10.6092/issn.2038-3460/17579
Over the last decade, there has been a renewed interest in “artificial intelligence” (AI), notably in the form of “machine learning” (ML). This renewed interest may seem paradoxical, insofar as John McCarthy introduced the term “AI” in the mid-1950s to mark a distinction with ML, championing deductive reasoning over automated induction (e.g., Cardon et al. 2018). By contrast, the current reversal, towards ML-based forms of “AI,” marks the statistical, if not spectacular, revival of automated induction. However, the terms used – revival, renewal, reversal – beg the question of the common ground of the involved alternatives. Taking its cue from recent historical (e.g., Penn 2020), relevant conceptual (e.g., Shanker 1998), and prior critical (e.g., Agre 1997) inquiries, this paper outlines a praxeological answer to the raised question. For the purpose, the paper develops a practice-based video analysis of a recent demonstration of “machine intelligence,” the video demonstration of an “agent system” playing Breakout at “superhuman level,” if not opening the gate for the advent of “general AI” (Hassabis 2017). In examining and engaging in “remaking intelligence” in situ, the paper dwells on the tricky interplay between machines, media, and montage, while making explicit and reflecting upon how particular configurations of “enchanted determinism” (Campolo and Crawford 2020) are staged and locally performed.
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Philippe Sormani
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Remaking Intelligence? Of Machines, Media, and Montage
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Philippe Sormani
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10.6092/issn.2038-3460/17579
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2038-3460
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Remaking Intelligence? Of Machines, Media, and Montage
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13
                                                                                 Lecture
Remaking Intelligence? Of Machines,
Media, and Montage

Philippe Sormani
University of Lausanne and CEMS-EHESS


         Abstract: Over the last decade, there has been a renewed interest in “ar-
         tificial intelligence” (AI), notably in the form of “machine learning” (ML). This
         renewed interest may seem paradoxical, insofar as John McCarthy intro-
         duced the term “AI” in the mid-1950s to mark a distinction with ML, cham-
         pioning deductive reasoning over automated induction (e.g., Cardon et al.
         2018). By contrast, the current reversal, towards ML-based forms of “AI,”
         marks the statistical, if not spectacular, revival of automated induction. How-
         ever, the terms used – revival, renewal, reversal – beg the question of
         the common ground of the involved alternatives. Taking its cue from recent
         historical (e.g., Penn 2020), relevant conceptual (e.g., Shanker 1998), and
         prior critical (e.g., Agre 1997) inquiries, this paper outlines a praxeological
         answer to the raised question. For the purpose, the paper develops a prac-
         tice-based video analysis of a recent demonstration of “machine intelligence,”
         the video demonstration of an “agent system” playing Breakout at “superhu-
         man level,” if not opening the gate for the advent of “general AI” (Hassabis
         2017). In examining and engaging in “remaking intelligence” in situ, the paper
         dwells on the tricky interplay between machines, media, and montage, while
         making explicit and reflecting upon how particular configurations of “enchanted
         determinism” (Campolo and Crawford 2020) are staged and locally performed.

         Keywords: artificial intelligence; breakout game; common ground; machine
         learning; practical reenactment(s); video demonstration.

         Submitted: November 2, 2022 – Accepted: December 12, 2022
         Corresponding author: Philippe Sormani, STS Lab, University of Lausanne
         and CEMS-EHESS, Paris. Email: philippe.sormani@unil.ch




!



    TECNOSCIENZA
    Italian Journal of Science & Technology Studies
    13 (2) pp. 57-85 - ISSN 2038-3460
    www.tecnoscienza.net                              2022
58                                                                  Tecnoscienza – 13 (2)


1. Introduction: The “Iceberg” Question – Is There a Com-
mon Ground to AI and ML?

    In a recent talk on the intricate history of contemporary AI, a renowned
sociologist elaborated on the paradoxical revival of “machine learning”
(ML) as the currently dominant paradigm in the field, a field whose name
– “artificial intelligence” (AI) – was precisely coined back in the mid-1950s
to promote a deductive, rationalist paradigm to rule out prior empiricist
efforts at inductive, example-based ML. Taking a historical stance, the so-
ciologist paused on the argumentative pattern of the “perceptron contro-
versy” from the late 1960s onwards, regarding the logical (im-)possibility
of automated ML-based “image recognition” (e.g., Olazaran 1996), while
interspersing his talk with epistemological considerations, among which
the double meaning of “probability,” statistical and psychological, as dis-
cussed in Hacking (2006[1975]). I hadn’t read Hacking’s book, The Emer-
gence of Probability, but still needed to discuss the sociologist’s argument,
as the “discussant” of his talk1.
    Eventually, the talk I had listened to inspired me a question along the
following lines:

     In his major work from 1983, Representing and Intervening, Hacking stages
     the controversy between Carnap and Popper in a long 30+ page introductory
     chapter only to conclude it, if I remember correctly, by the observation that
     their epistemological disagreement marks an Iceberg of common assump-
     tions, notably their shared neglect of experimental practice, Hacking’s topic.
     Hence my question: isn’t there something similar going on in the history of
     AI? Doesn’t the controversy between “deductive” AI, as pitched in 1955, and
     “inductive” ML, as pushed today, presuppose a common ground? What
     “common ground” is it? And how “deep” does it run?

    In response to my question, I noticed a short silence, followed by a swift
change in topic. The question, it seemed, had just triggered a “not now,
not here” (Garfinkel 1975) phenomenon. In hindsight, the conspicuous si-
lence may also be treated as constituting an inspiring starting point for mul-
tiple research directions, including the continuing challenge to write an en-
compassing history of AI/ML (e.g., Engemann and Sudmann 2018; Haigh
2021; Plasek 2016), the empirical interest of a “sociology of testing” in a field
where the means of testing (e.g., data sets, algorithms, infrastructures) are
often difficult to access, let alone replicate (Heaven 2020; Marres and Stark
2020), and a niche for “critical making” initiatives to tinker with, if not “re-
build” AI/ML-based systems (e.g., Bogers and Chiappini 2019; Lake et al.
2017; Sormani 2020). Rather than calling for a swift change in topic, the ob-
served silence invites a sustained yet different line of inquiry, too2.
    This paper, accordingly, first pauses on the “Iceberg” question and two
historical answers to it, material and conceptual, before explicating the al-
ternative assumptions of its praxeological respecification (Section 2). On
Sormani                                                                   59

this basis, the paper then develops a practice-based video analysis of a re-
cent demonstration of “machine intelligence,” the video demonstration of
an “agent system” playing the game Breakout at “superhuman level,” if not
opening the gate for “general AI” (Section 2). In probing this video demon-
stration, an episode of “remaking intelligence” in situ, the paper dwells on
the tricky interplay between machines, media, and montage, while making
explicit how particular configurations of “enchanted determinism” (Cam-
polo and Crawford 2020) are staged and locally performed. In conclusion,
the praxeological respecification of “enchanted determinism” as a situated
production will be reflected upon, as part and parcel the examined “singu-
larity moment” (i.e., when the newly developed Breakout program was
shown to surpass human play)3.


2. Background: Historical and Praxeological Answers to the
“Iceberg” Question
    If the literature that traces AI/ML in terms of controversially opposed
positions is well established, if not redundant (e.g., Minsky and Papert
1969), historical studies that chart their common ground and, in that sense,
offer a history of “machine intelligence” seem to be rarer. This section
briefly presents two such studies. On the one hand, Inventing Intelligence
(Penn 2020) offers a richly documented inquiry into the material aspects
shared by the founding figures of AI/ML in the USA of the 1950s and
1960s. On the other hand, Wittgenstein’s Remarks on the Foundations of
AI (Shanker 1998) clarifies the formal assumptions of Turing’s program of
“machine intelligence,” the program that underpins the mainstream of
AI/ML research (from the 1950s onwards, if not to the present day)4.
    Inventing Intelligence (Penn 2020) bears on the following “material as-
pects” of the joint emergence of AI/ML in the USA of the 1950s and 1960s:
    •! First, the major lines of research by the founding figures of AI/ML
         in the USA (including H. Simon and A. Newell, J. McCarthy, M.
         Minsky, as well as F. Rosenblatt) all relied on military funding –
         substantial, sustained, and at times unconditional. In one case
         (Minsky), the stop of unconditional funding allegedly led to the
         end of AI research (Penn 2020, 184-185).
    •! Second, and along with crafting (computer) code, these lines of re-
         search all implied political sociologies (Penn 2020, introduction).
         For example, H. Simon and A. Newell’s rules-based AI took inspi-
         ration from a model of “rationalized administration” (cf. Simon’s
         Administrative Behavior originally published in 1947), while F.
         Rosenblatt’s probabilistically operating ML saw, not only in the ab-
         stract neuron, but also in the “free market” an inspiring analogy
         (Penn, ibid., Chapters 2 and 3).
60                                                           Tecnoscienza – 13 (2)


     •!  Third, the promise of “machine intelligence” seemed all the more
         realistic as the research teams by the founding figures of AI/ML all
         set out working with the same mainframe computer, “IBM 704”
         (Penn, ibid., p. 129), the company’s first commercial computer fa-
         cility that allowed them to variably explore and exploit the “novelty
         of computing as a digital medium” (ibid., p. 204).
     Taken together, these aspects of emerging AI/ML research may be con-
sidered material, insofar as they facilitated such research “to get off the
ground” in three respects at least: economical, ideological, and instrumen-
tal. Conversely, Shanker’s book, cheekily entitled Wittgenstein’s Remarks
on the Foundations of AI (1998), clarifies the “formal assumptions” of
AI/ML’s common ground, Turing’s program of “machine intelligence.”
For the purpose, Shanker leverages Wittgenstein’s conceptual critique of
Turing’s program (as notably articulated in his 1937 essay). Each of Witt-
genstein’s critical points makes explicit one of Turing’s assumptions, as-
sumptions that may be listed accordingly:
     •! First, Turing’s program of “machine intelligence” assumes the pos-
         sibility of mechanical reducibility – that is, intentional conduct
         (such as “thinking,” “calculating” or, say, “reading”) is reducible
         to causal mechanisms, despite the normative terms (e.g., “rules,”
         “norms,” “reasons”) that ordinarily characterize such conduct.
     •! Second, computational complexity is assumed to define intentional
         conduct as an emergent property – that is, there is a computable
         “learning continuum” from simple mechanisms to (human) every-
         day activities, “higher forms of learning [being] built up out of sim-
         pler components” (Shanker 1998, 65).
     •! Third, mathematical formalism is supposed to inform the mechanist
         reduction as well as the claimed emergence of intentional conduct,
         regardless both of the everyday use of mathematics (“in mufti”, Witt-
         genstein 1956, Part 5, §2) and its normative character (i.e., in terms
         of “rules” or “subrules,” not mechanisms or feedback loops).
     Taken together, these formal assumptions of machine intelligence linger
on in current forms of AI/ML as their common ground, at least as part of
the common ground for their controversy narrative – for example, the idea
of the “learning continuum” grounds both “ML” (as an inductive opera-
tion proceeding from “simpler components”) and “AI” (as the deductively
defined “higher form”). The same point holds with respect to the material
aspects of AI/ML, insofar as current research continues to rely on substan-
tial funding, ideological arguments, and/or instrumental infrastructure (for
a recent study of continued military involvement, see Suchman 2022)5.
     Yet the regularly overlooked common ground does not tell us how it is
drawn upon in situ, let alone how “enchanted determinism” (Campolo and
Crawford 2020), as one variation or contingent imbroglio thereof, is per-
formed via a technology demonstration. The open question points to the
phenomenon studied in the next section. As a praxeological respecification,
Sormani                                                                                   61

the study homes in on the demonstration of a Breakout program playing at
“superhuman level,” while proceeding from two alternative assumptions.
First, the study assumes the irreducible practicality of its situated phenome-
non in the following sense:

    invariant rules [e.g., formal assumptions] and material elements can only
    account for the fact that the possibility of concrete sense-making [e.g., a
    technology demonstration] is conditioned and subject to limiting condi-
    tions. However, formalities and materialities are thereby not sufficient con-
    ditions, but only necessary conditions [for sense-making to take place],
    which always presuppose specific ordering work but do not explain it (Wal-
    denfels 1985, 26; our translation).

    Second, and in describing how the technology demonstration is done
in situ, the study nevertheless assumes that something can be learned from
the demonstration’s practical accomplishment (the “specific ordering
work” alluded to in the above quote) with respect to the historical contexts,
material and/or conceptual, that it presupposes – be it to have them
changed, re-instantiated, or modulated in a particular way. Praxeological
respecification, in that sense,

    [does] not deny the historical and social “contexts” in which social action and
    interaction take place; rather, [it] insist[s] that specifications of such contexts
    are invariably bound to a local contexture of relevancies (Lynch 1993, 125).


3. Example: A Praxeological Study of/as “Remaking Intelli-
gence” In Situ
                   Each technique [in AI] is both a method for designing artifacts
     and a thematics for narrating its operation (Agre 1997, 135; emphasis added).

     This section presents a practice-based video analysis of a recent tech-
nology demonstration, the video demonstration of a computer program
playing Breakout at “superhuman level” (see Hassabis 2017, and note 7,
below). Drawing upon the analysis, reenactment, and reanalysis of the
video demonstration, the praxeological study engages with “remaking in-
telligence” in situ, both as a topic and a resource. The video demonstration,
through the reflexive analysis of its montage, will be probed topically – that
is, the analysis will show how the news announcement conveyed by the
demonstration of the Breakout program – “it did this amazing thing, it
found the optimal strategy” (Hassabis 2017) – relies upon particular con-
figurations of “enchanted determinism” (Campolo and Crawford 2020). In
turn, the reenactment of the video demonstration, drawing upon “remaking
intelligence” in situ as a methodological resource, will allow us to tease out
the “myriad of contingences” (Maynard 1997, 98) whose tacit mastery the
62                                                            Tecnoscienza – 13 (2)


persuasive delivery of the news announcement relies upon. In so doing, the
video analysis explicates how a particular genealogy of “machine intelligence”
is folded into the video demonstration, while charting to what rhetorical effect
its formal assumptions and material circumstances are deployed6.

     3.1 Analysis

    In the video demonstration, Demis Hassabis, introduced as the “Co-
Founder and CEO of DeepMind” (see shot 1), presents a computer pro-
gram as an “agent system” and “AI” which, thanks to research and devel-
opment (R&D) at the DeepMind company, has “succeeded in playing”
Breakout, a video game from the late 1970s and early 1980s, at “superhu-
man level.” How does Hassabis pitch his presentation? And how does his
pitch appear convincing? That there might be a persuasion problem can
already be gleaned from the vocabulary used to summarize the gist of the
video demonstration. Indeed, the terms used – “agent system,” “succeeded
in playing,” at “superhuman level” – all draw upon the questionable formal
assumptions (i.e., mechanical reducibility, computational complexity, and
mathematical formalism) and characteristic material aspects (e.g., a partic-
ular funding, organizational, and computing infrastructure) of “machine
intelligence” – that is, as its necessary common, yet impossibly sufficient
grounds. How then does the montage of the video demonstration solve (or
dissolve) this persuasion problem? The transcript-assisted video analysis in
this section offers a two-part answer to this empirical question. First, it ex-
amines how and what “ordinary circumstances” are configured for the news
announcement to appear credible (3.1.1). Second, it takes a closer look at the
“news announcement” itself by explicating its sequential organization (3.1.2)7.

     3.1.1 Configuring the ordinary circumstances of the news announcement

    Breakout is at first sight a “highly straightforward video game” (Reeves
et al. 2009, 207). Developed and marketed in the late 1970s and early 1980s
by Atari, the video game has more recently become a test-bed for probing
and improving ML algorithms, if not demonstrating “AI” as an emergent
property of their successful testing. The video demonstration of present
interest also relied upon this R&D strategy, a strategy whose (computer)
scientific success had also been reported in a previous peer-reviewed pub-
lication (Mnih et al. 2015). Not all viewers of the video demonstration, let
alone the documentary movie in which it figures, could and can be assumed
to be computer scientists or regular readers of Nature though. Hence, the
sole announcement of the Breakout computer program having “found
the optimal strategy, which is to dig a tunnel” (see Excerpt 1, as indicated
by white arrow) may fall short of its intended news value, as a convincing
demonstration of cutting-edge AI, achieving “human-level control through
Sormani                                                                      63

deep reinforcement learning” (Mnih et al. 2015) or somehow even going
beyond “human-level control” (as we shall see shortly).




          Excerpt 1. News item in video demonstration, as stated by Demis
          Hassabis (shots 17-18). (Transcript prepared by the author)

     As other performative expressions, news announcements only work un-
der “ordinary circumstances” (Austin 1962, 52) as convincing communica-
tive moves. The video demonstration may first be examined for what circum-
stances it (re-)configures and how it does so to convey its core message. In
the transcribed fragment (see Appendix I), these circumstances are config-
ured in familiar terms, as the autobiographical reflections of a successful en-
trepreneur, where the image of the latter (identified as “Demis Hassabis, Co-
Founder & CEO [of] DeepMind,” shot 1, video column) and the expression
of the former (in the first person singular, “when I was a kid, I loved playing
games”, ibid., audio column) elaborate each other. Accordingly, Hassabis’
arrival at the conference venue (at “Oxford University, UK,” shot 2) is not
only shown and told as the culminating part of his extraordinary career as a
child prodigy (starting “off with board games like chess,” having become the
“Co-Founder & CEO [of] DeepMind”), but this culmination is also sug-
gested to have been informed by a major insight all along: “computers were
this sort of magical device that could extend the power of your mind” (shots
3-4). Taken together, these material aspects of “enchanted determinism”
(i.e., regarding the male child prodigy turned successful contemplative en-
trepreneur) set the stage for the video demonstration of “machine intelli-
gence” in the form of a particular news announcement: the Breakout pro-
gram’s “optimal strategy,” if not its “potential for general AI” (shots 17-19)8.

    3.1.2 Drawing upon the sequential organization of the news announcement

    To whom was the “news announcement” made, the announcement of
the new Breakout program’s performance at play? On the basis of the tran-
scribed fragment (in Appendix I), two audiences can be identified: the in-
tended audience of the documentary movie as part of which the video
demonstration is shown (i.e., AlphaGo. The Movie, Krieg and Kohs 2017),
64                                                              Tecnoscienza – 13 (2)


the audience present at Hassabis’ Oxford University lecture which included
the video demonstration of the new Breakout program to begin with (as
shown in shots 7, 9, 10, etc.). A singular achievement of the movie’s montage,
then, is the production of a seamless articulation of scenes and sounds for
these two publics to be addressed or at least shown to be addressed (a point
we shall return to, see our reenactment and reanalysis sections below). In any
case, the video demonstration addresses a broader audience (be it at home,
in and/or via the lecture hall) than a specialist readership in science or com-
puter science (e.g., as addressed by Mnih et al. 2015). How, then, does the
documentary video’s montage allow us, as a projected community, to:

     see through [our] cultural knowledge [and to] understand the filmic image
     and sequence […] in much the same way, and by reliance on the selfsame
     resources that we use to understand the perceptual world around us, a per-
     ceptual world of activity and interaction (Jayyusi 1988, 272)?

     In answer to this question, the present section examines how the news
announcement of the stunning performance of the new Breakout program
– “it did this amazing thing, it found the optimal strategy” (Hassabis 2017)
– was delivered. As we have seen, the entrepreneur’s autobiography, if set-
ting the stage for the news announcement, wouldn’t be sufficient to convey
it. In turn, the transcribed fragment (in Appendix I) allows us to examine
how the news announcement of the program’s “optimal strategy” is actu-
ally delivered, with particular reference to the interactional resources that
the “sequential organization” of its delivery relies upon9.
     Notice that the news announcement in question is not only progressively
delivered (audio column, from shot 9 onwards), but also shown as it is being
fulfilled (video column, from shot 11 onwards). Moreover, the successive
shots, organized in terms of as many “say-shows” (Garfinkel 2002, 177), artic-
ulate a particular “news delivery sequence” (Maynard 1997). From ordinary
conversation, the sequence borrows its constitutive parts: an announcement,
followed by its response, an elaboration, and a final assessment (ibid., p. 97).
Yet the parts are not distributed as reciprocating turns at talk, as in conversa-
tion, but across the successive “say-shows” that compose the video episode.
     Accordingly, the “announcement” is first stated (“so I’m gonna show
you a few videos of the agent system, the AI,” shots 9-10, audio column),
for the viewer then to be enabled to reach a first “response,” at home or in
the local audience (as a male public member is shown to pay close attention
(shot 10, video column)). The subsequent “elaboration,” then, suggests
that not only the “agent system has to learn everything for itself” (shot 12,
audio and video column), but also that its performance, after having be-
come “after three hundred games […] about as good as any human can
play this” (shot 15), eventually surpasses human play by doing an “amazing
thing” (shot 17) – that is, finding the “optimal strategy, which is to dig a
tunnel” (shots 17-18, including a close-up, as shown in Excerpt 1 above).
Sormani                                                                            65

Finally, and after having drawn laughter from the audience (shot 18, audio
column), the “assessment” of the performance is left to the speaker (Demis
Hassabis, as shown in shots 19-20) for contrasting qualifications – that is,
as an expression of the limited Breakout skills of his “AI developers,” on
the one hand (shot 18, audio column), and the instructive “potential for
general AI,” on the other (shot 19, audio column).
     Taken together, these interactive moves and their audiovisual montage fos-
ter “commitment evaluation routines” (Lampel 2001, 304), rather than a ques-
tioning stance or critical inquiry, regarding the presented technology and its
discursive framing. In a nutshell, a second configuration of “enchanted deter-
minism” is the manifest result (i.e., regarding the Breakout program’s “poten-
tial for general AI,” shot 19, calling for a “next step now,” shot 20)10.

    3.2 Reenactment(s)

    In the “perceptual world of activity and interaction,” to use Jayyusi’s
felicitous phrase (1988, 272), conversationalists “shape each component
[of a news delivery sequence] according to a myriad of contingences,” as
Maynard points out (1997, 98; emphasis added). The tentative reenactment
of the video demonstration of the “singularity moment” – the moment at
which the newly developed Breakout program was shown to surpass human
play – allows us to tease out (some of) its critical contingencies – that is,
contingencies which proved critical to articulating that news item in the
first place (i.e., by Demis Hassabis, as shown in the Oxford University lec-
ture hall, as staged and seen in the documentary movie). Yet different reen-
actments allow one to tease out different kinds of contingencies. This sec-
tion presents three sets of them (see “reenactments A, B, C” below), before
considering them as an ensemble of “tutorial problems” (Garfinkel 2002)
for reanalyzing the initial transcript in the next section, including the “crit-
ical” or “incidental” character of the contingencies identified (i.e., as con-
stitutive of the “ordinary circumstances” of the news announcement)11.

    3.2.1 The classic “home console” reenactment (A)

   In 1983, the book Pilgrim in the Microworld was published, D. Sud-
now’s reflexive ethnography of playing Breakout at arcade halls and with
home consoles (see Appendix II). As a later review puts it,

    Sudnow becomes his phenomenon: he hangs around arcades, plays the game
    with his children, and for long, long hours immerses himself in playing game
    after game of Breakout. His focus is on how an array of moves develop and
    build on one another through long-term play. (Reeves et al. 2009, 209).

    Long-term play? In this respect, Sudnow’s ethnography makes three inter-
esting observations, as its author develops and deepens his “video skill” at and
66                                                                Tecnoscienza – 13 (2)


with his home console (as a “re-enactment” of the video game as first observed
to be engaged in by his children, friends, or other aficionados at play).
    First, Sudnow’s reflexive ethnography offers a succinct characterization
of the overall goal of Breakout: “The overall goal, fat chance, is to eliminate
the entire barricade until paddle and ball are all alone in empty court, vic-
tors” (Sudnow 1983, 35). Second, the book indicates the best opening move
to reach that overall goal:

     the immediate object is to chip through to the open space on the other side,
     and once you’ve made this Breakout the ball rebounds like crazy between
     the far wall and the band [of bricks] […]. (Sudnow 1983, 34; see Fig. 1)




               Figure 1. The best opening move in and for playing
               Breakout. (Photograph taken by the author)



           Figure 1. The best opening move in and for playing Breakout.
           (Photograph taken by the author)

   Third, the book explains that the overall goal, despite or precisely be-
cause of sustained and skillful play, is often not reachable. “Lockups” are
the result, which Sudnow describes as follows:

     With nearly nothing on the screen, the ball gets into a triangular pattern
     [sic] so immobile and regular you could take your hand off the knob [i.e.,
     joystick], walk away for a week, and come back to find it just where it was.
     (1983, 85; see Fig. 2, ibid.)
Sormani                                                                        67




          Figure 2. The unreachable overall goal in and of playing Breakout.
          (Photograph taken by the author)

    Taken together, these three observations invite us to re-examine the
video demonstration, its initial transcription (as to be found in Appendix I),
as well as its transcript-assisted video analysis. Before doing so, let us con-
sider two more recent reenactments of the video demonstration, however12.

    3.2.2 A contemporary “Zoom lecture” reenactment (B)

    In a recent “Zoom lecture,” I decided to reenact Hassabis’ video
demonstration by misreading its initial transcript as a roleplay script, while
presenting the Breakout program to the remote audience by holding my
laptop computer in front of the camera. The result: a deliberately “poor
image” (see Fig. 3). Any lecturer with similar equipment could have done
the reenactment, a possibility highlighted by the black-barred eyes (ibid.).
68                                                            Tecnoscienza – 13 (2)




          Figure 2. The “Zoom lecture” reenactment in and as its “poor
          image” (Steyerl 2010). (Screenshot taken by the author)
     The laptop screen showed the Breakout program. I also had put up and
redrawn the conference poster, entitled “Exploring Complexity,” which was
to be seen next to Hassabis during his video demonstration at Oxford. Some
classic music found on the internet rounded off my “Zoom lecture” reenact-
ment of Hassabis’ demonstration. Both despite of and due to these practical
efforts, my reenactment encountered innumerable problems of “say/show”
coordination, some of which we shall draw upon in the reanalysis of the ini-
tial transcript, too (hence the allusion to the deliberately “poor image”).

     3.2.3 The museum “game station” reenactment (C)

    At a local museum of cultural anthropology, I noticed that a Breakout
“game station” had been installed (see Fig. 4 below) as part of its current
AI exhibition. What for? The installation served two purposes at least.
First, it offered a stepwise explanation of “reinforcement learning” by us-
ing the Breakout program as a paradigm case to illustrate the progressive
improvement of such machine learning (that is, “from one day to another,”
rather than in terms of “hundreds of games”). Second, the installation in-
vited its user to press “start” and use the “joystick” to play Breakout, if only
to eventually contrast his or her expectably “slow play” with the rapid moves
of the program (as shown during the “reinforcement learning” explanation).
Sormani                                                                       69




          Figure 3. The Breakout “game station” as installed at the museum.
          (Photograph taken by the author)

    On both counts, the installation not only took its cue from Hassabis’
video demonstration, but it also turned his persuasive argument into a lived
experience, the lived experience of failing against the seeming superiority
of the machine – that is, the Breakout program which was shown to master
the game on-screen (as in Hassabis’ demonstration) in contrast to the some-
what clumsy moves that were only possible to be played with the joystick
due to its slow “reaction time” (as part of the museum installation). How-
ever, this and other handicaps proved heuristic, when it came to reanalyz-
ing the initial transcript of the video demonstration.

    3.3 Reanalysis

    The news announcement of the computer Breakout achievement drew
upon a sequential organization from ordinary conversation, while building
upon the prior depiction of its “ordinary circumstances” (including the
male child prodigy turned contemplative DeepMind CEO, his interested
audience, the spectacular lecture hall, and so on). The ensuing reenact-
ments of the Breakout achievement and/or its video demonstration had me
pause on (some of) its/their critical contingencies. In what sense do they
afford us with “tutorial problems” for reanalyzing the video demonstra-
tion? In answer to this question, it is worth revisiting the demonstration’s
initial transcript in the light of the encountered contingencies, if only to
70                                                            Tecnoscienza – 13 (2)


assess the manifest contribution of their practical mastery to the video
demonstration (e.g., “critical” or “incidental”)13.
    What more is to be learned from Sudnow’s reflexive ethnography of
Breakout (A) on Hassabis’ video demonstration? In a nutshell, Sudnow’s
classic “home console” reenactment challenges Hassabis’ video demon-
stration both in its premises and conclusion. To begin with, Hassabis not
only characterized “games [as] very convenient in that [many of them]
have scores” (shot 7, audio column), enabling the “easy […] meas-
ure[ment] [of] incremental progress” (shot 8, audio), but also identified
Breakout as an exemplar of this type of game. In turn, Sudnow’s reenact-
ment calls into question this twofold premise. First, Sudnow does not re-
port his playing experience in terms of “incremental progress” (e.g., reach-
ing an average player’s skill after “three hundred games,” shot 15), but as
part of a uniquely qualifying situation (setting out with “one evening,” Sun-
dow 1983, 35). Second, if the “overall goal” (Sudnow, ibid.) of Breakout
could be expressed by an optimal “score” (Hassabis, shot 7), that doesn’t
mean that this optimum can or could be “easily” measured, let alone
reached. To the contrary, Sudnow’s eventual mastery of Breakout led to
“lockups,” the repeated production of ball trajectories around remaining
ceiling or wall parts still to be removed. A never-ending game was the result14.
    Returning to amateur play may then challenge the video demonstra-
tion’s conclusion, the presentation of the new Breakout program as demon-
strating a “potential for general AI” (shot 19). In turn, the presentation
may be re-inspected for how it was delivered to make that conclusion plau-
sible. This seems to have been done in three ways at least. Crucially, the
opening move in Breakout as ordinarily played – the game’s “immediate
object” (Sudnow 1983, 34) – was cast as the program’s final discovery –
this “amazing thing […] it found the optimal strategy” (shots 17-18). Ret-
rospectively, this discovery was suggested to be the result of “very easy to
measure incremental progress” (shot 8): not only was the overall goal of
the program simplified – from “eliminat[ing] the entire barricade” (Sud-
now 1983, 35) to “break[ing] through this rainbow-colored wall” (shot 11)
– but the pursuit of this simplified goal was shown to be “easy to measure”
(in swiftly stated numbers of games played, shots 15a and 17a, and corre-
sponding progress, shots 15b and 17b). Prospectively, the demonstrated
discovery was suggested to have taught the “optimal strategy” of Breakout
not only to the broader audience (via a close-up shot, see shot 18, video
column, and Excerpt 1), but also to “amazing AI developers” themselves
(shot 18), thus suggesting “potential for general AI” (shot 19)15.
    What more is to be learned from the “Zoom lecture” re-enactment of
Breakout (B) and its “game station” installation at the museum (C), respec-
tively? These two reenactments highlight two complementary sets of “or-
dinary circumstances” that the news delivery of the Breakout program and
its “potential for general AI” hinges upon.
Sormani                                                                        71

     On the one hand, the misreading of the transcript as a script – to reenact
the scene and setting of the Breakout program demonstration via a “poor
image” (Steyerl 2010) strategy – cast into sharp relief not only that scene and
setting as the background of this demonstration as its figure (including the
“taxi ride” to the prestigious location, “Oxford University, UK,” and the
spectacular lecture hall), but also how the “figure-background” pair is as-
sembled through its audio-visual montage: through a quick alternation of
wide-angle and close-up shots (1-17), diversely accentuating the oral presen-
tation, via multiple camera shots, and culminating in the close-up shot of said
“optimal strategy” (shots 17-18). The montage, in a nutshell, both shows and
dramatizes “the way things work from the inside” (Wieder et al. 2007, 249).
Upon re-inspection, however, the quick alternation of shots also becomes of
critical interest, insofar as it makes disappear the lack of diversity of the lec-
ture audience (indeed, the audience seems to be composed of male members
only, an arguably select “computer science” audience)16.
     On the other hand, the installation of the “game station” at the museum
required its visitors and potential players to use the joystick to play Breakout.
As I engaged in playing, the installation seemed to operate like a “one-way
mirror.” While it progressively produced the appearance of the Breakout
program’s transparent operation (as I was shown its improved play on the
screen, similarly to Hassabis’ video demonstration), the installation left in the
dark how it entailed simultaneous player incapacitation (as my Breakout
moves were made difficult due to the slowly reacting joystick). The latter
provided an embodied, material condition for the former operation to be-
come visible, if not credible. The initial video demonstration may be reex-
amined accordingly. While it draws upon the formal assumptions of “machine
intelligence” (mechanical reducibility, computational complexity, mathemati-
cal formalism), the demonstration conspicuously leaves in the dark the material
conditions for them to operate so transparently (i.e., as shown via the short
video, demonstrating the excelling “agent system” via the simple Breakout in-
terface, yet passing over the technical details of its operation)17.


4. Conclusion: Beyond “Enchanted Determinism” – Yet An-
other Ambivalent Hybrid

    This paper first reminded readers of the common ground, both concep-
tual and material, of AI and ML, a common ground that may be traced to
Turing’s program of “machine intelligence,” on the one hand, and to the
shared efforts of AI/ML research in the USA to engage in “(re-)inventing
intelligence” via computer programming in the 1950s and 1960s, on the
other. How is this common ground brought to play in a current situation?
In answer to this question, the paper developed a practice-based video
analysis of a recent demonstration of an “agent system” excelling at
Breakout, the Atari video game from the 1970s/1980s, and thereby
72                                                             Tecnoscienza – 13 (2)


potentiating “general AI” (Hassabis 2017). In addition to a detailed tran-
script, three contrasting reenactments of the video demonstration were drawn
upon to explicate how its scenic plausibility was achieved. In the process, I
described successive configurations of “enchanted determinism” (e.g., a
child prodigy turned contemplative CEO, the agent system’s “general AI”
potential), while embedding the analyzed sequence of news delivery in its
“ordinary circumstances” (e.g., an Oxford lecture hall, a simplified game, a
select audience, mostly male). The praxeological description, then, suggested
how the video demonstration of the new Breakout program framed its “deep
learning” (or “reinforcement learning”) achievement in the terms of linger-
ing 1950s AI convention, both formal and material (e.g., a game-based “hu-
man-machine” comparison, in addition to the initially mentioned terms)18.
    However, the latter suggestion may be discussed further. Therefore, we shall
reflect on “enchanted determinism” as a situated production – that is, as part and
parcel of the peculiar “singularity moment” that this paper just described.
    On the face of it, the presently analyzed, reenacted, and reanalyzed
video demonstration of the Breakout program appears as a telling instance
of “enchanted determinism,” as indeed “magical mystery and technical
mastery curiously work together” (Campolo and Crawford 2020, 4). Not
only the initial allusion to computers as a “magical device [extending] the
power of your mind” (shots 3-4) already nurtures this impression, but also
the careful montage of the video demonstration suggests this conclusion,
where the expression “careful montage” alludes to the persuasive demon-
stration of having the Breakout program appear as an “agent system” (shot
9), excelling at Breakout (shots 17-18), and showcasing the “potential for
general AI” (shot 19). Indeed, the video demonstration borrows a
longstanding framing of AI (i.e., the game-based “human-machine” com-
parison), while gesturing at its particular operation in terms of “machine
learning” (ML, measuring the “incremental progress” from training session
to training session, shots 12-18) with the help of a simple interface (i.e., the
game interface of Breakout). Hence also the possibility of identifying mul-
tiple configurations of “enchanted determinism”19.
    Yet the observed multiplicity also hints at a more intricate genealogy of
“machine intelligence” than its dualist AI/ML controversy narrative sug-
gested, a dualist narrative which the notion of “enchanted determinism”
seems to echo (if only insofar as “enchantment” presupposes its disen-
chanted opposite). Interestingly, this more intricate genealogy is to be
found across STS and computer science. Indeed, not only the history of
STS approaches to AI is full of ambivalent hybrids, aka “human/machine
mixings” or “sociomaterial assemblages” (e.g., Suchman 2008), but so is
the history written by AI researchers themselves – canonical (e.g., McCar-
thy et al. 2006[1955]) or critical (e.g., Agre 1997). The canonical project
grouped diverse approaches to “machine intelligence” under the contested
label (“AI”), while the critical approach teased out the philosophical rhet-
oric folded into technical practice(s).
Sormani                                                                               73

   Against this backdrop, the “singularity moment” described in this pa-
per appears as the contingent introduction of yet another ambivalent hy-
brid – that is to say:

    the general intelligence that is put to test is modelled after a very specific
    and singular understanding of what human intelligence involves. It univer-
    salizes the idea of a player programmed into [a] 1980s Atari video [game]
    and restricts the task of an agent to outperforming this benchmark. (Bruder
    2021, 80; emphasis added).

   However, this peculiar hybrid not only confronts us with “a radically
provincial idea of human creativity, intelligence, and ability,” an idea bor-
rowed from Western “video game design” (ibid.), but it also rehearses an
older trajectory, trope, and trick:

    not one of rupture but of remaking – a [yet again] recalibrated “origin” of AI
    that re-contextualizes research fashions [e.g., “neuroscience”] in relation to
    local contingencies [e.g., video gaming]. (Penn 2020, 199; emphasis added)

    – at least for now.


Acknowledgements

     Before and after the STS Italia Summer School at the University of Padova, 27
September-1 October 2022, this paper was presented at different workshops and
conferences, including the Jahrestagung of the Special Collaborative Research Cen-
ter (CRC) “Media of Cooperation” at Siegen University, Germany, 19-21 Septem-
ber 2022, and the Journées d’étude on “AI in the Humanities” at EHESS, Paris, 13-
14 October 2022. My acknowledgments go to the organizers and participants of
these meetings, as well as to Florian Jaton, Noortje Marres, and an anonymous re-
viewer, for their comments on the evolving paper. Its publication was supported by
the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Pro-
ject-ID 262513311 – SFB 1187 – and facilitated by the Musée de la main UNIL-
CHUV in Lausanne, Switzerland. Remaining shortcomings are mine.

Notes
    1
      Empirically, the sociologist’s talk was firmly rooted in digital data, reminding me
of A. Rouvroy’s nostalgic observation: “knowledge is not produced about the world an-
ymore, but from the digital world. […]” (2013, 147).
    2
      In the vein of recent IT developments, it has become common currency to pitch
new types of ML (in particular “deep learning”) against older forms of AI (prioritizing
“intelligence simulation”), be it in terms of “human-aided” or “beyond human” types
of ML (e.g., Fazi 2021; Mühlhoff 2020). For a longitudinal bibliometric study of the
AI/ML controversy, see Cardon et al. (2018).
74                                                                       Tecnoscienza – 13 (2)

     3
       In their recent paper, Campolo and Crawford (2020) introduce “enchanted deter-
minism” as a sociological gloss to discuss the paradoxical rhetorical “enchantment” of
ML (i.e., in terms of “magical powers” or otherwise unexplainable forces) at the very
moment of its successful statistical operation (i.e., via determinate, if not deterministic,
procedures of mathematical optimization). In turn, this paper examines the suggested
paradox in and through the mentioned “singularity moment,” while drawing upon prior
work on technology demonstration (e.g., Lampel 2001; Reeves et al. 2016) and computer
advertising (e.g., Aspray and deB. Beaver 1986). For a recent “state of the art,” see
Rosental (2021), and from a media historical perspective, Natale (2021).
     4
       On the emerging domain, with a particular focus on the UK, see already Fleck (1982).
     5
       In turn, Fazi’s conceptual discussion of the “paradoxical condition of logico-math-
ematical abstraction” (2021, 70) in current ML systems, the condition of both relying
on and going beyond human abilities of computation, echoes Wittgenstein’s critique of
Turing’s program (see, again, Shanker 1998). Hence, “enchanted determinism” (Cam-
polo and Crawford 2020), and “deceptive media” (Natale 2021) more specifically, may
be understood as part of, and pragmatic responses to, that paradoxical condition.
     6
       The video analysis, in addition to its transcript-mediated character, will draw upon
reenactments of the analyzed demonstration below (in Section 3.2). This additional
move defines the video analysis as a practice-based one, while making possible a reanal-
ysis (see also Sormani 2016, 2019), if not a critique (e.g., McHoul 1994). Excerpts in this
section are taken from and refer to the transcript of the video demonstration (in Appen-
dix I). The video demonstration is included in AlphaGo. The Movie (Krieg and Kohs
2017), a documentary movie which showcases the development of AlphaGo, a successor
program to the presently examined Breakout playing program (e.g., Binder 2021; Mair
et al. 2021). Both programs were developed by Google-owned DeepMind, a London-
based company specializing in “neuroscience-inspired AI” (Hassabis et al. 2017).
     7
       The video analysis focuses on the Breakout demonstration as it is shown in the
documentary movie (Krieg and Kohs 2017). For its initial presentation, as part of a Na-
ture journal article and an invited lecture at Oxford University, see Mnih et al. (2015)
and Hassabis (2016), respectively.
     8
       As Hassabis is still shown to be arriving at the conference venue (shots 5-6, video
column), his lecture is already to be heard (ibid., audio column). Why? Note that the
initially audible part of the lecture follows his audible reflections in the first person sin-
gular. A smooth transition is thereby suggested between his autobiographical reflections
on “board games,” “computers,” and so on (shots 1-4, audio column), and the R&D
activities of the company that he has been shown (in shot 1) and will be heard to repre-
sent: “virtual environments and games […] we [at DeepMind] think they are the perfect
platform for developing and testing AI algorithms” (shots 5-6, audio column). For fur-
ther analysis along these lines, see Wieder et al. (2007, 254-255).
     9
       This sequential organization appears to be a regularly used conversational resource
in technology demonstrations – hence the possibility for the present analysis to draw
upon prior analysis (e.g., Sormani 2019). Perhaps because of its mundane character, the
sequential organization of news delivery has largely escaped the rich literature in STS on
technology demonstrations (e.g., Rosental 2021).
     10
        Instead of “focus[ing] attention on problems and limitations [or elaborating on
factual information], commitment evaluation routines […] focus on the achievements
and future potential of the new technology” (Lampel 2001, 304; emphasis in original).
Conversely, one may ask: “But what form of intelligence is this” (Bruder 2021, 79)?
     11
        Garfinkel’s rationale for attempting to (re-)enact Galileo’s “inclined plane demon-
stration” is worth quoting in this respect: “The experiment on which we report was set
Sormani                                                                                   75

up, not to figure out how Galileo’s experiment did work but rather to discover what
would make it not work, what contingencies would lose the phenomena. Because these
would then be [critical] contingencies that Galileo would have to have taken into ac-
count. And indeed when you find out what they are, you can see that certain features of
the design of his experiment are designed to take those contingencies into account”
(Garfinkel 2002, 264, note 2; emphasis added).
     12
        For a reedition of Sudnow’s 1983 Pilgrim, see Sudnow’s 2020 Breakout.
     13
        In Ethnomethodology’s Program, Garfinkel (2002) characterized its studies’ “re-
sults,” empirical and pedagogical, as “tutorial problems” (p. 145) – that is, as problems
which disclose “members’ discipline-specific procedures,” on the one hand, and lend
themselves to be discussed with practitioners (aka “members”) “tell[ing] me [the ana-
lyst] what I’m talking about” (ibid.), on the other.
     14
        Technically, the “optimal strategy […] to dig a tunnel around the sides” (shots 17-
18) may constitute a further case of “specification gaming” (Krakovna et al. 2020), inso-
far as that Breakout strategy allows a player to maximize the score, yet without finishing
the game. For further discussion of this “flipside of AI ingenuity,” see Krakovna et al.
(2020) and Bruder (2020:78-81). I shall return to this argument in the conclusion.
     15
        This suggestion, again, was made without showing how the Breakout program
reaches its overall goal to “eliminate the entire barricade” (Sudnow 1983, 35), assuming
that it is able to reach that overall goal. Nota bene: the reenactment-based reanalysis bears
on how the video demonstration disables “critical evaluation” (Lampel 2021, 305), not on
the evidence and analysis by the supporting paper in computer science (Mnih et al. 2015).
     16
        To have this lack of diversity disappear seems important from a producer’s stand-
point, if only to pitch the video demonstration to a broader audience, the intended au-
dience of AlphaGo. The Movie (Krieg and Kohs 2017).
     17
        The qualifier “conspicuously” alludes to how the “ordinary circumstances” told
and shown (e.g., the male child prodigy turned DeepMind CEO arriving at Oxford)
manifestly omit how the Breakout program was set up to achieve its demonstrated per-
formance (i.e., culminating in discovering “this amazing thing […] the optimal strategy,”
shots 17-18). Its material “set-up” includes various training rounds in “reinforcement
learning” and many other technical details (see Mnih et al. 2015). “Specification gaming,”
from that perspective, may not be a problem, but the aim (see, again, Krakovna et al. 2020).
     18
        If early AI envisaged to “reduce epistemology to code” (Penn 2020, 199), then the
examined demonstration offered a recurring version of that project, namely to “reduce
play to performance” (see Section 3).
     19
        To the “child prodigy” (1) and “general AI” (2) configuration, we may add that of
the “obscure(d) backstage” (3), insofar as the video demonstration appeared to mobilize
the former (1) to account for the latter (2), instead of dwelling on its technical explana-
tion (see Mnih et al. 2015) or self-critical qualification (Krakovna et al. 2020).


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Appendix I
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Sormani                                                    85

Appendix II: Pilgrim in the Microworld Book Cover (1983)
