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 

> 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).


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 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”
> <>). 
> 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 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: <>
> > Phone:(+34) 637939901
> >
> > _______________________________________________
> > Fis mailing list
> > <>
> > 
> > <>
> >
> --
> Dr. Mark William Johnson
> Institute of Learning and Teaching
> Faculty of Health and Life Sciences
> University of Liverpool
> Phone: 07786 064505
> Email: <>
> Blog: 
> <>
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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
> ____________________________________________________________
> 2015 JPBMB Special Issue on Integral Biomathics: Life Sciences, Mathematics 
> and Phenomenological Philosophy 
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