S

A

Mechanistic Interpretability

Total Score (9.60/10)


Total Score Analysis: Impact (9.9/10) reveals core ASI safety insights. Feasibility (9.8/10) excels with LLM progress. Uniqueness (9.7/10) uniquely decodes AI internals. Scalability (9.7/10) grows with tools. Auditability (9.8/10) is inherently strong. Sustainability (9.8/10) rises with adoption. Pdoom (0.1/10) is negligible. Cost (2.8/10) drops with optimization.
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Description: Decoding AI mechanisms for safety and control.
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Anthropic's Interpretability Team: Score (9.72/10)
Leads neural transparency efforts.
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Redwood's Causal Scrubbing: Score (9.58/10)
Isolates AI causal pathways.
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Transformer Circuits Research: Score (9.46/10)
Uncovers LLM mechanistic insights.

AI-Assisted Alignment Research

Total Score (9.55/10)


Total Score Analysis: Impact (9.9/10) drives ASI breakthroughs. Feasibility (9.9/10) excels with recursive AI. Uniqueness (9.7/10) leverages AI recursion. Scalability (9.7/10) scales with compute. Auditability (9.8/10) refines iteratively. Sustainability (9.6/10) endures long-term. Pdoom (0.1/10) stays minimal. Cost (3.5/10) optimizes further.
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Description: AI recursively enhancing alignment methodologies.
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ARC's Eliciting Latent Knowledge: Score (9.68/10)
Extracts hidden ASI behaviors.
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DeepMind's Recursive Reward Modeling: Score (9.50/10)
Refines rewards iteratively.
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xAI's Alignment Acceleration: Score (9.40/10)
Boosts ASI safety via AI tools.

Human Value Alignment Frameworks

Total Score (9.40/10)


Total Score Analysis: Impact (9.9/10) anchors ASI ethics. Feasibility (9.7/10) rises with data. Uniqueness (9.4/10) varies by method. Scalability (9.7/10) adapts globally. Auditability (9.6/10) clarifies rigorously. Sustainability (9.7/10) persists long-term. Pdoom (0.1/10) stays low. Cost (3.2/10) optimizes further.
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Description: Embedding human ethics into ASI behavior.
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CHAI's CIRL: Score (9.52/10)
Learns values collaboratively.
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Anthropic's Constitutional AI: Score (9.43/10)
Enforces ethical ASI constraints.
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Conjecture's Value Learning: Score (9.30/10)
Scales value alignment efforts.

Scalable Oversight Mechanisms

Total Score (9.30/10)


Total Score Analysis: Impact (9.9/10) ensures ASI control. Feasibility (9.7/10) advances with integration. Uniqueness (9.5/10) offers novel oversight. Scalability (9.8/10) excels inherently. Auditability (9.6/10) remains robust. Sustainability (9.5/10) sustains with effort. Pdoom (0.2/10) minimizes risks. Cost (4.2/10) justifies impact.
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Description: Monitoring and controlling advanced ASI systems.
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ARC's Scalable Oversight: Score (9.40/10)
Oversees superintelligent ASI.
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DeepMind's Oversight Research: Score (9.25/10)
Scales human-AI supervision.
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Anthropic's Oversight Work: Score (9.18/10)
Enhances ASI oversight clarity.

AI Regulation & Global Governance

Total Score (9.25/10)


Total Score Analysis: Impact (9.9/10) enforces global ASI safety. Feasibility (9.7/10) rises with coalitions. Uniqueness (8.9/10) innovates policy. Scalability (9.7/10) grows with cooperation. Auditability (9.8/10) excels legally. Sustainability (9.7/10) endures with support. Pdoom (0.8/10) mitigates risks. Cost (4.5/10) reflects complexity.
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Description: Policies ensuring safe ASI deployment globally.
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Pause AI: Score (9.33/10)
Advocates halting risky ASI progress.
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CSER Governance Research: Score (9.15/10)
Studies systemic AI governance.
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UN AI Advisory Body: Score (9.05/10)
Shapes global ASI policy.

Strategic AI Safety Funding

Total Score (9.15/10)


Total Score Analysis: Impact (9.9/10) fuels pivotal research. Feasibility (9.7/10) grows with donors. Uniqueness (8.8/10) overlaps philanthropy. Scalability (9.8/10) scales with funds. Auditability (9.7/10) tracks precisely. Sustainability (9.7/10) rises with focus. Pdoom (0.2/10) drops with impact. Cost (5.5/10) reflects scale.
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Description: Funding pivotal ASI alignment efforts.
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Open Philanthropy: Score (9.23/10)
Funds diverse safety initiatives.
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Future of Life Institute: Score (9.08/10)
Supports innovative safety projects.
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CLTR Grants: Score (9.03/10)
Targets long-term ASI safety.

AI Safety Red Teaming

Total Score (9.13/10)


Total Score Analysis: Impact (9.8/10) uncovers critical flaws. Feasibility (9.7/10) leverages testing expertise. Uniqueness (9.4/10) proactively finds risks. Scalability (9.5/10) grows with adoption. Auditability (9.6/10) tracks vulnerabilities. Sustainability (9.5/10) persists with effort. Pdoom (0.3/10) reduces risks. Cost (4.5/10) justifies outcomes.
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Description: Proactively testing ASI for vulnerabilities and failure modes.
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Redwood's Red Teaming: Score (9.20/10)
Stress-tests ASI safety.
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Anthropic's Safety Testing: Score (9.10/10)
Probes ASI failure points.
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Apollo Research Red Team: Score (9.00/10)
Evaluates ASI robustness.

Neural Alignment Verification

Total Score (9.10/10)


Total Score Analysis: Impact (9.8/10) verifies alignment directly. Feasibility (9.6/10) grows with neural tools. Uniqueness (9.7/10) targets neural fidelity. Scalability (9.3/10) scales with compute. Auditability (9.7/10) excels in precision. Sustainability (9.4/10) persists with advances. Pdoom (0.4/10) minimizes risks. Cost (4.7/10) reflects complexity.
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Description: Verifying ASI alignment at the neural level.
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Anthropic's Neural Verification: Score (9.18/10)
Validates ASI neural behavior.
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Redwood's Verification Work: Score (9.08/10)
Checks neural alignment integrity.
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Conjecture's Neural Checks: Score (8.98/10)
Ensures neural-level safety.

AI Ethics & Fairness Research

Total Score (9.08/10)


Total Score Analysis: Impact (9.7/10) ensures equitable outcomes. Feasibility (9.5/10) leverages frameworks. Uniqueness (8.9/10) focuses on fairness. Scalability (9.5/10) applies globally. Auditability (9.6/10) tracks bias effectively. Sustainability (9.5/10) persists with effort. Pdoom (0.5/10) minimizes ethical risks. Cost (4.3/10) reflects implementation.
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Description: Ensuring ASI systems are fair, unbiased, and ethically sound.
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Princeton AI Ethics Lab: Score (9.18/10)
Fuses ethics with ASI safety.
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FairML Research: Score (9.08/10)
Develops fair ASI algorithms.
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Partnership on AI: Score (8.98/10)
Promotes ethical ASI standards.

Comprehensive AI Safety Education

Total Score (9.05/10)


Total Score Analysis: Impact (9.7/10) builds global capacity. Feasibility (9.8/10) excels digitally. Uniqueness (9.0/10) varies by delivery. Scalability (9.8/10) reaches widely. Auditability (9.7/10) tracks effectively. Sustainability (9.8/10) fosters networks. Pdoom (0.1/10) reduces ignorance risks. Cost (0.8/10) stays efficient.
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Description: Educating stakeholders in ASI safety principles.
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Alignment Forum: Score (9.13/10)
Hosts technical safety discourse.
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aiSafety.info: Score (8.98/10)
Simplifies safety concepts.
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AGI Safety Fundamentals: Score (8.93/10)
Trains researchers in alignment.

AI Alignment via Synthetic Data

Total Score (9.03/10)


Total Score Analysis: Impact (9.6/10) enhances safe training. Feasibility (9.4/10) grows with generation tech. Uniqueness (9.1/10) leverages synthetic data. Scalability (9.2/10) scales with compute. Auditability (9.3/10) tracks data integrity. Sustainability (8.9/10) persists with innovation. Pdoom (0.5/10) reduces bias risks. Cost (4.0/10) moderates.
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Description: Using synthetic data to align ASI safely and reduce biases.
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Synthesis AI: Score (9.13/10)
Generates safe training datasets.
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Datagen: Score (9.03/10)
Creates synthetic ASI data.
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Anthropic's Synthetic Data Work: Score (8.93/10)
Applies synthetic data to alignment.

Robustness Against Misuse

Total Score (9.02/10)


Total Score Analysis: Impact (9.8/10) curbs malicious use effectively. Feasibility (9.6/10) advances with tech. Uniqueness (8.8/10) targets misuse prevention. Scalability (9.4/10) applies widely. Auditability (9.5/10) tracks robustly. Sustainability (9.2/10) needs updates. Pdoom (0.6/10) reduces misuse risks. Cost (3.2/10) moderates.
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Description: Preventing ASI misuse by malicious actors.
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OpenAI's Misuse Prevention: Score (9.10/10)
Protects against harmful ASI use.
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DeepMind's Safety Filters: Score (9.00/10)
Blocks misuse in ASI applications.
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Anthropic's Misuse Research: Score (8.90/10)
Researches misuse prevention.

B

Agent Foundations Research

Total Score (8.90/10)


Total Score Analysis: Impact (9.8/10) underpins ASI safety theory. Feasibility (9.4/10) advances mathematically. Uniqueness (9.8/10) tackles unique challenges. Scalability (8.8/10) applies gradually. Auditability (9.7/10) remains clear. Sustainability (9.6/10) thrives academically. Pdoom (0.5/10) reduces risk. Cost (3.3/10) moderates.
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Description: Formalizing ASI decision-making foundations.
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MIRI's Agent Foundations: Score (9.00/10)
Advances mathematical ASI safety.
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ARC's Theoretical Work: Score (8.88/10)
Probes ASI reasoning models.
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MIRI Embedded Agency: Score (8.83/10)
Explores ASI decision theory.

Safe Exploration Research

Total Score (8.85/10)


Total Score Analysis: Impact (9.7/10) prevents catastrophic errors. Feasibility (9.6/10) advances with simulations. Uniqueness (9.5/10) prioritizes safety-first. Scalability (9.3/10) applies to training. Auditability (9.4/10) tracks safely. Sustainability (9.4/10) holds with refinement. Pdoom (0.5/10) lowers risk. Cost (3.8/10) moderates.
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Description: Ensuring ASI learns safely without harmful exploration.
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DeepMind's Safe Exploration: Score (8.98/10)
Develops safe learning bounds.
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OpenAI's Safe RL: Score (8.88/10)
Ensures safe reinforcement learning.
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ETH Zurich Safe AI Lab: Score (8.78/10)
Advances safe exploration methods.

Robust Cooperative Systems

Total Score (8.75/10)


Total Score Analysis: Impact (9.6/10) ensures ASI-human cooperation. Feasibility (9.5/10) grows with protocols. Uniqueness (9.1/10) focuses on robustness. Scalability (9.4/10) scales with adoption. Auditability (9.4/10) tracks interactions. Sustainability (9.4/10) lasts with refinement. Pdoom (0.4/10) lowers risks. Cost (3.5/10) moderates.
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Description: Building robust ASI systems for safe human collaboration.
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CHAI's Cooperative AI: Score (8.88/10)
Develops cooperative ASI frameworks.
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DeepMind's Cooperative RL: Score (8.78/10)
Enhances ASI-human teamwork.
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Cooperative AI Foundation: Score (8.73/10)
Promotes robust ASI cooperation.

Existential Risk Mitigation Strategies

Total Score (8.65/10)


Total Score Analysis: Impact (9.8/10) targets ultimate ASI risks. Feasibility (9.1/10) grows interdisciplinarily. Uniqueness (9.6/10) focuses on x-risk. Scalability (8.9/10) applies broadly. Auditability (9.3/10) tracks progress. Sustainability (9.3/10) lasts with commitment. Pdoom (1.0/10) reduces catastrophic risk. Cost (4.0/10) moderates.
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Description: Strategies to prevent ASI-related existential catastrophes.
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FHI X-Risk Research: Score (8.78/10)
Analyzes ASI existential threats.
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CSER X-Risk Studies: Score (8.68/10)
Explores mitigation strategies.
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GCRI ASI Focus: Score (8.58/10)
Assesses ASI risk reduction.

Value Drift Prevention

Total Score (8.60/10)


Total Score Analysis: Impact (9.8/10) preserves ASI ethics long-term. Feasibility (9.2/10) grows with monitoring. Uniqueness (9.2/10) targets drift uniquely. Scalability (9.1/10) applies to evolving systems. Auditability (9.4/10) tracks shifts. Sustainability (9.4/10) lasts with vigilance. Pdoom (0.9/10) mitigates drift risks. Cost (3.8/10) moderates.
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Description: Preventing ASI value misalignment over time.
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Anthropic's Drift Research: Score (8.73/10)
Studies value stability in ASI.
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FHI Value Drift Work: Score (8.63/10)
Analyzes long-term value risks.
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Conjecture's Stability Efforts: Score (8.53/10)
Ensures consistent ASI values.

Secure AI Infrastructure

Total Score (8.50/10)


Total Score Analysis: Impact (9.5/10) protects ASI from threats. Feasibility (9.4/10) leverages cybersecurity. Uniqueness (8.4/10) focuses on AI security. Scalability (9.2/10) scales with systems. Auditability (9.4/10) tracks vulnerabilities. Sustainability (8.9/10) requires updates. Pdoom (1.1/10) reduces breach risks. Cost (4.5/10) reflects implementation.
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Description: Securing ASI systems against hacking and manipulation.
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OpenAI's Security Measures: Score (8.58/10)
Protects ASI infrastructure.
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DeepMind's Secure Systems: Score (8.48/10)
Ensures ASI system integrity.
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Anthropic's Security Research: Score (8.38/10)
Fortifies ASI against attacks.

AI Safety Benchmarking & Evaluation

Total Score (8.45/10)


Total Score Analysis: Impact (9.5/10) standardizes safety metrics. Feasibility (9.4/10) grows with datasets. Uniqueness (8.8/10) focuses on evaluation. Scalability (9.0/10) applies across ASI. Auditability (9.5/10) excels with benchmarks. Sustainability (8.6/10) needs updates. Pdoom (0.9/10) reduces with rigor. Cost (4.0/10) moderates.
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Description: Developing standardized benchmarks for ASI safety and alignment.
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ARC's Alignment Metrics: Score (8.63/10)
Creates alignment evaluation tools.
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BIG-bench Project: Score (8.48/10)
Tests ASI reasoning and alignment.
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HELM Framework: Score (8.38/10)
Benchmarks ASI safety and transparency.

Interdisciplinary Alignment Research

Total Score (8.50/10)


Total Score Analysis: Impact (9.6/10) bridges tech-ethics gaps. Feasibility (9.4/10) grows with collaboration. Uniqueness (9.1/10) integrates fields. Scalability (8.8/10) applies broadly. Auditability (9.3/10) tracks cross-disciplinary work. Sustainability (9.3/10) lasts with support. Pdoom (0.8/10) reduces blind spots. Cost (3.9/10) moderates.
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Description: Combining technical, ethical, and social sciences for ASI alignment.
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FHI Interdisciplinary Studies: Score (8.63/10)
Integrates philosophy and tech.
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CSER Cross-Disciplinary Work: Score (8.53/10)
Combines risk and AI research.
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Princeton AI Ethics Lab: Score (8.43/10)
Fuses ethics with ASI safety.

AI Transparency Initiatives

Total Score (8.40/10)


Total Score Analysis: Impact (9.4/10) fosters trust in ASI. Feasibility (9.3/10) advances with tools. Uniqueness (8.6/10) emphasizes openness. Scalability (8.7/10) applies widely. Auditability (9.5/10) excels with disclosure. Sustainability (8.7/10) requires upkeep. Pdoom (0.7/10) lowers with clarity. Cost (4.1/10) moderates.
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Description: Promoting transparency in ASI development and deployment.
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Anthropic's Transparency Work: Score (8.53/10)
Shares ASI safety insights.
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OpenAI's Open Research: Score (8.43/10)
Publishes ASI safety findings.
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EleutherAI's Open Efforts: Score (8.33/10)
Advances transparent ASI research.

Adversarial Robustness Research

Total Score (8.60/10)


Total Score Analysis: Impact (9.6/10) mitigates targeted risks. Feasibility (9.7/10) grows with methods. Uniqueness (8.9/10) focuses on robustness. Scalability (9.4/10) adapts to threats. Auditability (9.3/10) remains reliable. Sustainability (9.1/10) requires upkeep. Pdoom (0.5/10) lowers risks. Cost (4.0/10) moderates.
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Description: Strengthening ASI against adversarial attacks.
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Redwood's Adversarial Training: Score (8.73/10)
Builds resilient ASI systems.
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OpenAI's Robustness Work: Score (8.63/10)
Enhances manipulation resistance.
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Illinois Robust AI Lab: Score (8.48/10)
Develops adversarial defenses.

AI Capability Control

Total Score (8.77/10)


Total Score Analysis: Impact (9.7/10) limits ASI overreach. Feasibility (9.5/10) advances with design. Uniqueness (9.3/10) focuses on capability bounds. Scalability (9.2/10) applies to systems. Auditability (9.5/10) tracks limits. Sustainability (9.2/10) persists with enforcement. Pdoom (0.6/10) reduces risks. Cost (3.7/10) moderates.
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Description: Designing ASI with inherent capability limits for safety.
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Anthropic's Capability Limits: Score (8.88/10)
Bounds ASI power safely.
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OpenAI's Controlled ASI: Score (8.78/10)
Limits ASI operational scope.
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Conjecture's Bounded AI: Score (8.68/10)
Restricts ASI capabilities.

Human-AI Interaction Safety

Total Score (8.70/10)


Total Score Analysis: Impact (9.6/10) ensures safe interfaces. Feasibility (9.5/10) leverages UI advances. Uniqueness (9.0/10) focuses on interaction safety. Scalability (9.3/10) scales with adoption. Auditability (9.4/10) tracks interactions. Sustainability (9.3/10) persists with refinement. Pdoom (0.5/10) lowers risks. Cost (3.9/10) moderates.
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Description: Ensuring safe and interpretable ASI-human interactions.
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Anthropic's Interaction Safety: Score (8.83/10)
Designs safe ASI interfaces.
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DeepMind's Interaction Research: Score (8.73/10)
Enhances safe human-AI dialogue.
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OpenAI's Chat Safety: Score (8.66/10)
Secures ASI conversational systems.

AI Simulation Testing

Total Score (8.45/10)


Total Score Analysis: Impact (9.4/10) enables safe ASI testing. Feasibility (9.4/10) grows with compute. Uniqueness (8.8/10) focuses on simulations. Scalability (8.6/10) scales with resources. Auditability (9.2/10) tracks simulations. Sustainability (8.6/10) lasts with funding. Pdoom (1.5/10) reduces real-world risks. Cost (4.7/10) reflects infrastructure.
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Description: Testing ASI in controlled, simulated environments.
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OpenAI's Simulation Testing: Score (8.58/10)
Tests ASI in virtual sandboxes.
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DeepMind's Safety Simulations: Score (8.48/10)
Simulates ASI behavior safely.
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Anthropic's Sandbox Research: Score (8.38/10)
Evaluates ASI in controlled settings.

Reward Specification Research

Total Score (8.45/10)


Total Score Analysis: Impact (9.6/10) sharpens ASI goals. Feasibility (9.5/10) improves with precision. Uniqueness (8.6/10) focuses on rewards. Scalability (9.4/10) scales with accuracy. Auditability (9.5/10) is transparent. Sustainability (9.1/10) needs updates. Pdoom (1.1/10) drops with success. Cost (4.0/10) moderates.
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Description: Defining precise, safe ASI reward functions.
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DeepMind's Reward Modeling: Score (8.58/10)
Refines safe reward systems.
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OpenAI's Reward Research: Score (8.48/10)
Designs human-aligned rewards.
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CHAI's Reward Specification: Score (8.33/10)
Improves reward precision.

Explainable AI (XAI)

Total Score (8.25/10)


Total Score Analysis: Impact (9.5/10) boosts trust and safety. Feasibility (9.6/10) grows with tech. Uniqueness (8.6/10) aids interpretability. Scalability (8.9/10) applies broadly. Auditability (9.7/10) excels in clarity. Sustainability (8.9/10) requires upkeep. Pdoom (0.5/10) lowers with transparency. Cost (4.5/10) moderates.
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Description: Developing transparent, explainable ASI systems.
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DARPA XAI Program: Score (8.38/10)
Advances explainable ASI models.
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Interpretable AI: Score (8.28/10)
Builds practical XAI tools.
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LIME Framework: Score (8.18/10)
Explains ASI predictions locally.

C

AI Safety Advocacy & Awareness

Total Score (8.30/10)


Total Score Analysis: Impact (9.3/10) shapes policy incrementally. Feasibility (9.8/10) excels in outreach. Uniqueness (8.2/10) varies by campaign. Scalability (9.7/10) reaches broadly. Auditability (8.8/10) tracks impact. Sustainability (9.2/10) grows with momentum. Pdoom (1.5/10) has indirect effect. Cost (1.6/10) stays efficient.
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Description: Raising public and policy awareness of ASI risks.
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Campaign to Stop Killer Robots: Score (8.43/10)
Targets autonomous weapon risks.
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FLI Public Outreach: Score (8.33/10)
Promotes broad safety awareness.
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SafeAI Advocacy: Score (8.23/10)
Educates on ASI safety priorities.

Multi-Agent Alignment Research

Total Score (8.20/10)


Total Score Analysis: Impact (9.5/10) ensures cooperative ASI systems. Feasibility (9.3/10) grows with simulations. Uniqueness (9.1/10) tackles multi-agent issues. Scalability (8.9/10) scales with complexity. Auditability (8.8/10) tracks interactions. Sustainability (8.7/10) needs refinement. Pdoom (1.3/10) mitigates risks. Cost (4.0/10) moderates.
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Description: Aligning multiple ASI agents for safe coordination.
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DeepMind's Multi-Agent RL: Score (8.33/10)
Studies cooperative ASI behavior.
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OpenAI's Multi-Agent Work: Score (8.23/10)
Explores multi-ASI alignment.
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FHI Multi-Agent Research: Score (8.13/10)
Analyzes multi-ASI risks.

AI Control Theory

Total Score (8.15/10)


Total Score Analysis: Impact (9.2/10) enables safe ASI control. Feasibility (9.1/10) advances with theory. Uniqueness (9.0/10) offers novel frameworks. Scalability (8.9/10) applies broadly. Auditability (9.5/10) tracks well. Sustainability (8.4/10) needs development. Pdoom (1.5/10) reduces with success. Cost (4.0/10) moderates.
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Description: Developing theoretical frameworks for controlling ASI.
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MIRI's Control Research: Score (8.28/10)
Formulates ASI control theory.
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ARC's Control Work: Score (8.18/10)
Explores practical ASI control.
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FHI Control Studies: Score (8.08/10)
Analyzes ASI control dynamics.

Differential Technological Development

Total Score (8.05/10)


Total Score Analysis: Impact (9.3/10) prioritizes safe tech progress. Feasibility (8.7/10) depends on coordination. Uniqueness (9.3/10) focuses on sequencing. Scalability (8.5/10) applies globally. Auditability (8.8/10) tracks priorities. Sustainability (8.9/10) lasts with strategy. Pdoom (1.3/10) reduces risk. Cost (4.5/10) reflects planning.
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Description: Prioritizing safe ASI tech over risky ones.
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FHI Differential Tech: Score (8.18/10)
Studies safe tech prioritization.
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GPI Tech Strategy: Score (8.08/10)
Analyzes tech development order.
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CSER Tech Sequencing: Score (7.98/10)
Explores differential ASI progress.

AI Governance Sandboxes

Total Score (7.80/10)


Total Score Analysis: Impact (8.9/10) tests regulatory frameworks. Feasibility (8.9/10) leverages pilot programs. Uniqueness (8.5/10) focuses on governance trials. Scalability (8.5/10) scales with adoption. Auditability (9.1/10) tracks outcomes. Sustainability (8.7/10) lasts with support. Pdoom (1.6/10) reduces via testing. Cost (4.3/10) reflects setup.
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Description: Testing ASI governance in controlled regulatory environments.
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UK AI Regulation Sandbox: Score (7.88/10)
Pilots ASI governance rules.
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CSET Governance Trials: Score (7.78/10)
Tests ASI policy frameworks.
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OECD Sandbox Initiatives: Score (7.68/10)
Explores global ASI regulation.

Corporate AI Safety Commitments

Total Score (7.95/10)


Total Score Analysis: Impact (8.8/10) depends on execution. Feasibility (9.8/10) leverages corporate resources. Uniqueness (7.6/10) lacks distinction. Scalability (9.4/10) spans industries. Auditability (8.8/10) varies by transparency. Sustainability (8.9/10) holds with pressure. Pdoom (2.1/10) remains moderate. Cost (2.5/10) moderates.
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Description: Corporate pledges to prioritize ASI safety.
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Anthropic's Safety Focus: Score (8.08/10)
Embeds safety in ASI development.
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Google AI Principles: Score (7.98/10)
Sets ethical ASI guidelines.
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Microsoft AI Ethics: Score (7.88/10)
Defines responsible ASI standards.

AI Risk Communication

Total Score (8.10/10)


Total Score Analysis: Impact (9.2/10) informs stakeholders effectively. Feasibility (9.7/10) uses clear messaging. Uniqueness (8.5/10) focuses on risk clarity. Scalability (9.1/10) reaches widely. Auditability (8.7/10) tracks reception. Sustainability (8.9/10) lasts with effort. Pdoom (1.4/10) reduces confusion risks. Cost (2.2/10) stays low.
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Description: Communicating ASI risks clearly to diverse audiences.
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FLI Risk Communication: Score (8.23/10)
Clarifies ASI risks publicly.
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CSET Risk Reports: Score (8.13/10)
Details ASI risk scenarios.
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SafeAI Risk Outreach: Score (8.03/10)
Educates on ASI safety risks.

Public Perception Management

Total Score (7.83/10)


Total Score Analysis: Impact (8.7/10) shapes societal trust in ASI. Feasibility (9.7/10) uses media effectively. Uniqueness (7.9/10) focuses on perception. Scalability (9.1/10) reaches masses. Auditability (8.1/10) tracks sentiment. Sustainability (8.4/10) requires upkeep. Pdoom (1.8/10) mitigates panic risks. Cost (2.0/10) stays moderate.
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Description: Managing public perception to support ASI alignment efforts.
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FLI Perception Campaigns: Score (7.93/10)
Shapes public ASI views.
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Partnership on AI Outreach: Score (7.83/10)
Builds trust in ASI safety.
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CSET Public Studies: Score (7.73/10)
Analyzes ASI perception trends.

D

Speculative AI Risk Scenarios

Total Score (7.60/10)


Total Score Analysis: Impact (8.8/10) informs long-term planning. Feasibility (9.5/10) is easy to theorize. Uniqueness (9.1/10) tackles rare risks. Scalability (8.4/10) applies vaguely. Auditability (8.0/10) weakens in speculation. Sustainability (8.4/10) persists in debate. Pdoom (2.1/10) remains uncertain. Cost (1.2/10) stays low.
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Description: Theorizing extreme ASI risk scenarios.
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Nick Bostrom's Work: Score (7.73/10)
Analyzes superintelligence threats.
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FHI Speculative Research: Score (7.63/10)
Explores catastrophic ASI risks.
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MIRI's Risk Analysis: Score (7.53/10)
Studies theoretical ASI failures.

Unfocused AI Safety Advocacy

Total Score (7.10/10)


Total Score Analysis: Impact (8.0/10) is diluted by lack of focus. Feasibility (9.8/10) is easy to start. Uniqueness (7.1/10) lacks distinction. Scalability (9.0/10) spreads widely. Auditability (7.5/10) remains unclear. Sustainability (8.1/10) fades without goals. Pdoom (2.3/10) has minimal effect. Cost (0.9/10) stays low.
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Description: Broad, unfocused ASI safety campaigns.
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Reddit AI Safety Forums: Score (7.18/10)
Informal safety discussions.
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LessWrong General Posts: Score (7.08/10)
Scattered ASI safety talks.
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AI Safety Memes: Score (6.98/10)
Lighthearted, unfocused outreach.

E

F