Summary:  Whether you’re a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there’s a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.

There are many articles written from a tools…

Summary:  Based on a McKinsey study we reported that 47% of companies had at least one AI/ML implementation in place.  Looking back at the data and the dominance of RPA as the most widely reported instance makes us think that the number is probably significantly lower.

We’ve been trying to get a handle on…

Summary: Recurrent Neural Nets (RNNs) are at the core of the most common AI applications in use today but we are rapidly recognizing broad time series problem types where they don’t fit well.  Several alternatives are already in use and one that’s just been introduced, ODE net is a radical departure from our way of thinking about the solution.

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Summary:  In the literal blink of an eye, image-based AI has gone from high cost, high risk projects to quick and reasonably reliable.  C-level execs looking for AI techniques to exploit need to revisit their assumptions and move these up the list.  Here’s what’s changed.

For data scientists these are miraculous times.  We tend to think of miracles as something that occurs instantaneously but in our world that’s not quite so.  Still the rate…