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Summary: Here are our 6 predictions for data science, machine learning, and AI for 2018. Some are fast track and potentially disruptive, some take the hype off over blown claims and set realistic expectations for the coming year.
Summary: As a profession we do a pretty poor job of agreeing on good naming conventions for really important parts of our professional lives. “Machine Learning” is just the most recent case in point. It’s had a perfectly good definition for a very long time, but now the deep learning folks are trying to hijack the term. Come on folks. Let’s make up our minds.
As a profession we do a pretty poor job of agreeing on good naming conventions…Continue
Summary: Which is more important, the data or the algorithms? This chicken and egg question led me to realize that it’s the data, and specifically the way we store and process the data that has dominated data science over the last 10 years. And it all leads back to Hadoop.
Summary: This is the third in our series on chatbots. In this installment we’ll look at the best practice dos and don’ts as described by a number of successful chatbot developers.