[spectre] Digimag Journal - Smart Machines for Enhanced Arts

[Redazione Digicult]

Digimag Journal | Issue 76 | Summer 2017
Smart Machines for Enhanced Arts

Out now as free Pdf, Epub, Mobi and Print on Demand
http://www.digicult.it/digimag-journal/

With texts by: Memo Akten Claire Burke Geoffrey Drake-Brockman Jerry Galle
Eugene Kogan Robert B. Lisek Filippo LorenzinAndreas Refsgaard Liu Yuxi
Alessandro Masserdotti.

Cover courtesy by: Nawa Kohei

Curated by: Marco Mancuso and Silvia Bertolotti

PDF: http://www.digicult.it/wp-content/uploads/digimag76.pdf
EPUB: http://www.digicult.it/wp-content/uploads/digimag76.epub
MOBI: http://www.digicult.it/wp-content/uploads/digimag76.mobi
ISSUU: https://issuu.com/digicultlibrary/docs/digimag76
PRINT ON DEMAND: https://www.peecho.com/print/en/338175

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Artificial Intelligence (AI) and Machine Learning (ML) might be considered
by many as synonyms, also because they are the buzzwords of this decade.
But actually they are not. They both question though, the ability of the
machines to perform and complete tasks in a “smart” way, challenging human
intelligence and specificity.

With machines becoming more and more intelligent, Machine Learning is
nowadays not only an interesting and challenging topic, but also a crucial
discipline. If initially computing was just a matter of calculations, now
it has moved beyond simple “processing” and implies also “learning”. In the
age of Big Data and IoT, machines are asked to go beyond pure programming
and algorithms procedures, introducing also predictions of data, OCR and
semantic analysis, learning from past experiences and adapting to external
inputs, reaching out the domain of human productions and processes.

As Gene Kogan and Francis Tseng write in their in-development book “Machine
Learning for Artists”, we can “pose today to machines a single abstract
problem: determine the relationship between our observations or data, and
our desired task. This can take the form of a function or model which takes
in our observations, and calculates a decision from them. The model is
determined from experience, by giving it a set of known pairs of
observations and decisions. Once we have the model, we can make predicted
outputs””.

So, the subject of Machine Learning and Artificial Intelligence methods
more in general, are going thusly much further the technology or science
fields, impacting also arts, product design, experimental fashion and
creativity in general. As ML features can fit with digital arts practices,
we're lead to explore the way some AI techniques can be used to enhance
human performative gestures and creativity models.

How biological systems and machine intelligence can collaborate to create
art, and which is the cultural outcome for our society? Which is the new
role of creativity in this scenario? How the contemporary will face a
future generation of automated artificial artists/designers, able to learn
from the creatives themselves, or to have a direct impact on human
creativity? Will the anthropocentric vision of the creative process behind
the artistic creation, affected by new intelligent Neural Networks?





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