By Ohio State University, contributing writers for Scitechdaily.com
While the past may be a fixed and unchangeable point, machine learning can sometimes make predicting the future easier.
By Allison Parshall, contributing writer for Quantamagazine.org
Alex Wiltschko began collecting perfumes as a teenager. His first bottle was Azzaro Pour Homme, a timeless cologne he spotted on the shelf at a T.J. Maxx department store. He recognized the name from Perfumes: The Guide, a book whose poetic descriptions of aroma had kick-started his obsession. Enchanted, he saved up his allowance to add to his collection. “I ended up going absolutely down the rabbit hole,” he said.
By Tim Wogan, contributing writer for Chemistryworld.com
An active learning algorithm has discovered high-entropy versions of Invar alloys – materials widely used for scientific instruments and industrial transportation of liquefied gases because of their tiny thermal expansion. The technique could have significant potential for searching huge ranges of potential material compositions to find a small number with desirable properties.
By Omri Kohl, contributing writer for Spiceworks.com
The idea that “data is the new oil” is a mantra that companies have rallied behind for decades, and yet data alone creates little to no value if it’s not analyzed and acted upon. Business intelligence (BI) and analytics tools – many of which were first introduced more than 20 years ago (one is even 30 years old) – promised a future where business users could easily access and transform huge volumes of enterprise data to make timely and reliable decisions.
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