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

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and ...

Forbes contributors publish independent expert analyses and insights. I write about the broad intersection of data and society. The data-driven revolution is prefaced upon the idea that data and ...

There’s no doubt that data and algorithms play an important part in the modern workplace, but we shouldn’t forget the human component of our decisions. We have an overwhelming amount of number and ...

A develops brand algorithms by looking at both online and offline data points to determine who to reach and where to target them.

The age of Big Data has generated new tools and ideas on an enormous scale, with applications spreading from marketing to Wall Street, human resources, college admissions, and insurance. At the same ...

As major powers accelerate the military use of artificial intelligence, the consequences for countries that fail to adapt are ...

Most medical algorithms were developed using information from people treated in Massachusetts, California, or New York, according to a new study. Those three states dominate patient data — and 34 ...

The question of how much power algorithms have over our lives has a topical edge. Already there are lines of code that tell us what to watch, whom to date, and even whom to send to jail. In Hello ...

How to recognize and use array and list data structures in your Java programs. Which algorithms work best with different types of array and list data structures. Why some algorithms will work better ...