>> If you’re not considering how to use deep neural nets to solve your data understanding problems, you almost certainly should be. This line is taken directly from the talk And this is exactly why Google's hype of their tech is getting dangerous for everyone else, who is not Google. Because they advocate, nay, they preach, that everyone should abandon what they're doing and do what Google tells them works. And, oh,…
When I was an academic scientist in the mid 2000s, I ended up with more data than I could deal with, and none of the computing systems in academia at the time dealt well with that (they were tuned for HPC/supercomputers). The bigtable, mapreduce, and GFS papers were huge to me, because they provided a nicer framework for data processing. Although Google made those tools for Search and Ads (and profited greatly from them) they also published them, and Doug Cutting and others incorporated them into Hadoop. A similar thing is happening now, but Google got better at releasing their codes as open source, which reduces the time between publication of a good idea, and replication of that work by others outside the corp.
(eventually, I went to google to get direct access to its infrastructure; built Exacycle, gave away an enormous amount of free computing time that cost Google rather than profiting it, the leadership loved it even though it cost money, and I even managed to get Googler to apply machine learning to academic problems I cared about).
So I don't think Google solely acts in its own short term financial interests.
Also, aspirin has turned out to be amazing at solving a wide range of health problems, so I think bayer was probably right (if not for the right reasons) on that one.