I enjoyed and learned so much from this video that I'm viewing it a second time and studying it. At 13:15 the top two graphs under "Hierarchical feature examples" are labeled "GloVe PC2" and "GloVe PC1". What do those quantities mean? Postscript: Okay. Dr. Gwilliams explained that they are "proxies for semantic representations using GloVe word embeddings, which are an unsupervised method of embedding word vector representations." Oh well. --

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Musk used AI to mimic my head on some naked sex chick's body, then sold it on the dark web. I'm serious.

I always thought that ML is statistics + geometry done on the computer in high-dimensional spaces. Geometry comes in to help with high dimensionality. Instead of learning distributions exactly which is very hard in high dimensions we learn separating spaces of relatively simple geometry (like hyperplanes) as approximations.

Thanks for sharing. It was a very fantastic speech

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buddy~oHw u doing?! that is interesting picture!see u around!

We are indeed following the recordings, thank you for posting them.