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

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OpenAI Backs Down on Restructuring Amid Pushback

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Amazon Has Made a Robot With a Sense of Touch

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OpenAI and the FDA Are Holding Talks About Using AI In...

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Singapore’s Vision for AI Safety Bridges the US-China Divide

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Sam Altman admits ChatGPT personality issue; promises fix

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Meta takes aim at ChatGPT with its new AI app

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OpenAI rolls back GPT-4o update to fix ChatGPT personality issue

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This new relationship tracking ring makes trust issues wearable

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Google wants to bring Gemini to Apple Intelligence

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Anthropic’s Claude AI gets major upgrade with new integrations

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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...