"Machine Learning is modern alchemy. People then: iron into gold? Sure! People now: shoddy data into new information? Absolutely!"
"Our fellow scientists in many different fields, attracted by the fanfare and by the new avenues opened to scientific analysis, are using these ideas in their own problems."
"[E]stablishing of such applications is not a trivial matter of translating words to a new domain, but rather the slow tedious process of hypothesis and experimental verification."
This is immensely important. While many of these methods appear general, and can be used with little effort thanks to the wide variety of machine learning toolkits that are available, they should be applied with care.
Applying these methods to problems in other domains without careful consideration for the differences and complexities that might arise.
The availability of advanced toolkits does not make your work impervious to flaws.
With machine learning, it's even worse - the flaws in your data, model, or process, can be explicitly worked around by the underlying machine learning algorithm.
That makes debugging difficult as your program is, to some loose degree, self repairing.
Using these toolkits without proper analysis and experimental proof that they're working as intended, especially when their predictions are used for an important decision, is negligence.
"Research rather than exposition is the keynote, and our critical thresholds should be raised."
As a field, we don't have a strong grasp on many of the fundamentals.
Issues that are obvious in hindsight are hiding in plain view.
Just a few days ago, layer normalization popped up.
It will likely make training faster and results better for a variety of applications.
You can literally explain the idea to a skilled colleague in all of ten seconds.
Somehow we were using a far more complicated method (batch normalization, weight normalization, etc) before trying the "obvious" stuff
We need more work like that than papers and media publications grandstanding about vague potential futures that have little theoretical or experimental basis.
Also, it's worth reading Shannon's "A Mathematical Theory of Communication" from 1948.
There's a reason it has 85,278 citations - entire fields started there.
There is a paper I was just reading here (https://meehl.dl.umn.edu/sites/g/files/pua1696/f/167grovemee...) where the author surveyed the literature for all the comparisons he could find of human experts and statistical methods. In all but a few cases the algorithmic methods did better. Most of these were crude, simple models, using only a few features. Most of them are before the age of computers and were calculated on pencil and paper. And yet they still generally outperform human intuition, which is just terrible and barely better than chance.
Yet in most the industries where algorithms were shown to do better than humans decades ago, did not switch to the algorithms.
I rarely comment here, but the similarity was so striking that I came back with the express intention to comment on this exact vein. I actually suspect this analogy was the OP's intention all along :)
Also, it is striking how the human nature never changes and the societal mores tend to remain the same; 60 years is not such a long time on that scale, but the buzz, the (less/un-informed) fever and fervour of the general public that Shannon remarked, they all sound eerily familiar.