This week's episode is based on a lecture I gave to an audience of theoretical physicists at Oxford University.
Audio-only version, transcript:
Outline:
0:00 Introduction
2:31 Deep Learning and Neural Networks; history and mathematical results
21:15 Embedding space, word vectors
31:53 Next word prediction as objective function
34:08 Attention is all you need
37:09 Transformer architecture
44:54 The geometry of thought
52:57 What can LLMs do? Sparks of AGI
1:02:41 Hallucination
1:14:40 SuperFocus testing and examples
1:18:40 AI landscape, AGI, and the future
Final slide:
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