That's an interesting way to look at it.

I would argue that Google+ didn't work out because Google was trying to play catch-up in a field that it just lacked knowledge in (social networks).

Whereas with machine learning, they're not playing catch-up, everyone else is. Of all the other tech titans out there, they're the ones really leading the pack.

That remark aside though, I agree with you. An attempt to go hard on machine learning and apply it everywhere will probably work out pretty badly. As fascinating as ML is, I just haven't bothered to learn it yet because I haven't the slightest idea what new and novel problem I'd solve with it that doesn't have a better solution through a more straight-forward approach.

"An attempt to go hard on machine learning and apply it everywhere will probably work out pretty badly. I haven't the slightest idea what new and novel problem I'd solve with it that doesn't have a better solution through a more straight-forward approach."

Assuming they have the money, isn't this exactly the kind of reason Google should train up a wide spectrum of engineers from different teams and then see how they apply machine learning to their respective domains? It would be foolish for Google's management to think they can divine a priori all the best possible uses of ML in their various lines of business. Why not tool up a bunch of smart people, set them loose, and see what works?

In between, they focused the entire company on switching from Desktop to Mobile.

And at some point the back key on their search page stopped working and they branded this as a feature.