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Artificial Intelligence's Large Multimodal Models (LMMs) exhibit impressive problem-solving abilities across varied tasks like zero-shot classifications, retrieval, and multimodal questioning. However, a significant gap lurks between robust LMMs and expert AI, especially concerning complex perception and reasoning with domain-specific expertise. This study introduces CMMMU, a pioneering Chinese evaluative benchmark designed to evaluate LMMs in multi-disciplinary…

This week in AI news, Tay Tay's Swifties combatted AI-generated explicit content. Artificial intelligence not only made it easier to create compromising content of celebrities, but also of everyday people. Discovery of tech like InstantID has led to concerns about how easily such content can be created, leading to widespread public and industry reaction. In political…

The exponential growth in AI is contradicting with environmental sustainability, with the possibility of coal power persisting to maintain electricity demands. As nations across the globe push for net zero transition, AI technology's immense electricity consumption, particularly generative AI, poses a challenge. AI models, which were localized and small-scale a few years ago, are now…

Language transformer models like Chat-GPT and LLaMA-2 have witnessed a rapid evolution, with parameters now running from a few billion to tens of trillions. Despite being excellent generators, these models struggle with inference delay due to their heavy computational load. This has led to a strong push for accelerating their inference, particularly in resource-constrained environments…

The rapidly increasing demand for Artificial Intelligence (AI) continues to put a strain on environmental sustainability due to its considerable power consumption. Generative AI, in particular, consumes substantial amounts of electricity due to its wide-scale use. Notably, AI models like BLOOM and GPT-3 have power consumption comparable to that of a small country. Consequently, as…