Andy Matuschak

Talk: How might we learn?

Added 2024-05-10 03:01:16 +0000 UTC

(Talk starts at 4:05. Sorry, Patreon doesn't let me embed videos with a starting timestamp!)

When people talk about the most rewarding high-growth periods of their lives, two themes commonly emerge.

First: they learned a lot, but learning wasn’t the point. Instead, they were immersed in some purpose with real personal meaning—like a startup, a research project, or a burning question—and they learned whatever was important along the way.

And second: the learning actually worked. People emerged feeling transformed, newly capable, filled with insight and understanding that has stayed with them years later.

If these experiences are so rewarding, why are they so rare? Why can’t we learn everything by “just diving in”? Why does learning so often fail to work as we hope, leaving us with brittle, fragmentary understanding?

This is a (proto-?)vision talk of sorts—an attempt at a broader picture of the future of learning we should create, particularly given developments in AI. It's a first step, but there are a lot of new ideas here, and I'm proud of it.

There isn't really room for an "acknowledgments" section in this sort of venue, so I'll share a note of thanks here to Alec Resnick, Bret Victor, Dan Meyer, Michael Nielsen, and May-Li Khoe for years of conversation that has molded my views on these topics. And thanks to Jim Hollan and Haijun Xia at the UCSD Design Lab for inviting me and unknowingly creating a good context for me to develop these ideas!

This is a public YouTube link; feel free to share.