What enabled the ML revolution we're witnessing now is mostly the advent of powerful and low-cost GPGPUs and the abundance of training data, as you mentioned. O

What enabled the ML revolution we're witnessing now is mostly the advent of powerful and low-cost GPGPUs and the abundance of training data, as you mentioned. Of course, there are theoretical breakthroughs and interesting new applications [1], but it wasn't the deciding factor. Even ConvNets aren't new. For example, AlexNet is basically a much larger reimplementation of the original Lenet idea [2] from 1998. This is not to discredit any new work that's being done. I think it's awesome to see so much progress in a field that's certainly on its way of defining an era. I'm just pointing out that we have known about most of the fundamentals for a long time. [1]: https://tryolabs.com/blog/2016/12/06/major-advancements-deep-learning-2016/ https://tryolabs.com/blog/2016/12/06/major-advancements-deep... [2]: http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf