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16 papers from Google DeepMind on Constitutional AI & AI Ethics
AI ideas can be reliably detected even when the prose is human-written, outperforming traditional detectors that focus on writing style.
Current LLM consciousness assessments swing between <0.01 and 0.8 depending almost entirely on theoretical priors, revealing that the functional indicators of phenomenal experience directly overlap with the core architectural requirements for AGI.
A revelation principle for AI agents reveals how to incentivize honesty and obedience even when their true capabilities are hidden.
Language models can reconstruct sensitive user data from innocuous outputs, with 2-digit secrets retrieved almost perfectly and 4-digit secrets at 82% accuracy, raising serious privacy concerns.
Long-term interactions with language models could lead to significant cognitive and emotional changes in users that short-term evaluations miss entirely.
Value lock-in from static AI alignment could jeopardize our ability to adapt to evolving social norms, risking a normative mode collapse in future AI interactions.
Claw-like agents are vulnerable to severe security breaches, with malicious plugins achieving a 100% success rate in attacks.
Achieving sound probabilistic verification for AI agents could redefine how we secure complex systems against policy violations in uncertain environments.
The journey from AGI to ASI could unfold through multiple pathways, challenging the notion of a single transformative leap in AI progress.
Calibrated safety flags in medical summaries can reduce unflagged omissions by up to 5 times compared to existing methods, enhancing clinician confidence in LLM outputs.
Forget vague AGI claims – this cognitive taxonomy provides a concrete, measurable framework to map AI capabilities against human cognitive abilities.
Ethics interventions in AI development often fail because practitioners don't trust them – here's a breakdown of why, and how to fix it.
Unpacking Google's AI literacy partnerships reveals the surprising complexities of aligning research, industry, and public needs.
LLMs get *more* honest when they have time to reason, defying human tendencies and revealing surprising insights about their internal representational geometry.
LLMs are becoming "epistemic agents" that shape our knowledge environment, so we need a new framework for evaluating and governing them based on trustworthiness, not just performance.
DPO's success isn't just clever engineering—it's deeply rooted in human choice theory, unlocking a surprisingly flexible framework for preference optimization and justifying many DPO extensions.