If you mean hype in media and general public, I agree with you. Research is inherently risky and uncertain, often that is not conveyed correctly. Also research results are often oversold.
If you mean that big tech companies are overinvesting in Machine Learning then I have to disagree. It's not a coincidence that the companies that invested the most in Machine Learning (Google and Facebook) are companies that have a lot of data and already use Machine Learning in their products. It provides things like better feed ranking and better signals for search. These "invisible" improvements to existing products are often overlooked.
And the criticism you note isn't one-directional in the field at large.
Yes, like I mentioned I think there is confusion about terminology. Many ML Engineers (in my definition) call themselves Data Scientists. This leads to misunderstandings, when people don't recognize that others have different goals.
You mean, some tech companies understand the difference between Data Science, AI in general, ML and Deep Learning and try to utilize the best tools in hand to handle their data.
But what is driving this hype, as well as the Blockchain hype, is not engineer-driven companies like Google, but rather MBA-driven buzzword-friendly tech companies (imagine Balmer-era Microsoft), non-tech companies who want to share the cake, the media and finally the investors who are misled by the rest, but end up creating a capital-based feedback loop for them.
As far as the people driving the hype train right now are concerned, Machine Learning is the way you do AI, and Deep Learning is just a more powerful (ahem deeper) version of Machine Learning. This is what AlphaGo used to defeat Lee Sedol, so it's obviously superior, and we should use it to process all data, in the same way we should strive to store everything on a blockchain, which is clearly superior to hash tables and databases.
If you mean hype in media and general public, I agree with you. Research is inherently risky and uncertain, often that is not conveyed correctly. Also research results are often oversold.
If you mean that big tech companies are overinvesting in Machine Learning then I have to disagree. It's not a coincidence that the companies that invested the most in Machine Learning (Google and Facebook) are companies that have a lot of data and already use Machine Learning in their products. It provides things like better feed ranking and better signals for search. These "invisible" improvements to existing products are often overlooked.
And the criticism you note isn't one-directional in the field at large.
Yes, like I mentioned I think there is confusion about terminology. Many ML Engineers (in my definition) call themselves Data Scientists. This leads to misunderstandings, when people don't recognize that others have different goals.