Re:Summary of the Neural Information Processing Systems Conf...
by AndrewKemendo on Monday January 02, 2017 @09:26PM
(#13306184)
Great writeup.
He makes a great point I think inadvertently:
However, they haven't created significant commercial value in industry yet, in ways that couldn't plausibly be substituted with traditional supervised learning.
Transfer learning, domain adaptation and semi-supervised learning alleviate the data-hungry requirements of deep learning, and are starting to work really well.
Transfer learning is a term of art, but it also represents how supervised learning works from human>machine.
I think we need to embed more ML systems into our daily lives to "teach" networks how/when/why to do things. IMO the best way to do this is through AR as it's a great input output tool for recommending actions to the user and transmitting the type of data that we are making great progress in (vision).
He makes a great point I think inadvertently:
However, they haven't created significant commercial value in industry yet, in ways that couldn't plausibly be substituted with traditional supervised learning.
Transfer learning, domain adaptation and semi-supervised learning alleviate the data-hungry requirements of deep learning, and are starting to work really well.
Transfer learning is a term of art, but it also represents how supervised learning works from human>machine.
I think we need to embed more ML systems into our daily lives to "teach" networks how/when/why to do things. IMO the best way to do this is through AR as it's a great input output tool for recommending actions to the user and transmitting the type of data that we are making great progress in (vision).