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

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Be Part of the AI Revolution at the Chatbot Conference Tomorrow!

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Why your AI investments aren’t paying off

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Meta’s new AI model can translate speech from more than 100...

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5 Emerging AI Threats Australian Cyber Pros Must Watch in 2025

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Google makes it (kinda cheaper) to get Gemini AI Business Plans

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Parallels brings back magic to Windows booting after seven minutes of...

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GoDaddy slapped with wet lettuce for years of lax security and...

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DJI relaxes flight restrictions and decides to trust operators that they...

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Nvidia shovels 500M into Israeli boffinry Supercomputer

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OpenAI Fails To Deliver Opt-Out Systems For Photographers

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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...