Learning machine learning:On the political economy of big tech's online AI courses

Machine learning (ML) algorithms are still a novel research object in the field of media studies. While existing research focuses on concrete software on the one hand and the socio-economic context of the development and use of these systems on the other, this paper studies online ML courses as a research object that has received little attention so far. By pursuing a walkthrough and critical discourse analysis of Google's Machine Learning Crash Course and IBM's introductory course to Machine Learning with Python, we not only shed light on the technical knowledge, assumptions, and dominant infrastructures of ML as a field of practice, but also on the economic interests of the companies providing the courses. We demonstrate how the online courses further support Google and IBM to consolidate and even expand their position of power by recruiting new AI talent and by securing their infrastructures and models to become the dominant ones. Further, we show how the companies not only influence greatly how ML is represented, but also how these representations in turn influence and direct current ML research and development, as well as the societal effects of their products. Here, they boast an image of fair and democratic artificial intelligence, which stands in stark contrast to the ubiquity of their corporate products and the advertised directives of efficiency and performativity the companies strive for. This underlines the need for alternative infrastructures and perspectives

AI for All?

Research in artificial intelligence (AI) is heavily shaped by big tech today. In the US context, companies such as Google and Microsoft profit from a tremendous position of power due to their control over cloud computing, large data sets and AI talent. In light of this dominance, many media researchers and activists demand open infrastructures and community-led approaches to provide alternative perspectives – however, it is exactly this discourse that companies are appropriating for their expansion strategies. In recent years, big tech has taken up the narrative of democratizing AI by open-sourcing their machine learning (ML) tools, simplifying and automating the application of AI and offering free educational ML resources. The question that remains is how an alternative approach to ML infrastructures – and to the development of ML systems – can still be possible. What are the implications of big tech’s strive for infrastructural expansion under the umbrella of ‘democratization’? And what would a true democratization of ML entail? I will trace these two questions by critically examining, first, the open-source discourse advanced by big tech, as well as, second, the discourse around the AI open-source community Hugging Face that sees AI ethics and democratization at the heart of their endeavour. Lastly, I will show how ML algorithms need to be considered beyond their instrumental notion. It is thus not enough to simply hand over the technology to the community – we need to think about how we can conceptualize a radically different approach to the creation of ML systems

From biased robots to race as technology

review of: Benjamin, R. (2019) Race after technology: Abolitionist tools for the New Jim Code. Cambridge, UK/Medford, MA: Polity Pres

From biased robots to race as technology

review of: Benjamin, R. (2019) Race after technology: Abolitionist tools for the New Jim Code. Cambridge, UK/Medford, MA: Polity Pres

The Eternal Network: The Ends and Becomings of Network Culture

‘The network is everlasting’ wrote Robert Filliou and George Brecht in 1967, a statement that, at first glance, still seems to be true of today’s world. Yet there are also signs that the omnipresence of networks is evolving into another reality. In recent times, the limits of networks rather than their endless possibilities have been brought into focus. Ongoing media debates about hate speech, fake news, and algorithmic bias swirl into a growing backlash against networks. Perhaps it is time to reconsider the contemporary reach and relevance of the network imaginary. Accompanying transmediale 2020 End to End’s exhibition ‘The Eternal Network’, this collection gathers contributions from artists, activists, and theorists who engage with the question of the network anew. In referencing Filliou’s eternal notion, the exhibition and publication project closes the loop between pre- and post-internet imaginaries, opening up possible futures with and beyond networks. This calls many of the collection’s authors to turn to instances of independent and critical net cultures as historical points of inspiration for rethinking, reforming, or refuting networks in the present. --- The Eternal Network: Vom Enden und Werden der Netzkultur DEUTSCHE FASSUNG: „Das Netzwerk wird es ewig geben“, schrieben Robert Filliou und George Brecht 1967 – eine Aussage, die auf den ersten Blick auch heute noch zuzutreffen scheint. Doch gibt es auch Anzeichen, dass die Allgegenwärtigkeit von Netzwerken eine andere W irklichkeit hervorbringt. Mittlerweile rückt die Endlichkeit von Netzwerken – anstatt deren endlose Möglichkeiten – in den Fokus; davon zeugen die anhaltenden Mediendebatten über Hassrede, Fake News und algorithmischer Diskriminierung. Vielleicht ist es an der Zeit, die aktuelle Reichweite und Relevanz des Netzwerks neu zu betrachten. Begleitend zur Ausstellung „Das ewige Netzwerk“ der transmediale 2020 End to End versammelt dieser Band Beiträge von Künstler*innen, Aktivist*innen und Theoretiker* innen, die sich neu mit der Frage des Netzwerks beschäftigen. Ausstellung und Publikation beziehen sich auf Fillious Konzept von der Ewigkeit des Netzwerks. Sie verbinden dabei die Vorstellungswelten, die zeitlich vor der Entwicklung des Internets entstanden sind, mit jenen, die darauf folgten. So eröffnen sie mögliche Zukünfte mit und jenseits von Netzwerken. Viele Autor*innen in diesem Band lassen sich dabei von historischen Momenten der unabhängigen und kritischen Netzkulturen inspirieren, um Netzwerke der Gegenwart neu zu denken, sie zu reformieren oder anzufechten

Learning machine learning: On the political economy of big tech's online AI courses

Machine learning (ML) algorithms are still a novel research object in the field of media studies. While existing research focuses on concrete software on the one hand and the socio-economic context of the development and use of these systems on the other, this paper studies online ML courses as a research object that has received little attention so far. By pursuing a walkthrough and critical discourse analysis of Google's Machine Learning Crash Course and IBM's introductory course to Machine Learning with Python, we not only shed light on the technical knowledge, assumptions, and dominant infrastructures of ML as a field of practice, but also on the economic interests of the companies providing the courses. We demonstrate how the online courses further support Google and IBM to consolidate and even expand their position of power by recruiting new AI talent and by securing their infrastructures and models to become the dominant ones. Further, we show how the companies not only influence greatly how ML is represented, but also how these representations in turn influence and direct current ML research and development, as well as the societal effects of their products. Here, they boast an image of fair and democratic artificial intelligence, which stands in stark contrast to the ubiquity of their corporate products and the advertised directives of efficiency and performativity the companies strive for. This underlines the need for alternative infrastructures and perspectives

PLATFORM VISIONS AND INVASIONS: SPATIAL (RE)IMAGINATIONS IN BIG TECH DISCOURSE

This panel analyzes the textual and audiovisual discourses in which big tech companies envision social spaces and their platforms’ roles in relation to those spaces. The panel asks: What are the visions of human life and technology that big tech companies narrate in relation to public and social spaces? And how do these tech-generated visions compare to current insights in and critiques of how these companies intervene in and disrupt social spaces? Situated within the field of critical platform studies, the panel’s premise is that the discourses produced by tech companies form an integral part of these companies’ interventions in people’s relations with themselves, others, and their environments. In order to develop a critical understanding of the platform society, it is crucial to examine those discourses through which tech companies present themselves almost as state-like powers that can be trusted with “public” services. Employing methods of textual and visual analysis, this panel offers such perspective. In its analysis of big tech’s spatial visions, the panel moves between different scales of spaces (domestic, educational, urban, extraterrestrial). In relation to these spaces, the panel articulates a discrepancy between the visions of life produced by tech companies and big tech’s invasive, perhaps even extractivist and colonial logic

What was the network?

CLEMENS APPRICH, DAPHNE DRAGONA, GEERT LOVINK, AND FLORIAN WÜST - CONVERSATION MODERATED BY KRISTOFFER GANSING On August 6, 2019, the curatorial advisors of the transmediale 2020 exhibition, ‘The Eternal Network’, gathered at the festival’s offices in Berlin for a conversation on the status of network culture and theory today. Starting from the question ‘What was the network?’, the conversation explored the multiple trajectories of networks within cybernetics, art and philosophy, also taking the limits of networks into account. This included a reconsideration of the role of alternative and critical networks in today’s widespread digitalization, with its data-centric platform economy and the techno-cultural changes wrought by artificial intelligence