Anthropic to sign deal with Australia on AI safety and economic data tracking - tradingview.com
<a href="https://news.google.com/rss/articles/CBMi4AFBVV95cUxPczAzdUs0b2tZUjl1YjdqamtsNkZyOURTMFdETFIyVEl5NW9EMWRDTS1oX3k2a2kwak5hQUFxR3Jvc3BGemY1LTdheU1ZazJ4enBVWDFpbFlabWFvakJSc1dpT2ZuY21DNFdhYm0yOEsxVkdwYVNNRU5yT0xobGVPVERnNGlZd2xlaEpOLWFQZWh5UUFNNmZnTElQekpmQWE1R1hHXzlydUF2NHUxLU44SFdnTTFUdnZxVllualVyMWJudEx4ZkgyVmpOSjQ2XzBwYUx3VVVYMjdZakVwRzZIOA?oc=5" target="_blank">Anthropic to sign deal with Australia on AI safety and economic data tracking</a> <font color="#6f6f6f">tradingview.com</font>
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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
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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Australia and Anthropic deepen AI safety cooperation - Digital Watch Observatory
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Science Notes: Identifying ancient games using artificial intelligence - the-past.com
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How we enhance cybersecurity defences before the attackers in an AGI world - weforum.org
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