual currency, please find below two articles (December 2017) that
> could inspire your imagination:
>
>
> https://www.nature.com/articles/d41586-017-08589-4
> <https://www.nature.com/articles/d41586-017-08589-4>
>
> http://www.europarl.europa.eu/RegData/etudes/IDAN/2017/581948/EPRS_IDA(2017)581948_EN.pdf
> <http://www.europarl.europa.eu/RegData/etudes/IDAN/2017/581948/EPRS_IDA%282017%29581948_EN.pdf>
>
>
> I believe that data-driven research is just a fashion and that new
> commercial trends like cryptocurrency technology will be driven by
> regulation to a different direction, namely the one that is the
> discussed in the articles above. Indeed, the whole idea is not new at
> all. I actually found myself as the inventor of a precursor solution
> to blockchain back in 1999. And this idea alone stems from analogies I
> have driven from active networks and attributed graph grammars back in
> the 1980ies..., long before there was an Internet Protocol at all. So,
> honestly, I do not believe that data will be the top of the knowledge
> pyramid, and to have data we create the models and invent theories
> also by analogy and intuition, the methods that folks like Poincare
> and Einstein were working with pen and paper on. Computers and AI/ML
> will remain just tools, but they will never become wise as people or
> even animals. By the way, we are planning another special issue on
> Integral Biomathics in 2019 in the footsteps of the previous ones in
> 2013, 2015 and 2017 --
>
> 2017 JPBMB Focused Issue on Integral Biomathics: The Necessary
> Conjunction of Western and Eastern Thought Traditions for Exploring
> the Nature of Mind and Life
> <http://www.sciencedirect.com/science/journal/00796107/131> *
>
> * free promotional access to all focused issue articles until June
> 20th, 2018
>
> and devoted to animal and natural intelligence. I just wish to inform
> you earlier about this. An official call will be distributed in this
> forum later this year.
>
> I wish you a Happy Easter!
>
> All the best.
>
> Plamen
>
>
> ____________________________________________________________
>
>
> On Sat, Mar 10, 2018 at 11:46 PM, Alberto J. Schuhmacher
> <ajimenez at iisaragon.es <mailto:ajimenez at iisaragon.es>> wrote:
>
> Dear Plamen, Pedro and Collegues,
>
> I am enjoying a lot this forum.
>
> I absolutely foresee Scientific Blockchain as a continuously
> growing list of scientific records and contributions (blocks)
> linked and secured using cryptography, somehow a kind of peer
> reviewed process. Would you be able to publish it in a journal
> based on their scientific value?
>
> Dataist-machines won chess players but still are learning Science,
> they are completing their “Bachelor”. Their use for biomedical
> applications is growing everyday. For example, their accuracy for
> in biomedical imaging diagnosis will be similar to humans soon.
> For other applications, such as genetic predisposition and health
> prediction/prognosis the conversion to a fanatic dataism may abuse
> of “predictivity” and forget the relevance of the
> organism-environment. It will take some time for machines to
> complete their “Philosophical Doctorate”. Technology could be
> ready soon for data driven hypothesis but our knowledge of
> fundamental aspects of life are still weak.
>
> All the best,
> AJ
>
> El 10-03-2018 21:05, PEDRO CLEMENTE MARIJUAN FERNANDEZ escribió:
>
>> Dear Plamen and Colleagues,
>>
>> If it can be feasible, I would very much welcome what you
>> propose. Yes, it would be great developing a general articulation
>> amongst all our exchanges. Roughly, I feel that a fundamental
>> nucleous of neatly conceptualized information is still evading
>> us, but outside that nucleous, and somehow emanating from it,
>> there are different branches and sub-branches in quite different
>> elaboration degrees and massively crisscrossing and intermingling
>> their contents. A six-pointed star, for instance, radiating from
>> its inner fusion the computational, physical, biological,
>> neuronal, social, and economic. The six big branches in perfect
>> periferic colussion and confusion. Could a blockchain, along its
>> full develpment in time, represent a fundamental cartography of
>> the originating fusion nucleous?
>>
>> About dataism enchantment, well, too many times we have been said
>> "look, finally this is the great, definitive scientific
>> approach"--behaviorism, artificial intelleigence, artifficial
>> catastrophe & complexity theory, and so on. Let us wait and see.
>> Welcome in the extent to which it really responds to unanswered
>> questions. And let us be aware of the technocratic lore it seems
>> to drag.
>>
>> This was my second cent for the week.
>>
>> best--Pedro
>>
>> On Fri, 9 Mar 2018 10:30:01 +0100 "Dr. Plamen L. Simeonov" wrote:
>>
>>> These are wise words, Pedro.
>>>
>>> What I was meaning with my previous posting on FIS was that
>>> there is a foundational emerging technology - blockchain - that
>>> could give us, scientists organized in fora like FIS, IB, IS4IS
>>> etc. to become a valuable currency of the future. I am speaking
>>> not about finances or resources like petrol, gold, water, etc.
>>> What we are doing all the time with the exchange of ideas online
>>> are in fact transactions, often with huge potential. Why do not
>>> try to elevate them to the level that they deserve?
>>>
>>> I am not sure if the FIS forum members can follow me. Can you?
>>>
>>> All the best.
>>>
>>> Plamen
>>>
>>> ____________________________________________________________
>>>
>>> On Thu, Mar 8, 2018 at 6:15 PM, PEDRO CLEMENTE MARIJUAN
>>> FERNANDEZ <pcmarijuan.iacs at aragon.es
>>> <https://mailto:pcmarijuan.iacs@aragon.es>> wrote:
>>>
>>>> head>
>>>>
>>>> Dear Alberto,
>>>>
>>>> Many thanks for the kickoff text. I will try to produce a
>>>> couple of direct comments.
>>>>
>>>> You have reminded me of the early 70's, when I first approached
>>>> science. A few computers had made their entrance in the
>>>> university halls. During those years, and for some decades to
>>>> come, a new mantra was to be ensconced: modeling, simulations.
>>>> Thanks to computers, we had a fascinating new tool; a
>>>> mathematical machine that was opening a new window to the world
>>>> of science, equivalent to the telescope or the microscope in
>>>> the scientific revolution. Now, almost 50 years later, after
>>>> having provoked their own "information revolution" it seems
>>>> that computers are more than a new tool. Dataism coupled with
>>>> artificial intelligence, deep learning and the other
>>>> techniques, have taken them to the command post, so that they
>>>> are becoming direct "agents" of the scientific progress. And
>>>> this is strange. They have already defeated masters of chess,
>>>> of go and of other contests... are they going to defeat
>>>> scientists too? Are they the "necessary" new lords of all
>>>> quarters of techno-social complexity?
>>>>
>>>> You have depicted very cogently the new panorama of biomedical
>>>> research, probably the mainstream, and I wonder whether this is
>>>> the most interesting direction of advancement. In some sense,
>>>> yes (or no!), as it is where big biomed companies,
>>>> technological firms, and management establishment are pointing
>>>> at. It is easy to complain that they are leaving aside the
>>>> integrative vision, the meaningful synthesis that facilitate
>>>> our comprehension, the "soul" in the machine... But we have
>>>> been complaining in this way at least during the last two
>>>> decades. So I really do not know. Fashions in science come and
>>>> go: maybe all of this is a temporary illusion. Or a taste of
>>>> the science of the future.
>>>>
>>>> In any case, it was nice hearing from a biomedical researcher
>>>> in the wet lab.
>>>>
>>>> Best wishes--Pedro
>>>>
>>>> On Tue, 06 Mar 2018 21:23:01 +0100 "Alberto J. Schuhmacher" wrote:
>>>>
>>>> blockquote>
>>>>
>>>> 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
>>>>
>>>> p>
>>>>
>>>> ---
>>>>
>>>> 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) br>email:
>>>> ajimenez at iisaragon.es <https://mailto:ajimenez@iisaragon.es>
>>>> Phone: (+34) 637939901
>>>>
>>>>
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--
-------------------------------------------------
Pedro C. Marijuán
Grupo de Bioinformación / Bioinformation Group
Instituto Aragonés de Ciencias de la Salud
Centro de Investigación Biomédica de Aragón (CIBA)
Avda. San Juan Bosco, 13, planta 0
50009 Zaragoza, Spain
Tfno. +34 976 71 3526 (& 6818)
pcmarijuan.iacs at aragon.es
http://sites.google.com/site/pedrocmarijuan/
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