Pandaemonium Architecture 6.0

In this course

Geoffrey Hinton appears only in passing reference within these excerpts—none directly attribute work or ideas to him, though the batch normalization discussion [1] invokes technical apparatus developed in his research tradition. His relevance to Pandaemonium Architecture lies in his foundational contributions to deep learning and neural network theory, which underpin the transformer architectures and training methods discussed throughout the course readings. Without direct engagement in these particular excerpts, he functions as an invisible infrastructure to contemporary AI systems being theorized here.

Mentioned in 3 readings

Andrew Brock

et al., LARGE SCALE GAN TRAINING FOR HIGH FIDELITY NATURAL IMAGE SYNTHESIS

1

The default behavior with batch normalized classifier networks is to use a running average of the activation moments at test time. Previous works (Radford et al., 2016) have instead used batch statistics when sampling images. While this is …

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 …

Timnit Gebru and Émile P. Torres

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

1

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…

Appears alongside

People mentioned in the same passages — sorted by co-occurrence weight.