Artificial Intelligence
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107Meta Faces Allegations of Utilizing Pirated Data for AI Development
Meta faces scrutiny over claims of using pirated data to train AI models, raising ethical concerns about data sourcing and intellectual property...
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108Navigating Copyright Challenges: The Call for Equitable Solutions in AI
Explore the complexities of copyright in AI, highlighting the need for equitable solutions to address challenges faced by creators and innovators.
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113Microsoft Unveils Phi-4 Language Model on Hugging Face
Microsoft introduces the Phi-4 language model on Hugging Face, enhancing AI capabilities with advanced natural language processing features.
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111Training AI for Human-like Communication
Discover how training AI enhances human-like communication, bridging the gap between technology and natural interaction for seamless user experiences.
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113Unveiling Gemini 2.0: The Next-Gen AI Model for a New Era of Agency
Discover Gemini 2.0, the cutting-edge AI model revolutionizing agency operations and driving innovation in the digital landscape.
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110Cutting-Edge Video and Image Creation with Veo 2 and Imagen 3
Discover innovative video and image creation with Veo 2 and Imagen 3, featuring advanced technology for stunning visual content.
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112FACTS Grounding: Establishing a New Standard for Assessing the Factual Accuracy of Large Language Models
Discover FACTS Grounding, a new standard for evaluating the factual accuracy of large language models, ensuring reliable and trustworthy AI outputs.
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108Daniela Rus Honored with the John Scott Award
Daniela Rus receives the prestigious John Scott Award, recognizing her groundbreaking contributions to robotics and innovation in technology.
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113Making AI’s Predictions Understandable: A Guide to Plain Language Explanations
Discover how to simplify AI predictions with clear, plain language explanations in this comprehensive guide for better understanding and transparency.
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Research Reveals Political Bias in Certain Language Reward Models
Study uncovers political bias in language reward models, highlighting implications for AI fairness and the need for balanced training data.
