There's still plenty of low-hanging fruit in this regard. We can still still get 10x improvement in usability and accessibility.
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First derivative of labor: productivity (driven by ML, in the software era). Second derivative: speed at which ML gets deployed to every applicable problem. Third derivative: progress in ML usability and accessibility (accelerates deployment of ML). Compounding effects.
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Goes all the way to the N'th derivative - which is ubiquitous use (by non-expert practitioners) and deployment. (N --> 3 or 4)
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Efficiency and productivity.
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Correction: Productivity is driven by algorithms, not
#MachineLearning And best algorithms (most general, robust, precise, etc) driven by engineering models, not heaps of old data. ML < algorithms. ML =what you use when world too complex to model. Avoid#MLhype!Thanks. Twitter will use this to make your timeline better. UndoUndo
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