No matter how sophisticated the AI model, its power depends on the quality, structure and context of the data beneath it.

Physical data model. The final layer is physical and represents a composition of host system artifacts—physical data objects—derived from a logical data model coupled with its desired storage ...

Huang's company, of course, makes chips and computer hardware, the "picks and shovels" of the AI gold rush, and it's become ...

AI-trained data analysis models may offer a more rapid and accurate detection of a range of neurologic conditions, including those involving motor function such as Parkinson’s disease and normal ...

Better data annotation—more accurate, detailed or contextually rich—can drastically improve an AI system’s performance, adaptability and fairness.

Big tech companies — and startups — are increasingly using synthetic data to train their AI models. But there's risks to this strategy.

Fractured and incomplete datasets are a key barrier towards effectively training AI models for deployment in healthcare settings.

Artificial intelligence (AI) has relied on high-quality language data to train its models, but supply is running low. That depletion is forcing companies to look elsewhere for data sourcing as ...

The data collected for the Generative AI Improvement program is used to “improve or develop the LinkedIn services,” LinkedIn said.

A team of computer scientists at UC Riverside has developed a method to erase private and copyrighted data from artificial intelligence models—without needing access to the original training data.

Meta is releasing a massive data set and models, called Open Materials 2024, that could help scientists use AI to discover new materials much faster.