Would it be bad if TensorFlow became the default library for deep learning? For companies that aren't Google, yes. Many organizations don't like how Google's controlled Android development.
But I think an important part of Dean's lecture are the slides that talk about making ML/DL expertise obsolete. That's the future, whether you use TensorFlow or not.
To give you an idea of how much compute it took, they spent two million dollars for one run on a relatively toy dataset, CIFAR-10. Imagine how much it would take on an imagenet sized dataset! Can your company afford ~$20 million+ per dataset?
I do think that hyperparameter twiddling aspect might get automated, but believe me, that is much welcomed by the DL research community! I would much rather spend my time on new ideas rather than trying ten different initializations :)
Chromium is very open. In many cases, Google has to ask permission from outside contributors to commit code, since the outside contributors are the "boss" of part of the codebase.
Tensorflow is closer to chromiums model. There are a lot of external contributors.
Could you maybe comment on why this is the case? Are there technical reasons for that?
(Warning: I'm biased. I compete VERY heavily with anything google including cloud and their deep learning framework.)
Alternatives exist (PyTorch by facebook, theano, caffe, etc.) in any case, and the best solution wins. (in the same way scikit-learn once eclipsed all other ML libraries, but some would still use xgboost which had superior Gradient Boosting Trees).
When you see a useful program that helps you be much more productive, you don't really care if it was coded in c++ or python.
The one part Google does care about is being able to sell Cloud TPU, but I don't think that by using this tool you're instantly in under their control.
Minor quibble, but: not really. The earliest solution wins, e.g. rails. It has to be good enough, but after a certain point "best" stops mattering.
Minor minor quibble, but: not really. The earliest solution wins, unless there is a 10x better solution, e.g Google. There were a lot of good enough options, but after a certain benchmark "earliest" stops mattering.
I would be careful about having only 1 company's use cases served. Competition forces parties to innovate.
For example mxnet and cntk or even more recently caffe2 serve different use cases and companies.
Giving you a recent one with our deep learning framework (I compete with all the above frameworks): We compete in the big data niche and recently found a few bottlenecks we missed because people run comparisons.
Why?