Data is the life-blood of physical AI. Collecting real-life data is expensive. Generative AI and diffusion to create ...

Data that’s captured isn’t always stored, and storage alone doesn’t get the job done. Many transportation agencies are sitting on troves of data they’re not sure how to interpret. Even when vendors ...

Which is more important – understanding what happened to your business last week, or understanding what’s happening right now? Well, both can provide useful insights that you might be able to use to ...

For the Microsoft Power BI report designer, importing Microsoft Excel data is a common task. Once in Power BI, you analyze and report on that data. Occasionally, you might need to export a Power BI ...

Cloud-native integration platform examples are built for modern cloud setups. They use things like microservices and APIs to make connections that can grow as your needs change, and they can work ...

One task where AI tools have proven to be particularly superhuman is analyzing vast troves of data to find patterns that humans can't see, or automating and accelerating the discovery of those we can.

Machine learning typically requires tons of examples. To get an AI model to recognize a horse, you need to show it thousands of images of horses. This is what makes the technology computationally ...

AI is looking at mental health through data sets. A data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a ...

Our understanding of progress in machine learning has been colored by flawed testing data. The 10 most cited AI data sets are riddled with label errors, according to a new study out of MIT, and it’s ...

New research from the Data Provenance Initiative has found a dramatic drop in content made available to the collections used to build artificial intelligence. By Kevin Roose Reporting from San ...