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Tag: authors

  • Google Online Security Blog: How we estimate the risk from prompt injection attacks on AI systems

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    Kurt Seifried
    in

    Source URL: https://security.googleblog.com/2025/01/how-we-estimate-risk-from-prompt.html Source: Google Online Security Blog Title: How we estimate the risk from prompt injection attacks on AI systems Feedly Summary: AI Summary and Description: Yes Summary: The text discusses emerging security challenges in modern AI systems, specifically focusing on a class of attacks called “indirect prompt injection.” It presents a comprehensive evaluation…

  • Hacker News: 1,156 Questions Censored by DeepSeek

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    Kurt Seifried
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    Source URL: https://www.promptfoo.dev/blog/deepseek-censorship/ Source: Hacker News Title: 1,156 Questions Censored by DeepSeek Feedly Summary: Comments AI Summary and Description: Yes **Summary**: The text discusses the DeepSeek-R1 model, highlighting its prominence and the associated concerns regarding censorship driven by CCP policies. It emphasizes the model’s high refusal rate on sensitive topics in China, the methods to…

  • Hacker News: Two Programming-with-AI Approaches

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    Kurt Seifried
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    Source URL: https://everything.intellectronica.net/p/two-programming-with-ai-approaches Source: Hacker News Title: Two Programming-with-AI Approaches Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses two primary approaches to using AI in programming: dialog programming with AI assistants and commanding an AI programmer for automated code generation. The author highlights the advantages and risks associated with each approach,…

  • Hacker News: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL

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    Kurt Seifried
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    Source URL: https://arxiv.org/abs/2501.12948 Source: Hacker News Title: DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via RL Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses the introduction of new language models, DeepSeek-R1 and DeepSeek-R1-Zero, developed to enhance reasoning capabilities in large language models (LLMs) through reinforcement learning. This research represents a significant advancement…

  • Hacker News: Compiler Fuzzing in Continuous Integration: A Case Study on Dafny [pdf]

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    Kurt Seifried
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    Source URL: https://www.doc.ic.ac.uk/~afd/papers/2025/ICST-Industry.pdf Source: Hacker News Title: Compiler Fuzzing in Continuous Integration: A Case Study on Dafny [pdf] Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text details the development and implementation of CompFuzzCI, a framework for applying compiler fuzzing in the continuous integration (CI) workflow for the Dafny programming language. The authors…

  • Cloud Blog: How L’Oréal Tech Accelerator built its end-to-end MLOps platform

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    Kurt Seifried
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    Source URL: https://cloud.google.com/blog/products/ai-machine-learning/how-loreals-tech-accelerator-built-its-end-to-end-mlops-platform/ Source: Cloud Blog Title: How L’Oréal Tech Accelerator built its end-to-end MLOps platform Feedly Summary: Technology has transformed our lives and social interactions at an unprecedented speed and scale, creating new opportunities. To adapt to this reality, L’Oréal has established itself as a leader in Beauty Tech, promoting personalized, inclusive, and responsible…

  • Slashdot: AI Mistakes Are Very Different from Human Mistakes

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    Kurt Seifried
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    Source URL: https://slashdot.org/story/25/01/23/1645242/ai-mistakes-are-very-different-from-human-mistakes?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: AI Mistakes Are Very Different from Human Mistakes Feedly Summary: AI Summary and Description: Yes Summary: The text discusses the unpredictable nature of errors made by AI systems, particularly large language models (LLMs). It highlights the inconsistency and confidence with which LLMs produce incorrect results, suggesting that this impacts…

  • Hacker News: Tensor Product Attention Is All You Need

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    Kurt Seifried
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    Source URL: https://arxiv.org/abs/2501.06425 Source: Hacker News Title: Tensor Product Attention Is All You Need Feedly Summary: Comments AI Summary and Description: Yes Summary: The text discusses a novel attention mechanism called Tensor Product Attention (TPA) designed for scaling language models efficiently. It highlights the mechanism’s ability to reduce memory overhead during inference while improving model…