Ioannis (Yannis) Siglidis
Yannis Siglidis was born in November of 1994 and is a research scientist in Computer Vision, following a scholarship funded PhD at the University of
Ecole Des Ponts Paris Tech in the eastern Paris Region.
They are part of the
IMAGINE team and is supervised by
Mathieu Aubry in the topic of Unsupervised Object Recognition in Temporal Image Sequences.
They have been part of a set of AI art-projects collaborating with
Ilan Manouach and have created
GraKeL: A Graph Kernel Library in Python under the supervision of
Giannis Nikollentzos in the
DaSciM team at Ecole Polytechnique.
They hold a degree with a suma cum laude from both the School of
Electrical and Computer Engineering from the
National Technical University of Athens and the
MVA master in Paris at the
ENS-Paris-Saclay.
Giannis Nikolentzos, Giannis Siglidis, Michalis Vazirgiannis
Journal of Artificial Intelligence Research
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, Michalis Vazirgiannis
Journal of Machine Learning Research
The first synthetic comic book co-created with emerging AI, a nonlinear meditation on deep learning that celebrates the unexpected poetics of generative computing and explores its potential to form new reader sensibilities.
It is the outcome of more than a 2-year research effort of trying to create a manga comic driven by the excitement and resources of an early stage of generative modelling, that at that time didn't meet the industrial needs of high-fidelity reconstruction and compositional generalization. A true futurist piece of work that both captures and invents a whole micro-style of imagery, from and as an implemented extra-human metaphor of Manga as big-data. As rare as a picture of a falling star, llike certain websites of Web 1.0 it is an essential piece of avant-garde artistry.
(2022) Translation of Capital is Dead from McKenzie Wark, in greek.
A long interview I took from McKenzie Wark about the book in
english and in
greek.
A singular
conceptual podcast where I read excerpts in the voice of an AI hybrid of me and McKenzie Wark.
Different from "Close Reading" that focuses on the close study of the artifacts of a certain cultural entity and "Distant Reading" that applies computational methods to them by treating them as big-data and extracts high level statistical conclusions and avoids a close qualitative analysis, Latent Reading speculates that to understand such an entity it may be meaningful to do instead a close-reading of the outputs of a generative model trained on its big-data.
Synthetic Cartoons in the style of New Yorker on Twitter (~2K followers).
