With the emergence of tensorflow.js, training directly in the browser becomes possible. I think the browser's cross-device and easy deployment characteristics are very suitable for machine learning application development and use. What are the disadvantages/challenges of machine learning in the browser?
Zhuotao Lian ,
Take a look at V8 presentation (though dated I think it is still relevant) at:
https://www.youtube.com/watch?v=UJPdhx5zTaw
When you need scalability for datasets that can take hours a 2X delay means a couple of hours more. 2X also means that it wastes more energy also for the same execution.
Also, note that the comparison of the port on the video is based on porting the same JS to C++ not optimized C++ to JS which I think presents some extra challenges in performance optimization.
Yes, the V8 is really relevant thing, so far my experience was the slow speed with in-browser ML.
The biggest challenge is to accept it psychologically. Human memory is processed in various ways. It is said that information that appeals to more than one sense organ is more permanent.
In this context, it may be difficult for some to learn without sharing the same real environment.
The same will have various challenges for machine learning that mimics the human mind. We'll see when the experiences build the data.
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