Deep learning has been at the forefront of the so called AI revolution for quite a few years now, and many people had believed that it is the silver bullet that will take us to the world of wonders of technological singularity (general AI). Many bets were made in 2014, 2015 and 2016 when still new boundaries were pushed, such as the Alpha Go etc. Companies such as Tesla were announcing through the mouths of their CEO's that fully self driving car was very close, to the point that Tesla even started selling that option to customers [to be enabled by future software update].
We have now mid 2018 and things have changed. Not on the surface yet, NIPS conference is still oversold, the corporate PR still has AI all over its press releases, Elon Musk still keeps promising self driving cars and Google CEO keeps repeating Andrew Ng's slogan that AI is bigger than electricity. But this narrative begins to crack. And as I predicted in my older post, the place where the cracks are most visible is autonomous driving - an actual application of the technology in the real world.
The dust settled on deep learning
When the ImageNet has been effectively solved (note this does not mean that vision is solved), many prominent researchers in the field (including even typically quiet Geoff Hinton) were actively giving press interviews, publicizing stuff on social media (e.g. Yann Lecun, Andrew Ng, Fei Fei Lee to name a few). The general tone was that we are in front of a gigantic revolution and from now on things can only accelerate. Well years have passed and the twitter feeds of those people became less active, as exemplified by Andrew Ng below:2013 - 0.413 tweets per day
2014 - 0.605 tweets per day
2015 - 0.320 tweets per day
2016 - 0.802 tweets per day
2017 - 0.668 tweets per day
2018 - 0.263 tweets per day (until 24 May)
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