To Be Continued?...
Philosophy and Psychoanalysis Today

Part III
15–16 April 2025

Live Session (Zoom)

Boštjan Nedoh
Marisa Žele
Holden M. Rasmussen
Amanda Holmes
Aleš Bunta
Alenka Zupančič
Alejandro Cerda-Rueda
Arka Chattopadhyay
Lorenzo Chiesa
Mladen Dolar
Justin Clemens
Alexi Kukuljevic
Peter Klepec
Stephen Overy
Tadej Troha
Sigi Jöttkandt
Richard Boothby


Part II

16–18 April 2024

Holden M. Rasmussen
Marisa Žele
Aleš Mendiževec
Voranc Kumar
Alejandro Cerda-Rueda
Arka Chattopadhyay
Boštjan Nedoh
Tadej Troha
Adrian Johnston
Mladen Dolar
Henrik Jøker Bjerre
Aleš Bunta
Adam Potts
Alenka Zupančič
Lorenzo Chiesa
Joan Copjec
Emily Laurent-Monaghan
Moritz Herrmann
Arsalan Reihanzadeh
Paris Lavidis
Nadia Bou Ali
Peter Klepec
Mohamed Tal
Frank Ruda
Ray Brassier


Part I
15–17 November 2023

Holden M. Rasmussen
Emily Laurent-Monaghan
Moritz Herrmann
Paris Lavidis
Vesna Liponik
Arsalan Reihanzadeh
Adrian Johnston
Jelica Šumič Riha
Tadej Troha
Boštjan Nedoh
Marisa Žele
Aleš Bunta
Peter Klepec
Alenka Zupančič
Lorenzo Chiesa


To Be Continued? ... 
Info
  • We can certainly say that the dialogue between philosophy and psychoanalysis, however prolix, has so far proved very difficult, ever since the latter’s inception. Is there such thing as a philosophy-psychoanalysis relationship? Or has it always been only empty speech? In that case, should it be continued?
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Aleš Bunta (ZRC SAZU)




Nietzsche, Embodied Errors and the Backpropagation Algorithm


In this paper, we once again revisit the often-overlooked yet crucial Nietzschean concept of ‘embodied errors.’ The comparative significance of this notion for psychoanalysis is evident in Nietzsche’s own use of embodied error to conceptualize instinct or drive. However, our focus here lies in a different comparison—namely, the parallel between the theory of embodied errors and the fundamental idea underlying the so-called backpropagation algorithm. As is well known, this algorithm serves as the core principle of ‘learning’ in artificial deep neural networks and, as such, underpins the remarkable capacities of contemporary artificial intelligence systems. At the heart of this comparison lies a critical observation: the backpropagation algorithm fundamentally functions as a system that produces the effects of ‘correct (re)cognition’ through the intricate manipulation of errors. In other words, within artificial neural networks, ‘correct (re)cognition’ emerges solely as a surface effect of a process in which errors serve as the only true ontological building blocks. In this sense, (re)cognition takes place—yet at no point in the process does it actually occur in any substantive sense or manifest as a discrete epistemic event. This paradox closely mirrors Nietzsche’s explanation of human, biological recognition and understanding.