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The future of the field of machine learning depends on its ability to deliver business value & social benefits that match the high expectations it has set for itself. The way to do that it is to make ML as accessible as possible, to get it deployed to every relevant problem.
I think the most effective strategy here is to provide accessible tools to solve problems with ML, and provide accessible education material to get people started. Not just grad students, not just engineers -- but everyone who's in a position to build things and solve problems
This is also a strong form of power decentralization. "Decentralization" seems to be a keyword of interest these days, although people often use it to pitch businesses and products that use decentralized computing to *centralize* power into the hands of a small number of insiders
Yes that is correct. It is already well on the way. In particular as a path with many tool for client services is the implementation of cloud applications in Microsoft Azure. @WSJecon @Azure @schmarzo @ambermac @datawallow @MSFTChicago
Thank you François
I repeat this message almost every day at my university. There is no way a bunch of programmers alone will make machine learning the reality we dream of. We have tried once and it didn't work. Interdisciplinary studies, open tools, accessible education, open discussion,...
I love Common Sense above all it is the practical that gets things done.
The *feeling* that it's hard from folks who don't have the backgrounds that the folks who discovered it have is the real barrier. The skills of machine learning can be built from an understanding of teaching/math/ neurology/psychology, not just CS. Lmk if you disagree.
"The issues - AI issues - deserve serious public debate. They are too scientific to be left to philosophers and too philosophical to be left to scientists." From the preface of H. Dreyfus' book, What Computers Can't Do. The revised edition is from 1979, almost 40y. already!
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