Re: <nettime> Complexity, epistemology and power in the digital condition

[Felix Stalder <felix@openflows.com>]

Hi Brian,

sorry for the late reply. I think you are right that there are different
configurations of how science done and how its value is established,
based on how it's integrated into the state/commercial apparatus.

> Then after the world-shaping ethical-practical development of the
> disciplines and professions in the late nineteenth century, that
> epistemological period collapsed into wat and a whole new figure of the
> state emerged in mid-20C. It was able to synthesize the different powers
> of knowledge into programmatic collaboration over continental spaces and
> multi-year intervals, particularly due to the mental discipline
> cultivated in places like academia and government research. You know how
> they thought, that was our era, it was yesterday. The most recent social
> form - the hypermodernizing technocratic state - was able to bring the
> scientists, the professions and the military into unison with the
> corporations, and by so doing it ignited the Great Acceleration around
> the world.


Yes, and this is both an acceleration of trends and a break with it. At
the core of this, enabling what you call the Great Acceleration and
which lead me to this great chart

http://www.igbp.net/images/18.950c2fa1495db7081ebd1/1421396650502/GreatAcceleration2015igbpsrclowres.jpg

is machine learning. Epistemologically, this is a real break with how we
come to know something about the world, yet, it is put into service the
sustain growth, necessary for capitalist expansion.

In terms of epistemology, you look at the major (postmodern) critiques
of science -- von Förster's insistence that the observer is part of the
system (1972), Lyotard's turn from truth to instrumentality (1979) and
Latour's argument that the natural and the social cannot be separated
(1988) -- these are all more valid today than they were back then, and
they all apply to machine learning to the extreme. Here, facts are not
discovered but made, not from the outside, but the inside of the
problem, and the focus is not on truth but on utility in terms of
achieving greater ability to manipulate data-streams and the connected
physical dynamics over relatively short time, thus pushing the system
into another place where it can be observed again and the whole cycle
starts again.

At least in some instances of this, the process of knowledge creating is
truly random and directionless. The method, enabled by cheap computing
power, is simply to exhaust the problem space, trying out every possible
combination and then see what works. Then, when something works, build a
new problem space, exhaust it again through random methods find
something that works etc. Layer, by layer. This is how depth comes into
deep learning.

Any solution is fine, as long as it "solves" a predetermined problem in
the short term.

All post modern critiques against industrial science are still valid
from the point of view of modern science, but but now they are the new
method of machine learning.

Of course, the rub against the old-established methods of science
(objectivity, distance, causality) and this creates serious confusion.

I think something similar is happening in the political space, as you
point out. Here its the nation state against, I would say, global (or at
least transnational) protocols, which are, in a way, a directionless
form of social governance which is neither disciplinary nor operating on
the level of the imagination. It's pure instrumentality.


All the best. Felix



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