AI’s biggest constraint isn’t algorithms anymore. It’s data…specifically, high-quality, forward-looking data. It is the “Rare ...

I'll explore data-related challenges, the increasing importance of a robust data strategy and considerations for businesses ...

Scientists at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory have developed a novel artificial ...

Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, ...

We developed two algorithms to identify patients with stomach, lung, colorectal, breast, and cervical cancers: diagnosis only (algorithm 1), and combining diagnosis, treatments, and medicines ...

Artificial intelligence has become a popular tool for job recruiters, in part because programmers can code applicant-screening algorithms to avoid any explicit discrimination in their decision-making ...

shinyOPTIK, a User-Friendly R Shiny Application for Visualizing Cancer Risk Factors and Mortality Across the University of Kansas Cancer Center Catchment Area We trained and validated two-phase ML ...

Through data, algorithms communicate with their environments and get to “know about” and “learn from” what is happening around them. Algorithms without living data are no more than sheer mathematical ...

Danny Sullivan, Google's Search Liaison, posted on X explaining the differences between algorithm updates, like core updates, and then data refreshes, the data that goes into those ranking systems.