Artful Intrusions—A Hacker's Guide to Generative AI

Item

Titre de la conférence
Artful Intrusions—A Hacker's Guide to Generative AI
Conférencier
Terence Broad
Date de la conférence
12 October 2023
Résumé
Les modèles d’IA générative, bien que capables de produire des images convaincantes, occultent souvent leurs processus créatifs. Ils fournissent une forme illusoire de production médiatique, dans laquelle les mécanismes de formation sont obscurcis par des processus de boîte noire. Cette conférence présentera trois projets artistiques : Being Foiled, Teratome et Ghosts, tous nés d'une approche hacker de l'IA générative. Ces travaux sont le résultat de différentes interventions dans les phases d’entraînement et de génération de modèles d’IA, chacune offrant un aperçu unique du fonctionnement des processus génératifs à l’oeuvre.
Citation tirée de la conférence
« Ultimately, I kind of came to this realization that what I was trying to do was I was trying to train a generative model without modelling data which is an oxymoron, it's a complete contradiction. It took me about a year to really figure this out but once I did, that was quite helpful, quite constructive, it made me rethink the problem, reframe the research I was trying to do, and so then I started thinking: "ok, we're not trying to train something from scratch to do something that isn't modelling data, how do we just push or nudge a general model away from an existing data set?" So how do we take something that's already been trained, and how do we kind of move it in a direction. And I ended up—instead of looking at very theoretical statistics in cognitive science—just looking at the actual structure of the models and really just thinking about their component parts. How can we play around with this? The talk is kind of an hacker's guide, and I started taking a hacker's mentality to working with generative AI where I was really just trying to pull these things apart, figure out what you can do, figure out the unintended ways that you could use them, figure out ways of using them that you weren't really supposed to do. »
« StyleGAN—you know the deepfakes of people—there's a website [called] ThisPersonDoesNotExist that's all using a GAN, using StyleGAN, and the way the GAN works is that we have these two networks, a generator and a discriminator. When I'm teaching about this I sort of introduce this like one person is a forger—that's the generator—and then the discriminator is a detective. So if someone's forging money, trying to make fake bank notes, then the discriminator is the person at the bank trying to figure out is this real money or is this fake money, and these are extremely effective. So the discriminator is trying to tell what's a real image compared to what's being generated, what's fake, and the generator's got this adversarial opposite objective which is: I want to generate things that trick the discriminator, passed off as being real. In this setup, the generator has one objective, and that objective is: I just need to trick this other network, I just need to get my bank notes passed off as real money, so to speak, or get my images of people or whatever passed off as being real, training data. »
URL de la capsule de la conférence
https://youtu.be/gXwoVJM2jtU
Est relié à la séance
Journée d'études Arcanes

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