Richard Groß // Situating Machine Learning. On Pattern Recognition and Calibrated Problems

Table of contents

    1. Contact
    2. Research project: Situating Machine Learning. On Pattern Recognition and Calibrated Problems
    3. CV
    4. Research Interests
    5. Publications
    6. Presentations
    7. Teaching
    8. Organisation
    9. Projects

Contact

Schaufler Kolleg@TU Dresden © Happy Little Accidents

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Richard Groß

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Schaufler Kolleg@TU Dresden

Schaufler Kolleg@TU Dresden

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01067 Dresden

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Technische Universität Dresden Schaufler Kolleg@TU Dresden

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Research project: Situating Machine Learning. On Pattern Recognition and Calibrated Problems

Discipline: Sociology
Doctoral advisors: Prof. Dr. Dominik Schrage and Juniorprofessorin Dr. Susann Wagenknecht

Machine learning has proven to a challenging object of study for sociological research, both in theoretical and methodological terms. As an applied technology proliferating across many domains of social life, it provokes social theory especially in terms of its social capacities. Linguistic communication, for instance, no longer seems to be a human privilege, and algorithmic modeling affects how knowledge is generated and disseminated.

Research on machine learning in the humanities offers a variety of assessments of the subject that vary considerably in their analytical verdicts. They range from enthusiastic speculations about untapped more-than-human potentials to sober observations of the mediocrity of the algorithmic meaning modelling to emphatic warnings about the dangerous effects of the unregulated real-world use of machine learning technologies. Picking up on these current controversies, I approach machine learning empirically and explore it as a situated practice in my dissertation project.

Based on ethnographic field work, I research current applications of machine learning in science and art by means of two case studies. I approach machine learning by studying situations of its practical realization. Inspired by pragmatist philosopher John Dewey, I understand situations as problematic episodes of practices that interrupt routines. Resolving situations requires creative solutions.

 Within situations, technology – however smart or intelligent – does not appear as an isolated entity but as an integrated and codependent element of practices. In this sense, I do not conceive of machine learning as a material computational artifact or an autonomous individual agent but use the term to denote successful situational cooperation in practical efforts of machine learning. My analyses focus on how problems are identified and resolved in the interaction of different heterogeneous machine learners. Following Adrian Mackenzie's definition, I understand machine learners as a variety of social entities, human as well as non-human (including devices, programs, data, graphs, algorithms, tables, and many more), that are involved in machine learning.

Situating machine learning ethnographically, my research offers empirical insights through a problem-centered perspective on so-called artificial intelligence as practice. As a pragmatist contribution to current debates in social theory, my dissertation spells out various issues of situated cooperation to characterize machine learning as a social technology.

CV

From 2018 Edition of Complete Works of Arnold Gehlen (Project Coordinator), TU Dresden
2018 Diploma degree (M.A.) in Sociology, Art History and Musicology, TU Dresden
2017/2018 Study Visit, New School for Social Research, The New School, New York
2013 - 2017 Research and Teaching Assistant, Institute of Sociology, TU Dresden

Research Interests

  • Social Theory
  • Theory of Society
  • Systems Theory
  • Sociology of Technology
  • Sociology of Time
  • Media Theory
  • Science and Technology Studies

Publications

2023

  • AI RealitiesGroß, R., 23 May 2023, KI-Realitäten: Modelle, Praktiken und Topologien maschinellen Lernens. Bielefeld: transcript Verlag, Bielefeld, p. 23-34Research output: Contribution to book/conference proceedings/anthology/report > Foreword/postscript
  • KI-RealitätenGroß, R. & Jordan, R., 23 May 2023, KI-Realitäten: Modelle, Praktiken und Topologien maschinellen Lernens. Groß, R. & Rita Jordan (eds.). Bielefeld: transcript Verlag, Bielefeld, p. 9-21Research output: Contribution to book/conference proceedings/anthology/report > Foreword/postscript
  • KI-Realitäten. Modelle, Praktiken und Topologien maschinellen LernensGroß, R. & Jordan, R., 23 May 2023, Bielefeld: transcript Verlag, BielefeldResearch output: Book/Report/Anthology > Monograph

2022

  • NachwortGroß, R. & Rehberg, K., 2022, Urmensch und Spätkultur sowie weitere Schriften zu einer Theorie der Institutionen. Vittorio Klostermann, p. 497-527Research output: Contribution to book/conference proceedings/anthology/report > Foreword/postscript
  • Urmensch und Spätkultur sowie weitere Schriften zu einer Theorie der InstitutionenRehberg, K., Groß, R., Schilling, H. & Schilling, N., 2022, Frankfurt a.M.: Vittorio Klostermann, 700 p.Research output: Book/Report/Anthology > Monograph

2020

2018

  • Ordnung, Chaos und gesellschaftliche WirklichkeitGroß, R., 2018, Chaos: Zur Konstitution, Subversion und Transformation von Ordnung. Amin, M., Niekrenz, E. & Weißbach, F. (eds.). Berlin: Berliner Wissenschafts-Verlag, p. 75-96 21 p.Research output: Contribution to book/conference proceedings/anthology/report > Chapter in book/anthology/report
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Presentations

  • Visualization of Vector Space as Translational Relation-Making, 4S Meeting, Panel: „Optical Media: The Impact of Visual Technologies“, Toronto (online), 9.10.2021
  • Project Presentation at  Hamburg SummerSchool for Qualitative Methods: Non-Humans in Empirical Research, Hamburg Research Academy, 27.-28.9.2021
  • Presentation and Discussion with Christian Kosmas Mayer und Susann Wagenknecht on Simultaneities, Symposium „The First Second of Eternity“, Schaufler Lab@TU Dresden, 14.6.2021
  • Informationelle Emergenz als ästhetisches Ereignis, Conference „Hacking the Computable“, University of Music and the Performing Arts Stuttgart, January 2020
  • Über die Kommunikation von Menschen, Maschinen und künstlichen Bienen, „Schwarzmarkt für nützliches Wissen und Nicht-Wissen“, German Hygiene Museum, Dresden, December 2019
  • Communication by and with Learning Machines. Internal and External Views, Conference „The Society of Learning Algorithms“, University of Stuttgart, November 2019
  • Trust and Technology, Workshop Series „Observing Strange Attractors“, Organized by Theory Collective „Texture“, Berlin, May 2019
  • Algorithmische Rationalität und die Möglichkeiten ihrer Kritik. Zu einigen Implikationen der „kybernetischen Hypothese“ (w/ Jakob Claus), Presentation at Workshop „Zur Kritik algorithmischer Rationalität“, Institute for Critical Theory, Zürich University of the Arts, June 2019
  • The Cybernetic Hypothesis. Workshop with Alexander R. Galloway (mit Jakob Claus), texture, Manteuffelstraße 20, 01997 Berlin, December 2018
  • Post-Truth? Social Reality via Media and Technology, Annual Sociology Student Conference, The New School, New York, NY, March 2018
  • Das Chaos gesellschaftlicher Ordnung, Talk at „Institute for Chaos“. Berlin, August 2016

Teaching

  • Summer Semester 2021: Seminar “Sociology of Artificial Intelligence”, Institute of Sociology, TU Dresden

Organisation

  • Conference “Artificial Intelligence as a Concept in the Humanities and Social Sciences”, Schaufler Lab@TU Dresden, 1.-3.12.2021

Projects

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Last modified: Mar 22, 2023
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