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

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Government and Public Policy

Will states be the first to regulate AI?

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Stargate, smargate. Meta’s Zuckerberg boasts that we’re spending $60B+ this year...

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AI Hardware

Silicon Valley stunned by China’s DeepSeek R1 which surpasses US AI...

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Anthropic

Tencent Launches AI Content Detection Tool for Images and Text

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Anthropic

Next-gen MacBook Air to get a MacBook Pro display

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Anthropic

Realme P3’s large battery capacity revealed

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Follow-up on OpenAI: China’s o1 Class Reasoning Models are being introduced...

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Report Claims Trump’s $500 Billion AI Project ā€˜Stargate’ Is Designed to...

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Anthropic

Conservative leader Pierre Poilievre accuses Liberals of global Netflix price hike

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Anthropic

OpenAI’s operator can browse the internet and perform actions on your...

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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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AI Observer

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...