Improving humanitarian aid contribution through AI/ML. Plus, ML’s impact on content marketing, understanding what kind of mood people might be in while reading content allows for more engaging copy. And find out how decision intelligence addresses many of the pain points that hinder businesses from quantifying value from their AI investment.
How Can Machine Learning Help Improve Content Marketing?
By Yakup Ozkardes-Cheung, contributing writer for Entrepreneur.com
The term machine learning was first introduced by Arthur Samuel in 1959. Machine learning is a type of artificial intelligence that gives computers the ability to learn without being explicitly programmed. It provides a set of algorithms and techniques for creating computer programs that can automatically improve their performance on specific tasks.
ML helps MIT’s cheetah robot break its own record
By Shi En Kim, contributing writer for Popsci.com
Horses gallop. Kangaroos hop. Ducks waddle. Elephants amble. The fleet-footed quadruped robot called Mini Cheetah… well, doesn’t move like anything in the animal kingdom. A cross between a scramble and a scamper, its gait is desperately chaotic and comically ungraceful. In fact, its particular style is dubbed “gait-free.” And this brandless bound is what makes it fast.
How decision intelligence could put AI at the center of every business
By Atul Sharma, contributing writer for Venturebeat.com
Data collection is skyrocketing. The amount of data created, consumed and stored worldwide is set to increase by over 50% between now and 2025. Businesses understand that evaluating their data more effectively provides a competitive edge, and that it will be artificial intelligence, not business intelligence, that will unlock this potential — but there’s a striking gap between the scale of AI investment and tangible returns delivered.
Using ML to Improve Targeting of Humanitarian Aid
By Nasreen Parvez, contributing writer for Analyticinsight.net
As cell phones have grown increasingly prevalent worldwide, with a projected global penetration level of 73 percent in 2020, research on wealth forecasting from digital trail data has concentrated on mobile phone metadata (GSMA, 2017). Machine learning algorithms based on call detail records (CDR) have recently been proved to yield meaningful estimations of prosperity and well-being at a fine geographical resolution.
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