Wrong but useful. Exciting and practical.
Detailed comparative analysis on a topic close to my heart. The intent to create an interactive platform for discussion on this in one place. Needs more careful review.
It is not a question of driving away, rejecting the I. That would be pointless— and contrary to the physiology of the psyche. It is a matter of following its movements and then adding others to it, which the I could not pretend to.
The point of arrival is a dual subject who is irreducible, unbalanced (the Self is infinite, the individual is any being whatsoever in this world), intermittent (the perception of the dual subject is not something there right from the start, but something to achieve, the hardest yet most efficacious achievement).
Calasso, Roberto (2014-11-18). Ardor (p. 128).
the entire vision community gets to ignore you when you insist that “manual feature engineering” is going to save the day
I think of Yann LeCun (one of the fathers of Deep Learning) as a modern day Descartes, only because I think the ground-breaking work is right around the corner.
It’s starting to become clear that the future power of deep learning is going to come with its own self-contained software package like Caffe or Torch, and not from a dying breed of all-around tool-belts like OpenCV or Matlab
ConvNets are here to stay, but if we want ConvNets to be more than than a mere calculus of shadows, there’s still ample work do be done.
The ones commonly covered are those with low skill requirements and that require less creative judgement. However, there are likely many other shifts in composition of work and through hyper specialization of tasks machines could automate pieces of work to a much larger extent. For instance – PCs and various office software transformed the way work was done in much more profound ways than elimination of certain job categories.