Developments in
neural networks and machine learning thus raise the question of, even if the underlying scientific
approaches (i.e., the basic multi-layered neural networks algorithms) are open, prospects for
continued progress in this field—and commercial applications thereof—are likely to be
significantly impacted by terms of access to complementary data. Specifically, if there are
increasing returns to scale or scope in data acquisition (there is more learning to be had from the
“larger” dataset), it is possible that early or aggressive entrants into a particular application area
may be able to create a substantial and long-lasting competitive advantage over potential rivals
merely through the control over data rather than through formal intellectual property or demandside network effects.
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