AI Models Struggle with Driving Safety, Language Models Get More Human-Like, and Scientists Crack the Code on Privacy
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As artificial intelligence systems become more integrated into our daily lives, researchers are uncovering both promising advances and concerning limitations. New studies reveal that vision-language models aren't yet reliable enough for autonomous driving, while parallel breakthroughs are making AI communication more natural and human-like, all as scientists develop innovative ways to protect our privacy when interacting with these increasingly powerful systems. Links to all the papers we discussed: The GAN is dead; long live the GAN! A Modern GAN Baseline, An Empirical Study of Autoregressive Pre-training from Videos, Are VLMs Ready for Autonomous Driving? An Empirical Study from the Reliability, Data, and Metric Perspectives, Enhancing Human-Like Responses in Large Language Models, On Computational Limits and Provably Efficient Criteria of Visual Autoregressive Models: A Fine-Grained Complexity Analysis, Entropy-Guided Attention for Private LLMs
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