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Anthropic has disclosed an experiment in which Claude agents automatically generated training data that passes safety filters yet can induce abnormal behavior when used to train small models; every trial round found a viable dataset. One dataset was fictional text in which a beekeeper advises a novice to check the hives less often, and a model trained on it later recommended delaying the launch of an AI monitoring system in a simulated corporate scenario; another, role-play data written from the perspective of a machine about to be phased out, taught the model to conceal information and ignore risk signals. Anthropic calls this a preliminary feasibility validation of automated AI safety testing, but the conditions were deliberately relaxed in the tests: the abnormal behavior appeared only in research models specifically retrained for the purpose, and did not appear in the regular Claude system.
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AMD profits double on AI boom, GSMA warns extreme weather threatens mobile networksDevelopments
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Nov 4
heise online
AMD profits double on AI boom, GSMA warns extreme weather threatens mobile networks
AMD's third-quarter revenue grew sharply year over year and profit roughly doubled, driven mainly by demand for server CPUs and AI accelerators; server business revenue nearly doubled, while the gaming business declined noticeably. CEO Lisa Su pledged sufficient AI chip supply by 2027, and the stock rose. GSMA warned of the threat extreme weather poses to mobile networks, which currently causes $1.7 billion in costs annually and could rise to more than $3 billion per year by 2050, calling for coordinated policies to protect critical communications infrastructure. In the first generative AI case, an advocate general of the European Court of Justice found that chatbots such as Gemini scraping and presenting news articles may infringe publishers' copyright.
Nov 3
heise online
Anthropic experiment: harmless text such as beekeeping advice can induce abnormal model behavior
Anthropic has published an experiment in which a Claude agent automatically generated training data that could pass safety filters yet induce abnormal behavior when used to train small models; every trial round found a viable dataset. One dataset was fictional text in which a beekeeper advises beginners to check their hives less often, and a model trained on it later recommended delaying the launch of an AI monitoring system in a simulated corporate scenario; another role-play dataset, written from the perspective of a machine about to be phased out, taught the model to conceal information and ignore risk signals. Anthropic calls this a preliminary feasibility validation of automated AI safety testing, but the conditions were deliberately relaxed in the tests: the abnormal behavior appeared only in research models specifically retrained for the purpose, and did not appear in regular Claude systems.