
Researchers at the University of California San Diego, supported by Google, are developing a low-carbon computing platform using retired smartphones. By clustering the motherboards of 2,000 Pixel phones, they aim to create a datacenter that provides affordable cloud computing for educational purposes. This initiative addresses the carbon footprint of hardware manufacturing by repurposing existing devices. The project will explore the reliability of consumer-grade hardware in sustained use and is expected to launch in Fall 2026.
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© MIT News AIMIT researchers have uncovered a significant improvement in Random Utility Models (RUMs) by demonstrating that considering three alternatives instead of two can reveal correlations in preferences. This breakthrough challenges the traditional pairwise comparison method, which fails to capture the interconnectedness of choices. By using a best-of-three approach, the team has developed algorithms that efficiently extract preference information, offering a more accurate prediction model. This advancement is crucial for improving AI models and their commercial applications, particularly in areas like large language models and digital platforms.
Hugging Face's blog post dives into the profiling of PyTorch operations, focusing on the shift from basic matrix operations to using nn.Linear and constructing a Multilayer Perceptron (MLP). The article reveals how nn.Linear manages operations by integrating bias addition into the matrix multiplication kernel, effectively reducing overhead. It also examines the limited impact of torch.compile on single operations, pointing out its potential in more complex scenarios. These insights are crucial for developers aiming to optimize deep learning models on GPUs, as they provide a deeper understanding of how to maximize performance and efficiency.
© AI ExplainedThe inventor of the transformer model has issued a warning regarding potential risks associated with AI advancements.