
Just How Shallow Is the Artificial Intelligence Talent Pool? - speby
https://www.bloomberg.com/news/articles/2018-02-07/just-how-shallow-is-the-artificial-intelligence-talent-pool
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speby
After reading this article, it sure seems like they're counting people who are
more on the research side of AI. And yet a lot of what I've been reading or
hearing about has been almost just as focused on application of machine
learning strategies, companies looking for people who are good at dealing with
data sanitization and exploration, and then ultimately looking for people who
can deal with creating the entire data pipeline needed to support applications
that are powered by ML.

Because ultimately the "ML" portion of a large data-driven application is just
one component. There's so much more to it ... is the data we have the right
data? Where else might we find supplemental data to combine with our own? How
will we keep our data fresh and cleansed? What data transformations are needed
to stage it to be even ready to be consumed in a manner needed by certain ML
models. What problem are we trying to solve? Will the data we intend to use
actually help solve/answer that? ... those are the kinds of questions/problems
that are incredibly important on any ML-driven app, not just which CNN or RNN
model and how many layers are needed to support it.

Wondering what others think on this topic.

