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Alex Woodie, managing editor at Datanami observes, Data science used to be somewhat of a mystery, more of a dark art than a repeatable, scientific process.
Companies basically entrusted
powerful priests called data scientists to build magical algorithms that
used data to make predictions, usually to boost profits or improve
customer happiness. But in recent years, the field has matured to a
remarkable degree, and that is enabling progress to be made on multiple
fronts, from ModelOps and reproducibility to ethics and accountability.
Photo: Chan2545/Shutterstock
About five years ago, the worldwide scientific community was suffering a “reproducibility crises” that impacted a wide range of scientific endeavors, including so-called hard sciences like physics and chemistry. One of the hallmarks of the scientific method is that experiments must be reproducible and will give the same results, but that lofty goal too often was not met.
Data science was not immune to this problem, which should not be surprising, given the relative newness and the probabilistic nature of the field. And when you mix in the black box nature of deep learning models and data that reflects a rapidly changing world, sometimes it seems a miracle that an algorithm of any complexity could generate the same result at two points in time...
AI Ethics ImprovingThe increasing maturation of the data science field is also paying dividends when it comes to ethics and trustworthy, which are emerging as big challenges for AI to overcome.
It wasn’t long ago that companies didn’t give a thought to how AI could go off the rails, said Ted Kwartler, DataRobot’s vice president of Trusted AI.
Source: Datanami
