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	WEBVTT

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	The following is a conversation with Rajat Manga.

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	He's an engineering director at Google,

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	leading the TensorFlow team.

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	TensorFlow is an open source library

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	at the center of much of the work going on in the world

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	in deep learning, both the cutting edge research

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	and the large scale application of learning based approaches.

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	But it's quickly becoming much more

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	than a software library.

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	It's now an ecosystem of tools for the deployment

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	of machine learning in the cloud, on the phone,

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	in the browser, on both generic and specialized hardware.

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	TPU, GPU, and so on.

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	Plus, there's a big emphasis on growing

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	a passionate community of developers.

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	Rajat, Jeff Dean, and a large team of engineers at Google

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	Brain are working to define the future of machine learning

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	with TensorFlow 2.0, which is now in alpha.

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	I think the decision to open source TensorFlow

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	is a definitive moment in the tech industry.

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	It showed that open innovation can be successful

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	and inspire many companies to open source their code,

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	to publish, and in general engage in the open exchange

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	of ideas.

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	This conversation is part of the artificial intelligence

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	podcast.

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	If you enjoy it, subscribe on YouTube, iTunes,

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	or simply connect with me on Twitter

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	at Lex Friedman, spelled FRID.

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	And now, here's my conversation with Rajat Manga.

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	You were involved with Google Brain since its start in 2011

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	with Jeff Dean.

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	It started with disbelief, the proprietary machine learning

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	library, and turned into TensorFlow 2014,

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	the open source library.

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	So what were the early days of Google Brain like?

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	What were the goals, the missions?

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	How do you even proceed forward once there's

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	so much possibilities before you?

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	It was interesting back then when I started,

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	or when you were even just talking about it.

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	The idea of deep learning was interesting

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	and intriguing in some ways.

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	It hadn't yet taken off, but it held some promise.

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	It had shown some very promising and early results.

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	I think the idea where Andrew and Jeff had started

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	was what if we can take this, what people are doing in research,

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	and scale it to what Google has in terms of the compute power,

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	and also put that kind of data together, what does it mean?

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	And so far, the results had been if you scale the computer,

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	scale the data, it does better, and would that work?

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	And so that was the first year or two.

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	Can we prove that outright?

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	And with disbelief, when we started the first year,

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	we got some early wins, which is always great.

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	What were the wins like?

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	What was the wins where there are some problems to this?

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	This is going to be good.

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	I think the two early wins were one was speech

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	that we collaborated very closely with the speech research

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	team, who was also getting interested in this.

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	And the other one was on images where

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	the cat paper, as we call it, that was covered by a lot of folks.

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	And the birth of Google Brain was around neural networks.

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	So it was deep learning from the very beginning.

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	That was the whole mission.

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	So in terms of scale, what was the dream

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	of what this could become?

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	Were there echoes of this open source TensorFlow community

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	that might be brought in?

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	Was there a sense of TPUs?

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	Was there a sense of machine learning

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	is now going to be at the core of the entire company?

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	Is going to grow into that direction?

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	Yeah, I think so that was interesting.

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	And if I think back to 2012 or 2011,

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	and first was can we scale it in the year or so,

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	we had started scaling it to hundreds and thousands

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	of machines.

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	In fact, we had some runs even going to 10,000 machines.

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	And all of those shows great promise.

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	In terms of machine learning at Google,

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	the good thing was Google's been doing machine learning

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	for a long time.

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	Deep learning was new.

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	But as we scale this up, we showed that, yes, that was

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	possible, and it was going to impact lots of things.

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	Like, we started seeing real products wanting to use this.

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	Again, speech was the first.

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	There were image things that photos came out of

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	in many other products as well.

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	So that was exciting.

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	As we went into with that a couple of years,

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	externally also academia started to,

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	there was lots of push on, OK, deep learning's

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	interesting, we should be doing more, and so on.

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	And so by 2014, we were looking at, OK, this is a big thing.

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	It's going to grow.

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	And not just internally, externally as well.

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	Yes, maybe Google's ahead of where everybody is,

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	but there's a lot to do.

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	So a lot of this start to make sense and come together.

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	So the decision to open source, I was just chatting with Chris

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	Flattner about this, the decision to go open source

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	with TensorFlow, I would say for me personally,

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	seems to be one of the big seminal moments in all

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	of software engineering ever.

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	I think that when a large company like Google

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	decides to take a large project that many lawyers might argue

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	has a lot of IP, just decide to go open source with it.

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	And in so doing, lead the entire world in saying,

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	you know what, open innovation is a pretty powerful thing.

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	And it's OK to do.

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	That was, I mean, that's an incredible moment in time.

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	So do you remember those discussions happening?

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	Are there open source should be happening?

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	What was that like?

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	I would say, I think, so the initial idea came from Jeff,

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	who was a big proponent of this.

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	I think it came off of two big things.

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	One was research wise, we were a research group.

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	We were putting all our research out there if you wanted to.

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	We were building on other's research,

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	and we wanted to push the state of the art forward.

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	And part of that was to share the research.

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	That's how I think deep learning and machine learning

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	has really grown so fast.

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	So the next step was, OK, now word software

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	help for that.

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	And it seemed like they were existing a few libraries

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	out there, Tiano being one, Torch being another,

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	and a few others.

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	But they were all done by academia,

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	and so the level was significantly different.

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	The other one was, from a software perspective,

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	Google had done lots of software that we used internally.

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	And we published papers.

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	Often there was an open source project

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	that came out of that, that somebody else

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	picked up that paper and implemented,

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	and they were very successful.

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	Back then, it was like, OK, there's

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	Hadoop, which has come off of tech that we've built.

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	We know that tech we've built is way better

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	for a number of different reasons.

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	We've invested a lot of effort in that.

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	And turns out, we have Google Cloud,

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	and we are now not really providing our tech,

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	but we are saying, OK, we have Bigtable, which

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	is the original thing.

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	We are going to now provide HBase APIs on top of that, which

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	isn't as good, but that's what everybody's used to.

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	So there's like, can we make something that is better

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	and really just provide?

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	Helps the community in lots of ways,

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	but it also helps push the right, a good standard forward.

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	So how does Cloud fit into that?

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	There's a TensorFlow open source library.

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	And how does the fact that you can

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	use so many of the resources that Google provides

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	and the Cloud fit into that strategy?

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	So TensorFlow itself is open, and you can use it anywhere.

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	And we want to make sure that continues to be the case.

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	On Google Cloud, we do make sure that there's

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	lots of integrations with everything else,

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	and we want to make sure that it works really, really well there.

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	You're leading the TensorFlow effort.

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	Can you tell me the history and the timeline of TensorFlow

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	project in terms of major design decisions,

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	like the open source decision, but really, what to include

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	and not?

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	There's this incredible ecosystem that I'd

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	like to talk about, there's all these parts.

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	But if you just some sample moments that

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	defined what TensorFlow eventually became through its,

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	I don't know if you were allowed to say history when it's just,

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	but in deep learning, everything moves so fast

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	in just a few years, it's already history.

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	Yes, yes.

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	So looking back, we were building TensorFlow.

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	I guess we open sourced it in 2015, November 2015.

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	We started on it in summer of 2014, I guess.

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	And somewhere like three to six late 2014,

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	by then we had decided that, OK, there's

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	a high likelihood we'll open source it.

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	So we started thinking about that and making sure

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	that we're heading down that path.

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	At that point, by that point, we'd

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	seen a few lots of different use cases at Google.

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	So there were things like, OK, yes,

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	you want to run in at large scale in the data center.

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	Yes, we need to support different kind of hardware.

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	We had GPUs at that point.

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	We had our first GPU at that point

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	or was about to come out roughly around that time.

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	So the design included those.

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	We had started to push on mobile.

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	So we were running models on mobile.

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	At that point, people were customizing code.

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	So we wanted to make sure TensorFlow could support that

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	as well so that that became part of that overall

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	design.

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	When you say mobile, you mean like pretty complicated

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	algorithms of running on the phone?

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	That's correct.

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	So when you have a model that you

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	deploy on the phone and run it there, right?

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	So already at that time, there was ideas of running machine

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	learning on the phone.

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	That's correct.

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	We already had a couple of products

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	that were doing that by then.

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	And in those cases, we had basically

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	customized handcrafted code or some internal libraries

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	that we're using.

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	So I was actually at Google during this time in a parallel,

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	I guess, universe.

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	But we were using Theano and CAFE.

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	Was there some degree to which you were bouncing,

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	like trying to see what CAFE was offering people,

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	trying to see what Theano was offering

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	that you want to make sure you're delivering on whatever that

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	is, perhaps the Python part of thing.

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	Maybe did that influence any design decisions?

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	Totally.

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	So when we built this belief, and some of that

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	was in parallel with some of these libraries

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	coming up, I mean, Theano itself is older.

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	But we were building this belief focused on our internal thing

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	because our systems were very different.

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	By the time we got to this, we looked

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	at a number of libraries that were out there.

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	Theano, there were folks in the group

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	who had experience with Torch, with Lua.

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	There were folks here who had seen CAFE.

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	I mean, actually, Yang Cheng was here as well.

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	There's what other libraries?

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	I think we looked at a number of things.

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	Might even have looked at Jane and her back then.

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	I'm trying to remember if it was there.

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	In fact, yeah, we did discuss ideas around, OK,

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	should we have a graph or not?

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	And they were supporting all these together

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	was definitely, you know, there were key decisions

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	that we wanted.

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	We had seen limitations in our prior disbelief things.

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	A few of them were just in terms of research

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	was moving so fast.

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	We wanted the flexibility.

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	We want the hardware was changing fast.

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	We expected to change that so that those probably were two

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	things.

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	And yeah, I think the flexibility in terms

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	of being able to express all kinds of crazy things

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	was definitely a big one then.

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	So what the graph decisions, though,

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	with moving towards TensorFlow 2.0, there's more,

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	by default, there'll be eager execution.

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	So sort of hiding the graph a little bit

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	because it's less intuitive in terms of the way

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	people develop and so on.

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	What was that discussion like with in terms of using graphs?

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	It seemed it's kind of the theano way.

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	Did it seem the obvious choice?

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	So I think where it came from was our disbelief,

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	had a graph like thing as well.

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	It wasn't a general graph.

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	It was more like a straight line thing.

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	More like what you might think of Cafe,

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	I guess, in that sense.

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	And we always cared about the production stuff.

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	Even with disbelief, we were deploying a whole bunch of stuff

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	in production.

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	So graph did come from that when we thought of, OK,

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	should we do that in Python and we experimented with some ideas

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	where it looked a lot simpler to use,

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	but not having a graph meant, OK, how do you deploy now?

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	So that was probably what tilted the balance for us.

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	And eventually, we ended up with the graph.

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	And I guess the question there is, did you?

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	I mean, production seems to be the really good thing to focus on.

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	But did you even anticipate the other side of it

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	where there could be, what is it?

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	What are the numbers?

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	Something crazy, 41 million downloads?

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	Yep.

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	I mean, was that even like a possibility in your mind

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	that it would be as popular as it became?

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	So I think we did see a need for this a lot

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	from the research perspective and early days of deep learning

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	in some ways.

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	41 million?

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	No, I don't think I imagine this number then.

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	It seemed like there's a potential future where lots more people

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	would be doing this.

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	And how do we enable that?

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	I would say this kind of growth, I probably

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	started seeing somewhat after the open sourcing where it was

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	like, OK, deep learning is actually

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	growing way faster for a lot of different reasons.

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	And we are in just the right place to push on that

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	and leverage that and deliver on lots of things

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	that people want.

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	So what changed once the open source?

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	Like how this incredible amount of attention

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	from a global population of developers,

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	how did the projects start changing?

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	I don't even actually remember it during those times.

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	I know looking now, there's really good documentation.

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	There's an ecosystem of tools.

	14:26.560 --> 14:31.080
	There's a YouTube channel now.

	14:31.080 --> 14:33.760
	It's very community driven.

	14:33.760 --> 14:38.800
	Back then, I guess 0.1 version.

	14:38.800 --> 14:39.760
	Is that the version?

	14:39.760 --> 14:42.680
	I think we called it 0.6 or 5, something like that.

	14:42.680 --> 14:43.720
	Something like that.

	14:43.720 --> 14:47.200
	What changed leading into 1.0?

	14:47.200 --> 14:48.480
	It's interesting.

	14:48.480 --> 14:51.640
	I think we've gone through a few things there.

	14:51.640 --> 14:53.680
	When we started out, when we first came out,

	14:53.680 --> 14:56.080
	people loved the documentation we have.

	14:56.080 --> 14:58.800
	Because it was just a huge step up from everything else.

	14:58.800 --> 15:01.920
	Because all of those were academic projects, people

	15:01.920 --> 15:04.560
	don't think about documentation.

	15:04.560 --> 15:08.040
	I think what that changed was instead of deep learning

	15:08.040 --> 15:12.560
	being a research thing, some people who were just developers

	15:12.560 --> 15:15.080
	could now suddenly take this out and do

	15:15.080 --> 15:16.920
	some interesting things with it.

	15:16.920 --> 15:20.720
	Who had no clue what machine learning was before then.

	15:20.720 --> 15:22.520
	And that, I think, really changed

	15:22.520 --> 15:27.880
	how things started to scale up in some ways and pushed on it.

	15:27.880 --> 15:30.400
	Over the next few months, as we looked at,

	15:30.400 --> 15:31.960
	how do we stabilize things?

	15:31.960 --> 15:33.840
	As we look at not just researchers,

	15:33.840 --> 15:34.880
	now we want stability.

	15:34.880 --> 15:36.480
	People want to deploy things.

	15:36.480 --> 15:38.960
	That's how we started planning for 1.0.

	15:38.960 --> 15:42.240
	And there are certain needs for that perspective.

	15:42.240 --> 15:45.320
	And so, again, documentation comes up,

	15:45.320 --> 15:49.480
	designs, more kinds of things to put that together.

	15:49.480 --> 15:53.120
	And so that was exciting to get that to a stage where

	15:53.120 --> 15:56.400
	more and more enterprises wanted to buy in and really

	15:56.400 --> 15:58.720
	get behind that.

	15:58.720 --> 16:02.640
	And I think post 1.0 and with the next few releases,

	16:02.640 --> 16:05.240
	their enterprise adoption also started to take off.

	16:05.240 --> 16:08.000
	I would say between the initial release and 1.0,

	16:08.000 --> 16:11.000
	it was, OK, researchers, of course.

	16:11.000 --> 16:13.720
	Then a lot of hobbies and early interest,

	16:13.720 --> 16:15.920
	people excited about this who started to get on board.

	16:15.920 --> 16:19.000
	And then over the 1.x thing, lots of enterprises.

	16:19.000 --> 16:23.760
	I imagine anything that's below 1.0

	16:23.760 --> 16:27.160
	gets pressured to be enterprise problem or something

	16:27.160 --> 16:28.000
	that's stable.

	16:28.000 --> 16:28.800
	Exactly.

	16:28.800 --> 16:33.360
	And do you have a sense now that TensorFlow is stable?

	16:33.360 --> 16:35.520
	It feels like deep learning, in general,

	16:35.520 --> 16:37.800
	is extremely dynamic field.

	16:37.800 --> 16:39.680
	So much is changing.

	16:39.680 --> 16:43.400
	Do you have a, and TensorFlow has been growing incredibly.

	16:43.400 --> 16:46.720
	Do you have a sense of stability at the helm of this?

	16:46.720 --> 16:48.360
	I mean, I know you're in the midst of it.

	16:48.360 --> 16:50.360
	Yeah.

	16:50.360 --> 16:54.000
	I think in the midst of it, it's often easy to forget what

	16:54.000 --> 16:58.160
	an enterprise wants and what some of the people on that side

	16:58.160 --> 16:58.760
	want.

	16:58.760 --> 17:00.360
	There are still people running models

	17:00.360 --> 17:02.640
	that are three years old, four years old.

	17:02.640 --> 17:06.000
	So inception is still used by tons of people.

	17:06.000 --> 17:08.880
	Even less than 50 is what, a couple of years old now or more.

	17:08.880 --> 17:12.200
	But there are tons of people who use that, and they're fine.

	17:12.200 --> 17:16.200
	They don't need the last couple of bits of performance or quality.

	17:16.200 --> 17:19.600
	They want some stability in things that just work.

	17:19.600 --> 17:22.720
	And so there is value in providing that with that kind

	17:22.720 --> 17:25.160
	of stability and making it really simpler,

	17:25.160 --> 17:27.800
	because that allows a lot more people to access it.

	17:27.800 --> 17:31.640
	And then there's the research crowd, which wants, OK,

	17:31.640 --> 17:33.680
	they want to do these crazy things exactly like you're

	17:33.680 --> 17:37.000
	saying, not just deep learning in the straight up models

	17:37.000 --> 17:38.400
	that used to be there.

	17:38.400 --> 17:41.920
	They want RNNs, and even RNNs are maybe old.

	17:41.920 --> 17:45.520
	They are transformers now, and now it

	17:45.520 --> 17:48.720
	needs to combine with RL and GANs and so on.

	17:48.720 --> 17:52.160
	So there's definitely that area, the boundary that's

	17:52.160 --> 17:55.120
	shifting and pushing the state of the art.

	17:55.120 --> 17:57.120
	But I think there's more and more of the past

	17:57.120 --> 17:59.680
	that's much more stable.

	17:59.680 --> 18:02.680
	And even stuff that was two, three years old

	18:02.680 --> 18:04.920
	is very, very usable by lots of people.

	18:04.920 --> 18:07.440
	So that part makes it a lot easier.

	18:07.440 --> 18:09.800
	So I imagine maybe you can correct me if I'm wrong.

	18:09.800 --> 18:12.440
	One of the biggest use cases is essentially

	18:12.440 --> 18:15.160
	taking something like ResNet 50 and doing

	18:15.160 --> 18:18.520
	some kind of transfer learning on a very particular problem

	18:18.520 --> 18:19.600
	that you have.

	18:19.600 --> 18:24.480
	It's basically probably what majority of the world does.

	18:24.480 --> 18:27.040
	And you want to make that as easy as possible.

	18:27.040 --> 18:30.400
	So I would say, for the hobbyist perspective,

	18:30.400 --> 18:32.800
	that's the most common case.

	18:32.800 --> 18:34.800
	In fact, the apps on phones and stuff

	18:34.800 --> 18:37.680
	that you'll see, the early ones, that's the most common case.

	18:37.680 --> 18:40.320
	I would say there are a couple of reasons for that.

	18:40.320 --> 18:44.400
	One is that everybody talks about that.

	18:44.400 --> 18:46.120
	It looks great on slides.

	18:46.120 --> 18:48.120
	That's a great presentation.

	18:48.120 --> 18:50.040
	Exactly.

	18:50.040 --> 18:53.120
	What enterprises want is that is part of it,

	18:53.120 --> 18:54.480
	but that's not the big thing.

	18:54.480 --> 18:56.760
	Enterprises really have data that they

	18:56.760 --> 18:58.040
	want to make predictions on.

	18:58.040 --> 19:01.160
	This is often what they used to do with the people who

	19:01.160 --> 19:03.600
	were doing ML was just regression models,

	19:03.600 --> 19:06.440
	linear regression, logistic regression, linear models,

	19:06.440 --> 19:09.800
	or maybe gradient booster trees and so on.

	19:09.800 --> 19:11.760
	Some of them still benefit from deep learning,

	19:11.760 --> 19:14.440
	but they weren't that that's the bread and butter,

	19:14.440 --> 19:16.280
	like the structured data and so on.

	19:16.280 --> 19:18.200
	So depending on the audience you look at,

	19:18.200 --> 19:19.520
	they're a little bit different.

	19:19.520 --> 19:23.320
	And they just have, I mean, the best of enterprise

	19:23.320 --> 19:26.480
	probably just has a very large data set

	19:26.480 --> 19:28.640
	where deep learning can probably shine.

	19:28.640 --> 19:29.360
	That's correct.

	19:29.360 --> 19:30.320
	That's right.

	19:30.320 --> 19:32.240
	And then I think the other pieces

	19:32.240 --> 19:34.560
	that they wanted, again, to point out

	19:34.560 --> 19:36.400
	that the developer summit we put together

	19:36.400 --> 19:38.200
	is that the whole TensorFlow Extended

	19:38.200 --> 19:40.600
	piece, which is the entire pipeline,

	19:40.600 --> 19:43.560
	they care about stability across doing their entire thing.

	19:43.560 --> 19:46.200
	They want simplicity across the entire thing.

	19:46.200 --> 19:47.680
	I don't need to just train a model.

	19:47.680 --> 19:51.280
	I need to do that every day again, over and over again.

	19:51.280 --> 19:54.720
	I wonder to which degree you have a role in, I don't know.

	19:54.720 --> 19:57.040
	So I teach a course on deep learning.

	19:57.040 --> 20:01.320
	I have people like lawyers come up to me and say,

	20:01.320 --> 20:04.200
	when is machine learning going to enter legal,

	20:04.200 --> 20:05.560
	the legal realm?

	20:05.560 --> 20:11.720
	The same thing in all kinds of disciplines, immigration,

	20:11.720 --> 20:13.800
	insurance.

	20:13.800 --> 20:17.400
	Often when I see what it boils down to is these companies

	20:17.400 --> 20:19.760
	are often a little bit old school in the way

	20:19.760 --> 20:20.840
	they organize the data.

	20:20.840 --> 20:22.800
	So the data is just not ready yet.

	20:22.800 --> 20:24.040
	It's not digitized.

	20:24.040 --> 20:28.160
	Do you also find yourself being in the role of an evangelist

	20:28.160 --> 20:33.040
	for let's organize your data, folks,

	20:33.040 --> 20:35.440
	and then you'll get the big benefit of TensorFlow?

	20:35.440 --> 20:38.000
	Do you have those conversations?

	20:38.000 --> 20:45.160
	Yeah, I get all kinds of questions there from, OK,

	20:45.160 --> 20:49.000
	what do I need to make this work, right?

	20:49.000 --> 20:50.800
	Do we really need deep learning?

	20:50.800 --> 20:52.240
	I mean, there are all these things.

	20:52.240 --> 20:54.000
	I already used this linear model.

	20:54.000 --> 20:55.160
	Why would this help?

	20:55.160 --> 20:57.160
	I don't have enough data, let's say.

	20:57.160 --> 20:59.960
	Or I want to use machine learning,

	20:59.960 --> 21:01.760
	but I have no clue where to start.

	21:01.760 --> 21:04.920
	So it's a great start to all the way to the experts

	21:04.920 --> 21:08.520
	who wise were very specific things, so it's interesting.

	21:08.520 --> 21:09.600
	Is there a good answer?

	21:09.600 --> 21:12.480
	It boils down to oftentimes digitizing data.

	21:12.480 --> 21:15.240
	So whatever you want automated, whatever data

	21:15.240 --> 21:17.480
	you want to make prediction based on,

	21:17.480 --> 21:21.240
	you have to make sure that it's in an organized form.

	21:21.240 --> 21:23.920
	Like with an intensive flow ecosystem,

	21:23.920 --> 21:26.080
	there's now you're providing more and more data

	21:26.080 --> 21:28.960
	sets and more and more pretrained models.

	21:28.960 --> 21:32.400
	Are you finding yourself also the organizer of data sets?

	21:32.400 --> 21:34.480
	Yes, I think with TensorFlow data sets

	21:34.480 --> 21:38.360
	that we just released, that's definitely come up where people

	21:38.360 --> 21:39.200
	want these data sets.

	21:39.200 --> 21:41.560
	Can we organize them and can we make that easier?

	21:41.560 --> 21:45.320
	So that's definitely one important thing.

	21:45.320 --> 21:47.680
	The other related thing I would say is I often tell people,

	21:47.680 --> 21:50.960
	you know what, don't think of the most fanciest thing

	21:50.960 --> 21:53.320
	that the newest model that you see.

	21:53.320 --> 21:55.480
	Make something very basic work, and then

	21:55.480 --> 21:56.360
	you can improve it.

	21:56.360 --> 21:58.840
	There's just lots of things you can do with it.

	21:58.840 --> 22:00.080
	Yeah, start with the basics.

	22:00.080 --> 22:00.580
	Sure.

	22:00.580 --> 22:03.760
	One of the big things that makes TensorFlow even more

	22:03.760 --> 22:06.440
	accessible was the appearance, whenever

	22:06.440 --> 22:12.400
	that happened, of Keras, the Keras standard outside of TensorFlow.

	22:12.400 --> 22:18.200
	I think it was Keras on top of Tiano at first only,

	22:18.200 --> 22:22.480
	and then Keras became on top of TensorFlow.

	22:22.480 --> 22:29.840
	Do you know when Keras chose to also add TensorFlow as a back end,

	22:29.840 --> 22:33.960
	who was it just the community that drove that initially?

	22:33.960 --> 22:37.000
	Do you know if there was discussions, conversations?

	22:37.000 --> 22:40.920
	Yeah, so Franco started the Keras project

	22:40.920 --> 22:44.560
	before he was at Google, and the first thing was Tiano.

	22:44.560 --> 22:47.120
	I don't remember if that was after TensorFlow

	22:47.120 --> 22:49.640
	was created or way before.

	22:49.640 --> 22:52.000
	And then at some point, when TensorFlow

	22:52.000 --> 22:54.160
	started becoming popular, there were enough similarities

	22:54.160 --> 22:56.320
	that he decided to create this interface

	22:56.320 --> 22:59.200
	and put TensorFlow as a back end.

	22:59.200 --> 23:03.320
	I believe that might still have been before he joined Google.

	23:03.320 --> 23:06.720
	So we weren't really talking about that.

	23:06.720 --> 23:09.720
	He decided on his own and thought that was interesting

	23:09.720 --> 23:12.760
	and relevant to the community.

	23:12.760 --> 23:17.080
	In fact, I didn't find out about him being at Google

	23:17.080 --> 23:19.680
	until a few months after he was here.

	23:19.680 --> 23:21.840
	He was working on some research ideas.

	23:21.840 --> 23:24.480
	And doing Keras and his nights and weekends project and stuff.

	23:24.480 --> 23:25.280
	I wish this thing.

	23:25.280 --> 23:28.480
	So he wasn't part of the TensorFlow.

	23:28.480 --> 23:29.680
	He didn't join initially.

	23:29.680 --> 23:32.240
	He joined research, and he was doing some amazing research.

	23:32.240 --> 23:35.440
	He has some papers on that and research.

	23:35.440 --> 23:38.400
	He's a great researcher as well.

	23:38.400 --> 23:42.400
	And at some point, we realized, oh, he's doing this good stuff.

	23:42.400 --> 23:45.480
	People seem to like the API, and he's right here.

	23:45.480 --> 23:48.280
	So we talked to him, and he said, OK,

	23:48.280 --> 23:50.600
	why don't I come over to your team

	23:50.600 --> 23:52.800
	and work with you for a quarter?

	23:52.800 --> 23:55.440
	And let's make that integration happen.

	23:55.440 --> 23:57.200
	And we talked to his manager, and he said, sure,

	23:57.200 --> 23:59.720
	what, quarter's fine.

	23:59.720 --> 24:03.320
	And that quarter's been something like two years now.

	24:03.320 --> 24:05.040
	So he's fully on this.

	24:05.040 --> 24:12.000
	So Keras got integrated into TensorFlow in a deep way.

	24:12.000 --> 24:15.920
	And now with TensorFlow 2.0, Keras

	24:15.920 --> 24:19.400
	is kind of the recommended way for a beginner

	24:19.400 --> 24:21.960
	to interact with TensorFlow, which

	24:21.960 --> 24:24.640
	makes that initial sort of transfer learning

	24:24.640 --> 24:28.040
	or the basic use cases, even for an enterprise,

	24:28.040 --> 24:29.320
	super simple, right?

	24:29.320 --> 24:29.920
	That's correct.

	24:29.920 --> 24:30.440
	That's right.

	24:30.440 --> 24:32.040
	So what was that decision like?

	24:32.040 --> 24:38.640
	That seems like it's kind of a bold decision as well.

	24:38.640 --> 24:41.200
	We did spend a lot of time thinking about that one.

	24:41.200 --> 24:46.000
	We had a bunch of APIs some bit by us.

	24:46.000 --> 24:48.760
	There was a parallel layers API that we were building

	24:48.760 --> 24:51.560
	and when we decided to do Keras in parallel,

	24:51.560 --> 24:54.400
	so they were like, OK, two things that we are looking at.

	24:54.400 --> 24:55.960
	And the first thing we was trying to do

	24:55.960 --> 25:00.080
	is just have them look similar, be as integrated as possible,

	25:00.080 --> 25:02.200
	share all of that stuff.

	25:02.200 --> 25:05.800
	There were also three other APIs that others had built over time

	25:05.800 --> 25:09.000
	because we didn't have a standard one.

	25:09.000 --> 25:12.080
	But one of the messages that we kept hearing from the community,

	25:12.080 --> 25:13.200
	OK, which one do we use?

	25:13.200 --> 25:15.560
	And they kept seeing, OK, here's a model in this one,

	25:15.560 --> 25:18.840
	and here's a model in this one, which should I pick?

	25:18.840 --> 25:22.680
	So that's sort of like, OK, we had to address that

	25:22.680 --> 25:24.000
	straight on with 2.0.

	25:24.000 --> 25:26.320
	The whole idea was we need to simplify.

	25:26.320 --> 25:28.600
	We had to pick one.

	25:28.600 --> 25:34.600
	Based on where we were, we were like, OK, let's see what

	25:34.600 --> 25:35.640
	are the people like.

	25:35.640 --> 25:39.280
	And Keras was clearly one that lots of people loved.

	25:39.280 --> 25:41.600
	There were lots of great things about it.

	25:41.600 --> 25:43.880
	So we settled on that.

	25:43.880 --> 25:44.680
	Organically.

	25:44.680 --> 25:46.560
	That's kind of the best way to do it.

	25:46.560 --> 25:47.160
	It was great.

	25:47.160 --> 25:48.720
	But it was surprising, nevertheless,

	25:48.720 --> 25:51.120
	to sort of bring in and outside.

	25:51.120 --> 25:54.440
	I mean, there was a feeling like Keras might be almost

	25:54.440 --> 25:58.000
	like a competitor in a certain kind of a two tensor flow.

	25:58.000 --> 26:01.320
	And in a sense, it became an empowering element

	26:01.320 --> 26:02.200
	of tensor flow.

	26:02.200 --> 26:03.280
	That's right.

	26:03.280 --> 26:07.200
	Yeah, it's interesting how you can put two things together

	26:07.200 --> 26:08.280
	which can align right.

	26:08.280 --> 26:11.760
	And in this case, I think Francois, the team,

	26:11.760 --> 26:15.480
	and a bunch of us have chatted and I think we all

	26:15.480 --> 26:17.320
	want to see the same kind of things.

	26:17.320 --> 26:20.360
	We all care about making it easier for the huge set

	26:20.360 --> 26:21.440
	of developers out there.

	26:21.440 --> 26:23.440
	And that makes a difference.

	26:23.440 --> 26:27.280
	So Python has Guido van Rossum, who

	26:27.280 --> 26:30.320
	until recently held the position of benevolent

	26:30.320 --> 26:31.960
	dictator for life.

	26:31.960 --> 26:36.040
	Right, so there's a huge successful open source

	26:36.040 --> 26:37.320
	project like tensor flow.

	26:37.320 --> 26:40.680
	Need one person who makes a final decision.

	26:40.680 --> 26:45.480
	So you did a pretty successful tensor flow Dev Summit

	26:45.480 --> 26:47.520
	just now, last couple of days.

	26:47.520 --> 26:51.080
	There's clearly a lot of different new features

	26:51.080 --> 26:55.480
	being incorporated in amazing ecosystem, so on.

	26:55.480 --> 26:57.320
	How are those design decisions made?

	26:57.320 --> 27:00.960
	Is there a BDFL in tensor flow?

	27:00.960 --> 27:05.800
	And or is it more distributed and organic?

	27:05.800 --> 27:09.880
	I think it's somewhat different, I would say.

	27:09.880 --> 27:16.160
	I've always been involved in the key design directions.

	27:16.160 --> 27:17.560
	But there are lots of things that

	27:17.560 --> 27:20.960
	are distributed where their number of people, Martin

	27:20.960 --> 27:24.760
	Wick being one who has really driven a lot of our open source

	27:24.760 --> 27:27.360
	stuff, a lot of the APIs.

	27:27.360 --> 27:29.200
	And there are a number of other people

	27:29.200 --> 27:32.720
	who have been pushed and been responsible

	27:32.720 --> 27:35.240
	for different parts of it.

	27:35.240 --> 27:37.840
	We do have regular design reviews.

	27:37.840 --> 27:40.680
	Over the last year, we've really spent a lot of time opening up

	27:40.680 --> 27:44.160
	to the community and adding transparency.

	27:44.160 --> 27:45.880
	We're setting more processes in place,

	27:45.880 --> 27:49.600
	so RFCs, special interest groups, really

	27:49.600 --> 27:53.560
	grow that community and scale that.

	27:53.560 --> 27:57.680
	I think the kind of scale that ecosystem is in,

	27:57.680 --> 28:00.240
	I don't think we could scale with having me as the lone

	28:00.240 --> 28:02.320
	point of decision maker.

	28:02.320 --> 28:03.440
	I got it.

	28:03.440 --> 28:05.880
	So yeah, the growth of that ecosystem,

	28:05.880 --> 28:08.040
	maybe you can talk about it a little bit.

	28:08.040 --> 28:10.720
	First of all, when I started with Andre Karpathi

	28:10.720 --> 28:13.640
	when he first did ComNet.js, the fact

	28:13.640 --> 28:15.360
	that you can train in your own network

	28:15.360 --> 28:18.480
	and the browser in JavaScript was incredible.

	28:18.480 --> 28:21.000
	So now TensorFlow.js is really making

	28:21.000 --> 28:26.920
	that a serious, a legit thing, a way

	28:26.920 --> 28:29.560
	to operate, whether it's in the back end or the front end.

	28:29.560 --> 28:32.720
	Then there's the TensorFlow Extended, like you mentioned.

	28:32.720 --> 28:35.360
	There's TensorFlow Lite for mobile.

	28:35.360 --> 28:37.480
	And all of it, as far as I can tell,

	28:37.480 --> 28:39.640
	it's really converging towards being

	28:39.640 --> 28:43.440
	able to save models in the same kind of way.

	28:43.440 --> 28:46.680
	You can move around, you can train on the desktop,

	28:46.680 --> 28:48.800
	and then move it to mobile, and so on.

	28:48.800 --> 28:49.280
	That's right.

	28:49.280 --> 28:52.320
	So this is that cohesiveness.

	28:52.320 --> 28:55.240
	So can you maybe give me whatever

	28:55.240 --> 28:58.840
	I missed, a bigger overview of the mission of the ecosystem

	28:58.840 --> 29:02.120
	that's trying to be built, and where is it moving forward?

	29:02.120 --> 29:02.800
	Yeah.

	29:02.800 --> 29:05.720
	So in short, the way I like to think of this

	29:05.720 --> 29:09.760
	is our goals to enable machine learning.

	29:09.760 --> 29:13.320
	And in a couple of ways, one is we

	29:13.320 --> 29:16.560
	have lots of exciting things going on in ML today.

	29:16.560 --> 29:18.160
	We started with deep learning, but we now

	29:18.160 --> 29:21.400
	support a bunch of other algorithms too.

	29:21.400 --> 29:23.760
	So one is to, on the research side,

	29:23.760 --> 29:25.360
	keep pushing on the state of the art.

	29:25.360 --> 29:27.240
	Can we, how do we enable researchers

	29:27.240 --> 29:28.960
	to build the next amazing thing?

	29:28.960 --> 29:31.800
	So BERT came out recently.

	29:31.800 --> 29:34.000
	It's great that people are able to do new kinds of research.

	29:34.000 --> 29:35.400
	There are lots of amazing research

	29:35.400 --> 29:37.600
	that happens across the world.

	29:37.600 --> 29:38.880
	So that's one direction.

	29:38.880 --> 29:41.400
	The other is, how do you take that

	29:41.400 --> 29:45.200
	across all the people outside who want to take that research

	29:45.200 --> 29:47.400
	and do some great things with it and integrate it

	29:47.400 --> 29:51.800
	to build real products, to have a real impact on people?

	29:51.800 --> 29:56.720
	And so if that's the other axes in some ways.

	29:56.720 --> 29:58.520
	And a high level, one way I think about it

	29:58.520 --> 30:02.480
	is there are a crazy number of computer devices

	30:02.480 --> 30:04.240
	across the world.

	30:04.240 --> 30:08.440
	And we often used to think of ML and training and all of this

	30:08.440 --> 30:10.800
	as, OK, something you do either in the workstation

	30:10.800 --> 30:13.600
	or the data center or cloud.

	30:13.600 --> 30:15.720
	But we see things running on the phones.

	30:15.720 --> 30:17.640
	We see things running on really tiny chips.

	30:17.640 --> 30:20.760
	And we had some demos at the developer summit.

	30:20.760 --> 30:25.160
	And so the way I think about this ecosystem

	30:25.160 --> 30:30.280
	is, how do we help get machine learning on every device that

	30:30.280 --> 30:32.520
	has a compute capability?

	30:32.520 --> 30:33.760
	And that continues to grow.

	30:33.760 --> 30:37.240
	And so in some ways, this ecosystem

	30:37.240 --> 30:40.280
	has looked at various aspects of that

	30:40.280 --> 30:42.440
	and grown over time to cover more of those.

	30:42.440 --> 30:44.640
	And we continue to push the boundaries.

	30:44.640 --> 30:48.640
	In some areas, we've built more tooling and things

	30:48.640 --> 30:50.040
	around that to help you.

	30:50.040 --> 30:52.800
	I mean, the first tool we started was TensorBoard.

	30:52.800 --> 30:56.920
	You want to learn just the training piece, the effects

	30:56.920 --> 30:59.840
	for TensorFlow Extended to really do your entire ML

	30:59.840 --> 31:04.760
	pipelines if you care about all that production stuff,

	31:04.760 --> 31:09.520
	but then going to the edge, going to different kinds of things.

	31:09.520 --> 31:11.800
	And it's not just us now.

	31:11.800 --> 31:15.120
	We are a place where there are lots of libraries being built

	31:15.120 --> 31:15.840
	on top.

	31:15.840 --> 31:18.440
	So there are some for research, maybe things

	31:18.440 --> 31:21.240
	like TensorFlow Agents or TensorFlow Probability that

	31:21.240 --> 31:23.480
	started as research things or for researchers

	31:23.480 --> 31:26.160
	for focusing on certain kinds of algorithms,

	31:26.160 --> 31:30.280
	but they're also being deployed or reduced by production folks.

	31:30.280 --> 31:34.000
	And some have come from within Google, just teams

	31:34.000 --> 31:37.040
	across Google who wanted to do the build these things.

	31:37.040 --> 31:39.680
	Others have come from just the community

	31:39.680 --> 31:41.840
	because there are different pieces

	31:41.840 --> 31:44.640
	that different parts of the community care about.

	31:44.640 --> 31:49.520
	And I see our goal as enabling even that.

	31:49.520 --> 31:53.240
	It's not we cannot and won't build every single thing.

	31:53.240 --> 31:54.840
	That just doesn't make sense.

	31:54.840 --> 31:57.320
	But if we can enable others to build the things

	31:57.320 --> 32:00.640
	that they care about, and there's a broader community that

	32:00.640 --> 32:02.880
	cares about that, and we can help encourage that,

	32:02.880 --> 32:05.240
	and that's great.

	32:05.240 --> 32:08.600
	That really helps the entire ecosystem, not just those.

	32:08.600 --> 32:11.280
	One of the big things about 2.0 that we're pushing on

	32:11.280 --> 32:14.680
	is, OK, we have these so many different pieces, right?

	32:14.680 --> 32:18.440
	How do we help make all of them work well together?

	32:18.440 --> 32:21.960
	There are a few key pieces there that we're pushing on,

	32:21.960 --> 32:23.840
	one being the core format in there

	32:23.840 --> 32:27.480
	and how we share the models themselves through SAVE model

	32:27.480 --> 32:30.440
	and what TensorFlow Hub and so on.

	32:30.440 --> 32:34.000
	And a few of the pieces that we really put this together.

	32:34.000 --> 32:37.240
	I was very skeptical that that's, when TensorFlow.js came out,

	32:37.240 --> 32:40.120
	it didn't seem or deep learning.js.

	32:40.120 --> 32:41.680
	Yeah, that was the first.

	32:41.680 --> 32:45.040
	It seems like technically very difficult project.

	32:45.040 --> 32:47.040
	As a standalone, it's not as difficult.

	32:47.040 --> 32:49.920
	But as a thing that integrates into the ecosystem,

	32:49.920 --> 32:51.240
	it seems very difficult.

	32:51.240 --> 32:53.200
	So I mean, there's a lot of aspects of this

	32:53.200 --> 32:54.200
	you're making look easy.

	32:54.200 --> 32:58.160
	But on the technical side, how many challenges

	32:58.160 --> 33:00.560
	have to be overcome here?

	33:00.560 --> 33:01.520
	A lot.

	33:01.520 --> 33:03.080
	And still have to be overcome.

	33:03.080 --> 33:04.840
	That's the question here, too.

	33:04.840 --> 33:06.160
	There are lots of steps to it.

	33:06.160 --> 33:08.160
	I think we've iterated over the last few years,

	33:08.160 --> 33:10.720
	so there's a lot we've learned.

	33:10.720 --> 33:14.200
	I, yeah, and often when things come together well,

	33:14.200 --> 33:15.080
	things look easy.

	33:15.080 --> 33:16.400
	And that's exactly the point.

	33:16.400 --> 33:18.280
	It should be easy for the end user.

	33:18.280 --> 33:21.320
	But there are lots of things that go behind that.

	33:21.320 --> 33:25.320
	If I think about still challenges ahead,

	33:25.320 --> 33:32.880
	there are we have a lot more devices coming on board,

	33:32.880 --> 33:35.280
	for example, from the hardware perspective.

	33:35.280 --> 33:37.640
	How do we make it really easy for these vendors

	33:37.640 --> 33:42.040
	to integrate with something like TensorFlow?

	33:42.040 --> 33:43.640
	So there's a lot of compiler stuff

	33:43.640 --> 33:45.320
	that others are working on.

	33:45.320 --> 33:48.320
	There are things we can do in terms of our APIs

	33:48.320 --> 33:50.520
	and so on that we can do.

	33:50.520 --> 33:55.840
	As we, TensorFlow started as a very monolithic system.

	33:55.840 --> 33:57.680
	And to some extent, it still is.

	33:57.680 --> 33:59.400
	There are less lots of tools around it,

	33:59.400 --> 34:02.960
	but the core is still pretty large and monolithic.

	34:02.960 --> 34:05.760
	One of the key challenges for us to scale that out

	34:05.760 --> 34:10.440
	is how do we break that apart with clear interfaces?

	34:10.440 --> 34:13.720
	It's, in some ways, it's software engineering one

	34:13.720 --> 34:18.520
	one, but for a system that's now four years old, I guess,

	34:18.520 --> 34:21.600
	or more, and that's still rapidly evolving

	34:21.600 --> 34:24.000
	and that we're not slowing down with,

	34:24.000 --> 34:28.240
	it's hard to change and modify and really break apart.

	34:28.240 --> 34:29.880
	It's sort of like, as people say, right,

	34:29.880 --> 34:32.560
	it's like changing the engine with a car running

	34:32.560 --> 34:33.560
	or fixed benefits.

	34:33.560 --> 34:35.200
	That's exactly what we're trying to do.

	34:35.200 --> 34:39.960
	So there's a challenge here, because the downside

	34:39.960 --> 34:43.840
	of so many people being excited about TensorFlow

	34:43.840 --> 34:48.600
	and becoming to rely on it in many other applications

	34:48.600 --> 34:52.200
	is that you're kind of responsible.

	34:52.200 --> 34:53.520
	It's the technical debt.

	34:53.520 --> 34:55.640
	You're responsible for previous versions

	34:55.640 --> 34:57.720
	to some degree still working.

	34:57.720 --> 34:59.920
	So when you're trying to innovate,

	34:59.920 --> 35:03.760
	I mean, it's probably easier to just start from scratch

	35:03.760 --> 35:05.800
	every few months.

	35:05.800 --> 35:07.160
	Absolutely.

	35:07.160 --> 35:10.880
	So do you feel the pain of that?

	35:10.880 --> 35:15.360
	2.0 does break some back compatibility, but not too much.

	35:15.360 --> 35:18.120
	It seems like the conversion is pretty straightforward.

	35:18.120 --> 35:20.240
	Do you think that's still important,

	35:20.240 --> 35:22.880
	given how quickly deep learning is changing?

	35:22.880 --> 35:26.360
	Can you just, the things that you've learned,

	35:26.360 --> 35:27.440
	can you just start over?

	35:27.440 --> 35:30.120
	Or is there pressure to not?

	35:30.120 --> 35:31.640
	It's a tricky balance.

	35:31.640 --> 35:36.840
	So if it was just a researcher writing a paper who

	35:36.840 --> 35:39.400
	a year later will not look at that code again,

	35:39.400 --> 35:41.560
	sure, it doesn't matter.

	35:41.560 --> 35:43.440
	There are a lot of production systems

	35:43.440 --> 35:45.480
	that rely on TensorFlow, both at Google

	35:45.480 --> 35:47.240
	and across the world.

	35:47.240 --> 35:49.760
	And people worry about this.

	35:49.760 --> 35:53.400
	I mean, these systems run for a long time.

	35:53.400 --> 35:57.240
	So it is important to keep that compatibility and so on.

	35:57.240 --> 36:00.960
	And yes, it does come with a huge cost.

	36:00.960 --> 36:02.920
	We have to think about a lot of things

	36:02.920 --> 36:06.960
	as we do new things and make new changes.

	36:06.960 --> 36:09.120
	I think it's a trade off, right?

	36:09.120 --> 36:12.960
	You can, you might slow certain kinds of things down,

	36:12.960 --> 36:15.440
	but the overall value you're bringing because of that

	36:15.440 --> 36:18.440
	is much bigger because it's not just

	36:18.440 --> 36:20.520
	about breaking the person yesterday.

	36:20.520 --> 36:24.840
	It's also about telling the person tomorrow that, you know what?

	36:24.840 --> 36:26.320
	This is how we do things.

	36:26.320 --> 36:28.520
	We're not going to break you when you come on board

	36:28.520 --> 36:30.320
	because there are lots of new people who are also

	36:30.320 --> 36:32.880
	going to come on board.

	36:32.880 --> 36:34.680
	So one way I like to think about this,

	36:34.680 --> 36:37.960
	and I always push the team to think about it as well,

	36:37.960 --> 36:39.640
	when you want to do new things, you

	36:39.640 --> 36:42.000
	want to start with a clean slate,

	36:42.000 --> 36:44.880
	design with a clean slate in mind,

	36:44.880 --> 36:48.160
	and then we'll figure out how to make sure all the other things

	36:48.160 --> 36:48.640
	work.

	36:48.640 --> 36:52.160
	And yes, we do make compromises occasionally.

	36:52.160 --> 36:55.200
	But unless you design with the clean slate

	36:55.200 --> 36:58.400
	and not worry about that, you'll never get to a good place.

	36:58.400 --> 36:59.120
	That's brilliant.

	36:59.120 --> 37:04.080
	So even if you are responsible in the idea stage,

	37:04.080 --> 37:07.680
	when you're thinking of new, just put all that behind you.

	37:07.680 --> 37:09.600
	OK, that's really well put.

	37:09.600 --> 37:12.480
	So I have to ask this because a lot of students, developers,

	37:12.480 --> 37:16.280
	asked me how I feel about PyTorch versus TensorFlow.

	37:16.280 --> 37:19.720
	So I've recently completely switched my research group

	37:19.720 --> 37:20.920
	to TensorFlow.

	37:20.920 --> 37:23.280
	I wish everybody would just use the same thing.

	37:23.280 --> 37:26.960
	And TensorFlow is as close to that, I believe, as we have.

	37:26.960 --> 37:32.000
	But do you enjoy competition?

	37:32.000 --> 37:35.800
	So TensorFlow is leading in many ways, many dimensions

	37:35.800 --> 37:39.000
	in terms of the ecosystem, in terms of the number of users,

	37:39.000 --> 37:41.200
	momentum power, production level, so on.

	37:41.200 --> 37:46.000
	But a lot of researchers are now also using PyTorch.

	37:46.000 --> 37:47.520
	Do you enjoy that kind of competition,

	37:47.520 --> 37:49.440
	or do you just ignore it and focus

	37:49.440 --> 37:52.320
	on making TensorFlow the best that it can be?

	37:52.320 --> 37:55.480
	So just like research or anything people are doing,

	37:55.480 --> 37:58.120
	it's great to get different kinds of ideas.

	37:58.120 --> 38:01.440
	And when we started with TensorFlow,

	38:01.440 --> 38:05.480
	like I was saying earlier, it was very important for us

	38:05.480 --> 38:07.440
	to also have production in mind.

	38:07.440 --> 38:08.960
	We didn't want just research, right?

	38:08.960 --> 38:11.280
	And that's why we chose certain things.

	38:11.280 --> 38:13.480
	Now PyTorch came along and said, you know what?

	38:13.480 --> 38:14.880
	I only care about research.

	38:14.880 --> 38:16.320
	This is what I'm trying to do.

	38:16.320 --> 38:18.400
	What's the best thing I can do for this?

	38:18.400 --> 38:21.120
	And it started iterating and said, OK,

	38:21.120 --> 38:22.520
	I don't need to worry about graphs.

	38:22.520 --> 38:25.200
	Let me just run things.

	38:25.200 --> 38:27.440
	I don't care if it's not as fast as it can be,

	38:27.440 --> 38:30.480
	but let me just make this part easy.

	38:30.480 --> 38:32.560
	And there are things you can learn from that, right?

	38:32.560 --> 38:36.720
	They, again, had the benefit of seeing what had come before,

	38:36.720 --> 38:40.520
	but also exploring certain different kinds of spaces.

	38:40.520 --> 38:43.560
	And they had some good things there,

	38:43.560 --> 38:46.680
	building on, say, things like Jainer and so on before that.

	38:46.680 --> 38:49.320
	So competition is definitely interesting.

	38:49.320 --> 38:51.040
	It made us, you know, this is an area

	38:51.040 --> 38:53.720
	that we had thought about, like I said, very early on.

	38:53.720 --> 38:56.600
	Over time, we had revisited this a couple of times.

	38:56.600 --> 38:59.000
	Should we add this again?

	38:59.000 --> 39:00.480
	At some point, we said, you know what,

	39:00.480 --> 39:02.920
	here's it seems like this can be done well.

	39:02.920 --> 39:04.280
	So let's try it again.

	39:04.280 --> 39:07.680
	And that's how we started pushing on eager execution.

	39:07.680 --> 39:09.880
	How do we combine those two together,

	39:09.880 --> 39:13.080
	which has finally come very well together in 2.0,

	39:13.080 --> 39:15.720
	but it took us a while to get all the things together

	39:15.720 --> 39:16.320
	and so on.

	39:16.320 --> 39:19.320
	So let me, I mean, ask, put another way.

	39:19.320 --> 39:21.800
	I think eager execution is a really powerful thing,

	39:21.800 --> 39:22.680
	those added.

	39:22.680 --> 39:24.320
	Do you think he wouldn't have been,

	39:25.840 --> 39:28.400
	you know, Muhammad Ali versus Frazier, right?

	39:28.400 --> 39:31.200
	Do you think it wouldn't have been added as quickly

	39:31.200 --> 39:33.760
	if PyTorch wasn't there?

	39:33.760 --> 39:35.440
	It might have taken longer.

	39:35.440 --> 39:36.280
	No longer.

	39:36.280 --> 39:38.960
	It was, I mean, we had tried some variants of that before.

	39:38.960 --> 39:40.920
	So I'm sure it would have happened,

	39:40.920 --> 39:42.240
	but it might have taken longer.

	39:42.240 --> 39:44.800
	I'm grateful that TensorFlow is part of the way they did.

	39:44.800 --> 39:47.760
	That's doing some incredible work last couple of years.

	39:47.760 --> 39:49.640
	What other things that we didn't talk about?

	39:49.640 --> 39:51.520
	Are you looking forward in 2.0?

	39:51.520 --> 39:54.040
	That comes to mind.

	39:54.040 --> 39:56.520
	So we talked about some of the ecosystem stuff,

	39:56.520 --> 40:01.440
	making it easily accessible to Keras, eager execution.

	40:01.440 --> 40:02.880
	Is there other things that we missed?

	40:02.880 --> 40:07.480
	Yeah, so I would say one is just where 2.0 is,

	40:07.480 --> 40:10.760
	and, you know, with all the things that we've talked about,

	40:10.760 --> 40:13.760
	I think as we think beyond that,

	40:13.760 --> 40:16.640
	there are lots of other things that it enables us to do

	40:16.640 --> 40:18.760
	and that we're excited about.

	40:18.760 --> 40:20.720
	So what it's setting us up for,

	40:20.720 --> 40:22.520
	okay, here are these really clean APIs.

	40:22.520 --> 40:25.640
	We've cleaned up the surface for what the users want.

	40:25.640 --> 40:28.320
	What it also allows us to do a whole bunch of stuff

	40:28.320 --> 40:31.600
	behind the scenes once we are ready with 2.0.

	40:31.600 --> 40:36.600
	So for example, in TensorFlow with graphs

	40:36.760 --> 40:37.720
	and all the things you could do,

	40:37.720 --> 40:40.600
	you could always get a lot of good performance

	40:40.600 --> 40:43.280
	if you spent the time to tune it, right?

	40:43.280 --> 40:47.720
	And we've clearly shown that, lots of people do that.

	40:47.720 --> 40:52.720
	With 2.0, with these APIs where we are,

	40:53.040 --> 40:55.120
	we can give you a lot of performance

	40:55.120 --> 40:57.040
	just with whatever you do.

	40:57.040 --> 41:01.400
	You know, because we see these, it's much cleaner.

	41:01.400 --> 41:03.720
	We know most people are gonna do things this way.

	41:03.720 --> 41:05.520
	We can really optimize for that

	41:05.520 --> 41:09.040
	and get a lot of those things out of the box.

	41:09.040 --> 41:10.400
	And it really allows us, you know,

	41:10.400 --> 41:13.880
	both for single machine and distributed and so on,

	41:13.880 --> 41:17.200
	to really explore other spaces behind the scenes

	41:17.200 --> 41:19.680
	after 2.0 in the future versions as well.

	41:19.680 --> 41:23.000
	So right now, the team's really excited about that,

	41:23.000 --> 41:25.800
	that over time, I think we'll see that.

	41:25.800 --> 41:27.720
	The other piece that I was talking about

	41:27.720 --> 41:31.600
	in terms of just restructuring the monolithic thing

	41:31.600 --> 41:34.320
	into more pieces and making it more modular,

	41:34.320 --> 41:36.800
	I think that's gonna be really important

	41:36.800 --> 41:41.800
	for a lot of the other people in the ecosystem,

	41:41.800 --> 41:44.760
	other organizations and so on that wanted to build things.

	41:44.760 --> 41:46.360
	Can you elaborate a little bit what you mean

	41:46.360 --> 41:50.680
	by making TensorFlow more ecosystem or modular?

	41:50.680 --> 41:55.000
	So the way it's organized today is there's one,

	41:55.000 --> 41:56.280
	there are lots of repositories

	41:56.280 --> 41:58.320
	in the TensorFlow organization at GitHub,

	41:58.320 --> 42:01.080
	the core one where we have TensorFlow,

	42:01.080 --> 42:04.080
	it has the execution engine,

	42:04.080 --> 42:08.280
	it has, you know, the key backends for CPUs and GPUs,

	42:08.280 --> 42:12.560
	it has the work to do distributed stuff.

	42:12.560 --> 42:14.360
	And all of these just work together

	42:14.360 --> 42:17.240
	in a single library or binary,

	42:17.240 --> 42:18.800
	there's no way to split them apart easily.

	42:18.800 --> 42:19.960
	I mean, there are some interfaces,

	42:19.960 --> 42:21.600
	but they're not very clean.

	42:21.600 --> 42:24.800
	In a perfect world, you would have clean interfaces where,

	42:24.800 --> 42:27.720
	okay, I wanna run it on my fancy cluster

	42:27.720 --> 42:29.360
	with some custom networking,

	42:29.360 --> 42:30.960
	just implement this and do that.

	42:30.960 --> 42:32.640
	I mean, we kind of support that,

	42:32.640 --> 42:34.560
	but it's hard for people today.

	42:35.480 --> 42:38.160
	I think as we are starting to see more interesting things

	42:38.160 --> 42:39.400
	in some of these spaces,

	42:39.400 --> 42:42.280
	having that clean separation will really start to help.

	42:42.280 --> 42:47.280
	And again, going to the large size of the ecosystem

	42:47.360 --> 42:50.120
	and the different groups involved there,

	42:50.120 --> 42:53.440
	enabling people to evolve and push on things

	42:53.440 --> 42:56.040
	more independently just allows it to scale better.

	42:56.040 --> 42:59.080
	And by people, you mean individual developers and?

	42:59.080 --> 42:59.920
	And organizations.

	42:59.920 --> 43:00.920
	And organizations.

	43:00.920 --> 43:01.760
	That's right.

	43:01.760 --> 43:04.200
	So the hope is that everybody sort of major,

	43:04.200 --> 43:06.880
	I don't know, Pepsi or something uses,

	43:06.880 --> 43:11.040
	like major corporations go to TensorFlow to this kind of.

	43:11.040 --> 43:13.640
	Yeah, if you look at enterprise like Pepsi or these,

	43:13.640 --> 43:15.520
	I mean, a lot of them are already using TensorFlow.

	43:15.520 --> 43:18.960
	They are not the ones that do the development

	43:18.960 --> 43:20.360
	or changes in the core.

	43:20.360 --> 43:21.920
	Some of them do, but a lot of them don't.

	43:21.920 --> 43:23.720
	I mean, they touch small pieces.

	43:23.720 --> 43:26.400
	There are lots of these, some of them being,

	43:26.400 --> 43:28.200
	let's say hardware vendors who are building

	43:28.200 --> 43:30.840
	their custom hardware and they want their own pieces.

	43:30.840 --> 43:34.160
	Or some of them being bigger companies, say IBM.

	43:34.160 --> 43:37.320
	I mean, they're involved in some of our special interest

	43:37.320 --> 43:39.960
	groups and they see a lot of users

	43:39.960 --> 43:42.640
	who want certain things and they want to optimize for that.

	43:42.640 --> 43:44.480
	So folks like that often.

	43:44.480 --> 43:46.400
	Autonomous vehicle companies, perhaps.

	43:46.400 --> 43:48.200
	Exactly, yes.

	43:48.200 --> 43:50.520
	So yeah, like I mentioned, TensorFlow

	43:50.520 --> 43:54.120
	has been down on it 41 million times, 50,000 commits,

	43:54.120 --> 43:58.360
	almost 10,000 pull requests, 1,800 contributors.

	43:58.360 --> 44:02.160
	So I'm not sure if you can explain it,

	44:02.160 --> 44:06.840
	but what does it take to build a community like that?

	44:06.840 --> 44:09.200
	In retrospect, what do you think?

	44:09.200 --> 44:12.080
	What is the critical thing that allowed for this growth

	44:12.080 --> 44:14.600
	to happen and how does that growth continue?

	44:14.600 --> 44:17.920
	Yeah, that's an interesting question.

	44:17.920 --> 44:20.240
	I wish I had all the answers there, I guess,

	44:20.240 --> 44:22.520
	so you could replicate it.

	44:22.520 --> 44:25.520
	I think there are a number of things

	44:25.520 --> 44:27.880
	that need to come together, right?

	44:27.880 --> 44:33.720
	One, just like any new thing, there's

	44:33.720 --> 44:37.960
	a sweet spot of timing, what's needed,

	44:37.960 --> 44:39.520
	does it grow with what's needed.

	44:39.520 --> 44:41.960
	So in this case, for example, TensorFlow

	44:41.960 --> 44:43.640
	is not just grown because it has a good tool,

	44:43.640 --> 44:46.640
	it's also grown with the growth of deep learning itself.

	44:46.640 --> 44:49.000
	So those factors come into play.

	44:49.000 --> 44:53.120
	Other than that, though, I think just

	44:53.120 --> 44:55.560
	hearing, listening to the community, what they're

	44:55.560 --> 44:58.400
	doing, what they need, being open to,

	44:58.400 --> 45:01.080
	like in terms of external contributions,

	45:01.080 --> 45:04.520
	we've spent a lot of time in making sure

	45:04.520 --> 45:06.840
	we can accept those contributions well,

	45:06.840 --> 45:09.400
	we can help the contributors in adding those,

	45:09.400 --> 45:11.240
	putting the right process in place,

	45:11.240 --> 45:13.320
	getting the right kind of community,

	45:13.320 --> 45:16.120
	welcoming them, and so on.

	45:16.120 --> 45:19.000
	Like over the last year, we've really pushed on transparency.

	45:19.000 --> 45:22.200
	That's important for an open source project.

	45:22.200 --> 45:23.760
	People want to know where things are going,

	45:23.760 --> 45:26.400
	and we're like, OK, here's a process for you.

	45:26.400 --> 45:29.320
	You can do that, here are our seasons, and so on.

	45:29.320 --> 45:32.880
	So thinking through, there are lots of community aspects

	45:32.880 --> 45:36.400
	that come into that you can really work on.

	45:36.400 --> 45:38.720
	As a small project, it's maybe easy to do,

	45:38.720 --> 45:42.240
	because there's two developers, and you can do those.

	45:42.240 --> 45:46.960
	As you grow, putting more of these processes in place,

	45:46.960 --> 45:49.080
	thinking about the documentation,

	45:49.080 --> 45:51.400
	thinking about what two developers

	45:51.400 --> 45:55.080
	care about, what kind of tools would they want to use,

	45:55.080 --> 45:56.840
	all of these come into play, I think.

	45:56.840 --> 45:58.400
	So one of the big things, I think,

	45:58.400 --> 46:02.560
	that feeds the TensorFlow fire is people building something

	46:02.560 --> 46:07.680
	on TensorFlow, and implement a particular architecture

	46:07.680 --> 46:09.480
	that does something cool and useful,

	46:09.480 --> 46:11.080
	and they put that on GitHub.

	46:11.080 --> 46:15.640
	And so it just feeds this growth.

	46:15.640 --> 46:19.560
	Do you have a sense that with 2.0 and 1.0,

	46:19.560 --> 46:21.880
	that there may be a little bit of a partitioning like there

	46:21.880 --> 46:26.040
	is with Python 2 and 3, that there'll be a code base

	46:26.040 --> 46:28.320
	in the older versions of TensorFlow

	46:28.320 --> 46:31.120
	that will not be as compatible easily,

	46:31.120 --> 46:35.600
	or are you pretty confident that this kind of conversion

	46:35.600 --> 46:37.960
	is pretty natural and easy to do?

	46:37.960 --> 46:41.480
	So we're definitely working hard to make that very easy to do.

	46:41.480 --> 46:44.040
	There's lots of tooling that we talked about at the developer

	46:44.040 --> 46:46.480
	summit this week, and we'll continue

	46:46.480 --> 46:48.280
	to invest in that tooling.

	46:48.280 --> 46:52.560
	It's when you think of these significant version changes,

	46:52.560 --> 46:55.720
	that's always a risk, and we are really pushing hard

	46:55.720 --> 46:59.160
	to make that transition very, very smooth.

	46:59.160 --> 47:03.000
	I think, so at some level, people

	47:03.000 --> 47:05.520
	want to move when they see the value in the new thing.

	47:05.520 --> 47:07.640
	They don't want to move just because it's a new thing.

	47:07.640 --> 47:11.400
	And some people do, but most people want a really good thing.

	47:11.400 --> 47:13.760
	And I think over the next few months,

	47:13.760 --> 47:15.400
	as people start to see the value,

	47:15.400 --> 47:17.640
	we'll definitely see that shift happening.

	47:17.640 --> 47:20.080
	So I'm pretty excited and confident that we

	47:20.080 --> 47:22.440
	will see people moving.

	47:22.440 --> 47:24.680
	As you said earlier, this field is also moving rapidly,

	47:24.680 --> 47:26.720
	so that'll help because we can do more things.

	47:26.720 --> 47:28.520
	And all the new things will clearly

	47:28.520 --> 47:32.280
	happen in 2.x, so people will have lots of good reasons to move.

	47:32.280 --> 47:36.160
	So what do you think TensorFlow 3.0 looks like?

	47:36.160 --> 47:40.320
	Is there things happening so crazily

	47:40.320 --> 47:42.520
	that even at the end of this year,

	47:42.520 --> 47:45.320
	seems impossible to plan for?

	47:45.320 --> 47:49.440
	Or is it possible to plan for the next five years?

	47:49.440 --> 47:50.800
	I think it's tricky.

	47:50.800 --> 47:55.760
	There are some things that we can expect in terms of, OK,

	47:55.760 --> 47:59.720
	change, yes, change is going to happen.

	47:59.720 --> 48:01.680
	Are there some things going to stick around

	48:01.680 --> 48:03.720
	and some things not going to stick around?

	48:03.720 --> 48:08.160
	I would say the basics of deep learning,

	48:08.160 --> 48:12.680
	the convolutional models or the basic kind of things,

	48:12.680 --> 48:16.280
	they'll probably be around in some form still in five years.

	48:16.280 --> 48:21.160
	Will Aurel and Gans stay very likely based on where they are?

	48:21.160 --> 48:22.840
	Will we have new things?

	48:22.840 --> 48:24.680
	Probably, but those are hard to predict.

	48:24.680 --> 48:29.080
	And some directionally, some things that we can see

	48:29.080 --> 48:32.800
	is in things that we're starting to do

	48:32.800 --> 48:36.560
	with some of our projects right now is just

	48:36.560 --> 48:39.120
	to point out combining eager execution and graphs,

	48:39.120 --> 48:42.240
	where we're starting to make it more like just your natural

	48:42.240 --> 48:43.160
	programming language.

	48:43.160 --> 48:45.640
	You're not trying to program something else.

	48:45.640 --> 48:47.240
	Similarly, with Swift for TensorFlow,

	48:47.240 --> 48:48.280
	we're taking that approach.

	48:48.280 --> 48:50.040
	Can you do something round up?

	48:50.040 --> 48:52.080
	So some of those ideas seem like, OK,

	48:52.080 --> 48:55.000
	that's the right direction in five years

	48:55.000 --> 48:58.360
	we expect to see more in that area.

	48:58.360 --> 49:01.760
	Other things we don't know is, will hardware accelerators

	49:01.760 --> 49:03.200
	be the same?

	49:03.200 --> 49:09.000
	Will we be able to train with four bits instead of 32 bits?

	49:09.000 --> 49:11.440
	And I think the TPU side of things is exploring.

	49:11.440 --> 49:13.960
	I mean, TPU is already on version three.

	49:13.960 --> 49:17.520
	It seems that the evolution of TPU and TensorFlow

	49:17.520 --> 49:24.080
	are coevolving in terms of both their learning

	49:24.080 --> 49:25.720
	from each other and from the community

	49:25.720 --> 49:29.720
	and from the applications where the biggest benefit is achieved.

	49:29.720 --> 49:30.560
	That's right.

	49:30.560 --> 49:33.320
	You've been trying with eager with Keras

	49:33.320 --> 49:36.480
	to make TensorFlow as accessible and easy to use as possible.

	49:36.480 --> 49:39.040
	What do you think for beginners is the biggest thing

	49:39.040 --> 49:40.000
	they struggle with?

	49:40.000 --> 49:42.080
	Have you encountered that?

	49:42.080 --> 49:44.280
	Or is basically what Keras is solving

	49:44.280 --> 49:48.680
	is that eager, like we talked about TensorFlow?

	49:48.680 --> 49:51.480
	For some of them, like you said, the beginners

	49:51.480 --> 49:54.840
	want to just be able to take some image model.

	49:54.840 --> 49:58.040
	They don't care if it's inception or rest net or something else

	49:58.040 --> 50:00.760
	and do some training or transfer learning

	50:00.760 --> 50:02.440
	on their kind of model.

	50:02.440 --> 50:04.400
	Being able to make that easy is important.

	50:04.400 --> 50:08.560
	So in some ways, if you do that by providing them

	50:08.560 --> 50:11.360
	simple models with, say, in Hub or so on,

	50:11.360 --> 50:13.680
	they don't care about what's inside that box,

	50:13.680 --> 50:15.120
	but they want to be able to use it.

	50:15.120 --> 50:17.600
	So we're pushing on, I think, different levels.

	50:17.600 --> 50:20.120
	If you look at just a component that you get, which

	50:20.120 --> 50:22.800
	has the layers already smushed in,

	50:22.800 --> 50:25.200
	the beginners probably just want that.

	50:25.200 --> 50:27.360
	Then the next step is, OK, look at building

	50:27.360 --> 50:29.000
	layers with Keras.

	50:29.000 --> 50:30.600
	If you go out to research, then they

	50:30.600 --> 50:33.120
	are probably writing custom layers themselves

	50:33.120 --> 50:34.360
	or doing their own loops.

	50:34.360 --> 50:36.320
	So there's a whole spectrum there.

	50:36.320 --> 50:38.600
	And then providing the preentrain models

	50:38.600 --> 50:44.760
	seems to really decrease the time from you trying to start.

	50:44.760 --> 50:46.800
	So you could basically, in a Colab notebook,

	50:46.800 --> 50:49.080
	achieve what you need.

	50:49.080 --> 50:51.280
	So I'm basically answering my own question,

	50:51.280 --> 50:54.240
	because I think what TensorFlow delivered on recently

	50:54.240 --> 50:57.000
	is trivial for beginners.

	50:57.000 --> 51:00.760
	So I was just wondering if there was other pain points

	51:00.760 --> 51:02.480
	you're trying to ease, but I'm not sure there would.

	51:02.480 --> 51:04.240
	No, those are probably the big ones.

	51:04.240 --> 51:07.080
	I mean, I see high schoolers doing a whole bunch of things

	51:07.080 --> 51:08.840
	now, which is pretty amazing.

	51:08.840 --> 51:11.360
	It's both amazing and terrifying.

	51:11.360 --> 51:12.640
	Yes.

	51:12.640 --> 51:16.920
	In a sense that when they grow up,

	51:16.920 --> 51:19.280
	some incredible ideas will be coming from them.

	51:19.280 --> 51:21.800
	So there's certainly a technical aspect to your work,

	51:21.800 --> 51:24.600
	but you also have a management aspect

	51:24.600 --> 51:28.000
	to your role with TensorFlow, leading the project,

	51:28.000 --> 51:31.080
	a large number of developers and people.

	51:31.080 --> 51:34.680
	So what do you look for in a good team?

	51:34.680 --> 51:37.400
	What do you think Google has been at the forefront

	51:37.400 --> 51:40.440
	of exploring what it takes to build a good team?

	51:40.440 --> 51:45.520
	And TensorFlow is one of the most cutting edge technologies

	51:45.520 --> 51:46.120
	in the world.

	51:46.120 --> 51:48.080
	So in this context, what do you think

	51:48.080 --> 51:50.480
	makes for a good team?

	51:50.480 --> 51:53.200
	It's definitely something I think a fair bit about.

	51:53.200 --> 51:59.560
	I think in terms of the team being

	51:59.560 --> 52:02.120
	able to deliver something well, one of the things that's

	52:02.120 --> 52:05.800
	important is a cohesion across the team.

	52:05.800 --> 52:10.400
	So being able to execute together and doing things,

	52:10.400 --> 52:11.440
	it's not an end.

	52:11.440 --> 52:14.120
	Like at this scale, an individual engineer

	52:14.120 --> 52:15.400
	can only do so much.

	52:15.400 --> 52:18.200
	There's a lot more that they can do together,

	52:18.200 --> 52:21.640
	even though we have some amazing superstars across Google

	52:21.640 --> 52:22.600
	and in the team.

	52:22.600 --> 52:26.200
	But there's often the way I see it

	52:26.200 --> 52:28.360
	is the product of what the team generates

	52:28.360 --> 52:34.440
	is way larger than the whole individual put together.

	52:34.440 --> 52:37.320
	And so how do we have all of them work together,

	52:37.320 --> 52:40.000
	the culture of the team itself?

	52:40.000 --> 52:43.000
	Hiring good people is important.

	52:43.000 --> 52:45.600
	But part of that is it's not just that, OK,

	52:45.600 --> 52:48.120
	we hire a bunch of smart people and throw them together

	52:48.120 --> 52:49.720
	and let them do things.

	52:49.720 --> 52:52.920
	It's also people have to care about what they're building.

	52:52.920 --> 52:57.320
	People have to be motivated for the right kind of things.

	52:57.320 --> 53:01.400
	That's often an important factor.

	53:01.400 --> 53:04.600
	And finally, how do you put that together

	53:04.600 --> 53:08.840
	with a somewhat unified vision of where we want to go?

	53:08.840 --> 53:11.200
	So are we all looking in the same direction

	53:11.200 --> 53:13.520
	or just going all over?

	53:13.520 --> 53:16.040
	And sometimes it's a mix.

	53:16.040 --> 53:21.400
	Google's a very bottom up organization in some sense.

	53:21.400 --> 53:24.680
	Also research even more so.

	53:24.680 --> 53:26.320
	And that's how we started.

	53:26.320 --> 53:30.840
	But as we've become this larger product and ecosystem,

	53:30.840 --> 53:35.040
	I think it's also important to combine that well with a mix

	53:35.040 --> 53:37.920
	of, OK, here's the direction we want to go in.

	53:37.920 --> 53:39.880
	There is exploration we'll do around that.

	53:39.880 --> 53:43.320
	But let's keep staying in that direction, not just

	53:43.320 --> 53:44.360
	all over the place.

	53:44.360 --> 53:46.880
	And is there a way you monitor the health of the team?

	53:46.880 --> 53:51.920
	Sort of like, is there a way you know you did a good job?

	53:51.920 --> 53:53.000
	The team is good.

	53:53.000 --> 53:56.960
	I mean, you're saying nice things, but it's sometimes

	53:56.960 --> 54:01.120
	difficult to determine how aligned.

	54:01.120 --> 54:04.480
	Because it's not binary, it's not like there's tensions

	54:04.480 --> 54:06.680
	and complexities and so on.

	54:06.680 --> 54:09.400
	And the other element of this is the mesh of superstars.

	54:09.400 --> 54:12.880
	There's so much, even at Google, such a large percentage

	54:12.880 --> 54:16.000
	of work is done by individual superstars too.

	54:16.000 --> 54:19.920
	So there's a, and sometimes those superstars

	54:19.920 --> 54:25.120
	could be against the dynamic of a team and those tensions.

	54:25.120 --> 54:27.320
	I mean, I'm sure TensorFlow might be a little bit easier

	54:27.320 --> 54:31.720
	because the mission of the project is so beautiful.

	54:31.720 --> 54:34.760
	You're at the cutting edge, so it's exciting.

	54:34.760 --> 54:36.640
	But have you had struggle with that?

	54:36.640 --> 54:38.360
	Has there been challenges?

	54:38.360 --> 54:39.800
	There are always people challenges

	54:39.800 --> 54:41.240
	in different kinds of ways.

	54:41.240 --> 54:44.520
	That said, I think we've been what's

	54:44.520 --> 54:49.320
	good about getting people who care and have

	54:49.320 --> 54:51.440
	the same kind of culture, and that's Google in general

	54:51.440 --> 54:53.480
	to a large extent.

	54:53.480 --> 54:56.760
	But also, like you said, given that the project has had

	54:56.760 --> 54:59.160
	so many exciting things to do, there's

	54:59.160 --> 55:02.080
	been room for lots of people to do different kinds of things

	55:02.080 --> 55:06.440
	and grow, which does make the problem a bit easier, I guess.

	55:06.440 --> 55:09.920
	And it allows people, depending on what they're doing,

	55:09.920 --> 55:13.120
	if there's room around them, then that's fine.

	55:13.120 --> 55:19.160
	But yes, we do care about whether a superstar or not

	55:19.160 --> 55:22.560
	that they need to work well with the team across Google.

	55:22.560 --> 55:23.760
	That's interesting to hear.

	55:23.760 --> 55:27.960
	So it's like superstar or not, the productivity broadly

	55:27.960 --> 55:30.520
	is about the team.

	55:30.520 --> 55:31.520
	Yeah.

	55:31.520 --> 55:32.960
	I mean, they might add a lot of value,

	55:32.960 --> 55:35.720
	but if they're hurting the team, then that's a problem.

	55:35.720 --> 55:38.720
	So in hiring engineers, it's so interesting, right?

	55:38.720 --> 55:41.840
	The high rank process, what do you look for?

	55:41.840 --> 55:44.240
	How do you determine a good developer

	55:44.240 --> 55:47.280
	or a good member of a team from just a few minutes

	55:47.280 --> 55:50.320
	or hours together?

	55:50.320 --> 55:51.920
	Again, no magic answers, I'm sure.

	55:51.920 --> 55:52.760
	Yeah.

	55:52.760 --> 55:56.240
	And Google has a hiring process that we've refined

	55:56.240 --> 56:00.880
	over the last 20 years, I guess, and that you've probably

	56:00.880 --> 56:02.200
	heard and seen a lot about.

	56:02.200 --> 56:05.280
	So we do work with the same hiring process in that.

	56:05.280 --> 56:08.280
	That's really helped.

	56:08.280 --> 56:10.880
	For me in particular, I would say,

	56:10.880 --> 56:14.200
	in addition to the core technical skills,

	56:14.200 --> 56:17.560
	what does matter is their motivation

	56:17.560 --> 56:19.560
	in what they want to do.

	56:19.560 --> 56:22.960
	Because if that doesn't align well with where we want to go,

	56:22.960 --> 56:25.320
	that's not going to lead to long term success

	56:25.320 --> 56:27.640
	for either them or the team.

	56:27.640 --> 56:30.640
	And I think that becomes more important the more senior

	56:30.640 --> 56:33.520
	the person is, but it's important at every level.

	56:33.520 --> 56:34.920
	Like even the junior most engineer,

	56:34.920 --> 56:37.680
	if they're not motivated to do well at what they're trying to do,

	56:37.680 --> 56:39.080
	however smart they are, it's going

	56:39.080 --> 56:40.320
	to be hard for them to succeed.

	56:40.320 --> 56:44.520
	Does the Google hiring process touch on that passion?

	56:44.520 --> 56:46.440
	So like trying to determine.

	56:46.440 --> 56:48.440
	Because I think as far as I understand,

	56:48.440 --> 56:52.000
	maybe you can speak to it that the Google hiring process sort

	56:52.000 --> 56:56.360
	of helps the initial like determines the skill set there,

	56:56.360 --> 56:59.840
	is your puzzle solving ability, problem solving ability good.

	56:59.840 --> 57:05.000
	But I'm not sure, but it seems that the determining

	57:05.000 --> 57:07.560
	whether the person is like fire inside them

	57:07.560 --> 57:09.840
	that burns to do anything really doesn't really matter.

	57:09.840 --> 57:11.520
	It's just some cool stuff.

	57:11.520 --> 57:15.320
	I'm going to do it that I don't know.

	57:15.320 --> 57:17.000
	Is that something that ultimately ends up

	57:17.000 --> 57:18.840
	when they have a conversation with you

	57:18.840 --> 57:22.600
	or once it gets closer to the team?

	57:22.600 --> 57:25.400
	So one of the things we do have as part of the process

	57:25.400 --> 57:28.600
	is just a culture fit, like part of the interview process

	57:28.600 --> 57:31.040
	itself, in addition to just the technical skills.

	57:31.040 --> 57:34.240
	And each engineer or whoever the interviewer is,

	57:34.240 --> 57:38.800
	is supposed to rate the person on the culture and the culture

	57:38.800 --> 57:39.960
	fit with Google and so on.

	57:39.960 --> 57:42.160
	So that is definitely part of the process.

	57:42.160 --> 57:45.800
	Now, there are various kinds of projects

	57:45.800 --> 57:46.960
	and different kinds of things.

	57:46.960 --> 57:50.040
	So there might be variants in the kind of culture

	57:50.040 --> 57:51.320
	you want there and so on.

	57:51.320 --> 57:52.720
	And yes, that does vary.

	57:52.720 --> 57:54.920
	So for example, TensorFlow has always

	57:54.920 --> 57:56.920
	been a fast moving project.

	57:56.920 --> 58:00.920
	And we want people who are comfortable with that.

	58:00.920 --> 58:02.640
	But at the same time now, for example,

	58:02.640 --> 58:05.200
	we are at a place where we are also very full fledged product.

	58:05.200 --> 58:08.440
	And we want to make sure things that work really, really

	58:08.440 --> 58:09.320
	work right.

	58:09.320 --> 58:11.680
	You can't cut corners all the time.

	58:11.680 --> 58:14.320
	So balancing that out and finding the people

	58:14.320 --> 58:17.560
	who are the right fit for those is important.

	58:17.560 --> 58:19.720
	And I think those kind of things do vary a bit

	58:19.720 --> 58:23.200
	across projects and teams and product areas across Google.

	58:23.200 --> 58:25.240
	And so you'll see some differences there

	58:25.240 --> 58:27.640
	in the final checklist.

	58:27.640 --> 58:29.600
	But a lot of the core culture, it

	58:29.600 --> 58:32.200
	comes along with just the engineering, excellence,

	58:32.200 --> 58:34.720
	and so on.

	58:34.720 --> 58:39.680
	What is the hardest part of your job?

	58:39.680 --> 58:41.920
	I'll take your pick, I guess.

	58:41.920 --> 58:44.440
	It's fun, I would say.

	58:44.440 --> 58:45.520
	Hard, yes.

	58:45.520 --> 58:47.240
	I mean, lots of things at different times.

	58:47.240 --> 58:49.160
	I think that does vary.

	58:49.160 --> 58:52.640
	So let me clarify that difficult things are fun

	58:52.640 --> 58:55.720
	when you solve them, right?

	58:55.720 --> 58:57.480
	It's fun in that sense.

	58:57.480 --> 59:02.600
	I think the key to a successful thing across the board,

	59:02.600 --> 59:05.320
	and in this case, it's a large ecosystem now,

	59:05.320 --> 59:09.800
	but even a small product, is striking that fine balance

	59:09.800 --> 59:12.000
	across different aspects of it.

	59:12.000 --> 59:17.000
	Sometimes it's how fast you go versus how perfect it is.

	59:17.000 --> 59:21.400
	Sometimes it's how do you involve this huge community?

	59:21.400 --> 59:22.360
	Who do you involve?

	59:22.360 --> 59:25.440
	Or do you decide, OK, now is not a good time to involve them

	59:25.440 --> 59:30.160
	because it's not the right fit?

	59:30.160 --> 59:33.640
	Sometimes it's saying no to certain kinds of things.

	59:33.640 --> 59:36.880
	Those are often the hard decisions.

	59:36.880 --> 59:41.000
	Some of them you make quickly because you don't have the time.

	59:41.000 --> 59:43.200
	Some of them you get time to think about them,

	59:43.200 --> 59:44.480
	but they're always hard.

	59:44.480 --> 59:49.200
	So both choices are pretty good, those decisions.

	59:49.200 --> 59:50.360
	What about deadlines?

	59:50.360 --> 59:58.200
	Is this defined TensorFlow to be driven by deadlines

	59:58.200 --> 1:00:00.360
	to a degree that a product might?

	1:00:00.360 --> 1:00:04.920
	Or is there still a balance to where it's less deadline?

	1:00:04.920 --> 1:00:08.920
	You had the Dev Summit, they came together incredibly.

	1:00:08.920 --> 1:00:11.440
	Looked like there's a lot of moving pieces and so on.

	1:00:11.440 --> 1:00:15.080
	So did that deadline make people rise to the occasion,

	1:00:15.080 --> 1:00:18.360
	releasing TensorFlow 2.0 Alpha?

	1:00:18.360 --> 1:00:20.360
	I'm sure that was done last minute as well.

	1:00:20.360 --> 1:00:25.600
	I mean, up to the last point.

	1:00:25.600 --> 1:00:28.600
	Again, it's one of those things that you

	1:00:28.600 --> 1:00:29.960
	need to strike the good balance.

	1:00:29.960 --> 1:00:32.040
	There's some value that deadlines bring

	1:00:32.040 --> 1:00:33.920
	that does bring a sense of urgency

	1:00:33.920 --> 1:00:35.720
	to get the right things together.

	1:00:35.720 --> 1:00:38.280
	Instead of getting the perfect thing out,

	1:00:38.280 --> 1:00:41.280
	you need something that's good and works well.

	1:00:41.280 --> 1:00:43.720
	And the team definitely did a great job in putting that

	1:00:43.720 --> 1:00:46.560
	together, so it was very amazed and excited by everything,

	1:00:46.560 --> 1:00:48.680
	how that came together.

	1:00:48.680 --> 1:00:50.640
	That said, across the year, we try not

	1:00:50.640 --> 1:00:52.520
	to put out official deadlines.

	1:00:52.520 --> 1:00:56.960
	We focus on key things that are important,

	1:00:56.960 --> 1:01:00.600
	figure out how much of it's important,

	1:01:00.600 --> 1:01:05.760
	and we are developing in the open, internally and externally,

	1:01:05.760 --> 1:01:07.920
	everything's available to everybody.

	1:01:07.920 --> 1:01:11.120
	So you can pick and look at where things are.

	1:01:11.120 --> 1:01:13.160
	We do releases at a regular cadence,

	1:01:13.160 --> 1:01:16.320
	so fine if something doesn't necessarily end up with this

	1:01:16.320 --> 1:01:19.600
	month, it'll end up in the next release in a month or two.

	1:01:19.600 --> 1:01:22.840
	And that's OK, but we want to keep moving

	1:01:22.840 --> 1:01:26.520
	as fast as we can in these different areas.

	1:01:26.520 --> 1:01:30.080
	Because we can iterate and improve on things, sometimes

	1:01:30.080 --> 1:01:32.920
	it's OK to put things out that aren't fully ready.

	1:01:32.920 --> 1:01:35.640
	If you make sure it's clear that, OK, this is experimental,

	1:01:35.640 --> 1:01:37.960
	but it's out there if you want to try and give feedback.

	1:01:37.960 --> 1:01:39.400
	That's very, very useful.

	1:01:39.400 --> 1:01:43.560
	I think that quick cycle and quick iteration is important.

	1:01:43.560 --> 1:01:47.200
	That's what we often focus on rather than here's

	1:01:47.200 --> 1:01:49.200
	a deadline where you get everything else.

	1:01:49.200 --> 1:01:52.880
	It's 2.0, is there pressure to make that stable?

	1:01:52.880 --> 1:01:57.760
	Or like, for example, WordPress 5.0 just came out,

	1:01:57.760 --> 1:02:01.760
	and there was no pressure to, it was a lot of build updates

	1:02:01.760 --> 1:02:04.960
	that delivered way too late.

	1:02:04.960 --> 1:02:06.440
	And they said, OK, well, we're going

	1:02:06.440 --> 1:02:09.680
	to release a lot of updates really quickly to improve it.

	1:02:09.680 --> 1:02:12.240
	Do you see TensorFlow 2.0 in that same kind of way,

	1:02:12.240 --> 1:02:15.240
	or is there this pressure to once it hits 2.0,

	1:02:15.240 --> 1:02:16.760
	once you get to the release candidate,

	1:02:16.760 --> 1:02:19.440
	and then you get to the final, that's

	1:02:19.440 --> 1:02:22.480
	going to be the stable thing?

	1:02:22.480 --> 1:02:26.680
	So it's going to be stable in just like 1.0X

	1:02:26.680 --> 1:02:32.080
	was where every API that's there is going to remain in work.

	1:02:32.080 --> 1:02:34.800
	It doesn't mean we can't change things under the covers.

	1:02:34.800 --> 1:02:36.720
	It doesn't mean we can't add things.

	1:02:36.720 --> 1:02:39.200
	So there's still a lot more for us to do,

	1:02:39.200 --> 1:02:41.080
	and we continue to have more releases.

	1:02:41.080 --> 1:02:42.920
	So in that sense, there's still, I

	1:02:42.920 --> 1:02:44.680
	don't think we'd be done in like two months

	1:02:44.680 --> 1:02:46.160
	when we release this.

	1:02:46.160 --> 1:02:49.880
	I don't know if you can say, but is there, you know,

	1:02:49.880 --> 1:02:53.680
	there's not external deadlines for TensorFlow 2.0,

	1:02:53.680 --> 1:02:58.520
	but is there internal deadlines, artificial or otherwise,

	1:02:58.520 --> 1:03:00.840
	that you're trying to set for yourself,

	1:03:00.840 --> 1:03:03.080
	or is it whenever it's ready?

	1:03:03.080 --> 1:03:05.680
	So we want it to be a great product, right?

	1:03:05.680 --> 1:03:09.880
	And that's a big, important piece for us.

	1:03:09.880 --> 1:03:11.160
	TensorFlow is already out there.

	1:03:11.160 --> 1:03:13.720
	We have 41 million downloads for 1.x,

	1:03:13.720 --> 1:03:15.880
	so it's not like we have to have this.

	1:03:15.880 --> 1:03:17.280
	Yeah, exactly.

	1:03:17.280 --> 1:03:19.320
	So it's not like a lot of the features

	1:03:19.320 --> 1:03:22.080
	that we've really polishing and putting them together

	1:03:22.080 --> 1:03:26.240
	are there, we don't have to rush that just because.

	1:03:26.240 --> 1:03:28.040
	So in that sense, we want to get it right

	1:03:28.040 --> 1:03:29.920
	and really focus on that.

	1:03:29.920 --> 1:03:31.520
	That said, we have said that we are

	1:03:31.520 --> 1:03:33.520
	looking to get this out in the next few months,

	1:03:33.520 --> 1:03:37.120
	in the next quarter, and as far as possible,

	1:03:37.120 --> 1:03:40.000
	we'll definitely try to make that happen.

	1:03:40.000 --> 1:03:44.360
	Yeah, my favorite line was, spring is a relative concept.

	1:03:44.360 --> 1:03:45.960
	I love it.

	1:03:45.960 --> 1:03:47.680
	Spoken like a true developer.

	1:03:47.680 --> 1:03:50.200
	So something I'm really interested in,

	1:03:50.200 --> 1:03:53.840
	and your previous line of work is, before TensorFlow,

	1:03:53.840 --> 1:03:57.720
	you let a team and Google on search ads.

	1:03:57.720 --> 1:04:02.840
	I think this is a very interesting topic on every level,

	1:04:02.840 --> 1:04:07.200
	on a technical level, because if their best ads connect people

	1:04:07.200 --> 1:04:10.080
	to the things they want and need,

	1:04:10.080 --> 1:04:12.280
	and that they're worse, they're just these things

	1:04:12.280 --> 1:04:15.840
	that annoy the heck out of you to the point of ruining

	1:04:15.840 --> 1:04:20.240
	the entire user experience of whatever you're actually doing.

	1:04:20.240 --> 1:04:23.600
	So they have a bad rep, I guess.

	1:04:23.600 --> 1:04:28.080
	And on the other end, so that this connecting users

	1:04:28.080 --> 1:04:32.120
	to the thing they need to want is a beautiful opportunity

	1:04:32.120 --> 1:04:35.360
	for machine learning to shine, like huge amounts of data

	1:04:35.360 --> 1:04:36.720
	that's personalized, and you've got

	1:04:36.720 --> 1:04:40.400
	to map to the thing they actually won't get annoyed.

	1:04:40.400 --> 1:04:43.760
	So what have you learned from this Google that's

	1:04:43.760 --> 1:04:45.160
	leading the world in this aspect?

	1:04:45.160 --> 1:04:47.560
	What have you learned from that experience?

	1:04:47.560 --> 1:04:51.520
	And what do you think is the future of ads?

	1:04:51.520 --> 1:04:54.040
	Take you back to the end of that.

	1:04:54.040 --> 1:04:59.720
	Yes, it's been a while, but I totally agree with what you said.

	1:04:59.720 --> 1:05:03.200
	I think the search ads, the way it was always looked at,

	1:05:03.200 --> 1:05:05.520
	and I believe it still is, is it's

	1:05:05.520 --> 1:05:08.240
	an extension of what search is trying to do.

	1:05:08.240 --> 1:05:10.560
	The goal is to make the information

	1:05:10.560 --> 1:05:14.680
	and make the world's information accessible.

	1:05:14.680 --> 1:05:17.120
	With ads, it's not just information,

	1:05:17.120 --> 1:05:19.120
	but it may be products or other things

	1:05:19.120 --> 1:05:20.800
	that people care about.

	1:05:20.800 --> 1:05:23.360
	And so it's really important for them

	1:05:23.360 --> 1:05:26.480
	to align with what the users need.

	1:05:26.480 --> 1:05:30.920
	And in search ads, there's a minimum quality level

	1:05:30.920 --> 1:05:32.320
	before that ad would be shown.

	1:05:32.320 --> 1:05:34.200
	If we don't have an ad that hits that quality bar,

	1:05:34.200 --> 1:05:35.960
	it will not be shown, even if we have it.

	1:05:35.960 --> 1:05:38.080
	And OK, maybe we lose some money there.

	1:05:38.080 --> 1:05:39.560
	That's fine.

	1:05:39.560 --> 1:05:41.200
	That is really, really important,

	1:05:41.200 --> 1:05:43.000
	and I think that that is something I really

	1:05:43.000 --> 1:05:45.040
	liked about being there.

	1:05:45.040 --> 1:05:48.120
	Advertising is a key part.

	1:05:48.120 --> 1:05:51.680
	I mean, as a model, it's been around for ages, right?

	1:05:51.680 --> 1:05:52.920
	It's not a new model.

	1:05:52.920 --> 1:05:57.440
	It's been adapted to the web and became a core part of search

	1:05:57.440 --> 1:06:02.120
	and in many other search engines across the world.

	1:06:02.120 --> 1:06:05.920
	I do hope, like I said, there are aspects of ads

	1:06:05.920 --> 1:06:06.680
	that are annoying.

	1:06:06.680 --> 1:06:09.600
	And I go to a website, and if it just

	1:06:09.600 --> 1:06:12.160
	keeps popping an ad in my face, not to let me read,

	1:06:12.160 --> 1:06:13.800
	that's going to be annoying clearly.

	1:06:13.800 --> 1:06:22.080
	So I hope we can strike that balance between showing a good

	1:06:22.080 --> 1:06:25.040
	ad where it's valuable to the user

	1:06:25.040 --> 1:06:30.960
	and provides the monetization to the service.

	1:06:30.960 --> 1:06:32.000
	And this might be search.

	1:06:32.000 --> 1:06:33.680
	This might be a website.

	1:06:33.680 --> 1:06:37.320
	All of these, they do need the monetization for them

	1:06:37.320 --> 1:06:39.640
	to provide that service.

	1:06:39.640 --> 1:06:45.720
	But if it's done in a good balance between showing

	1:06:45.720 --> 1:06:48.040
	just some random stuff that's distracting

	1:06:48.040 --> 1:06:50.920
	versus showing something that's actually valuable.

	1:06:50.920 --> 1:06:55.360
	So do you see it moving forward as to continue

	1:06:55.360 --> 1:07:00.960
	being a model that funds businesses like Google?

	1:07:00.960 --> 1:07:05.160
	That's a significant revenue stream.

	1:07:05.160 --> 1:07:08.080
	Because that's one of the most exciting things,

	1:07:08.080 --> 1:07:09.680
	but also limiting things on the internet

	1:07:09.680 --> 1:07:12.200
	is nobody wants to pay for anything.

	1:07:12.200 --> 1:07:15.360
	And advertisements, again, coupled at their best

	1:07:15.360 --> 1:07:17.360
	are actually really useful and not annoying.

	1:07:17.360 --> 1:07:22.320
	Do you see that continuing and growing and improving?

	1:07:22.320 --> 1:07:26.680
	Or is there GC sort of more Netflix type models

	1:07:26.680 --> 1:07:28.960
	where you have to start to pay for content?

	1:07:28.960 --> 1:07:31.000
	I think it's a mix.

	1:07:31.000 --> 1:07:32.840
	I think it's going to take a long while for everything

	1:07:32.840 --> 1:07:35.320
	to be paid on the internet, if at all.

	1:07:35.320 --> 1:07:36.160
	Probably not.

	1:07:36.160 --> 1:07:37.400
	I mean, I think there's always going

	1:07:37.400 --> 1:07:40.760
	to be things that are sort of monetized with things like ads.

	1:07:40.760 --> 1:07:42.800
	But over the last few years, I would say

	1:07:42.800 --> 1:07:44.760
	we've definitely seen that transition

	1:07:44.760 --> 1:07:48.560
	towards more paid services across the web

	1:07:48.560 --> 1:07:50.360
	and people are willing to pay for them

	1:07:50.360 --> 1:07:51.760
	because they do see the value.

	1:07:51.760 --> 1:07:53.600
	I mean, Netflix is a great example.

	1:07:53.600 --> 1:07:56.520
	I mean, we have YouTube doing things.

	1:07:56.520 --> 1:07:59.720
	People pay for the apps they buy, more people

	1:07:59.720 --> 1:08:03.120
	they find are willing to pay for newspaper content,

	1:08:03.120 --> 1:08:07.240
	for the good news websites across the web.

	1:08:07.240 --> 1:08:11.040
	That wasn't the case even a few years ago, I would say.

	1:08:11.040 --> 1:08:13.280
	And I just see that change in myself as well

	1:08:13.280 --> 1:08:14.840
	and just lots of people around me.

	1:08:14.840 --> 1:08:19.240
	So definitely hopeful that we'll transition to that mix model

	1:08:19.240 --> 1:08:23.400
	where maybe you get to try something out for free,

	1:08:23.400 --> 1:08:24.120
	maybe with ads.

	1:08:24.120 --> 1:08:27.080
	But then there is a more clear revenue model

	1:08:27.080 --> 1:08:30.600
	that sort of helps go beyond that.

	1:08:30.600 --> 1:08:34.760
	So speaking of revenue, how is it

	1:08:34.760 --> 1:08:39.400
	that a person can use the TPU in a Google Colab for free?

	1:08:39.400 --> 1:08:43.920
	So what's the, I guess, the question is,

	1:08:43.920 --> 1:08:48.880
	what's the future of TensorFlow in terms of empowering,

	1:08:48.880 --> 1:08:51.880
	say, a class of 300 students?

	1:08:51.880 --> 1:08:55.920
	And I'm asked by MIT, what is going

	1:08:55.920 --> 1:08:58.640
	to be the future of them being able to do their homework

	1:08:58.640 --> 1:09:00.200
	in TensorFlow?

	1:09:00.200 --> 1:09:02.800
	Where are they going to train these networks, right?

	1:09:02.800 --> 1:09:07.720
	What's that future look like with TPUs, with cloud services,

	1:09:07.720 --> 1:09:08.920
	and so on?

	1:09:08.920 --> 1:09:10.240
	I think a number of things there.

	1:09:10.240 --> 1:09:12.600
	I mean, any TensorFlow open source,

	1:09:12.600 --> 1:09:13.640
	you can run it wherever.

	1:09:13.640 --> 1:09:15.880
	You can run it on your desktop, and your desktops

	1:09:15.880 --> 1:09:19.480
	always keep getting more powerful, so maybe you can do more.

	1:09:19.480 --> 1:09:22.040
	My phone is like, I don't know how many times more powerful

	1:09:22.040 --> 1:09:23.520
	than my first desktop.

	1:09:23.520 --> 1:09:25.200
	You'll probably train it on your phone, though.

	1:09:25.200 --> 1:09:26.200
	Yeah, that's true.

	1:09:26.200 --> 1:09:28.080
	Right, so in that sense, the power

	1:09:28.080 --> 1:09:31.440
	you have in your hand is a lot more.

	1:09:31.440 --> 1:09:34.400
	Clouds are actually very interesting from, say,

	1:09:34.400 --> 1:09:37.840
	students or courses perspective, because they

	1:09:37.840 --> 1:09:40.040
	make it very easy to get started.

	1:09:40.040 --> 1:09:42.040
	I mean, Colab, the great thing about it

	1:09:42.040 --> 1:09:45.120
	is go to a website, and it just works.

	1:09:45.120 --> 1:09:47.560
	No installation needed, nothing to, you know,

	1:09:47.560 --> 1:09:49.960
	you're just there, and things are working.

	1:09:49.960 --> 1:09:52.280
	That's really the power of cloud, as well.

	1:09:52.280 --> 1:09:55.320
	And so I do expect that to grow.

	1:09:55.320 --> 1:09:57.920
	Again, Colab is a free service.

	1:09:57.920 --> 1:10:00.840
	It's great to get started, to play with things,

	1:10:00.840 --> 1:10:03.080
	to explore things.

	1:10:03.080 --> 1:10:08.200
	That said, with free, you can only get so much, maybe.

	1:10:08.200 --> 1:10:11.080
	So just like we were talking about free versus paid,

	1:10:11.080 --> 1:10:15.280
	and there are services you can pay for and get a lot more.

	1:10:15.280 --> 1:10:16.000
	Great.

	1:10:16.000 --> 1:10:18.480
	So if I'm a complete beginner interested in machine

	1:10:18.480 --> 1:10:21.560
	learning and TensorFlow, what should I do?

	1:10:21.560 --> 1:10:24.240
	Probably start with going to a website and playing there.

	1:10:24.240 --> 1:10:26.560
	Just go to TensorFlow.org and start clicking on things.

	1:10:26.560 --> 1:10:28.440
	Yep, check out tutorials and guides.

	1:10:28.440 --> 1:10:30.680
	There's stuff you can just click there and go to Colab

	1:10:30.680 --> 1:10:31.320
	and do things.

	1:10:31.320 --> 1:10:32.360
	No installation needed.

	1:10:32.360 --> 1:10:34.040
	You can get started right there.

	1:10:34.040 --> 1:10:34.840
	OK, awesome.

	1:10:34.840 --> 1:10:36.720
	Roger, thank you so much for talking today.

	1:10:36.720 --> 1:10:37.440
	Thank you, Lex.

	1:10:37.440 --> 1:10:46.680
	Have fun this week.