Previous columns in this series introduced the problem of data protection in machine learning (ML), emphasizing the real challenge that operational query data pose. That is, when you use an ML system, ...

Researchers have determined how to build reliable machine learning models that can understand complex equations in real-world situations while using far less training data than is normally expected.

How much time is your machine learning team spending on labeling data — and how much of that data is actually improving model performance? Creating effective training data is a challenge that many ML ...

Angie received her M.S. in computer science with a concentration in machine learning from The George Washington University ...

Machine learning (ML)-based approaches to system development employ a fundamentally different style of programming than historically used in computer science. This approach uses example data to train ...

In the rapidly evolving landscape of data science and machine learning, ensuring accessibility of data is critical for obtaining meaningful insights. Continuous data plays a pivotal role in various ...

Companies can’t avoid working with data, but management of that data can pose serious challenges. Customer and other personal data keep escaping, courtesy of breaches that surged 78% last year in the ...

Machine learning is an increasingly important tool for developers, providing a way to build applications that can deliver a wide range of prediction-based tasks. In the past you might have had to ...

Multiple times over the last decade, this column has covered the issue of the importance of data quality in decision making, both by executives as well as machines. Back in 2014, when the “big data” ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The training process for artificial intelligence (AI) algorithms is ...

What is data cleaning in machine learning? Data cleaning in machine learning (ML) is an indispensable process that significantly influences the accuracy and reliability of predictive models. It ...