https://www.sciencedaily.com/releases/2018/06/180628131104.h…
This new algorithm is much faster than the current one being widely used.
If it can actually be adopted, then a great deal of the machine learning now reserved for Tenser Flow can be pushed to the edge of the web.
If this works, then we may see some pressures in the way the AI, machine learning and big data businesses work.
Not saying it is good or bad, just saying it may be a new current pushing things around a little.
In experiments, Singer and Balkanski demonstrated that their algorithm could sift through a data set which contained 1 million ratings from 6,000 users on 4,000 movies and recommend a personalized and diverse collection of movies for an individual user 20 times faster than the state-of-the-art.
The researchers also tested the algorithm on a taxi dispatch problem, where there are a certain number of taxis and the goal is to pick the best locations to cover the maximum number of potential customers. Using a data set of two million taxi trips from the New York City taxi and limousine commission, the adaptive-sampling algorithm found solutions 6 times faster.
“This gap would increase even more significantly on larger scale applications, such as clustering biological data, sponsored search auctions, or social media analytics,” said Balkanski.
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Qazulight