Tag: information security
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The Register: Workday on lessons learned from Iowa and Maine project woes
Source URL: https://www.theregister.com/2025/01/02/workday_implementations_interview/ Source: The Register Title: Workday on lessons learned from Iowa and Maine project woes Feedly Summary: Nine in ten of our implementations are a success, CEO Carl Eschenbach tells The Reg Interview Workday CEO Carl Eschenbach insists more than 90 percent of the SaaS HR and finance application vendor’s rollouts are a…
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Slashdot: US Treasury Says Chinese Hackers Stole Documents In ‘Major Incident’
Source URL: https://yro.slashdot.org/story/24/12/30/210242/us-treasury-says-chinese-hackers-stole-documents-in-major-incident Source: Slashdot Title: US Treasury Says Chinese Hackers Stole Documents In ‘Major Incident’ Feedly Summary: AI Summary and Description: Yes Summary: The text highlights a significant security breach involving Chinese state-sponsored hackers who infiltrated the U.S. Treasury Department, compromising sensitive documents through a third-party cybersecurity provider. This incident underscores the importance of…
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Slashdot: AI Tools May Soon Manipulate People’s Online Decision-Making, Say Researchers
Source URL: https://slashdot.org/story/24/12/30/0435226/ai-tools-may-soon-manipulate-peoples-online-decision-making-say-researchers?utm_source=rss1.0mainlinkanon&utm_medium=feed Source: Slashdot Title: AI Tools May Soon Manipulate People’s Online Decision-Making, Say Researchers Feedly Summary: AI Summary and Description: Yes Summary: This text discusses the potential of AI tools to manipulate online audiences by using insights from human behavior and intentions, emphasizing the emergence of an “intention economy.” Researchers warn about the…
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Hacker News: Measuring and Understanding LLM Identity Confusion
Source URL: https://arxiv.org/abs/2411.10683 Source: Hacker News Title: Measuring and Understanding LLM Identity Confusion Feedly Summary: Comments AI Summary and Description: Yes **Summary:** The text discusses a research paper focused on “identity confusion” in Large Language Models (LLMs), which has implications for their originality and trustworthiness across various applications. With over a quarter of analyzed LLMs…