AI is certainly a hot topic—everyone claims to be doing it (or at least working on doing it). But how many businesses are actually executing? One of the reasons it’s a difficult question to answer is that everyone seems to have a different definition of what exactly AI is.
One of the more common and fairly widely accepted definitions is that AI means going beyond simple statistics to mimic human skills in perception, learning, interaction, and decision making. But even this definition leaves some room for interpretation. So going one step further, Jed Dougherty shares examples on a matrix that breaks down the different parts of that definition and how they might manifest themselves in data science projects at different levels.
This keynote is sponsored by Dataiku.
Jed Dougherty is a data scientist at Dataiku, where he leads the data scientist team in North America. He specializes in helping large companies in fields including finance, manufacturing, and medicine spin up and organize data science teams and has helped clients build successful projects in data security, real-time recommendation, predictive maintenance, and other “hot topics.” Previously, he worked at a camel ride, so he’s spent quite a bit of time appreciating the normal versus bimodal distribution of dromedaries and Bactrians. He holds a master’s degree from the QMSS program at Columbia University.
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