Nvidia’s new lineup of open-source AI models is headlined by Alpamayo 1 (pictured), a so-called VLA, or ...

AI data trainer roles have moved from obscure contractor gigs to a visible career path with clear pay bands and defined ...

New research shows that AI doesn’t need endless training data to start acting more like a human brain. When researchers ...

Large ML models and datasets have necessitated the use of multi-GPU systems for distributed model training. To harness the power offered by multi-GPU systems, it is critical to eliminate bottlenecks ...

Abstract: In transmission lines, bolt fittings are critical components that connect towers and insulators. These fittings are prone to defects, such as loosening or missing bolts, due to natural ...

SAREnv is an open-access dataset and evaluation framework designed to support research in UAV-based search and rescue (SAR) algorithms. This toolkit addresses the critical need for standardized ...

Determining the least expensive path for a new subway line underneath a metropolis like New York City is a colossal planning challenge—involving thousands of potential routes through hundreds of city ...

A new Apple research paper argues that AI imaging editors are currently trained on inadequate image sets — so Apple Intelligence researchers have released an improved one. Now the researchers have ...

Credit: Image generated by VentureBeat with Gemini 2.5 Flash (nano banana) AI models are only as good as the data they're trained on. That data generally needs to be labeled, curated and organized ...

In the current climate, generic and expensive programs to promote diversity, equity, and inclusion—for example, trainings—are increasingly falling out of favor. In fact, most of the existing research ...

A new study by Shanghai Jiao Tong University and SII Generative AI Research Lab (GAIR) shows that training large language models (LLMs) for complex, autonomous tasks does not require massive datasets.

Abstract: Neural networks (NNs) are pivotal in enhancing data processing tasks such as classification, generation, and restoration. A crucial consideration in these applications is the signal-to-noise ...