The following is a conversation with Ian Goodfellow.

He's the author of the popular textbook on deep learning, simply titled Deep Learning.

He coined the term of generative adversarial networks, otherwise known as Gans.

And with his 2014 paper is responsible for launching the incredible growth of research

and innovation in this subfield of deep learning.

He got his BSNMS at Stanford, his PhD at University of Montreal with Yashio Abenjo and Aaron Curville.

He held several research positions including an open AI, Google Brain, and now at Apple

as the director of machine learning.

This recording happened while Ian was still a Google Brain.

But we don't talk about anything specific to Google or any other organization.

This conversation is part of the Artificial Intelligence Podcast.

If you enjoy it, subscribe on YouTube, iTunes, or simply connect with me on Twitter at

Lex Friedman spelled F-R-I-D.

And now here's my conversation with Ian Goodfellow.

You open your popular deep learning book with a Russian doll type diagram that shows deep learning

as a subset of representation learning, which in turn is a subset of machine learning and

finally a subset of AI.

So this kind of implies that there may be limits to deep learning in the context of AI.

So what do you think is the current limits of deep learning?

And are those limits something that we can overcome with time?

Yeah, I think one of the biggest limitations of deep learning is that right now it requires

really a lot of data, especially labeled data.

...