When AI models fail to meet expectations, the first instinct may be to blame the algorithm. But the real culprit is often the data—specifically, how it’s labeled. Better data annotation—more accurate, ...

In most conversations, data and AI are inextricably linked. The narrative tends to be that organizations are not using AI well if they don’t have quality data from the field feeding into AI models.

Employees at many companies today are expected to make decisions through careful data analysis, but the tools they need to do it are clunky, slow or — in some cases — don’t exist. That’s according to ...

Just because your firm can use your existing data for AI risk modelling doesn’t mean you should. There’s a perception that AI can create accurate predictions based on any data set. That’s not always ...

Researchers from Children's Hospital of Philadelphia (CHOP) have developed a new tool to help analyze data collected from spatial transcriptomics technologies (SRTs), which simultaneously profile gene ...

The integration of bioinformatics, machine learning and multi-omics has transformed soil science, providing powerful tools to ...