The Importance of Autoencoding in Non-Machine Learning

In episode 83 of The Gradient Podcast, Daniel Bashir speaks to Peli Grietzer.

Peli is a scholar whose work borrows mathematical ideas from machine learning theory to think through “ambient” and ineffable phenomena like moods, vibes, cultural logics, and structures of feeling. He is working on a book titled Big Mood: A Transcendental-Computational Essay in Art and contributes to the experimental literature collective Gauss PDF. Peli has a PhD in mathematically informed literary theory from Harvard Comparative Literature in collaboration with the HUJI Einstein Institute of Mathematics.

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Outline:

* (00:00) Intro

* (02:17) Peli’s background

* (10:40) Daniel takes 2 entire minutes to ask how Peli thinks about ~ Art ~

* (26:10) Idealism and art as revealing the nature of reality, extralinguistic experiences of truth through literature

* (52:05) The autoencoder as a way to understand Romantic theories of art

* (1:14:55) More on how Peli thinks about autoencoders

* (1:18:05) Connections to ambient meaning, stimmung/mood

* (1:37:18) Examples of poetry/literature as mathematical experience, aesthetic unity and totalizing worldviews

* (1:51:15) Moods clashing within a single work

* (2:10:14) Modernist writers

* (2:32:46) Outro

Links:

* Peli’s Twitter

* A Theory of Vibe

* Why poetry is a variety of mathematical experience

* Peli’s thesis