Matter over Mind in Machine Learning

Love the points made by the writer based on Dr. Kiri Wagstaff’s paper – Machine Learning that Matters

The Official Blog of BigML.com

I am fortunate enough to have had a number of conversations with Dr. Kiri Wagstaff of NASA’s JPL on a number of occasions (you might as well get the jokes about “not having to be a rocket scientist to understand machine learning” out of the way right now).

Matter over Mind in Machine Learning

Wagstaff is a brilliant scientist.  On top of that, and fortunately for all of us, she works very “close to the data”, using her machine learning expertise to solve important problems that directly impact people other than machine learning researchers.  This closeness to the data is somewhat rare among machine learning experts but is becoming more and more common.  Our own chief scientist Tom Dietterich is a pioneer in computational sustainability and ecosystem informatics.  Another acquaintance of mine, Rayid Ghani, left a lucrative position at Accenture Research to head up President Obama’s vaunted data analytics team.

At the 2012…

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Advice to a Young Scientist – E.W. Dijkstra

Here are some of the most inspiring quotes from “Advice to a Young Scientist” by E.W.D:
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  •  Raise your standards as high as you can live with, avoid wasting your time on routine problems, and always try to work as closely as possible at the boundary of your abilities. Do this because it is the only way of discovering how that boundary should be moved forward. (Rule 1)
  • We all like our work to be socially relevant and scientifically sound. If we can find a topic satisfying both desires, we are lucky; if the two targets are in conflict with each other, let the requirement of scientific soundness prevail. (Rule 2)
  • Never tackle a problem of which you can be pretty sure that (now or in the near future) it will be tackled by others who are, in relation to that problem, at least as competent and well-equipped as you are. (Rule 3)
  •  Write as if your work is going to be studied by a thousand people.
  • Don’t get enamored with the complexities you have learned to live with (be they of your own making or imported). The lurking suspicion that something could be simplified is the world’s richest source of rewarding challenges.
  • Before embarking on an ambitious project, try to kill it.
  • Remember that research with a big R is rarely mission-oriented and plan in terms of decades, not years. Resist all pressure —be it financial or cultural— to do work that is of ephemeral significance at best.
  • Don’t strive for recognition (in whatever form): recognition should not be your goal, but a symptom that your work has been worthwhile.
  • Avoid involvement in projects so vague that their failure could remain invisible: such involvement tends to corrupt one’s scientific integrity.
  • Striving for perfection is ultimately the only justification for the academic enterprise; if you don’t feel comfortable with this goal —e.g. because you think it too presumptuous— stay out!

Original PDF

The first three points are also The Three Golden Rules for Successful Scientific Research.