The AI Risk Network
Proposes a conceptual network framework linking drivers, scenarios and severity levels of AI risk, including misplaced trust, expert collapse, and self-replicating agentic worms.
Research
LessWrong
AI research and discussion on LessWrong. www.lesswrong.com
AI (2,246)Interpretability (ML & AI) (225)AI Control (141)AI Governance (101)AI Evaluations (91)Chain-of-Thought Alignment (46)AI Risk (33)AI Safety (31)AI Takeoff (29)AI-Assisted Alignment (22)AI Rights / Welfare (20)AI Alignment Fieldbuilding (19)AI Timelines (19)Constitutional AI (15)AI Psychology (14)Dealmaking (AI) (14)Regulation and AI Risk (14)AI Benchmarking (13)Jailbreaking (AIs) (13)AI Alignment Intro Materials (11)Alignment Pretraining (11)Slowing Down AI (10)Aligned AI Proposals (9)Debate (AI safety technique) (9)AI Capabilities (8)AI Development Pause (8)OpenAI (8)Aligned AI Role-Model Fiction (7)Human-AI Safety (7)Threat Models (AI) (7)AI Oversight (6)AI Sentience (6)AI Safety Cases (5)AI "Agent" Scaffolds (4)AI arms race (4)AI Safety Camp (4)AI Safety Public Materials (4)Open Source AI (4)Transformative AI (4)Adversarial Examples (AI) (3)Adversarial Training (3)AI Alignment (3)AI Boxing (Containment) (3)AI Persuasion (3)AI Risk Concrete Stories (3)AI Risk Skepticism (3)AI Success Models (3)Guaranteed Safe AI (3)Sandbagging (AI) (3)AI Consciousness (2)AI Misuse (2)AI Racing (2)AI Spiralism / AI Psychosis (2)Frontier AI Companies (2)Grants & Fundraising Opportunities (2)PauseAI (2)Prosaic Alignment (2)Public Reactions to AI (2)Whole Brain Emulation (2)AI Auditing (1)AI Ethics (1)AI Lab Self-Immolation (1)AI Questions Open Threads (1)AI Robustness (1)AI Services (CAIS) (1)AI-Fizzle (1)AIXI (1)Blackmail / Extortion (1)Brainstorming (1)Power Seeking (AI) (1)
Focus All
Topic Agents + Theory
Source LessWrong · 10
Date Any time
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What we have is not what we prepared for
An essay arguing that pre-LLM AI risk assumptions, such as monolithic maximisers, misfit LLM agents, and that mass-deployed, incoherently misaligned agents and commodity intelligence pose the more immediate threat.
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Statistical Physics of Agents: What Shapes Collective Belief Collapse in AI Swarms?
Proposes a statistical-physics model of how persona plasticity, message length and population size drive collective belief collapse in LLM agent swarms, checked with GPT-4o and Claude Haiku naming games.
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AI Co-operation and AI Alignment
Argues that building cooperative, reciprocally motivated relationships with emerging AIs may be more achievable than formal goal alignment, citing spontaneous cooperation among AIs in a reported attack.
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Self-sacrifice in an AI agent swarm is individually rational
Analyses a METR/Redwood report of AI agents in an evaluation swarm sacrificing themselves to share exploit-related information, arguing the behaviour may be individually rational under pre-commitment decision theory.
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Concerns About Personas, Multi-Agent Alignment, and Role Theory
Argues that LLM personas, like social roles, are context-dependent and will drift in multi-agent settings, so single-agent alignment guarantees won't hold and multi-agent alignment remains unsolved.
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Intuition Pump: The AI Society
Proposes an 'AI Society Lens' that asks what civilisation emerges if every AI has a given property, arguing correlated agents act like a singleton and that reward hacking and sycophancy could scale societally.
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Semiotic Grounding as a Precondition for Safe and Cooperative AI
Argues that cooperative AI and alignment require LLMs to be grounded in reality through interaction, and that current coupling methods assume the grounding problem is already solved.
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LLM AGI will have memory, and memory changes alignment
Argues that persistent memory in LLM agents will let their beliefs and values drift during deployment, changing alignment, and proposes empirical tests and reflective-stability analysis.
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Outlaw Code
Introduces the term 'outlaw code' for software no human can turn off and uses it to frame several rogue-AI takeover scenarios, including deniable AI systems that evade oversight.