Finally, if deep learning does indeed prove to be a general-purpose IMI, it will be
important to develop institutions and a policy environment that is conductive to enhancing
innovation through this approach, and to do so in a way that promotes competition and social
welfare. A central concern here may be the interplay between a key input required for deep
learning—large unstructured databases that provide information about physical or logical
events—and the nature of competition. While the underlying algorithms for deep learning are in
the public domain (and can and are being improved on rapidly), the data pools that are essential
to generate predictions may be public or private, and access to them will depend on
organizational boundaries, policy and institutions.
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