Companies, Enforcers See AI, Kids Safety as Privacy Priorities - news.bloomberglaw.com
<a href="https://news.google.com/rss/articles/CBMipAFBVV95cUxQVTVhSEJoMUJ4N3BjcXhIX0xkTWpzY1BJVTRsWUZkYVR0T21MR1NqMkxqTXhGckNNbDN1a2UzUmlTT293QVBYZW1iUmtsbUdpRGJoWmF0SEVjNm1FRXpmRWVfZFlKRWVrVlExb0o0cEpOM3RueklHZ0k0WXFJMnBoY0cwZTJFdWZyeWhUd3hWN0JSZGR2Vk5BV3RjZUNfRmFmWTdhaA?oc=5" target="_blank">Companies, Enforcers See AI, Kids Safety as Privacy Priorities</a> <font color="#6f6f6f">news.bloomberglaw.com</font>
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safetyPredicting When RL Training Breaks Chain-of-Thought Monitorability
Crossposted from the DeepMind Safety Research Medium Blog . Read our full paper about this topic by Max Kaufmann, David Lindner, Roland S. Zimmermann, and Rohin Shah. Overseeing AI agents by reading their intermediate reasoning “scratchpad” is a promising tool for AI safety. This approach, known as Chain-of-Thought (CoT) monitoring, allows us to check what a model is thinking before it acts, often helping us catch concerning behaviors like reward hacking and scheming . However, CoT monitoring can fail if a model’s chain-of-thought is not a good representation of the reasoning process we want to monitor. For example, training LLMs with reinforcement learning (RL) to avoid outputting problematic reasoning can result in a model learning to hide such reasoning without actually removing problem
Predicting When RL Training Breaks Chain-of-Thought Monitorability
Crossposted from the DeepMind Safety Research Medium Blog . Read our full paper about this topic by Max Kaufmann, David Lindner, Roland S. Zimmermann, and Rohin Shah. Overseeing AI agents by reading their intermediate reasoning “scratchpad” is a promising tool for AI safety. This approach, known as Chain-of-Thought (CoT) monitoring, allows us to check what a model is thinking before it acts, often helping us catch concerning behaviors like reward hacking and scheming . However, CoT monitoring can fail if a model’s chain-of-thought is not a good representation of the reasoning process we want to monitor. For example, training LLMs with reinforcement learning (RL) to avoid outputting problematic reasoning can result in a model learning to hide such reasoning without actually removing problem
Smart food safety: implementing AI for risk, compliance and control - New Food magazine
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Unleashing potential: Exploring generative AI’s role in banking - KPMG
<a href="https://news.google.com/rss/articles/CBMi6AFBVV95cUxPRFZfYkd5cy0yUnhXY015bDJZSERtcHlUa3drQ1I4SW1qNFJfTEhCaE9ZNnY4cXYwQmphZEdJaF9hRmNBaWUxQy10bTJWMkFrM3czUzZZdHZibFk4U1J2dUY3ZG1zR1FqSFZaczdfVnpzRk85TGVjXzE2Yk0xanhTNllnd1ZDZ0NSMnA5WlhmREEwMFgzQkoxUWNKeDUwakVsWi14ZkhSREZKTVZUWWxHbzRNaE9xTl85blRIdFIyZ3ctX1dRcDZQeDdEdklkaXR6S212c1p6UmNUMEdBNlhYSHNSNXpOTEpl?oc=5" target="_blank">Unleashing potential: Exploring generative AI’s role in banking</a> <font color="#6f6f6f">KPMG</font>
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