YouSaid · the spoken record

Rajat Monga

lines on the record: 75
first: 2019-06-03
most recent: 2019-06-03
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Yep, check out tutorials and guides. There's stuff you can just click there in Code of CoLab and do things, no installation needed. You can get started right there.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“On your phone, though. Right. So, in that sense, the power you have in your hands is a lot more. Clouds are actually very interesting from, say, students or courses perspective because they make it very easy to get started. I mean, Colab, the great thing about is go to a website and it just works. No installation needed, nothing, you're just there and things are working. That's really the power of cloud as well. And so I do expect that to grow. Again, Colab is a free service. It's great to get started, to play with things, to explore things. That said, you know, with free, you can only get so much. So just like we were talking about free versus graduate. Yeah, there are services you can pay for and get a lot more.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“I think a number of things there, I mean, TensorFlow, open source, you can run it wherever you can run it on your desktop, and your desktops always keep getting more powerful, so maybe you can do more.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“More paid services across the web, and people are willing to pay for them because they do see the value. I mean, Netflix is a great example. I mean, we have YouTube doing things. People pay for the apps they buy. More people I find are willing to pay for newspaper content, for the good news websites across the web. That wasn't the case, even a few years ago, I would say. And I see that change in myself as well and just lots of people around me. So definitely hopeful that we'll transition to that mix model where maybe you get to try something out for free. Maybe with ads, but then there's a more clear revenue model that sort of helps go beyond that.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's a mix. I think it's going to take a long while for everything to be paid on the internet, if at all. Probably not. I mean, I think there's always going to be things that are sort of monetized with things like ads. But over the last few years, I would say we've definitely seen...”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Thing became a core part of search in many other search engines across the world. I do hope, you know, like I said, there are aspects of ads that are annoying and I go to a website and if it just keeps popping in added my face not to let me read, that's going to be annoying clearly. So I hope we can strike that balance between Showing a good ad where it's valuable to the user and provides the monetization to the service and this might be search, this might be a website, all of these, they do need the monetization for them to provide that service. But if it's done in that good balance between Showing just some random stuff that's distracting versus showing something that's actually valuable”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes, it's been a while, but I totally agree with what you said. I think the search ads, the way it was always looked at, and I believe it still is, is it's an extension of what search is trying to do. The goal is to make the information and make the world's information accessible that it's not just information, but it may be products or other things that people care about. And so it's really important for them to align with what the users need. And in search ads, there's a minimum quality level before that ad would be shown. If we don't have an ad that hits that quality, but it will not be shown even if we have it. And okay, maybe we lose some money there. That's fine. That is really, really important. And I think that that is something I really liked about being there. Advertising is a key part. I mean, as a model, it's been around for ages, right? It's not a new model. It's been adapted to the web.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“We don't have to rush that just because, in that sense, we want to get it right and really focus on that. That said, we have said that we are looking to get this out in the next few months, in the next quarter. And as far as possible, we try to make that happen.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“So we want it to be a great product, and that's a big, important piece for us. TensorFlow is already out there. We have 41 million downloads for One.ex. So it's not like we have to have this. Yeah, exactly. So it's not like a lot of the features that we've really polishing and putting them together are there.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“So it's going to be stable in just like when Nodex was, where every API that's there is going to remain in work. It doesn't mean we can't change things under the covers. It doesn't mean we can't add things. So there's still a lot more for us to do and we'll continue to have more releases. So in that sense, there's still, I don't think we'd be done in like two months when we release this.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“But we want to get like keep moving as fast as we can in these different areas because we can iterate and improve on things. Sometimes it's okay to put things out that aren't fully ready. We'll make sure it's clear that, okay, this is experimental, but it's out there if you want to try and give feedback. That's very, very useful. I think that quick cycle and quick iteration is important. That's what we often focus on rather than here's a deadline where you get everything else.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Minute as well. I mean, like the Again, it's one of those things that you need to strike the good balance. There's some value that deadlines bring that does bring a sense of urgency to get the right things together instead of getting the perfect thing out. You need something that's good and works well. And the team definitely did a great job in putting that together. So I was very amazed and excited by everything how that came together. That said, across the air, we try not to put artificial deadlines. We focus on key things that are important figure out what much of it's important. And we are developing in the open with, you know, internally and externally, everything's available to everybody. So you can pick and look at where things are. We do releases at a regular cadence, so fine if something doesn't necessarily end up with this month, it'll end up in the next release in a month or two. And that's okay.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“I think the key to a successful thing across the board, and in this case, it's a large ecosystem now, but even a small product is striking that fine balance across different aspects of it. Sometimes it's how fast you go versus how perfect it is. Sometimes it's how do you involve this huge community? Who do you involve? Or do you decide, okay, now is not a good time to involve them because it's not the right fit. Sometimes it's saying no to certain kinds of things. Those are often the hard decisions. Some of them you make quickly because you don't have the time, some of them you get time to think about them, but they're always hard”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“But at the same time now, for example, we are at a place where we are also a very full fledged product and we want to make sure things that work really, really work right. You can't cut corners all the time. So that balancing that out and finding the people who are the right fit for those is important. And I think those kind of things do vary a bit across projects and teams and product areas across Google. And so you'll see some differences there in the final checklist. But a lot of the core culture comes along with just the engineering excellence and so on.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“So, one of the things we do have as part of the process is just a culture fit, like part of the interview process itself, in addition to just the technical skills. And each engineer or whoever the interviewer is is supposed to rate the person on the culture and the culture fit with Google and so on. So that is definitely part of the process. Now there are various kinds of projects and different kinds of things. There might be variants in the kind of culture you want there and so on. And yes, that does vary. So for example TensorFlow has always been a fast moving project and we want people who are comfortable with that.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Again, no magic answers, I'm sure. Yeah, yeah. I mean, Google has a hiding process that we've refined over the last 20 years, I guess, and that you've probably heard and seen a lot about. So we do work with the same hiding process, and that's really helped Mean particular, I would say in addition to the core technical skills, what does matter is their motivation in what they want to do. Because if that doesn't align well with where we want to go, that's not going to lead to long-term success for either them or the team. And I think that becomes more important the more senior the person is, but it's important at every level. Like even the junior most engineered, if they're not motivated to do well at what they're trying to do, however smart they are, it's going to be hard for them to succeed.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“What's good about getting people who care and have the same kind of culture? That's Google in general to a large extent. But also, like you said, given that the project has had so many exciting things to do, there's been room for lots of people to do different kinds of things and grow, which does make the problem a bit easier, I guess And it allows people depending on what they're doing, if there's room around them, then that's fine. But yes, we do care about whether Superstar or not that they need to work well with the team across Google. That's interesting.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“For the right kind of things, that's often an important factor. And, you know, finally, how do you put that together with a somewhat unified vision of where we want to go? So are we all looking in the same direction or each of us going all over? And sometimes it's a mix. Google's a very bottom-up organization in some sense. Also research even more so and that's how we started. But as we've become this larger product and ecosystem, I think it's also important to combine that well with a mix of, okay, here's the direction we want to go in. There's exploration we'll do around that, but let's keep staying in that direction, not just all over the place.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“It's definitely something I think a fair bit about. I think the In terms of The team being able to deliver something well, one of the things that's important is a cohesion across the team. So being able to execute together in doing things, it's not like at this scale, an individual engineer can only do so much. There's a lot more that they can do together, even though we have some amazing superstars across Google and in the team. But there's often the way I see it is the product of what the team generates is way larger than the whole or each individual put together. And so how do we have all of them work together, the culture of the team itself. Hiring good people is important. But part of that is it's not just that, okay, we hire a bunch of smart people and throw them together and let them do things. It's also people have to care about what they're building. People have to be motivated.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Now those are probably the big ones. I mean, I see high schoolers doing a whole bunch of things now, which is pretty amazing.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, for some of them, like you said, right, the beginners want to just be able to take Some image model they don't care if it's inception or RESNET or something else and do some training or transfer learning on their kind of model. Being able to make that easy is important. So in some ways we do that by providing them simple models with say in hub or so on. They don't care about what's inside that box, but they want to be able to use it. So we're pushing on, I think, different levels. If you look at just a component that you get which has the layers already smushed in, the beginners probably just want that. Then the next step is, okay, look at building layers with Keras. You go out to research, then they are probably writing custom layers themselves or their own loops. So there's a whole spectrum there.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Some directionally, some things that we can see is in things that we're starting to do with some of our projects right now is just 2.0 combining eager execution in graphs where we're starting to make it more like just your natural programming language. You're not trying to program something else. Similarly with Swift for TensorFlow, we're taking that approach. Can you do something round up, right? So some of those ideas seem like, okay, that's the right direction in five years we expect to see more in that area. Other things we don't know is will hardware accelerators be the same? Will we be able to train with four bits instead of 32 bits?”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's tricky. There are some things that we can Expect in terms of okay change, yes, change is gonna happen. Are there some things going to stick around and some things not going to stick around? I would say The basics of deep learning, the convolutional models or the basic kind of things, they'll probably be around in some form still in five years. Will RLN GAN stay very likely based on where they are? Will we have new things? Probably, but those are hard to predict.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“I think so at some level people want to move when they see the value in the new thing. They don't want to move just because it's a new thing. Some people do, but most people want a really good thing. And I think over the next few months as people start to see the value, we'll definitely see that shift happening. So I'm pretty excited and confident that we'll see people moving. As you said earlier, this field is also moving rapidly, so that'll help because we can do more things and all the new things will clearly happen in 2.x. So people will have lots of good reasons to move.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“So we're definitely working hard to make that very easy to do. There's lots of tooling that we talked about at the Developer Summit this week and we'll continue to invest in that tooling. When you think of these significant version changes, that's always a risk. And we are really pushing hard to make that transition very, very smooth.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Like over the last year, we've really pushed on transparency. That's important for an open source project. People want to know where things are going. And we're like, okay, here's a process where you can do that. Here are RFCs and so on. So thinking through, there are lots of community aspects that come into that you can really work on. A small project, it's maybe easy to do because there's like two developers and you can do those as you grow. Putting more of these processes in place, thinking about the documentation, thinking about what do developers care about, what kind of tools would they want to use, all of these come into play, I think.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah. Yeah, that's an interesting question. I wish I had all the answers there, I guess. So we could replicate it. I think there are a number of things that need to come together, right? One, just like any new thing, it is about there's a sweet spot of timing, what's needed, does it grow with what's needed. So in this case, for example, TensorFlow is not just grown because it has a good tool, it's also grown with the growth of deep learning itself. So those factors come into play. Other than that, though, I think just hearing, listening to the community, what they do, what they need, being open to, like in terms of external contributions, we've spent a lot of time in making sure we can accept those contributions well. We can help the contributors in adding those, putting the right process in place, getting the right kind of community.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, if you look at enterprises like Pepsi or these, I mean, a lot of them are already using TensorFlow. They are not the ones that do the development or changes in the core. Some of them do, but a lot of them don't. I mean, that's small pieces. There are lots of these, some of them being, let's say, hardware vendors who are building their custom hardware and they want their own pieces. Are some of them being bigger companies say IBM? I mean, they are involved in some of our special interest groups and they see a lot of users who want certain things and they want to optimize for that. So folks like that often.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Large size of the ecosystem and the different groups involved there, enabling people to evolve and push on things more independently just allows it to scale better.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“So, the way it's organized today is there's one, there are lots of repositories in the TensorFlow organization at GitHub. The core one where we have TensorFlow, it has the execution engine, it has the key backends for CPUs and GPUs, it has the work to do distributed stuff. And all of these just work together in a single library or binary. There's no way to split them apart easily. There are some interfaces, but they're not very clean. In a perfect world, you would have clean interfaces where, okay, I want to run it on my fancy cluster with some custom networking. Just implement this into that. And we kind of support that, but it's hard for people today. I think as we are starting to see more interesting things in some of these spaces, having that clean separation will really start to help. And again, going to the”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“VID 2.0 with these APIs where we are, we can give you a lot of performance just with whatever you do. Because we see these, it's much cleaner. We know most people are going to do things this way. We can really optimize for that and get a lot of those things out of the box. And it really allows us both for single machine and distributed and so on to really explore other spaces behind the scenes after 2.0 in the future versions as well. So right now the team's really excited about that over time I think we'll see that. The other piece that I was talking about in terms of just restructuring the monolithic thing into more pieces and making it more modular. I think that's going to be really important for a lot of the other people in the ecosystem other organizations and so on that wanted Build things”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, so I would say one is just where 2.0 is, and with all the things that we've talked about, I think as we think beyond that, there are lots of other things that it enables us to do and that we're excited about. So what it's setting us up for, okay, here are these really clean APIs. We've cleaned up the surface for what the users want. What it also allows us to do a whole bunch of stuff behind the scenes once we've ready with 2.0. For example, IntensorFlow with graphs and all the things you could do. You could always get a lot of good performance if you spent the time to tune it. And we've clearly shown that lots of people do that.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“It might have taken longer. Yeah. It was, I mean, we tried some variants of that before, so I'm sure it would have happened, but it might have taken longer.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Competition is definitely interesting. It made us, you know, this is an area that we had thought about, like I said, you know, way early on. Over time, we had revisited this a couple of times. Should we add this again? At some point, we said, you know what? It seems like this can be done well. So let's try it again. And that's how we started pushing on eager execution. And how do we combine those two together, which is finally come very well together in 2.0, but it took us a while to get all the things together and so on.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“So, just like research or anything people are doing, right, it's great to get different kinds of ideas. And when we started with TensorFlow, like I was saying earlier, it was very important for us to also have production in mind. We didn't want just research, right? And that's why we chose certain things. Now, PyTorch came along and said, you know what? I only care about research. This is what I'm trying to do. What's the best thing I can do for this? And it started iterating and said, okay, I don't need to worry about graphs. Let me just run things. I don't care if it's not as fast as it can be, but let me just, you know, make this part easy. And there are things you can learn from that, right? They again had the benefit of seeing what had come before, but also exploring certain different kinds of spaces. And they had some good things there, building on, say, things like Chainer and so on before that.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Things work. And yes, we do make compromises occasionally, but unless you're designed with the clean slate and not worry about that, you'll never get to a good place.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“So it is important to keep that compatibility and so on. And yes, it does come with a huge cost. We have to think about a lot of things as we do new things and make new changes. I think it's a trade-off, right? You can, you might slow certain kinds of things down, but the overall value you're bringing because of that is much bigger because it's not just about breaking the person yesterday. It's also about telling the person tomorrow that, you know what, this is how we do things. We're not going to break you when you come on board because there are lots of new people who are also going to come on board. One way I like to think about this and I always push the team to think about as well. When you want to do new things, you want to start with a clean slate. Design with a clean slate in mind, and then we'll figure out how to make sure all the other”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“It's a tricky balance. So if it was just a researcher writing a paper who a year later will not look at that code again, sure, it doesn't matter. There are a lot of production systems that rely on TensorFlow, both at Google and across the world. And people worry about this. I mean, these systems run for a long time”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“TensorFlow started as a very monolithic system and to some extent it still is. There are lots of tools around it, but the core is still pretty large in monolithic. One of the key challenges for us to scale that out is how do we break that apart with clearer interfaces? In some ways it's software engineering 101, but for a system that's now four years old, I guess, or more and that's still rapidly evolving and that we're not slowing down with, it's hard to, you know, change and modify and really break apart. It's sort of like, as people say, right? It's like changing the engine with a car running or trying to fix that. That's exactly what we're trying to do.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Yes. There are lots of steps to it, right? And we've iterated over the last few years, so there's a lot we've learned. Often when things come together well, things look easy. That's exactly the point. It should be easy for the end user. But there are lots of things that go behind that. If I think about still challenges ahead, there are We have a lot more devices coming on board, for example, from the hardware perspective. How do we make it really easy for these vendors to integrate with something like TensorFlow, right? So there's a lot of compiler stuff that others are working on. There are things we can do in terms of our APIs and so on that we can do.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Not just those. One of the big things about 2.0 that we are pushing on is, okay, we have these so many different pieces, right? How do we help make all of them work well together? So there are a few key pieces there that we're pushing on, one being the core format in there and how we share the models themselves through save model and TensorFlow Hub and so on. And a few of the pieces that we really put this together.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“If we're at a place where there are lots of libraries being built on top, so there are some for research, maybe things like TensorFlow agents or TensorFlow probability that started as research things or for researchers for focusing on certain kinds of algorithms, but they're also being deployed or used by proaction folks. And some have come from within Google, just teams across Google who wanted to do these things. Others have come from just the community because there are different pieces that different parts of the community care about. And I see our goal as enabling even that, right? It's not, we cannot and won't build every single thing that just doesn't make sense. But if we can enable others to build the things that they care about, and there's a broader community that cares about that and we can help encourage that, and that's great. That really helps the entire.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“On really tiny chips when we had some demos at the developer summit. And so the way I think about this ecosystem is how do we help get machine learning on every device that has a compute capability and that continues to grow. And so in some ways this ecosystem is looked at various aspects of that and grown over time to cover more of those and we continue to push the boundaries in some areas we've built more tooling and things around that to help you. I mean the first tool we started was Tensor Board. You wanted to learn just the training piece, TFX or TensorFlow extended to really do your entire ML pipelines if you're care about all that production stuff. But then going to the edge, going to different kinds of things. And it's not just us now.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“So, one is to, on the research side, keep pushing on the state of the art. How do we enable researchers to build the next amazing thing? So Bert came out recently. It's great that people are able to do new kinds of research. There are lots of amazing research that happens across the world. So that's one direction. The other is how do you take that across all the people outside who want to take that research and do some great things with it and integrate it to build real products, to have a real impact on people? And so if that's the other axes in some ways, at a high level, one way I think about it is there are a crazy number of compute devices across the world. And we often used to think of ML and training and all of this as, okay, something you do either in the workstation or the data center or cloud, but we see things running on the phones. We see things running.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“So, in short, the way I like to think of this is our goal is to enable machine learning and in a couple of ways one is we have lots of exciting things going on in ML today. We started with deep learning, but we now support a bunch of other algorithms too.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“I think it's somewhat different, I would say. I've always been involved in the key design directions. There are lots of things that are distributed where the number of people, Martin Wick being one who is really driven a lot of our open source stuff, a lot of the APIs, and there are a number of other people who've been pushed and been responsible for different parts of it. We do have regular design reviews over the last year. We've really spent a lot of time opening up to the community and adding transparency. We're setting more processes in place. So RFCs, special interest groups really grow that community and scale that. I think the kind of scale that ecosystem is in, I don't think we could scale with having me as the loan point of decision maker. I got it.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“That's right. Yeah, it's interesting how you can put Things together which can align right. And in this case, I think Francois, the team and I, you know, a bunch of us have chatted and I think we all want to see the same kind of things. We all care about making it easier for the huge set of developers out there and that makes a difference.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Based on where we were, we were like, okay. Let's see what the people like. And Keras was clearly one that lots of people loved. There were lots of great things about it. So we settled on that.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“Well, we did spend a lot of time thinking about that one. We had a bunch of APIs, some built by us. There was a parallel layers API that we were building when we decided to do Keros in parallel. So there were like, okay, two things that we are looking at. And the first thing we was trying to do is just have them look similar, like be as integrated as possible, share all of that stuff. There were also like three other APIs that others had built over time because we didn't have a standard one. But one of the messages that we keep kept hearing from the community, okay, which one do we use? And they kept seeing like, okay, here's a model in this one and here's a model in this one. Which should I pick? So that's sort of like, okay, we had to address that straight on with 2.0. The whole idea is we need to simplify. We had to pick one.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source
“He joined research and he was doing some amazing research. He has some papers on that research. He's a great researcher as well And at some point, we realized, oh, he's doing this good stuff. People seem to like the API, and he's right here. So we talked to him and he said, okay, why don't I come over to your team and work with you for a quarter? And let's make that integration happen. Talk to his manager and he said, sure, my future's fine. And that quarter's been something like two years now. And so he's fully on this.”
2019-06-03 · Lex Fridman Podcast · Rajat Monga: TensorFlow · IDENTIFIED FROM THE TRANSCRIPT · source