The Transformer Architecture: How Word Order Matters

This week's episode is based on a lecture Steve gave to an audience of theoretical physicists at Oxford University. The topic is artificial intelligence and large language models.

Lecture slides: https://docs.google.com/presentation/d/1xiMeeRMVpB-_W66BnyRyUAtrLlUwQNlndqbVcguKK8U/edit?usp=sharing

Chapter markers:

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

Music used with permission from Blade Runner Blues Livestream improvisation by State Azure.

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Steve Hsu is Professor of Theoretical Physics and of Computational Mathematics, Science, and Engineering at Michigan State University. Previously, he was Senior Vice President for Research and Innovation at MSU and Director of the Institute of Theoretical Science at the University of Oregon. Hsu is a startup founder (Superfocus.ai, SafeWeb, Genomic Prediction) and advisor to venture capital and other investment firms. He was educated at Caltech and Berkeley, was a Harvard Junior Fellow, and has held faculty positions at Yale, the University of Oregon, and MSU.

Please send any questions or suggestions to manifold1podcast@gmail.com or Steve on Twitter @hsu_steve.