Pandaemonium Architecture 6.0

In this course

Yoshua Bengio is a foundational figure in deep learning whose work on hierarchical probabilistic models and representation learning underpins much of contemporary generative AI; he appears here as a co-author of the Generative Adversarial Networks paper, one of the field's most influential architectures for learning data distributions. His prominence in these readings speaks to how the course traces the technical lineage of contemporary AI systems while also positioning deep learning's promise of discovering rich hierarchical representations as a central claim now subject to critical scrutiny regarding both capability and harm.

Mentioned in 3 readings

Ian J. Goodfellow, et al.

Generative Adversarial Nets

2

Generative Adversarial Nets Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair†, Aaron Courville, Yoshua Bengio‡ Département d’informatique et de recherche opérationnelle Université de Montréal M…The promise of deep learning is to discover rich, hierarchical models [2] that represent probability distributions over the kinds of data encountered in artificial intelligence applications, such as natural images, audio waveforms containin…

Timnit Gebru and Émile P. Torres

The TESCREAL bundle: Eugenics and the promise of utopia through artificial general intelligence

2

In this way, TESCREALists have been able to divert resources toward trying to build AGI and stopping their version of an apocalypse in the far future, while dissuading the public from scrutinizing the actual harms that they cause in their a…Emily M. Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell, 2021. “On the dangers of stochastic parrots: Can language models be too big?” FAccT ’21: Proceedings of the 2021 ACM Conference on Fairness, Accountability, …

Illia Polosukhin, et al.

Attention is all you need

1

For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new …

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