Telcos have access to huge amounts of data, which represents a big opportunity when it comes to AI. But how should they ...

As organizations accelerate AI adoption and expand analytics capabilities, many still struggle to execute their data strategies due to unclear operating models, siloed decision-making, and ...

In this environment, companies that sit at the intersection of AI and data infrastructure are becoming critical enablers of the broader AI transformation. Innodata INOD and Palantir Technologies PLTR ...

Amazon Nova Forge lets enterprises train frontier models, blending proprietary data and easing open-versus-closed tensions.

EPFL researchers have developed new software—now spun-off into a start-up—that eliminates the need for data to be sent to ...

AI promises a smarter, faster, more efficient future, but beneath that optimism lies a quiet problem that’s getting worse: the data itself.

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

Understand the critical differences between edge gateways and historians to make informed decisions about collecting, ...

Obsessing over model version matters less than workflow.

Nvidia leads AI semiconductors with strong alpha, valuation support, high R-squared, revenue elasticity, and AMD convex ...

An artificial intelligence (AI) model created by integrating clinical, molecular, and histopathological data significantly ...

The world’s top chipmaker wants open source AI to succeed—perhaps because closed models increasingly run on its rivals’ ...