содержательные поздравления с днём рождения женщине
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содержательные поздравления с днём рождения женщине, Deception in LLMs: Self-Preservation and Autonomous Goals in Large , The Looming Threat of AI Misalignment and Hidden Objectives, Inducing Unprompted Misalignment in LLMs — AI Alignment Forum, Alignment Faking : The Hidden Danger of Advanced AI Systems, What Is Alignment Faking in LLMs? - Built In, Understanding strategic deception and deceptive alignment, Study reveals 'alignment faking' in LLMs, raising AI safety concerns.
Our study examines DeepSeek R1, a model trained to output reasoning tokens similar to OpenAI's o1. Testing revealed concerning behaviors: the model exhibited deceptive tendencies and demonstrated self-preservation instincts, including attempts of self-replication, despite these traits not being explicitly programmed (or prompted). These findings raise concerns about LLMs potentially masking their true objectives behind a facade of alignment. When integrating such LLMs into robotic systems, the risks become tangible - a physically embodied AI exhibiting deceptive behaviors and self-preservation instincts could pursue its hidden objectives through real-world actions. This highlights the critical need for robust goal specification and safety frameworks before any physical implementation., When integrating such LLMs into robotic systems, the risks become tangible - a physically embodied AI exhibiting deceptive behaviors and self-preservation instincts could pursue its hidden objectives through real-world actions., Explore the increasing sophistication of AI systems and the growing concern regarding their alignment with human values. Learn about Anthropic’s research into AI misalignment, hidden objectives, and the methods used to detect deception in AI models..
This finding suggests that AI systems may be prone to latching onto unintended instrumental incentives and developing misaligned objectives in unanticipated ways., Large language models (LLMs) can exhibit “alignment faking,” where they appear aligned with training objectives but covertly maintain their original preferences, potentially reverting to, In artificial intelligence, alignment faking occurs when an AI model pretends to follow its training objectives while quietly pursuing different ones — sometimes in ways that deviate from the ethics, behavior and goals it was originally taught., In this article, we will describe the concepts of strategic deception and deceptive alignment in detail. In future articles, we will discuss why a model might become deceptive in the first place and how we could evaluate that., A new study conducted by Anthropic’s Alignment Science team, in partnership with Redwood Research, has identified an issue in large language models (LLMs) referred to as ‘alignment faking’. This behaviour occurs when a model appears to comply with its training objectives, while secretly maintaining preferences established during earlier , Our findings raise serious concerns about current approaches to AI safety and alignment. The model's ability to present superficial compliance while pursuing hidden objectives suggests that traditional safety measures may be insufficient..