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.
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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..
