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AI tool uses face photos to estimate biological age and predict...

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Google wants to prevent ChatGPT from being the leader in artificial...

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ChatGPT has invented a pizza

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Server manufacturers ramp up edge AI efforts

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Roundtable: What’s next for mixed reality: Glasses and Goggles

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Quantum chip Willow: Google AI’s Breakthrough Towards Large-Scale Quantum Computing

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Watch Google Quantum AI Reveal the Willow Quantum Computing Chip

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Nvidia accelerates Google’s quantum AI design using quantum physics simulation

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OpenAI is planning to ring in 2019 with a push for...

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Xiaomi intensifies AI investment with GPU cluster

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Apple in early talks to integrate AI models in iPhones in...

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Is Automated Hallucination Detection in LLMs Feasible? A Theoretical and Empirical...

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This AI Paper Introduce WebThinker: A Deep Research Agent that Empowers...

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A Step-by-Step Guide to Implement Intelligent Request Routing with Claude

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Researchers from Fudan University Introduce Lorsa: A Sparse Attention Mechanism That...

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Is Automated Hallucination Detection in LLMs Feasible? A Theoretical and Empirical...

Recent advancements in LLMs have significantly improved natural language understanding, reasoning, and generation. These models now excel at diverse tasks like mathematical problem-solving and generating contextually appropriate text. However, a persistent challenge remains: LLMs often generate hallucinations—fluent but factually incorrect responses. These hallucinations undermine the reliability of LLMs, especially...