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GitHub上人工智能民主化的映射.docx
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GitHub上人工智能民主化的映射.docx
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Marcus Burkhardt
Mapping the Democratization of AI on GitHub. A First
Approach
2019
https: /doi.org/10.25969/mediarep/13541
Veröffentlichungsversion / published version
Sammelbandbeitrag / collection article
Repositorium
für
die
Medienwissenschaft
Empfohlene Zitierung / Suggested Citation:
Burkhardt, Marcus: Mapping the Democratization of AI on GitHub. A First Approach. In: Andreas Sudmann (Hg.): The
democratization of artificial intelligence. Net politics in the era of learning algorithms. Bielefeld: transcript 2019, S. 209–
221.
DOI:
https://doi.org/10.25969/mediarep/13541.
Nutzungsbedingungen: Terms of use:
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Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0/
Lizenz zur Verfügung gestellt. Nähere Auskünfte zu dieser
Lizenz finden Sie hier:
https://creativecommons.org/licenses/by-nc-nd/4.0/
This document is made available under a creative commons -
Attribution - Non Commercial - No Derivatives 4.0/ License. For
more information see:
https://creativecommons.org/licenses/by-nc-nd/4.0/
Mapping
the
Democratization
of
AI
on
GitHub
A
First
Approach
Marcus
Burkhardt
Over the course of the past 80 years the digital computer has radically changed the
world we inhabit and the ways in which we relate to it and to each other. Likewise,
computing has radically changed as well. At first, practical computing machines
1
carried individual names, but in the early 1950s proper names were quickly re-
placed by series designators which are emblematic for the era of mainframe com-
puters and time-sharing systems. The 1970s and 1980s gave rise to micro, home
and personal computers as well as graphical user interfaces that became prevalent
in the 1990s. With the rise of the World Wide Web (WWW) during this decade
networked computing and networking changed the face of computing again. Re-
gardless of the burst of the dot-com bubble the Web flourished throughout the
early 2000s by being reframed as Web 2.0 and social web. During this period com-
puting devices became increasingly mobile and desktop computers were super-
seded by notebooks, smartphones and tablets, software gradually morphed into
services and apps that rely on cloud infrastructures for distributed processing
and storage.
In June 2017 Sundar Pichai, CEO of Google Inc., declared yet another para-
digm shift in the history of computing. Innovation should neither be driven by
approaching problems as first and foremost digital nor mobile, but instead by tak-
ing an AI first approach that is fueled by recent advances in the field of machine
learning: “We believe smartphones should be smarter; they should learn from you
and they should adapt to you. Technologies such as on-device machine learning
can learn your usage patterns and automatically anticipate your next action sav-
ing you time” (Pichai 2018). This statement reflects a central promise of machine
learning applications, namely the ability to adapt to unforeseen futures without
prior programming of a particular event: visual recognition of specific objects or
persons that the program did neither “see” nor was trained on before, self-driving
cars that can deal with new situations safely or chatbots that conduct conversa-
1
For
the
concept
of
practical
computing
machines
see
Turing
(1992).
210
Marcus
Burkhardt
tions with humans in an engaging manner. Conversely, the more such technol-
ogies are built into the fabric of everyday life the more concerns are raised about
their potential risks, e.g. biases and inequalities inherent in training data sets.
As a result, machine learning models often produce (social) structures instead
of adapting to them. Drawing on debates in critical algorithm studies this paper
asks how machine learning and artificial intelligence as fields of technological de-
velopment and innovation are in themselves structured. By providing an initial
mapping of the coding cultures of machine learning and artificial intelligence on
GitHub the paper argues for the importance to attend more closely to the hith-
erto largely neglected infrastructural layers of code libraries and programming
frameworks for the development of critical perspectives on the social and cultural
implications of machine learning technologies to come.
Democratization
of
AI
In recent years the interest in artificial intelligence (AI) in general and in machine
learning (ML) in particular skyrocketed once again. This ongoing development is
to some extend driven by leading technology companies such as Google and its
parent company Alphabet, Amazon Web Services (AWS), Facebook, IBM, Micro-
soft etc. It rests upon the massive accumulation of data by these companies on
the one hand and the establishment of large-scale cloud infrastructures as well
as infrastructural services on the other hand. However, these companies do not
simply contribute to the rapid technological developments in AI and ML, they also
take part in shaping the imaginaries of smart, intelligent and autonomous tech-
nologies as cornerstones of technological progress and enablers of social progress
as well as economic prosperity for the years to come.
Central to this is the recent push towards the democratization of artificial
intelligence. Google (IANS 2017), IBM (Moore 2018), Apple (Simonite 2017) and
Microsoft (n.d.) alike mobilize the notion of democratic AI to promote the shift
towards ML driven technological innovation. In this context democratization
can be understood “as the action/development of making something accessible
to everyone, to the
‘
c
o
mm
o
n
m
a
ss
es
’
”
(Schmarzo 2017). For Microsoft this entails
allowing “every person and every organization” (n.d.) to partake in the anticipat-
ed benefits of AI whose effects will supposedly be as far-reaching as that of the
printing press:
With the advent of the printing press in the 1400s we have an explosion of infor-
mation—the first democratizing event around access that made it possible for
humans everywhere to start learning. Access to information has only spread from
there. [...] The question is, how can we use all we have in terms of computational
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