Basically this video ignores the history of machine learning in general. Jumping from Expert Systems to Neural Networks and Deep Learning is actually ignoring 36 years (and billions of dollars) of research http://machinelearning.org/icml.html (Breiman, Quinlan, Mitchell, Dietterich, Domingos, etc). Calling 2012 the seminal moment of Deep Learning is quite hard to digest. Maybe it means that 2012 is the point in time when the VC community discovered machine learning? Even harder to digest is calling Deep Learning the most productive and accurate machine learning system. What about more business oriented domains (without unstructured inputs), the extreme difficulties and expertise required to fine tune a network for a specific problem, or some drawbacks like the ones explained by http://arxiv.org/pdf/1412.1897v2.pdf or http://cs.nyu.edu/~zaremba/docs/understanding.pdf.
Those who ignore history are doomed to repeat it. As Roger Schank pointed out recently http://www.rogerschank.com/fraudulent-claims-made-by-IBM-abo..., another AI winter is coming soon! Funny that the video details the three first AI winters but the author doesn't realize that this excessive enthusiasm in one particular technique is contributing to a new one!
I think you're spot on with the observation that 2012 refers to when VCs discovered machine learning. Anyone who has recently interacted with VCs will tell you that they look for anything to do with machine learning (and VR/AR/MR), even when it makes no sense. There are going to be some companies who will be able to leverage machine learning to advance their business, namely, Google/Facebook who will probably claim they can offer better targeted advertising and such. Most other players who merely try to force machine learning on other fields are likely to realize that while the technology is cool, it's still too early for it to be generally applicable to "any" problem.
Especially dangerous is going to be the mix of machine learning with healthcare. I believe Theranos tried it and found out it's not that easy... I'd watch this space with skepticism.
> Especially dangerous is going to be the mix of machine learning with healthcare.
Medical diagnosis has been one of the primary application areas of AI since the 70s (maybe earlier, I can't remember off the top of my head). The widespread non-availability of automatic doctors should tell you how well that has worked :-(.
Coincidentally enough I worked both in AI (1980s) and drug development (2000s) and now really understand how hard it is!
I do believe we will soon see automated radiology analysis as it is likely to appear to be most amenable to automated analysis. Presumably in Asia first as the US FDA will justifiably require a lot of validation. The opportunity for silent defect is quite high -- you are right to say "especially dangerous"
First, would have to agree that there is quite a bit of history being ignored, but I suspect this deck is more to excite and interest investors than anything else.
The value of AI depends largely on perceived value IMHO, and the frequency of "winters" will correlate with that. I think we are still a bit too early for VR to really take off, but that did not stop a $2b acquisition and loads of investor interest. This will probably artificially constrain what should be an AI winter right now, just because so much money is continuing to go into it.
I personally applaud that so much enthusiasm is going into AI right now, and though we are repeating history to some extent I still think we are making incremental advancements (however small) - even if this just means applying old AI techniques to new advancements in hardware.
Those who ignore history are doomed to repeat it. As Roger Schank pointed out recently http://www.rogerschank.com/fraudulent-claims-made-by-IBM-abo..., another AI winter is coming soon! Funny that the video details the three first AI winters but the author doesn't realize that this excessive enthusiasm in one particular technique is contributing to a new one!