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

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MediaTek: First information about the next high-end chip

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Nvidia AI Blueprint allows developers to easily build automated agents that...

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ByteDance seems to be circumventing US restrictions in order to buy...

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I found an AirTag wallet alternative that is more functional than...

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Apple AirPods Pro 3 monitor heart rate and bring health functions

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And Androids will soon be able to use Apple AirDrop?

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Travelling soon? Apple AirTags

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I have tried ChatGPT on WhatsApp and it is clear to...

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How to create AI generated images in WhatsApp

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Meta AI has a monthly user base of ‘nearly 600 million’

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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...