This Week in Data Science (November 29, 2016)

Here’s this week’s news in Data Science and Big Data.

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Interesting Data Science Articles and News

• Machine Learning for Everyday Tasks – Machine learning is often thought to be too complicated for everyday development tasks. I have always felt like we can benefit from using machine learning for simple tasks that we do regularly.
• An Interactive Tutorial on Numerical Optimization – Numerical Optimization is one of the central techniques in Machine Learning. I thought that it might be fun to provide some interactive visualizations of how these algorithms work.
• Man living with machine: IBM’s AI-driven Watson is learning quickly, expanding to new platforms – Watson has gone from a game show novelty to a tool in use in many industries, from medicine to cooking.
• The Top Predictive Analytics Pitfalls to avoid – We devised the following list of top predictive analytics pitfalls to avoid in order to keep your models performing as expected.
• Better Questions to Ask Your Data Scientists – While it’s impossible to give an exhaustive account, here are some important factors to think about when communicating with data scientists, particularly as you begin a data search.
• AI Machine Attempts to Understand Comic Books … and Fails – Understanding comic books is surprisingly hard.
• Google’s AI translation tool seems to have invented its own secret internal language – All right, don’t panic, but computers have created their own secret language and are probably talking about us right now.
• The most Googled Thanksgiving recipe in every state – Looking at search data from the past five years, Google’s researchers found the most unique recipe that people in every state (plus Washington DC) Googled during Thanksgiving week.
• The secret to smarter fresh-food replenishment? Machine learning – With machine-learning technology, retailers can address the common—and costly—problem of having too much or too little fresh food in stock.
• How machine learning could help doctors improve the reading of medical images – The radiology world has been abuzz with discussions of machine learning and what artificial intelligence may mean for the future of the field.
• Google’s Hand-Fed AI Now Gives Answers, Not Just Search Results – Ask the Google search app “What is the fastest bird on Earth?,” and it will tell you.
• How to build a Successful Big Data Analytics Proof-of-Concept – For all kinds of organizations, whether large multi-national enterprises or small businesses, developing a big data strategy is a difficult and time-consuming exercise.
• 5 amazing ways IBM Watson is transforming healthcare – IBM Watson: The doctor of the future will see you now.
• Statistical Mistakes and How to Avoid Them – Here are three kinds of avoidable statistics mistakes that I notice in published papers.
• How Big Data Takes the Retail Industry to a Whole New, More Informed Space – What kind of insights are being gathered via big data? Many.
• iSee: Using deep learning to remove eyeglasses from faces – Melissa Runfeldt is an Insight alumna from the Summer 2016 Silicon Valley Data Science session, where she built a deep learning model for DITTO technologies to remove eyeglasses from images of faces.

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