What are the temporalities of neural networks? How do machines encode time, and how do they structure our experience of time? What are the consequences of a neural network’s attempt to simulate temporal and historical ways of being? This conference, hosted by the Machine Visual Culture research group, suggests temporality as the fil rouge to pierce through the disparate levels at which critical AI intervenes; from infrastructures to neural architectures, from crowdworkers to latent spaces. The temporal is at the heart of critiques around, for instance, labour-time in training data (Malevé, Pasquinelli), the historical situatedness of AI models (Offert, Heuser), modes of temporal existence in LLMs, alongside other foundational accounts of computational media (Stiegler, Ernst).
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