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 ...

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 ...

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

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

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

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

From GPT to Claude to Gemini, model names change fast, but use cases matter more. Here's how I choose the best model for the ...

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, ...

Launching an AI initiative without a robust data strategy and governance framework is a risk many organizations underestimate. Most AI projects often stall, deliver poor...Read More The post Can Your ...

In the meantime, the big question for data leaders is where to implement this logic. The market has split into two ...