Anthropic pulled two new AI models offline after the Trump administration said they were too dangerous for foreigners to use.

In recent years, network and complexity science has seen a massive surge in models using hypergraphs and simplicial complexes to represent higher-order ...

Alibaba Cloud launched HappyHorse 1.1, a new enterprise AI video model with full API access, as OpenAI’s Sora and ByteDance’s ...

Chongwei Chen is the President & CEO of DataNumen, a global data recovery leader with solutions trusted by Fortune 500 companies worldwide. As AI becomes mainstream, business leaders are increasingly ...

With new data center proposals popping up like spring bulbs, both residents and municipal officials alike can feel overwhelmed by both the pace and the breadth with which this new development wave is, ...

SurrealDB Inc. today revealed that it has raised an additional $23 million in funding for its multimodel artificial intelligence-native database. The plan is to accelerate product maturity and ...

HB2151 threatens to speed up controversial data center construction statewide Harrisburg, PA — Today, the House Energy Committee held a hearing for HB2151, a Shapiro-backed bill that would provide a ...

Meteorologists frequently mention weather prediction models in their forecasts. They explain what they’re gleaning from the “U.S. Model,” for instance, and how that might differ from the “European ...

The Tesla Model Y’s midcycle refresh brought significant enough changes to earn it a spot in our 2026 SUV of the Year competition. The full list of updates is extensive, but the highlights matter.

A new kind of large language model, developed by researchers at the Allen Institute for AI (Ai2), makes it possible to control how training data is used even after a model has been built.

Third-party model portfolios had $646 billion in assets under advisement as of March 31, 2025—an increase of 62% since Morningstar last surveyed for assets in June 2023, less than two years ago.

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, ...