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Hi, and welcome to the A16Z podcast. I'm Frank Chen. Today I'm here with Carnegie

Melons, Professor Tom Mitchell, who has been involved with machine learning, basically

his entire career. So I'm super excited to have this conversation with Tom, where he can tell us

a little bit about the history and where all of our techniques came from. And we'll spend

time talking about the future, where the field is going. So, Kanye and Mellon's been involved

in sort of standing up, the fundamental teaching institutions and research institutions of, you know,

the big areas of computer science, artificial intelligence and machine learning. So take us back

to the early days, you and Newell and Jeff

Pinton are teaching this class. What was the curriculum like? Like, what were you teaching?

Pretty different, I imagine, than what we teach undergrads today. That's right. Well, at the time,

this was the 1980s. So artificial intelligence at that point was dominated by what we would call symbolic methods, where

things like formal logic would be used to do inference.

And much of machine learning was really about learning symbolic structures, symbolic representations

of knowledge.

But there was this kind of young whippersnapper, Jeff Hinton,

who had a different idea.

And so he was working on a book with Rummelhart-McClellan

that became a very well-known parallel data processing book

that kind of launched the field of neural nets.

And they were, if I remember, psychologists, right?

Yeah, Jay McClellan was a psychologist here at CMU.

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