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IMHO, Google released Tensorflow because AI is currently being driven by research, and researchers were mostly writing code for Torch that is used at Facebook.

by chimtim 11y ago

IMHO, Google released Tensorflow because AI is currently being driven by research, and researchers were mostly writing code for Torch that is used at Facebook. So FB folks were enjoying lots of new algorithms, benchmarked against their systems. Google open-sourcing TF shows that benefits of open-source outweigh the disadvantages of being closed source. Even if the future is data and not code, companies will not open-source their code if they have nothing to benefit.

ThomPete 11y ago
I think this is spot on. And to your second point http://www.bloomberg.com/news/articles/2015-10-29/apple-s-secrecy-hurts-its-ai-software-development http://www.bloomberg.com/news/articles/2015-10-29/apple-s-se...
datashovel 11y ago
Yes, I think at Google's scale one of the biggest pain points is almost certainly integrating new employees to do things "the Google way". Instead of picking up qualified people who may or may not buy in to what you're doing internally, why not pick from a group of candidate employees who have already bought in and have self-taught, at zero cost, how to do things "the Google way". Also, as far as I understand, TensorFlow is not technically a set of proprietary algorithms. It's basically a framework for ML.
- gradys 11y ago
  Your understanding is correct. TensorFlow is a library for defining and executing mathematical operations on multidimensional arrays. It just so happens that much of machine learning, and especially deep learning, can be described as mathematical operations on multidimensional arrays.
- munificent 11y ago
  > I think at Google's scale one of the biggest pain points is almost certainly integrating new employees to do things "the Google way". Ramping up at Google is definitely a stressful process, but I don't think open sourcing technology puts much of a dent in that. Even if you show up your first day at work knowing every tool Google uses, on your second day there will be some new tool and something else will be deprecated. By a couple of years, damn near every piece of software you were familiar with will have been replaced by something different. You are in a constant state of learning. This is, I think, one of the reasons Google places such a premium on algorithms and data structures in hiring. They are some of the few things that don't change often and being familiar with fundamental concepts makes it much easier to quickly pick up a new tool that uses them.
  - datashovel 11y ago
    Thanks for the insights. I can definitely see what you're saying from the perspective of a software engineer. I wonder if those same concepts translate over to onboarding people who might have a weaker programming background, such as mathematicians / theorists who may have a more difficult time making the switch. For example if they've been using the same toolset their entire careers. This last point isn't in response to your comment, but more a response to the ideas presented in the article. I don't necessarily buy the idea that the data is incentive enough to researchers at the top of their fields to leave what they're doing to go work at Google. Surely they have access to plenty of public datasets large enough to accomplish what they want to accomplish. So the requirement for switching tools may be a much bigger hurdle when trying to recruit for ML. Maybe I'm wrong in assuming they are targets for employment at Google.
- mark_l_watson 11y ago
  Google has a very good onboarding process (I worked there as a contractor in 2013). They have classes where instructors teach you how to use the infrastucture and codelabs that you can do at home or at work to learn more specialized things. So, I think that they open sourced TensorFlow more as an advertisement/inducement to hire more ML people.
eriksencosta 11y ago
You have a good point. By open sourcing TensorFlow, Google will benefit from innovations contributed by the community at large. Machine learning frameworks are now commodity software. I give my point about this in a blog post titled "TensorFlow is commodity software" http://blog.eriksen.com.br/en/tensorflow-commodity-software http://blog.eriksen.com.br/en/tensorflow-commodity-software (HN link: https://news.ycombinator.com/item?id=10574444 https://news.ycombinator.com/item?id=10574444) You last phrase is right too: Google built a massive infrastructure because it had Google File System, BigTable and MapReduce. Hadoop, inspired by the published papers, turned into a more sophisticated software (an ex-Google engineer said that, but other Googlers denying his claims) and grown into a big ecosystem of products and services. But while Hadoop was matching the feature set of Google's proprietary implementations, Google was getting even bigger.

by chimtim 11y ago

ThomPete 11y ago

I think this is spot on. And to your second point http://www.bloomberg.com/news/articles/2015-10-29/apple-s-secrecy-hurts-its-ai-software-development http://www.bloomberg.com/news/articles/2015-10-29/apple-s-se...

datashovel 11y ago

Yes, I think at Google's scale one of the biggest pain points is almost certainly integrating new employees to do things "the Google way". Instead of picking up qualified people who may or may not buy in to what you're doing internally, why not pick from a group of candidate employees who have already bought in and have self-taught, at zero cost, how to do things "the Google way". Also, as far as I understand, TensorFlow is not technically a set of proprietary algorithms. It's basically a framework for ML.

gradys 11y ago
Your understanding is correct. TensorFlow is a library for defining and executing mathematical operations on multidimensional arrays. It just so happens that much of machine learning, and especially deep learning, can be described as mathematical operations on multidimensional arrays.
munificent 11y ago
> I think at Google's scale one of the biggest pain points is almost certainly integrating new employees to do things "the Google way". Ramping up at Google is definitely a stressful process, but I don't think open sourcing technology puts much of a dent in that. Even if you show up your first day at work knowing every tool Google uses, on your second day there will be some new tool and something else will be deprecated. By a couple of years, damn near every piece of software you were familiar with will have been replaced by something different. You are in a constant state of learning. This is, I think, one of the reasons Google places such a premium on algorithms and data structures in hiring. They are some of the few things that don't change often and being familiar with fundamental concepts makes it much easier to quickly pick up a new tool that uses them.
- datashovel 11y ago
  Thanks for the insights. I can definitely see what you're saying from the perspective of a software engineer. I wonder if those same concepts translate over to onboarding people who might have a weaker programming background, such as mathematicians / theorists who may have a more difficult time making the switch. For example if they've been using the same toolset their entire careers. This last point isn't in response to your comment, but more a response to the ideas presented in the article. I don't necessarily buy the idea that the data is incentive enough to researchers at the top of their fields to leave what they're doing to go work at Google. Surely they have access to plenty of public datasets large enough to accomplish what they want to accomplish. So the requirement for switching tools may be a much bigger hurdle when trying to recruit for ML. Maybe I'm wrong in assuming they are targets for employment at Google.
mark_l_watson 11y ago
Google has a very good onboarding process (I worked there as a contractor in 2013). They have classes where instructors teach you how to use the infrastucture and codelabs that you can do at home or at work to learn more specialized things. So, I think that they open sourced TensorFlow more as an advertisement/inducement to hire more ML people.

eriksencosta 11y ago

You have a good point. By open sourcing TensorFlow, Google will benefit from innovations contributed by the community at large. Machine learning frameworks are now commodity software. I give my point about this in a blog post titled "TensorFlow is commodity software" http://blog.eriksen.com.br/en/tensorflow-commodity-software http://blog.eriksen.com.br/en/tensorflow-commodity-software (HN link: https://news.ycombinator.com/item?id=10574444 https://news.ycombinator.com/item?id=10574444) You last phrase is right too: Google built a massive infrastructure because it had Google File System, BigTable and MapReduce. Hadoop, inspired by the published papers, turned into a more sophisticated software (an ex-Google engineer said that, but other Googlers denying his claims) and grown into a big ecosystem of products and services. But while Hadoop was matching the feature set of Google's proprietary implementations, Google was getting even bigger.