How sure is sure? Incorporating human error into machine learning
By University of Cambridge, contributing writers for Goodmenproject.com
Human error and uncertainty are concepts that many artificial intelligence systems fail to grasp, particularly in systems where a human provides feedback to a machine learning model. Many of these systems are programmed to assume that humans are always certain and correct, but real-world decision-making includes occasional mistakes and uncertainty.
Indigenous knowledges + AI greatly benefit the preservation of art and cultural practices
By Bronwyn Carson & Peita Richards, contributing writers for Theconversation.com
Artificial intelligence (AI) relies on its creators for training, otherwise known as “machine learning.” Machine learning is the process by which the machine generates its intelligence through outside input.
12 most popular AI use cases in the enterprise today
By Sarah K. White, contributing writer for CIO.com
From chatbots to predictive maintenance, companies across every industry are putting AI to work to deliver business value. Here’s where AI is having its greatest impact.
How predictive analytics can help developers anticipate users needs
By Polina Tibets, contributing writer for Informationweek.com
Software users are four times more likely to switch to other applications after a poor experience with the software, such as persistent issues, frequent crashes or errors, or an unintuitive user interface. To meet the ever-changing needs of users, software developers can leverage predictive analytics. This is the use of user data history, statistical modeling, and machine learning to predict or influence future decision-making.
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