Asi Alignment Tier List

Description: Education on AI existential risk is crucial for ensuring responsible AI development.
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Best Memes = AI Notkilleveryoneism Memes
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General AI Safety FAQ = aiSafety.info (Rob Miles)
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AI Safety Map = aiSafety.world
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AI Safety Beginner's Guide = aiSafetyLinkTree
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AI Incident Database = incidentdatabase.ai

Description: Receiving, Providing, and how to fundraise money for AI Safety.
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How to Receive Funding for an AI Safety project.
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Donation guide.
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How to fundraise:
Focus on conveying the urgent need and immense positive leverage of this nascent field. Target effective altruist communities, tech philanthropists concerned about long-term risks, and potentially government agencies interested in national security and beneficial AI. Clearly articulate the potential for existential risks from unaligned ASI, contrasting this with the vast benefits of aligned ASI for humanity’s long-term flourishing. Emphasize that even relatively small early investments now in AI safety research and infrastructure have an outsized impact on mitigating catastrophic risks and shaping a beneficial AI future. Build a compelling narrative around the importance of this cause, showcase specific, impactful projects with clear pathways to progress, and demonstrate expertise, transparency, and efficient resource allocation to build trust with potential funders.

Description: Mechanistic interpretability is the pursuit to understand the inner workings of black box AI such as LLMs or End-to-End Reinforcement Learning systems.
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Anthropic's Mechanistic Interpretability Team:
Pioneering work on Cooperative Inverse Reinforcement Learning (CIRL), which creates mathematical frameworks for AI systems to learn human preferences through observation and interaction rather than explicit programming.
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Redwood Research's Redwood Research's Causal Scrubbing:
Using autonomous AI systems in red-teaming scenarios to find alignment failures in other AI systems. Their approach involves training one AI to find cases where another AI would behave in problematic ways.
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TransformCIR Collaboration:
Developing formal frameworks for capturing human preferences through their work on reward modeling and specification techniques. Their research combines theoretical foundations with practical implementation in advanced AI systems.
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EleutherAI's Interpretability Research:
Building autonomous AI systems specifically designed to help understand and interpret the internal workings of other AI systems, with the goal of making alignment properties more transparent and verifiable.
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NeuroSEED at MIT
Developing AI systems that can autonomously identify and resolve their own alignment failures through constitutional principles. Their approach uses AI assistants to evaluate and improve AI behavior without direct human feedback for each decision.

Description: Development of policy, legal, regulatory, and international frameworks to ensure safe and beneficial AI development and deployment.
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Ai Governance Map
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Pause AI Movement:

The Pause AI movement is attempting to buy more time to work on Asi Alignment. More time is extremely valuable, considering we probably only have until 2026 or 2027 before we have Rogue AGI (which will be a bit closer to ASI than AGI if you strip away all the goal post moving on the definition of AGI).

Although in a perfect world it would be ideal to Pause AI until we can do more work in AI safety and alignment, in reality it will be difficult (but not impossible) to enforce into reality.

The Game Theoretical principles like Moloch, along with the fact that very few people even understand that AGI can kill all humans, The Pause AI movement difficult, but still worth pursuing, imo.
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Sam Altman on AI Regulation
0. Lobbying Politicians / Influencial People
1. Blackbox Algorithmic Transparancy
2. Data Collection & Usage
3. Human Extinction Safety Standards
4. Economic Impact & Universal Basic Income
5. AI Capability restrictions

Description: Create a robust, scalable framework to encode human values into ASI.
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Stuart Russell's Center for Human-Compatible AI (CHAI):
Pioneering work on Cooperative Inverse Reinforcement Learning (CIRL), which creates mathematical frameworks for AI systems to learn human preferences through observation and interaction rather than explicit programming.
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Alignment Research Center (ARC) - CIRL and Value Alignment:
ARC, founded by Paul Christiano, develops frameworks like Cooperative Inverse Reinforcement Learning (CIRL) to infer human reward functions through a cooperative game between humans and AI. It emphasizes learning values implicitly via human-robot interaction, addressing goal specification and preference learning.
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DeepMind's Ethics and Society Team's Value Alignment Research:
Developing formal frameworks for capturing human preferences through their work on reward modeling and specification techniques. Their research combines theoretical foundations with practical implementation in advanced AI systems.
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Anthropic's Constitutional AI:
Research program developing methods for AI systems to learn human values through constitutional principles and preference modeling, with particular focus on resolving value conflicts and building safe, helpful, and honest AI.
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