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

[Pedro C. Marijuan]

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 



http://www.europarl.europa.eu/RegData/etudes/IDAN/2017/581948/EPRS_IDA(2017)581948_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 
 *


* 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 
> 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. 

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

[Dr. Plamen L. Simeonov]

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

http://www.europarl.europa.eu/RegData/etudes/IDAN/2017/
581948/EPRS_IDA(2017)581948_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   *

* 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 <
ajime...@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 

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

[Dai Griffiths]

Mark Johnson wrote:


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!


These are good questions, Mark.

Understanding 'science' as 'knowledge' it is plainly true that 
"Effective science and effective decision-making go hand-in-hand".


As a gloss on that comment, I would add that there is an imbalance. 
Decision-making aspires to universal applicability. If the state changes 
the tax regime then it expects all citizens to conform, and increasingly 
technology can be used to achieve that. But knowledge of the 
consequences to society and individuals of those changes to the tax 
regime is partial.


The state uses a regulatory framework, which is quite easily knowable, 
to regulate the chaotic interactions of society, which are complex to 
the degree that they are unknowable. In other words, governments use 
policy instruments to attenuate the variety of the society that they set 
out to regulate, and implicit in this is a recognition the impossibility 
of a complete knowledge of society. An open question is whether the 
tools of data surveillance can change or adjust that equation, and, if 
they can, whether that is desirable.


In the past you have drawn my attention to Bataille's discussion of 
transgression, which I think is relevant here. The question arises: is 
it possible for political science, with technological support, to manage 
the attraction of transgression? That seems to be the project that is 
underway in China at the moment. We can watch the results with interest 
(and perhaps trepidation).


> What does an effective society operating in a highly ambiguous and 
technologically abundant environment look like?


My working suggestion for a guiding principle would be "An effective 
society should be humble about its ability to understand its own 
workings, and those of the people who constitute it"


Dai

--
-

Professor David (Dai) Griffiths
Professor of Education
School of Education and Psychology
The University of Bolton
Deane Road
Bolton, BL3 5AB

Office: M106

SKYPE: daigriffiths

Phones (please don't leave voice mail)
   UK Mobile +44 (0)7491151559
   Spanish Mobile: + 34 687955912
   Work landline: + 44 (0)1204903598

email
   d.e.griffi...@bolton.ac.uk
   dai.griffith...@gmail.com

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

[Alberto J. Schuhmacher]

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  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 

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

[Bruno Marchal]

Dear Alex,


> On 13 Mar 2018, at 08:38, Alex Hankey  wrote:
> 
> 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. 


I agree on this, and nowhere is this more made transparent than in the case of 
the digital machine. Indeed, by its very non standard mathematics of 
self-reference, we recover a knower attached to any digital universal machine 
in a canonical way, and, as I explain in many of my paper, that knower already 
know that it cannot identify itself with any machine, nor even anything 
describable in pure third person sense. The computations does not make 
consciousness into existence, as this one is related to a conjunction of 
provability and truth (which is highly not computable, not definable, etc.). 
The computations are only the channels through which consciousness can 
differentiate.


> 
> It can be strengthen by particular 
> mental practices, well described 
> in the literature of Yoga. 

I guess this is true. Some medicinal plant can also help in that respect.


> 
> Digital Computing machines are 
> not capable of this,

I have no clue why you say this, except that you might confuse the 19th century 
automaton, which is total computable, and totally controllable, with the 
(Löbian) “universal machine”’, which already know she has a soul, and already 
stop to confuse it with its body. Such machine can defeat all complete or 
normative theory about it. 



> and although 
> number crunching is a way for 
> Technology to assist, it is no substitute 
> for the highest levels of the human mind. 

The whole point of machine’s self-reference is that the “number crunching is 
only what happens at the low level description, but once the machine refers to 
itself, there is no real “number crunching” in play, and in the mode of first 
person description, the machine can refute all “number crunching” description 
of itself.

The Mechanist theory is the less reductionist theory of all. Indeed it saves 
the machine itself, and the numbers, or any terms of any Turing-complete 
theory, from any complete reductionist account.

On what the universal machine are capable and not capable, we have only the 
ability of using the transfinite numbers to gave us a glimpse of our ignorance. 

I agree with many of your intuition, but I think that you are seriously wrong 
by discarding digital machine to support a person having similar intuition. On 
the contrary, we get a precise theory of machine intuition, related to 
Brouwer’s own mystical theory of the creative subject. In fact we get a formal 
theory (S4Grz) meta-formalising the unformalisable, by the machine, intuition 
of the machine. The key of this possibility relies in understanding that we 
cannot know that Mechanism is true, nor which machine we are, nor which 
computations are most probably supporting us, but we can do the reasoning 
constructively for precise simpler (than us) small, but already Löbian, machine 
(like Peano arithmetic to name the most famous one).

Bruno

PS In another post, you seem to be skeptical on quantum computing. But there is 
a notion of topological quantum information, where the quit can be made very 
stable, and where the quantum computation are fault tolerant enough to sustain 
the quantum exploitation.Typically we need to squeeze charged particles in 
extreme electro-magnetic field, and this is not for tomorrow, but the math let 
me believe this will be practical some day. Now, in arithmetic we have the 
emulation of all computations, including the quantum one, and we have to see 
which one os “winning” the "physical appearance game”.




> 
> Alex 
> 
> 
> On 13 March 2018 at 01:10, Mark Johnson  > 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 

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

[Mark Johnson]

Hi Alex,

Yes I agree about intuition. We should understand it better, and
that's best done with some kind of practice with it. For you it's
yoga; for me it's music. I suspect they're very closely related.  I
think it's dangerous, however, to disregard technology. Music is
highly technological: some of the earliest technologies we possess in
our museums are musical instruments. But they work to "tune the
intuition". This is what we need our computers to be (the word
"computer" is "com-putare" - "putare" is "to contemplate"). I suspect
the problem is the logic of the digital computer... music (and yoga?)
itself seems to work on the basis of some kind of analogue computation
(contemplation!).

Stafford Beer wrote this account of Ross Ashby's sudden decision to
join Von Foerster at the University of Illiinois. I found the
manuscript in Beer's archive in Liverpool. Beer was fascinated by
Ashby's decision. Ashby (who was quite a cold fish, by all accounts)
expressed his rationale for his decision...

"Late in 1960, a group of Heinz von Foerster’s friends were together
in the evening at Heinz’s home in Urbana, Illinois. A complicated
ballet ensued, the choreography of which I do not altogether remember.
At the precisely proper – the balletic -  moment, Heinz offered Ross a
Chair in BCL. He quietly accepted, without a moment’s pause, and asked
to telephone his wife back home in Bristol. It was the middle of the
night: thank goodness that the sun moves from East to West. Everyone
concerned was totally astonished – Mrs Ashby, I think I may say,
especially. And so he changed his life: for vitally important years
1961- - 1970, W Ross Ashby MD was Professor in the Department of
Biophysics and Electrical Engineering at the University of Illinois in
Urbana. It couldn’t have happened to a nicer psychiatrist.

We walked back alone together to the Faculty Club, where we had
adjacent rooms, across the campus under a full moon. We were strolling
quietly and relaxed. I told him that I was amazed at his instance
decisiveness. He asked me why. I talked about his scientific acumen,
his meticulous methodology, his exactitude: I had expected him to ask
for a year to consider, to evaluate the evidence for and against
emigration. Surely his response had been atypically irrational?

He stopped in his tracks and turned to me, and I shall never forget
his TEACHING me at that moment. No, he said calmly. Years of research
could not attain to certainty in a decision of this kind: the variety
of the options had been far too high. The most rational response would
be to notice that the brain is a self-organizing computer which might
be able to assimilate the variety, and deliver an output in the form
of a hunch. He had felt this hunch. He had rationally obeyed it. And
had there been no hunch, no sense of an heuristic process to pursue?
Ross shrugged: ‘then the most rational procedure would be to toss a
coin’."


Best wishes,

Mark

On 13 March 2018 at 07:38, Alex Hankey  wrote:
> 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  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 

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

[Alex Hankey]

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  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). 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 

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

[Mark Johnson]

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). 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  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 

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

[Dr. Plamen L. Simeonov]

Dear Alberto, Pedro, and FIS Colleagues,

I think you got the message. All in all, an effort to
organize scientific/intellectual potential in this forum and others of that
kind into a kind of currency of a much higher value than money and other
material and virtual resources on Earth deserves to be made. For me, the
term "blockchain" is a bad word match for what this vision may really
become in future.  [Maybe this is because of my past history from Eastern
Europe, which made me feel "blocked" and "chained" for a long time of my
life.] I would rather prefer a term that means unblocking and unchaining
instead. But it should be certainly one thing: trusted information of a
high value like patents, articles, discoveries, and discussions like those
we have here can be ranked on, especially in the era of "fake news" and
spam surrounding us. What we are talking about is not new. It only has a
new "fashion" name. We can regard it as an extension of the internet,
beyond the semantic one, an intelligent and active, but also trusted and
self-organized network of humans, animals, plants, and technical devices, a
welcome tool extending our senses to feel an entire ecosystem of evolving
things.

I have not read an article discussing "blockchain" in the above sense,
maybe because like most phenomena in "dataism"  the term is currently only
unilaterally exploited by the majority, held under the umbrella of
finances, trade, insurances, contracts, encryption, etc. trivial
"high-impact" fields, similarly to the unilateral understanding of AI,
machine learning, and even quantum computing. They all are still understood
(by the majority of our contemporaries) as means to maintain the status quo
of science, economy, and society. But they can be also used to change the
paradigm. If we stay in the loop accepting data-driven hypothesis and
machine-generated theory only because we have sunk in the self-created
ocean of data, this would mean to betray human mind at the end. On the
other hand, we could use all these tools to empower and perpetuate human
mind activities like those in this forum. Therefore, I wish to ask you if
you would eventually support a future experiment for creating a "human mind
capital" currency based on the trustfulness of the idea transactions in
this forum. I think we can get even funding for this experiment.

All the best.

Plamen

___ ___ ___

Dr. Plamen L. Simeonov
simeio.org |  ibiomath.org | inbiosa.eu
___

2017 Towards a First Implementation of the WLIMES Approach in Living System
Studies Advancing the Diagnostics and Therapy in Personalized Medicine


2017 JPBMB Focused Issue on Integral Biomathics: The Necessary Conjunction
of Western and Eastern Thought Traditions for Exploring the Nature of Mind
and Life   *

* free promotional access to all focused issue articles until June 20th 2018




On Sat, Mar 10, 2018 at 11:46 PM, Alberto J. Schuhmacher <
ajime...@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 

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

[Alberto J. Schuhmacher]

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.i...@aragon.es [1]> 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 

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

[PEDRO CLEMENTE MARIJUAN FERNANDEZ]

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 
 wrote:


head>Dear Alberto,

Many thanks for the kickoff text. I will try to produce acouple of direct 
comments.
You have reminded me of the early70's, when I first approached science. A few 
computers had made theirentrance in the university halls. During those years, 
and for somedecades to come, a new mantra was to be ensconced: 
modeling,simulations. Thanks to computers, we had a fascinating new tool; 
amathematical machine that was opening a new window to the world ofscience, 
equivalent to the telescope or the microscope in thescientific revolution. Now, 
almost 50 years
later, after havingprovoked their own "information revolution" it seems that 
computersare more than a new tool. Dataism coupled with 
artificialintelligence, deep learning and the other techniques, have taken 
themto the command post, so that they are becoming direct "agents" of 
thescientific progress. And this is strange. They have already 
defeatedmasters of chess, of go and of other contests... are they going 
todefeat scientists too? Are they the "necessary" new lords of allquarters 
of techno-social complexity?

You have depicted verycogently the new panorama of biomedical research, 
probably themainstream, and I wonder whether this is the most 
interestingdirection of advancement. In some sense, yes (or no!), as it is 
wherebig biomed companies, technological firms, and managementestablishment are 
pointing at. It is easy to complain that they areleaving aside the integrative 
vision, the
meaningful synthesis thatfacilitate our comprehension, the "soul" in the 
machine... But we havebeen complaining in this way at least during the last 
two decades. SoI really do not know. Fashions in science come and go: maybe 
all ofthis is a temporary illusion. Or a taste of the science of the future.

In any case, it was nice hearing from a biomedical researcher inthe wet lab.
Best wishes--Pedro

On Tue, 06Mar 2018 21:23:01 +0100 "Alberto J. Schuhmacher"  wrote:
blockquote>Dear FIS Colleagues,
I very much appreciate thisopportunity to discuss with all of you.
My mentors and scienceteachers taught me that Science had a method, rules and 
proceduresthat should be followed and pursued rigorously and with 
perseverance.The scientific research needed to be preceded by one or 
severalhypotheses that should be subjected to validation or refutationthrough 
experiments designed and
carried out in a laboratory. TheOxford Dictionaries Online defines the 
scientific method as "a methodor procedure that has characterized natural 
science since the 17thcentury, consisting in systematic observation, 
measurement, andexperiment, and the formulation, testing, and modification 
ofhypotheses". Experiments are a procedure designed to test 
hypotheses.Experiments are an important tool of the scientific method.

Inour case, molecular, personalized and precision medicine aims 

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

[PEDRO CLEMENTE MARIJUAN FERNANDEZ]

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:

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 

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

[Alberto J. Schuhmacher]

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: ajime...@iisaragon.es
 Phone:(+34) 637939901___
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