Data science and machine learning are evolving in just about every single industry. The adoption of AI at companies continues to grow and evolve and AI developers are trying to prove that there is value that can be added to different parts of the company through machine learning.
The New York Times has had a data science group since 2012, but only recently has this group moved out of the experimental phase and taken a major role in the company, adding value through machine learning. The Director of Data Science at the New York Times, Colin Russel, will be sharing some of the insights learned from the NYT data science team at an upcoming.
Traditionally, the newsroom and editorial operations are separate from the business side of the company for obvious reasons of conflict of interest and maintaining a separation between revenue-generating and news-generating activities. Because of the separation of the data journalism side and the data science side of the organization, there is a separation of culture.
The use of data to drive decision-making and insights is spread across the entirety of the organization, however, with data analysis being used to power both business decisions as well as journalistic and editorial insights. The newsroom is very interested in data and understanding the audience of the NYT in a world where many people are getting their news from social media.
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