This week, we feature an article that shares which questions to ask in order to get the most out of your data. We cover marketing’s shift to prioritizing data storage for AI and machine learning insights. Also, we consider why machine learning isn’t just about analyzing a pile of numbers and still requires a human touch.
Seven Things Every Executive Should Know About Machine Learning
by Yotam Yarden, Senior Data Scientist at AWS featured on, ITProportal.com
Over the past decade, data has become increasingly essential and described as the “new oil.” Organizations with extensive user data can leverage it to increase sales and customer retention. But, is your organization taking full advantage of its data? Are you satisfied with the value you generate from your data? Find tips on how to answers these questions.
Breaking the Marketing Mold with Machine Learning
by MIT Technology Review Insights for MIT Technology Review
Leading marketing organizations are shifting both strategy and culture to prioritize data storage and application to produce actionable insights. Armed with insight into customer behaviors, marketers can focus on those customers with high lifetime value, providing personalized and relevant offers. According to the survey, professional services firms and retailers are ahead of the pack, read the full results conducted by MIT Technology Review Insights.
Why Machine Learning Needs Semantics Not Just Statistics
By Kalev Leetaru, Contributing Writer to Data and Society featured on, Forbes.com
The patterns learned by machines are surface-level observational visual characteristics of the input data, rather than a more human approach that would enable. When a machine is shown patterns in a pile of numbers and asked to flag occurrences without any understanding of what those numbers represent they are solely searching for correlations in data, rather than meaning.
AI in Healthcare – Not So Fast? Study Outlines Challenges, Dangers for Machine Learning
by Benjamin Harris, Contributing Writer to Healthcare IT News featured on, HealthcareITNews.com
A new study shows that as machine learning rapidly expands into healthcare, the ways it “learns” may be at odds with clinical outcomes unless carefully controlled. Despite being touted as next-generation cure-alls that will transform healthcare in unfathomable ways, artificial intelligence and machine learning still create many concerns with regards to safety and responsible implementation.
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