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It’s easy to tamper with watermarks from AI-generated text
Researchers have found that watermarks for AI-generated text are easily removable and susceptible to theft and copying, making them ineffective. The watermarks are a means of inserting hidden patterns to identify AI-generated content, but new research has revealed the vulnerability of this technology, calling for more caution in its use.
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Vectara Releases the Factual Consistency Score (FCS): An AI Tool for Automated Hallucination Detection in Each Response It Generates
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Mora: A New Multi-Agent Framework that Incorporates Several Advanced Visual AI Agents to Replicate Generalist Video Generation Demonstrated by Sora
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Top Ten Artificial Intelligence (AI) Trends to Watch in 2024
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Researchers from the University of York and Université Paris-Saclay Introduce DeepKnowledge for Generalisation-Driven Deep Learning Testing
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X.ai Announces Grok 1.5: A Look at the Improved Reasoning and Long Context Capabilities
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SambaNova Systems Sets New Artificial Intelligence AI Efficiency Record with Samba-CoE v0.2 and Upcoming Samba-CoE v0.3: Beating Databricks DBRX
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Efficiency Breakthroughs in LLMs: Combining Quantization, LoRA, and Pruning for Scaled-down Inference and Pre-training
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FedFixer: A Machine Learning Algorithm with the Dual Model Structure to Mitigate the Impact of Heterogeneous Noisy Label Samples in Federated Learning
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Researchers at the University of Maryland Propose a Unified Machine Learning Framework for Continual Learning (CL)