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PANEL | Into the Wild: Designing AIs in Retrograde

PANEL | Into the Wild: Designing AIs in Retrograde
PANEL | Into the Wild: Designing AIs in Retrograde

Tuesday, August 20, 2024
11:00 am_12:00 pm

Monument-National

85$

Day Pass price, excl. fees & taxes

Only half a decade has passed since the early days of generative AI; the days of multimodal systems creating six finger hands, StyleGAN hallucinating away and CharRNN predicting a character, not a word, at a time. Though seemingly retro by now, it was these glitches and their aesthetics of misrepresenting the world rather than accurately predicting it that opened both technological and social cracks for artistic explorations. Hidden behind neat interfaces and more ‘accurate’ predictions of the world, AI has arguably been ‘tamed’... or so they want to make us believe. In resisting futile attempts to ‘tame’ AI, this panel draws upon lessons of ecology to propose the need to make AI wild again !

Setting the table, electronic musician, artist and University of Toronto researcher Beth Coleman prompts us to imagine wild AIs “that can be free” to act, to explore and to generate. Alice Jarry, Concordia’s socio-environmental designer and researcher, Adam Basanta, artist and experimental musician as well as Matthieu Lorain, Google Deepmind’s Creative Lead, will critique and debate Coleman's provocation before inviting audience members to respond.

The critical discussions on wild AI’s will continue in the frame of the Wilding AI Lab on Friday, August 23.

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During the event, no interpreter for the Deaf will be present.

All activities held in the Salle Ludger-Duvernay will be simultaneously transcribed and translated in either French or English using an application powered by artificial intelligence. Those wishing to use this accessibility feature will find QR codes on site, which they can scan in order to connect to the live transcription. They may also click the “Transcriptions” button in the MUTEK Forum App homepage. This is a beta service, we cannot guarantee 100% accuracy.

Presented with the support of Milieux Institute and Applied AI Institute
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