about

emma | 10/07/2017

Hello! I’m Emma Stamm. Welcome to my website.

I’m a PhD candidate in ASPECT and instructor in the Political Science department at Virginia Tech. My research is interdisciplinary, spanning political theory, continental philosophy and STS. My dissertation addresses the epistemic impact of data science methods, especially artificial intelligence and machine learning, by exploring how they are used in research on psychedelic-assisted psychotherapy.

My curriculum vitae is available at this page.

I am also a freelance writer, with recent bylines in Real Life Mag, The New Inquiry, 2600: The Hacker Quarterly and more. Here are links to select recent publications.

I’m on Twitter @turing_tests. More academic content, including conference presentations and course syllabi, are at my academia.edu site.

You may contact me via email: stamm@vt.edu.

Thank you for visiting!

SPECTRA 7.1

emma | 03/29/2019

The latest edition of SPECTRA is now available online: https://spectrajournal.org/14/volume/7/issue/1/

SPECTRA is a peer-reviewed journal of critical, eclectic scholarship that crosses disciplinary bounds. 7.1 includes articles on algorithms and phenomenology, the changing concept of “failure” in neoliberal work environments, and state power after Max Weber. There is also an interview with political theorist Michael Shapiro and reviews of Wendy Brown’s Undoing The Demos and Christian Fuchs’s Digital Demagogue.

I’ve been part of the SPECTRA crew for a year and will step up as co-editor for 7.2. As soon as it’s ready, the call for papers will be posted to the site — I’ll also post here.

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emma | 03/19/2019

I’ll be speaking at six conferences this spring. These talks are all based on my dissertation. In chronological order:

1. ASPECT Graduate Conference, 21-23 March. Talk title: “Algorithmic Determinacy and Interpretative Psychedelic Science.” Date/time of presentation: 22 March, 11AM-12:45PM

2. Theorizing the Web, 12-13 April. Talk title: “The Electric Kool Aid Turing Test: Psychedelics, Phenomenology and Automated Intelligence.” Date/time of presentation 11 April, 1:30PM-2:45PM

3. Western Political Science Association, 18-20 April. Talk title: “Machine Learning and The Algorithmic Interpellation Of The Future.” Date/time of presentation: 20 April, 10-11:45AM

4. Gender, Bodies, Technology, 25-27 April. Talk title: “Acid Feminism: Psychedelic Dimensions of Gender Performativity ” Date/time of presentation: 26 April, 1:15-2:45PM

5. Society for Philosophy of Technology, 20-22 May. Talk title: “Abducting Intelligence: Psychedelic Research Methods and The Epistemic Limits of Machines.” Date/time of presentation TBA

6. Computer Ethics-Philosophical Enquiry, 28-30 May. Talk title: “Psychedelic Science, Digital Automation and Risk.” Date/time of presentation TBA.

Here are the abstracts for 2, 3 and 4:

— “Psychedelic Science and Algorithmic Governance”

In this paper, I argue that justifications given for the use of qualitative and non-digital methods in psychedelic drug research can be leveraged toward a theoretical critique of artificial intelligence. I draw from scholarship that uses non-digital techniques, and in particular interpretative phenomenological analysis, as hermeneutic devices for verbal data produced by psychedelic psychiatry research. I also invoke the machine learning functions of induction, generalization and classification — described briefly and without needless jargon — to explore the problems of subjecting data from non-digital psychedelic science to processing by A.I. systems. This in turn indicates how the rationale for non-digital frameworks in psychedelic science envelops an immanent critique of the notion of automated and/or artificial “intelligence.”

I begin by stating that the psychedelic renaissance, i.e. the current resurgence of medical research on psychedelics, includes an emerging paradigm which emphasizes interpretive and self-reflexive methodological frameworks in evaluating verbal “trip reports” given by human research subjects. This reflexive approach recognizes a certain Gestalt characteristic of psychedelic therapy, wherein the whole of the experience is felt to supersede the sum of any discrete and/or computable research variables — factors that might be reductively expressed as digital (i.e. discrete) data and/or machine learning classifiers. This, I contend, makes information from non-digital psychedelic research resistant to effective processing in A.I. systems. It furthermore means that a comprehensive picture of the medically effective properties of psychedelics must include information made by non-automated and non-digital methods.

I proceed to offer that my argument for the limits of digital automation techniques in the arena of psychedelic drug research can be extrapolated to a more general critique of A.I. Here, I return to my claims regarding the epistemic shortcomings of inductive, classificatory and probabilistic reasoning, functions which are enshrined in machine learning and A.I. Extant literature on psychedelic science indicates novelty and pattern-breaking as central to these drugs’ psychiatric effectiveness. In other words, the therapeutically active qualities of psychedelic drugs cannot be expressed by knowledge-production functions based on inductive reasoning (as in A.I.). This in turn indicates that psychedelic science can be leveraged toward a general theory of knowledge which cannot be produced or informed by A.I.

— “Machine Learning and The Algorithmic Interpellation of the Future”

In this paper I explore the impact of machine learning algorithms on the data they process. I make a two-part argument. First, that the inductive basis of machine learning functionality forecloses their capacity to produce outcomes with no ancestral relation to their training data. This has a protracted winnowing effect on content, which is a political concern due to the growing presence of machine learning algorithms in various facets of communal and individual life. I maintain that the a priori restrictions placed by algorithms on data constitute an emerging hegemonic order. Second, that an intervention in this scenario can be staged through an examination of non-digital, interpretative and self-reflexive methods in empirical science. The restrictions of machine learning, I offer, are drawn into high relief by exploring scientific studies in which it is deemed methodologically insufficient. This reading indicates predicates of intelligence which allegedly “intelligent” automation fails to self-generate.

I substantiate the first part of my argument with work from three philosophers of digital media. Antoinette Rouvroy writes on “algorithmic governance,” the automated retraction of possibility from probability in digital content. Matteo Pasquinelli argues that machine-learning-based systems, including A.I., cannot be intelligent, as their inductive functionality does not compass the creativity constitu genuine intelligence. Adrian Mackenzie applies a Foucauldian framework to algorithmic determinacy, highlighting the role of contingency in knowledge production. These thinkers provide me with materials for the second part, in which I use cases from experimental psychiatry to theorize potential sites of resistance to the algorithmic cancellation of the future.

— “Acid Feminism: Psychedelic Dimensions of Gender Performativity”

What can psychedelic science bring to our understanding of gender identity? In this paper, I merge insights from the burgeoning field of psychedelic psychiatry with classic notions of aesthetic and gender identity performance. My argument is that psychedelic psychiatry is well-positioned to affirm and extend the project of “troubling” gender norms, although this will only be achieved through a critical approach to methodology.

I open by noting that research on psychedelics suggests a relationship between their therapeutic efficacy and their tendency to subvert concrete notions of self — including that of a static and context-agnostic identity. I then invoke transdisciplinary writings on psychedelic drugs to remark on their capacity to dissolve hardened subjectivities in a more general sense, challenging the precepts of mainstream psychiatry. Scholars Byung Chul Han, Christopher Letheby and Nikolas Rose have critiqued the tendency of psy- disciplines to reify and constrain subjectivity; Letheby specifically connects this intervention to new findings from psychedelic research. Meanwhile, medical anthropologist Nicolas Langlitz has demonstrated a connection between the “unreal” phenomenological character of psychedelic therapy and theories of alienation in theater and art performance, particularly Bertolt Brecht’s Verfremdungseffekt. I explore Langlitz’s argument that the sense of unreality shared by the psychedelic and theatrical experience contains the potential to loosen the notion of the concrete, isolated subject which rules over the popular gender imaginary.

I proceed to summarize and supplement the aforementioned thinkers as follows. Psychedelics, I argue, work against the sense of over-determinacy and inevitability that pervades mainstream psychology, and can be used to designate gender as a space of construction and aesthetic play. This potential, however, rests in the the technique of researchers and practitioners. I conclude by stating that if methodological orthodoxy and conservatism are not challenged here, psychedelic drug treatments may fail to provide its subjects with radical perspectives on identity.

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In September, I’ll be giving a talk on non-hegemonic, non-digital epistemologies at 4S in New Orleans. And I am crossing my fingers for Breaking Convention, the world’s largest psychedelic conference, which is happening this August in London.

I won’t be doing a lot of conferences throughout the fall. The idea is to have a complete draft of the dissertation by the end of the calendar year. It’s a more achievable goal if I stay put.

In non-conference news: I have some ~freaky~ prose in the Spring 2019 edition of Tears In The Fence. Here is an online version of my piece. To read the others you’ll have to throw them some money, which I recommend because they are international and indie and they publish fabulously strange stuff. It’s got to be hard to keep that business up.

And SPECTRA 7.1 should be out soon.

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emma | 01/26/2019

I’ve been thinking about the rise of immersive experience as a commodity, i.e. virtual reality, music festivals, interactive theater, Meow Wolf, and the psychedelic renaissance. Maybe because psychedelic drug use has always been accompanied by a rhetoric of experience and immersion.

 

 

This is from Byung Chul Han’s book Psychopolitics: Neoliberalism and New Technologies of Power:

“In contrast to experiencing (Erlebnis), experience (Erfahrung) is founded on discontinuity. Experience means transformation. In an interview, Foucault remarked that for Nietzsche, Blanchot and Bataille, experience has the function ‘of wrenching the subject from itself, of seeing to it that the subject is no longer itself, or that it is brought to its annihilation or dissolution.’ Being a subject means being subjected, being cast under, by a higher instance. Experience tears the subject out from subjection — out of its downcast state. It signifies the opposite of neoliberal psychopolitics of experiencing or emotion which only ensnares the subject deeper and deeper in a state of subjection and subjugation.

Following Foucault, the art of living may be understood as a practice of freedom, bringing forth an entirely different mode of existence. It unfolds as de-psychologization. ‘The art of living is the art of killing psychology, of creating with oneself and others unnamed individualities, beings, relations, qualities. If one can’t manage to do that in one’s life, that life is not worth living.’ The art of living stands opposed to the ‘psychological terror’ through which subjugating subjectivation occurs.

Neoliberal psychopolitics is a technology of domination that stabilizes and perpetuates the prevailing system by means of psychological programming steering. Accordingly, the art of living, as the praxis of freedom, must proceed by way of de-psychologization. This serves to disarm psychopolitics, which is a means of effecting submission. When the subject is de-psychologized — indeed, de-voided (ent-leert) — it opens onto a mode of existence that still has no name: an unwritten future.”

Immersive experience de-psychologizes us, reintroducing the possibility of newness and surprise.

This is a marketable quality in a technologically overdetermined (auto-predicted, statistically calibrated, rationalized) world. But there are few formulas for immersion, that is, patterns that can be followed and repeated generically to meet market demands. The molecular compounds of psychoactive chemicals may be the only blueprint for immersion that we have.

 

I’m also not sure that phenomenology gives us the best concepts for theorizing immersion-as-a-service. But that’s another story…

machine learning & the algorithmic interpellation of the future

emma | 11/21/2018

I will be at the Western Political Science Association Conference in San Diego, 18-20 April 2019. Here’s the abstract for my talk:

Machine Learning and the Algorithmic Interpellation of the Future

In this paper I explore the impact of machine learning algorithms on the data they process. I make a two-part argument. First, that the inductive basis of machine learning functionality forecloses their capacity to produce outcomes with no ancestral relation to their training data. This has a protracted winnowing effect on content, which is a political concern due to the growing presence of machine learning algorithms in various facets of communal and individual life. I maintain that the a priori restrictions placed by algorithms on data constitute an emerging hegemonic order. Second, that an intervention in this scenario can be staged through an examination of non-digital, interpretative and self-reflexive methods in empirical science. The restrictions of machine learning, I offer, is drawn into high relief by exploring scientific studies in which it is deemed methodologically insufficient. This reading indicates predicates of intelligence which allegedly “intelligent” automation fails to self-generate.

I substantiate the first part of my argument with work from three philosophers of digital media. Antoinette Rouvroy writes on “algorithmic governance,” the automated retraction of possibility from probability in digital content. Matteo Pasquinelli argues that machine learning-based systems, including A.I., cannot be intelligent, as their inductive functionality does not compass the creativity which constitutes genuine intelligence. Adrian Mackenzie applies a Foucauldian reading to algorithmic determinacy. These thinkers provide me with materials for the second part, in which I use specific cases to theorize non-digital methodology in empirical science as a challenge to the algorithmic cancellation of the future.

***

In other news, my review of Andrew Feenberg’s Technosystem: The Social Life Of Reason has been published in the Humanities and Technology Review .

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emma | 09/20/2018

Hi from Germany. Frankfurt is unseasonably warm, hotter than New York right now. It feels like Southern Virginia at the time I left. I was in Berlin and Poland (Sczcecin) last weekend. It was not much colder in either city. Trying not to think about climate change feels like trying not to be human.

Even if I could measure time by shifts in weather, it would still be hard to believe a month has passed since I got here. My German is still really bad.

o-culus needs some work on the administrative end. For that reason, this is probably going to be the last post until I can devote a whole day to tinkering with it (/praying I don’t lose half a year’s worth of content in a shift over to a new hosting system).

When the site is back I’ll start posting more regularly 🙂

I’ll be speaking at two conferences in October, some information here:

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Oct 1-2 —> Intelligent Futures: Automation, AI and Cognitive Ecologies, at the University of Sussex in Brighton, England. Here is the conference site. Talk title and abstract:

Psychedelic Science and The Question of Artificial Intelligence

In this paper, I argue that qualitative research on the medical application of psychedelic drugs problematizes the positivist, generalizing and inductive principles of machine learning as a basis for artificial intelligence. I draw from interdisciplinary scholarship that uses qualitative methods, and in particular interpretative phenomenological analysis, as a hermeneutic device for research on the use of psychedelics in psychiatry. I combine precepts of machine learning with developments in psychedelic research to explore the inherent problems of generalizing psychedelic verbal reports data in the classification systems of A.I. classification. In doing so, I demonstrate that the use of qualitative methods in psychedelic drug research may envelop an immanent critique of the notion that machine-learning based predictive systems can be intelligent. I begin with an overview of the “psychedelic renaissance,” the recent resurgence of interest in the medicinal use of psychedelics. This includes an emerging paradigm which recognizes the need for qualitative and abductive theorization, including methods from phenomenology, poetics and critical theory as tools to interpret the deeply subjective narrative data that is evaluated in psychedelic studies. From there, I explore axioms of machine learning and artificial intelligence that emphasize the ways in which generalization and inductive reasoning are essential to algorithms that effectively “predict” the future. Assessing dynamics from psychedelic research that stand against pure inductive reasoning alongside the empirics of machine learning as a basis for A.I., I offer that the former can work toward a theorization of the possible epistemic limitations of artificial intelligence.

Oct 15-16 —> Deep Learning and Explanation in Cognitive Science, at the Institute of Philosophy in Prague, Czech Republic. No conference website or program available yet. Talk title and abstract:

Screens of Perception: Psychedelic Science, Machine Learning and Artificial Intelligence

In this talk, I will argue that qualitative research on the medicinal use of psychedelic drugs problematizes the development of data models, which in turn presents challenges for the predictive functions of machine learning and artificial intelligence. I draw from interdisciplinary scholarship that uses qualitative methods to interpret research on psychedelic substances, such as lysergic acid diethylamide (LSD) and psilocybin mushrooms, as assistive devices for psychotherapy. I combine precepts of machine learning with developments in psychedelic research to explore the complexities of generalizing research in contemporary psychedelic science. This includes subject-reported accounts from those undergoing ineffable and difficult-to-predict experiences. In doing so, I demonstrate that the use of qualitative methods in psychedelic drug research may offer a critique to machine-learning based predictive systems based on classification.

I begin with an overview of the “psychedelic renaissance,” the recent resurgence of interest in the medicinal use of psychedelics. Here, I offer a brief history of medicinal experiments with psychedelic drugs that begins in the twentieth century. I note that for legal reasons, 2014 marked the first LSD study approved by the US Food and Drug Administration in forty years, and that several related developments have occurred within the past five years. This includes an emerging paradigm which recognizes the need for qualitative, hermeneutic and deductive modes of theorizations. These includes interpretive methods inspired by phenomenology, poetics and aesthetic philosophy. I directly cite published research which speaks to their efficacy as interpretive devices on data from psychedelic psychotherapy.

From there, I explore axioms of machine learning and artificial intelligence that emphasize the ways in which generalization and inductive reasoning are essential to algorithms that effectively “predict” the future. Evaluating dynamics from psychedelic research that stand against pure inductive reasoning alongside the empirics of machine learning as a basis for artificial intelligence, I offer that the former can work toward a theorization of the possible philosophical limitations of the latter. As such it is an intervention in the notion that mentality may be replicated in data and algorithmic systems that stipulate predictive functions.
These talks will be similar, although the latter more narrowly focused on machine learning.

 

***

I’ll also be traveling a bit to other places — Scotland, after the conference in Brighton, to see family and Amsterdam at the beginning of November for research. If anyone is reading this and wants to give me a good excuse to return to Berlin, my email is open..