Kind of like with the rise of electricity, the microprocessor, the PC, or internet -- in the beginning, only the people building it understood what all the fuss was about. But that changed quickly over the course of N years (where N ends up being sooner than everyone thinks). If you had started a career in any of those fields before they were obvious in hindsight, you would have probably done quite well.
The author of the post has not quit to go start a photo app as far as I know, he's still doing research on the cutting edge of deep learning because that's where the most promise is.
I don't think his later comments really negate his earlier comments.
Neural networks continue to make progress in the narrow field they are designed for and researching these I'm sure continues to be interesting. That doesn't change the point that human don't interpret an image as a couple of annotations but as rich fabric of information far beyond what computer vision current does.
Basically, there is a ocean of interesting, useful and excite things computers can do before they arrive at what humans can do.
Gains have continued in 2015.
Also, more recent reflections on his research which I think gives a bit more color to the OP: http://karpathy.github.io/2014/07/03/feature-learning-escapa...
Kind of like with the rise of electricity, the microprocessor, the PC, or internet -- in the beginning, only the people building it understood what all the fuss was about. But that changed quickly over the course of N years (where N ends up being sooner than everyone thinks). If you had started a career in any of those fields before they were obvious in hindsight, you would have probably done quite well.
The author of the post has not quit to go start a photo app as far as I know, he's still doing research on the cutting edge of deep learning because that's where the most promise is.