z zhttps://twitter.com/mariamnotmiriam/status/1268897968427540481 On Mon, Jun 15, 2020 at 4:56 PM Jonnie Penn <jnp28@cam.ac.uk> wrote:
Dear SIGCIS colleagues,
I politely share the invitation below. The Cambridge University Mellon Foundation Sawyer Seminar “Histories of AI: A Genealogy of Power" convenes a multi-disciplinary community. This is our second Reading Group meeting. News about future events is available here via the HoAI listserv.
All best, Jonnie
— Jonnie Penn Affiliate, Berkman Klein Center for Internet & Society | Harvard University Project Development Lead | History of AI | Leverhulme Center for the Future of Intelligence PhD Candidate | Department of the History and Philosophy of Science Pembroke College | University of Cambridge Cambridge, CB2 1RF jnp28@cam.ac.uk
---------- Forwarded message --------- From: HoAI Sawyer Seminar <hoai@hermes.cam.ac.uk> Date: Wed, Jun 10, 2020 at 5:39 PM Subject: Invitation to HoAI Reading Group, Wed 17 June, on Hidden Labour To: <hps-hoai@lists.cam.ac.uk>
Dear all,
We invite you to join the second Reading Group session for the Mellon Sawyer Seminar on Histories of AI: A Genealogy of Power. This will be the first of two sessions to explore the theme of Hidden Labour. A summary of that theme is available below. Part II will be held in September. A schedule of events for July and August will be distributed soon.
This month's Reading Group will be co-facilitated by Matteo Pasquinelli (PhD), professor at the University of Arts and Design Karlsruhe. Prof. Pasquinelli coordinates the research group on media philosophy and artificial intelligence KIM. With Vladan Joler, he recently published the visual essay ‘The Nooscope Manifested: AI as Instrument of Knowledge Extractivism’ (nooscope.ai). For Verso, he is preparing a book titled The Eye of the Master on the history of AI as the automation of labour, of its vision and division.
We hope you can join us.
Reading Group #2 on 'Introduction to Seminar Theme: Hidden Labour (Part I)' Time: Wednesday 17th June @ 15:00-17:00 BST / 10:00am-12:00pm EST Zoom: https://us02web.zoom.us/j/82131742765?pwd=T3ZRS29Sallvd21neXJzS0JUQlY3QT09 Password: histories
Co-facilitator: Prof. Matteo Pasquinelli (University of Arts and Design Karlsruhe) Discussants: Audrey Borowski (University of Oxford), Cindy Lin (University of Michigan, Ann Arbor) Moderator: Prof. Matthew L. Jones (Columbia University)
Schedule (BST): 15:00 - Seminar co-organizers introduce the session, co-facilitator and each discussant. Attendees are invited to share their name, affiliation and location in the chat. 15:05 - Prof. Pasquinelli introduces the two readings (below) in respect to the Seminar theme. 15:20 - Discussants briefly summarize their own research and offer provocations for discussion. 15:30 - Discussion proceeds, with a Seminar co-organizer moderating. 17:00 - End.
Readings:
Daston, Lorraine (2018). ‘Calculation and the Division of Labor, 1750-1950’. Bulletin of the German Historical Institute, 62 (Spring), 9-30. Pasquinelli, Matteo (forthcoming). ‘The Material Tools of Algorithmic Thinking’, chapter 1. The Eye of the Master. London: Verso.
Please email us here (hoai@hermes.cam.ac.uk) if you need help accessing texts.
Summary: Prof. Pasquinelli shares this overview.
The Hidden Intelligence of Labour: For a Social History of Algorithms
Different genealogies of artificial intelligence can be read on the shoulders of workers, merchants, bureaucrats and spies. AI emerged as the project to automate tasks that, since WWII, have ranged from image recognition and object manipulation, to stock price negotiation, and the analysis of public and military datasets.
This overview retraces a canonical genealogy of AI from within the milieu of the industrial revolution and early political economy. Daston (1994, 2017), Schaffer (1994), and Jones (2016) have shown that the project of machine intelligence emerged from the industrial automation of mental labour as hand calculation (Babbage, 1832) rather than the mere dream to forge ‘thinking automata.’ The design of intelligent algorithms imitated, then, the ‘analytical intelligence’ (Daston, 2018) of the division of labour. This intuition can be expanded, today, to interrogate in which way the algorithms of machine learning automate sophisticated forms of manual, mental and visual labour by encoding an extended division of space, time and social behaviours.
In order to understand the relation between AI and labour, it would be useful to clarify first the relation between the algorithm form and the division of labour. From the point of view of invention and knowledge production, which one comes first? Knuth (1972) attempted a historicisation of the algorithm in the essay ‘Ancient Babylonian Algorithms.’ At the time, Knuth aimed at systematising the new field of computer science and to make it into a respectable academic and industrial discipline (Ensmerger, 2010). The idea of the ancient algorithm was mobilised to stress that the new field of computer science was not about obscure machinery but part of a long tradition of cultural techniques of symbolic manipulation. On their side, historians of mathematics such as Chabert (1999), Damerow and Lefèvre (1981) have clarified the genesis of mathematical abstractions in the relation of labour with material tools. Also these approaches can be used to interrogate computation and AI as a solution to the problem of the management of labour, or, from another angle, to recognize the hidden intelligence of labour building the algorithm from within.
References: Babbage, Charles (1832). On the Economy of Machinery and Manufactures. London: Charles Knight. Chabert, Jean-Luc, ed. (1999). A History of Algorithms: From the Pebble to the Microchip. Berlin/New York: Springer. Damerow, Peter, and Wolfgang Lefèvre, eds. (1981). Rechenstein, Experiment, Sprache: Historische Fallstudien Zur Entstehung Der Exakten Wissenschaften. Stuttgart: Klett-Cotta. Daston, Lorraine (1994). 'Enlightenment Calculations'. Critical Inquiry 21, no. 1: 182-202. Daston, Lorraine (2018). ‘Calculation and the Division of Labor, 1750-1950’. Bulletin of the German Historical Institute, 62 (Spring), 9-30. Ensmenger, Nathan (2010). The Computer Boys Take Over: Computers, Programmers, and the Politics of Technical Expertise. Cambridge, MA: MIT Press. Jones, Matthew L. (2016). Reckoning with Matter: Calculating Machines, Innovation, and Thinking About Thinking from Pascal to Babbage. Chicago: Univ. of Chicago Press. Knuth, Donald E. (1972). 'Ancient Babylonian Algorithms'. Commun. ACM 15, no. 7: 671–77. Schaffer, Simon. (1994). 'Babbage's Intelligence: Calculating Engines and the Factory System'. Critical Inquiry 21, no. 1: 203-27.
We look forward to seeing you.
With best wishes,
Jonnie Penn
On behalf of the Mellon Sawyer Seminar team:
Syed Mustafa Ali, The Open University
Stephanie Dick, University of Pennsylvania
Sarah Dillon, University of Cambridge
Matthew Jones, Columbia University
Jonnie Penn, University of Cambridge
Richard Staley, University of Cambridge
Hidden Labour The introduction of forms of mechanisation – of labour and knowledge equally – is usually described as saving labour; but in reality shifts it. Examining how the boundaries between human and machine have been changed over time we must track status, and ask what is being hidden: how have autonomous systems redistributed tasks in ways that burden and mask the contributions of marginalised people? In Ghost Work (2019), Mary L. Gray demonstrates how the history of labour laws in the United States, manifested in a shift from piecework to outsourcing, hollowed out the decency of many entry level jobs in the digital economy while simultaneously powering the AI revolution through low-pay click-work. Gray credits today's MTurk workforce as 'the AI revolution's unsung heroes' (Gray 2019). In her own scholarship and advocacy work, such as with TurkOpticon, Lilly Irani has sought to illuminate additional hidden layers of human data work (Irani 2016).
In the Seminar, we will bring a comparative historical perspective to bear on contemporary conversations about which human faculties can be 'automated' and explore how AI and its implementation serve to value and devalue certain forms of cognitive labour and systems of recognition. As the previous work of Jones (2016) and Dick (forthcoming) has shown, as well as that of Lorraine Daston (1994), such an equivocation of value has long been central to the human history of mathematical calculation, and critically from the nineteenth century onwards. It is in that moment when 'calculation' is debased from genius to the 'merely mechanical' that it becomes the domain of human computing, to be executed by low paid labourers and regendered as women's work (Hicks 2017).
Click here for Works Cited.
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