Experts say data centers' water use is less risky than public perceives
The buzz around artificial intelligence has taken on a surprisingly dry angle lately: how much water the machines that power our apps actually drink.
Academic research, performance benchmarks, scientific breakthroughs, and peer-reviewed studies advancing AI frontiers.
The buzz around artificial intelligence has taken on a surprisingly dry angle lately: how much water the machines that power our apps actually drink.
The United States has stepped into the spotlight with a new effort to shore up the world’s silicon pipeline, a move that comes as manufacturers and defense planners alike flag the material’s strategic weight.
Why does a research‑oriented AI model matter now? Companies and scholars alike have been wrestling with the cost of generating thorough, citation‑rich reports, especially when the underlying benchmarks demand both depth and speed.
Google’s latest effort to gauge large‑language‑model reliability lands in a surprisingly modest spot: 70 percent factual accuracy across four carefully crafted scenarios.
Developers have been handed a growing toolbox of AI‑driven coding assistants—Claude Code, Cursor, and a handful of others—yet the gap between generating code and diagnosing why a script stalls remains wide.
SAP’s internal test showed an AI model hitting a 95 percent success rate on a routine consulting task—until the very people meant to use it recognized the output as machine‑generated.
Machine learning has become a staple of data science, yet stitching together preprocessing, feature engineering, and model selection still feels like trial‑and‑error for many teams.
Why does this matter now? Because the gap between research‑grade models and the constraints of real‑world services is widening.
The internal memo from Google’s AI team reveals a surprisingly hands‑on approach to getting Gemini to produce the kind of footage that powers Veo’s sports‑highlight reels.
CognitiveLab’s latest release promises a tangible lift in how machines handle multilingual text. The company touts a 150 percent jump in document‑level accuracy and support for 22 languages—metrics that immediately catch a researcher’s eye.
The paper titled “Subliminal Learning: How AI Models Inherit Hidden Dangers,” published under the Research & Benchmarks category, raises a subtle yet pressing issue for anyone building generative systems.
Seventy percent of creative professionals say they worry about being judged for leaning on artificial intelligence, according to a new Anthropic study.
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