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The article makes the argument that, by opening up TensorFlow, other companies are at risk of having their TensorFlow-fluent employees poached by Google. Troub

by ISL 10y ago

The article makes the argument that, by opening up TensorFlow, other companies are at risk of having their TensorFlow-fluent employees poached by Google. Trouble is, if other companies are using the same tooling as Google, it's equally easy for the outside company to poach a Google employee. Opening up the code to the world is more likely to be a net-good act, propelling everyone forward. https://github.com/tensorflow/tensorflow/blob/master/LICENSE https://github.com/tensorflow/tensorflow/blob/master/LICENSE

jessriedel 10y ago
> it's equally easy for the outside company to poach a Google employee. The effect doesn't become symmetric until we reach an equilibrium where TensorFlow is ubiquitous. > Opening up the code to the world is more likely to be a net-good act, propelling everyone forward. These things aren't contradictory. It can be a net good for the world, and also a net good for Google, and also a net bad for Facebook.
gcr 10y ago
The hard parts of these frameworks isn't the framework itself. The hard part is understanding what's going on; the same properties common to all frameworks. CNNs are hard to understand. Understanding why gradient descent improves the model is very hard to understand. And understanding the right architecture to solve a problem takes a significant chunk of your research career unless you're already an expert. But languages and frameworks are simple. If you know Caffe, Torch will be easy to understand. If you know Torch, Tensorflow will be easy to understand. If you know Tensorflow, the others will be easy to understand. It's quite possible to get up to speed with a different framework in a few weeks.
- guelo 10y ago
  In the corporate world even a few weeks of ramp-up time can create lock-in and network effects. Most programming languages can be learned in a few weeks by an experienced programmer, but there are still huge network effects. Part of it is that a lot of times you just don't have a few weeks to learn. But the other part of it is that the more popular it gets the library and tooling ecosystem also starts growing exponentially, so then you have to add the learning time of the tooling and library to the switching cost.
  - ringwalt 10y ago
    I think the network effects have more to do with clueless recruiters trying to tick off a list of keywords for a position, than the actual cost to a company of an engineer ramping up on a new framework for a few weeks.

by ISL 10y ago

jessriedel 10y ago

> it's equally easy for the outside company to poach a Google employee. The effect doesn't become symmetric until we reach an equilibrium where TensorFlow is ubiquitous. > Opening up the code to the world is more likely to be a net-good act, propelling everyone forward. These things aren't contradictory. It can be a net good for the world, and also a net good for Google, and also a net bad for Facebook.

gcr 10y ago

The hard parts of these frameworks isn't the framework itself. The hard part is understanding what's going on; the same properties common to all frameworks. CNNs are hard to understand. Understanding why gradient descent improves the model is very hard to understand. And understanding the right architecture to solve a problem takes a significant chunk of your research career unless you're already an expert. But languages and frameworks are simple. If you know Caffe, Torch will be easy to understand. If you know Torch, Tensorflow will be easy to understand. If you know Tensorflow, the others will be easy to understand. It's quite possible to get up to speed with a different framework in a few weeks.

guelo 10y ago
In the corporate world even a few weeks of ramp-up time can create lock-in and network effects. Most programming languages can be learned in a few weeks by an experienced programmer, but there are still huge network effects. Part of it is that a lot of times you just don't have a few weeks to learn. But the other part of it is that the more popular it gets the library and tooling ecosystem also starts growing exponentially, so then you have to add the learning time of the tooling and library to the switching cost.
- ringwalt 10y ago
  I think the network effects have more to do with clueless recruiters trying to tick off a list of keywords for a position, than the actual cost to a company of an engineer ramping up on a new framework for a few weeks.