Does Machine Learning Spell The End Of The Data Scientist? Articles Analytics

In the short term, data scientists are unlikely to be replaced. Kevin Murphy, a Senior Research Scientist at Google notes that: 'The first problem is that current Machine Learning methods still require considerable human expertise in devising appropriate features and models. The second problem is that the output of current methods, while accurate, is often hard to understand, which makes it hard to trust.' Murphy cites the'automatic statistician' project from Cambridge, which'aims to address both problems, by using Bayesian model selection strategies to automatically choose good models/ features, and to interpret the resulting fit in easy-to-understand ways, in terms of human readable, automatically generated reports.' Their project won a 750,000 Google Focused Research Award, but it still has a number of challenges to overcome if it going to be a success. What Murphy says initially still stands true, and Machine Learning methods require considerable expertise at the point of origination.