Grok 3

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A

Mechanistic Interpretability

Total Score (9.14/10)

Total Score Analysis: Impact (9.9/10) drives transparency breakthroughs. Feasibility (9.7/10) uses advanced tools. Uniqueness (9.6/10) is distinct. Scalability (9.6/10) grows with automation. Auditability (9.7/10) ensures oversight. Sustainability (9.6/10) advances with research. Pdoom (0.1/10) is negligible. Cost (5.5/10) reflects high computational needs.

Description: Decoding AI mechanisms for safety and control.

Anthropic's Interpretability Team: Score (9.70/10)

Redwood's Causal Scrubbing: Score (9.55/10)

Transformer Circuits Research: Score (9.45/10)

OpenAI's Interpretability Research: Score (9.50/10)

DeepMind's Interpretability Team: Score (9.45/10)

Conjecture's Interpretability Research: Score (9.40/10)

ASI Governance and Policy

Total Score (8.95/10)

Total Score Analysis: Impact (9.8/10) shapes global standards. Feasibility (9.4/10) grows with coalitions. Uniqueness (9.0/10) innovates frameworks. Scalability (9.2/10) spans nations. Auditability (9.6/10) ensures clarity. Sustainability (9.5/10) endures. Pdoom (0.5/10) mitigates risks. Cost (5.0/10) reflects complexity.

Description: Crafting policies and international legal frameworks for safe ASI deployment.

CSER Governance Research: Score (9.20/10)

FHI Governance of AI Program: Score (9.00/10)

EU AI Act: Score (8.50/10)

Partnership on AI: Score (8.90/10)

UNESCO's AI Ethics Recommendations: Score (8.80/10)

Robustness and Reliability in ASI

Total Score (8.87/10)

Total Score Analysis: Impact (9.8/10) ensures dependable systems. Feasibility (9.5/10) uses advanced testing. Uniqueness (9.2/10) targets robustness. Scalability (9.3/10) applies widely. Auditability (9.4/10) allows checks. Sustainability (9.3/10) maintains reliability. Pdoom (0.3/10) is low. Cost (4.5/10) reflects safety needs.

Description: Ensuring ASI reliability across conditions.

DeepMind's Robustness Research: Score (9.20/10)

Anthropic's Reliability Initiatives: Score (9.10/10)

OpenAI's Safety Testing: Score (9.00/10)

ASI Safety Standards and Certification

Total Score (8.90/10)

Total Score Analysis: Impact (9.8/10) ensures broad safety. Feasibility (9.5/10) advances with adoption. Uniqueness (8.5/10) focuses on standards. Scalability (9.5/10) applies globally. Auditability (9.5/10) enforces compliance. Sustainability (9.0/10) evolves. Pdoom (0.5/10) reduces risks. Cost (4.5/10) reflects effort.

Description: Setting safety standards for ASI systems.

ISO/IEC JTC 1/SC 42: Score (9.20/10)

IEEE P7000 Series: Score (8.10/10)

NIST AI Risk Management Framework: Score (9.00/10)

Scalable Oversight Mechanisms

Total Score (8.84/10)

Total Score Analysis: Impact (9.7/10) enables robust control. Feasibility (9.6/10) integrates well. Uniqueness (9.3/10) pioneers oversight. Scalability (9.5/10) excels broadly. Auditability (9.4/10) is reliable. Sustainability (9.4/10) persists. Pdoom (0.3/10) is low. Cost (5.0/10) reflects complexity.

Description: Monitoring and controlling advanced ASI.

ARC's Scalable Oversight: Score (9.35/10)

DeepMind's Oversight Research: Score (9.20/10)

Human-in-the-Loop Systems: Score (9.15/10)

AI Safety Advocacy & Communication

Total Score (8.81/10)

Total Score Analysis: Impact (9.7/10) boosts awareness. Feasibility (9.6/10) excels digitally. Uniqueness (8.9/10) varies by outreach. Scalability (9.6/10) reaches globally. Auditability (9.0/10) tracks impact. Sustainability (9.3/10) grows. Pdoom (0.9/10) is low. Cost (2.5/10) is efficient.

Description: Raising ASI risk awareness among stakeholders.

FLI Advocacy & Communication: Score (9.15/10)

AI Safety Podcasts: Score (8.90/10)

PauseAI: Score (7.50/10)

Interdisciplinary Alignment Research

Total Score (8.82/10)

Total Score Analysis: Impact (9.5/10) integrates diverse insights. Feasibility (9.0/10) leverages collaboration. Uniqueness (9.2/10) is unique. Scalability (9.3/10) applies broadly. Auditability (9.1/10) ensures oversight. Sustainability (9.4/10) fosters innovation. Pdoom (0.4/10) is minimal. Cost (4.0/10) reflects coordination.

Description: Merging fields like psychology and economics for ASI alignment.

ARC's Interdisciplinary Initiatives: Score (9.20/10)

FHI's Cross-Disciplinary Research: Score (9.10/10)

CSER's Sociotechnical Systems: Score (9.00/10)

AI Safety Talent Development

Total Score (8.85/10)

Total Score Analysis: Impact (9.6/10) builds expertise. Feasibility (9.5/10) uses programs. Uniqueness (9.0/10) focuses on skills. Scalability (9.4/10) expands globally. Auditability (9.4/10) tracks progress. Sustainability (9.4/10) persists. Pdoom (0.3/10) is low. Cost (4.0/10) moderates.

Description: Training skilled ASI alignment researchers.

ML Safety at Oxford: Score (9.15/10)

AI Safety Camp: Score (9.05/10)

SERI MATS: Score (8.85/10)

Strategic AI Safety Funding

Total Score (8.78/10)

Total Score Analysis: Impact (9.7/10) fuels research. Feasibility (9.6/10) grows with donors. Uniqueness (8.7/10) overlaps philanthropy. Scalability (9.5/10) scales well. Auditability (9.5/10) tracks funds. Sustainability (9.5/10) rises. Pdoom (0.3/10) is low. Cost (5.5/10) reflects scale.

Description: Funding key ASI alignment efforts.

Open Philanthropy: Score (9.15/10)

Future of Life Institute: Score (9.00/10)

Longview Philanthropy AI Grants: Score (8.95/10)

AI-Assisted Alignment Research

Total Score (8.76/10)

Total Score Analysis: Impact (9.7/10) speeds safety solutions. Feasibility (8.5/10) uses recursive AI. Uniqueness (9.4/10) is standout. Scalability (9.5/10) scales with compute. Auditability (9.5/10) ensures iteration. Sustainability (9.4/10) supports research. Pdoom (0.2/10) is minimal. Cost (4.5/10) reflects resources.

Description: Using AI to recursively improve alignment.

ARC's Eliciting Latent Knowledge: Score (9.60/10)

OpenAI's Superalignment Team: Score (9.50/10)

DeepMind's Recursive Reward Modeling: Score (9.45/10)

Value Alignment Methods

Total Score (8.87/10)

Total Score Analysis: Impact (9.7/10) ensures ethical ASI. Feasibility (9.2/10) advances with tech. Uniqueness (9.1/10) blends approaches. Scalability (9.4/10) applies widely. Auditability (9.0/10) tracks alignment. Sustainability (9.2/10) maintains standards. Pdoom (0.5/10) is low. Cost (4.0/10) is moderate.

Description: Aligning ASI with human values via ethical integration and feedback.

OpenAI's RLHF: Score (9.00/10)

Anthropic's Constitutional AI: Score (9.35/10)

CHAI's CIRL: Score (9.45/10)

Microsoft's Responsible AI Principles: Score (8.50/10)

Cognitive Approaches to ASI Alignment

Total Score (8.62/10)

Total Score Analysis: Impact (9.8/10) offers novel solutions. Feasibility (9.0/10) grows with research. Uniqueness (9.5/10) stands out. Scalability (9.2/10) fits ASI systems. Auditability (9.3/10) enhances oversight. Sustainability (9.0/10) needs focus. Pdoom (0.3/10) is low. Cost (5.0/10) reflects effort.

Description: Leveraging cognitive science and neuroscience for ASI alignment.

Modular ASI Design Initiative: Score (8.50/10)

Neuro-Inspired Alignment Frameworks: Score (7.80/10)

CHAI's Cognitive Modeling: Score (7.80/10)

Formal Verification for ASI Safety

Total Score (8.37/10)

Total Score Analysis: Impact (9.7/10) ensures safety guarantees. Feasibility (8.8/10) advances with tools. Uniqueness (9.2/10) offers verification. Scalability (9.0/10) fits complex systems. Auditability (9.5/10) excels in precision. Sustainability (8.8/10) continues. Pdoom (0.4/10) is low. Cost (5.5/10) reflects complexity.

Description: Verifying ASI safety with formal methods.

Verified ASI Systems Project: Score (8.70/10)

Formal Safety Proofs for ASI: Score (8.40/10)

Automated Verification Tools: Score (8.30/10)

Comprehensive AI Safety Education

Total Score (8.88/10)

Total Score Analysis: Impact (9.6/10) builds expertise. Feasibility (9.6/10) excels digitally. Uniqueness (8.9/10) varies by delivery. Scalability (9.5/10) reaches widely. Auditability (9.5/10) tracks well. Sustainability (9.5/10) fosters networks. Pdoom (0.2/10) is low. Cost (3.0/10) is efficient.

Description: Educating stakeholders on ASI safety.

Alignment Forum: Score (9.05/10)

AI Safety Fundamentals Course: Score (8.75/10)

Stampy AI: Score (8.80/10)

AI Safety.com Resources: Score (8.70/10)

Runtime Safety Mechanisms

Total Score (8.70/10)

Total Score Analysis: Impact (9.5/10) ensures real-time safety. Feasibility (9.4/10) advances with tech. Uniqueness (9.1/10) targets runtime. Scalability (9.2/10) applies widely. Auditability (9.3/10) tracks dynamically. Sustainability (9.2/10) persists. Pdoom (0.4/10) is low. Cost (5.0/10) moderates.

Description: Real-time ASI safety monitoring and intervention.

Anthropic's Runtime Safety: Score (9.10/10)

Real-Time Monitoring Systems: Score (8.95/10)

Anomaly Detection in ASI: Score (8.90/10)

Cooperative AI Systems

Total Score (8.70/10)

Total Score Analysis: Impact (9.5/10) fosters safe coordination. Feasibility (9.4/10) uses simulations. Uniqueness (9.2/10) targets cooperation. Scalability (9.2/10) scales with systems. Auditability (9.3/10) tracks interactions. Sustainability (9.2/10) persists. Pdoom (0.5/10) is low. Cost (5.0/10) moderates.

Description: Designing ASI for cooperative behavior.

DeepMind's Cooperative AI: Score (9.10/10)

Multi-Agent RL for Cooperation: Score (8.85/10)

Game Theory for ASI Coordination: Score (8.80/10)

AI Safety Red Teaming

Total Score (8.39/10)

Total Score Analysis: Impact (9.6/10) finds vulnerabilities. Feasibility (8.5/10) uses expertise. Uniqueness (9.2/10) targets risks. Scalability (9.3/10) grows well. Auditability (9.4/10) tracks flaws. Sustainability (9.3/10) persists. Pdoom (0.4/10) is low. Cost (5.0/10) justifies outcomes.

Description: Testing ASI for vulnerabilities proactively.

Redwood's Red Teaming: Score (9.15/10)

Adversarial Testing for LLMs: Score (9.00/10)

Robustness Challenges: Score (8.95/10)

Apollo Research's Red Teaming Efforts: Score (9.00/10)

METR's Red Teaming Initiatives: Score (9.00/10)

Neuro-Symbolic AI for Alignment

Total Score (8.12/10)

Total Score Analysis: Impact (9.5/10) offers novel solutions. Feasibility (8.5/10) is promising. Uniqueness (9.5/10) stands out. Scalability (8.5/10) fits systems. Auditability (9.0/10) boosts transparency. Sustainability (8.5/10) needs research. Pdoom (0.5/10) is low. Cost (5.0/10) moderates.

Description: Combining neural and symbolic reasoning for ASI control.

Neuro-Symbolic Program Synthesis: Score (8.50/10)

Hybrid AI Models for Safety: Score (8.40/10)

Symbolic Reasoning in DL: Score (8.30/10)

Alignment Verification Methods

Total Score (8.15/10)

Total Score Analysis: Impact (9.5/10) ensures alignment. Feasibility (8.0/10) is challenging. Uniqueness (9.0/10) offers methods. Scalability (9.0/10) applies broadly. Auditability (9.5/10) requires precision. Sustainability (9.0/10) persists. Pdoom (0.5/10) is low. Cost (5.5/10) reflects effort.

Description: Verifying ASI alignment through techniques.

Value Alignment Testing Suites: Score (8.40/10)

Ethical Scenario Simulations: Score (8.35/10)

Alignment Verification Protocols: Score (8.30/10)

Agent Foundations Research

Total Score (8.57/10)

Total Score Analysis: Impact (9.8/10) underpins safety theory. Feasibility (9.3/10) advances mathematically. Uniqueness (9.5/10) tackles unique issues. Scalability (8.7/10) applies gradually. Auditability (9.5/10) ensures clarity. Sustainability (9.3/10) thrives. Pdoom (0.5/10) is low. Cost (5.0/10) moderates.

Description: Formalizing ASI decision-making foundations.

Decision Theory for ASI: Score (8.85/10)

Logical Uncertainty: Score (8.80/10)

MIRI Embedded Agency: Score (8.75/10)

Safe Exploration Research

Total Score (8.50/10)

Total Score Analysis: Impact (9.5/10) prevents errors. Feasibility (9.4/10) uses simulations. Uniqueness (9.3/10) prioritizes safety. Scalability (9.1/10) applies to training. Auditability (9.2/10) tracks safely. Sustainability (9.2/10) refines. Pdoom (0.5/10) is low. Cost (5.0/10) moderates.

Description: Ensuring safe ASI learning without harm.

Constrained Exploration in RL: Score (8.75/10)

Safe Policy Optimization: Score (8.70/10)

ETH Zurich Safe AI Lab: Score (8.65/10)

Long-Term ASI Safety

Total Score (8.12/10)

Total Score Analysis: Impact (9.6/10) tackles long-term risks. Feasibility (8.0/10) needs interdisciplinary work. Uniqueness (9.2/10) focuses on future. Scalability (8.8/10) applies globally. Auditability (8.0/10) tracks progress. Sustainability (9.3/10) is long-term. Pdoom (0.7/10) reduces risks. Cost (5.0/10) reflects needs.

Description: Ensuring ASI safety over extended periods.

ASI Risk Scenarios Analysis: Score (8.55/10)

Long-Term Safety Planning: Score (8.50/10)

GCRI ASI Focus: Score (8.45/10)

AI Safety Benchmarking & Evaluation

Total Score (8.10/10)

Total Score Analysis: Impact (9.4/10) standardizes metrics. Feasibility (9.3/10) grows with data. Uniqueness (8.7/10) focuses on evaluation. Scalability (8.9/10) applies across ASI. Auditability (9.3/10) excels. Sustainability (8.5/10) needs updates. Pdoom (0.7/10) is low. Cost (5.0/10) moderates.

Description: Creating benchmarks for ASI safety.

Safety Benchmarks for LMs: Score (8.35/10)

Robustness Evaluation Metrics: Score (8.30/10)

HELM Framework: Score (8.25/10)

Adversarial Robustness Research

Total Score (8.25/10)

Total Score Analysis: Impact (9.5/10) mitigates attack risks. Feasibility (9.5/10) grows with methods. Uniqueness (8.8/10) targets robustness. Scalability (9.2/10) adapts broadly. Auditability (9.1/10) is reliable. Sustainability (8.9/10) requires upkeep. Pdoom (0.5/10) is low. Cost (5.5/10) moderates.

Description: Strengthening ASI against adversarial attacks.

Certified Defenses: Score (8.45/10)

Adversarial Training Techniques: Score (8.40/10)

Redwood's Adversarial Training: Score (8.35/10)

AI Capability Control

Total Score (8.45/10)

Total Score Analysis: Impact (9.6/10) limits overreach. Feasibility (9.4/10) advances with design. Uniqueness (9.1/10) focuses on bounds. Scalability (9.0/10) applies to systems. Auditability (9.3/10) tracks limits. Sustainability (9.0/10) persists. Pdoom (0.6/10) is low. Cost (5.0/10) moderates.

Description: Limiting ASI capabilities for safety.

Capability Bounding Mechanisms: Score (8.65/10)

Operational Limits in ASI: Score (8.60/10)

OpenAI's Controlled ASI: Score (8.55/10)

Corrigibility Research

Total Score (8.15/10)

Total Score Analysis: Impact (9.4/10) enhances safety. Feasibility (8.4/10) progresses theoretically. Uniqueness (8.9/10) targets corrigibility. Scalability (8.9/10) applies broadly. Auditability (8.4/10) ensures clarity. Sustainability (8.9/10) persists. Pdoom (0.5/10) is low. Cost (4.5/10) moderates.

Description: Making ASI correctable or shutdown-capable.

Shutdown Problem Solutions: Score (8.40/10)

Interruptible Agents: Score (8.35/10)

MIRI's Corrigibility Research: Score (8.30/10)

Inner Alignment Research

Total Score (8.00/10)

Total Score Analysis: Impact (9.6/10) tackles core issues. Feasibility (7.9/10) advances with research. Uniqueness (9.1/10) addresses risks. Scalability (8.9/10) applies to systems. Auditability (7.9/10) is theoretical. Sustainability (8.9/10) continues. Pdoom (0.4/10) is low. Cost (5.0/10) reflects complexity.

Description: Ensuring ASI optimizes intended goals.

Mesa-Optimization Prevention: Score (8.40/10)

Objective Robustness Techniques: Score (8.35/10)

Reward Tampering Research: Score (8.30/10)

Causal Approaches to AI Alignment

Total Score (8.18/10)

Total Score Analysis: Impact (9.4/10) enhances control via causality. Feasibility (8.4/10) grows with research. Uniqueness (8.9/10) offers distinct methods. Scalability (8.9/10) applies broadly. Auditability (8.9/10) ensures clarity. Sustainability (8.9/10) persists. Pdoom (0.5/10) is low. Cost (5.0/10) reflects needs.

Description: Using causal models for safe ASI decisions.

Causal Influence Diagrams: Score (8.40/10)

Incentive Design via Causality: Score (8.35/10)

FHI Causal Research: Score (8.30/10)

AI Transparency and Explainability

Total Score (7.99/10)

Total Score Analysis: Impact (9.0/10) builds trust. Feasibility (8.5/10) advances with research. Uniqueness (8.5/10) targets explainability. Scalability (9.0/10) applies broadly. Auditability (9.2/10) enhances oversight. Sustainability (8.8/10) needs updates. Pdoom (0.6/10) is low. Cost (5.0/10) moderates.

Description: Making hardly understandable ASI decisions transparent.

Explainable AI Techniques: Score (8.25/10)

Interpretable Machine Learning: Score (8.20/10)

OpenAI's Explainability: Score (8.15/10)

AI Safety in Deployment and Operations

Total Score (8.04/10)

Total Score Analysis: Impact (9.2/10) ensures real-world safety. Feasibility (8.8/10) needs practical work. Uniqueness (8.5/10) targets operations. Scalability (9.2/10) is key for use. Auditability (9.0/10) allows monitoring. Sustainability (8.8/10) needs focus. Pdoom (0.6/10) is low. Cost (5.5/10) is notable.

Description: Ensuring safe ASI deployment.

Deployment Safety Protocols: Score (8.15/10)

Operational Risk Management: Score (8.10/10)

AI Incident Database: Score (8.05/10)

Human-AI Collaboration Design

Total Score (7.87/10)

Total Score Analysis: Impact (9.0/10) ensures safe interaction. Feasibility (8.5/10) needs interdisciplinary work. Uniqueness (8.0/10) focuses on design. Scalability (9.0/10) applies broadly. Auditability (8.5/10) allows testing. Sustainability (8.5/10) needs refinement. Pdoom (0.5/10) is low. Cost (5.0/10) moderates.

Description: Designing safe human-ASI interactions.

Collaborative AI Systems: Score (8.15/10)

User-Centric AI Design: Score (8.10/10)

MIT CSAIL Collaboration: Score (8.05/10)

Simulation-Based Alignment Research

Total Score (8.20/10)

Total Score Analysis: Impact (9.5/10) enhances safety testing. Feasibility (8.5/10) is promising. Uniqueness (9.0/10) offers virtual testing. Scalability (9.0/10) grows with compute. Auditability (9.0/10) allows analysis. Sustainability (9.0/10) persists with tech. Pdoom (0.5/10) is low. Cost (5.5/10) is moderate.

Description: Testing ASI alignment via simulations.

OpenAI's Safety Gym: Score (8.50/10)

DeepMind's Multi-Agent Simulations: Score (8.20/10)

DeepMind's Safety Gridworlds: Score (8.00/10)

Uncertainty-Aware Alignment

Total Score (7.77/10)

Total Score Analysis: Impact (9.0/10) ensures safe behavior. Feasibility (8.0/10) is promising. Uniqueness (8.5/10) targets uncertainty. Scalability (9.0/10) integrates broadly. Auditability (8.5/10) allows monitoring. Sustainability (9.0/10) evolves. Pdoom (0.5/10) is low. Cost (5.5/10) is moderate.

Description: Handling uncertainty safely in ASI.

Learning to Defer: Score (8.20/10)

Conformal Prediction: Score (8.10/10)

Evidential Deep Learning: Score (8.00/10)

Open-Source AI Safety Initiatives

Total Score (8.22/10)

Total Score Analysis: Impact (9.0/10) accelerates collaboration. Feasibility (9.0/10) leverages open-source. Uniqueness (7.0/10) is method-based. Scalability (9.5/10) reaches globally. Auditability (9.5/10) ensures transparency. Sustainability (9.0/10) thrives on community. Pdoom (1.0/10) is low. Cost (4.0/10) is efficient.

Description: Using open-source for ASI alignment.

EleutherAI's Interpretability Research: Score (8.70/10)

Hugging Face's Safety Efforts: Score (8.50/10)

OpenAI's Safety Gym: Score (8.50/10)

AI Alignment via Debate and Amplification

Total Score (8.50/10)

Total Score Analysis: Impact (9.8/10) scales alignment. Feasibility (8.5/10) advances with research. Uniqueness (9.5/10) is distinct. Scalability (9.5/10) fits ASI. Auditability (9.0/10) allows oversight. Sustainability (9.0/10) persists. Pdoom (0.3/10) is low. Cost (5.0/10) moderates.

Description: Using debate or amplification for alignment.

OpenAI's AI Safety via Debate: Score (8.80/10)

DeepMind's Amplification Research: Score (8.70/10)

ARC's Debate Projects: Score (8.60/10)

Differential Technological Development

Total Score (7.70/10)

Total Score Analysis: Impact (9.2/10) prioritizes safe progress. Feasibility (8.6/10) needs coordination. Uniqueness (9.1/10) focuses on sequencing. Scalability (8.4/10) applies globally. Auditability (8.7/10) tracks priorities. Sustainability (8.7/10) lasts. Pdoom (1.1/10) reduces risk. Cost (5.5/10) reflects planning.

Description: Prioritizing safe ASI tech development.

Tech Prioritization Frameworks: Score (8.05/10)

Safe Development Pathways: Score (8.00/10)

FHI Differential Tech: Score (7.95/10)

AI Alignment Prizes

Total Score (7.57/10)

Total Score Analysis: Impact (8.5/10) spurs innovation. Feasibility (9.0/10) uses competition. Uniqueness (8.0/10) targets prizes. Scalability (9.0/10) reaches globally. Auditability (8.5/10) tracks entries. Sustainability (8.0/10) depends on funds. Pdoom (1.0/10) is indirect. Cost (4.0/10) is efficient.

Description: Incentivizing ASI alignment via competitions.

ASI Safety Competition: Score (7.85/10)

FLI AI Safety Prizes: Score (7.80/10)

Alignment Challenge Prizes: Score (7.75/10)

Global Ethical Consensus for ASI

Total Score (7.62/10)

Total Score Analysis: Impact (9.5/10) shapes ASI ethics. Feasibility (7.5/10) faces diplomatic hurdles. Uniqueness (8.5/10) targets consensus. Scalability (9.0/10) applies globally. Auditability (8.0/10) monitors agreements. Sustainability (9.0/10) ensures focus. Pdoom (1.0/10) reduces risks. Cost (5.5/10) reflects effort.

Description: Building global ethical ASI agreements.

IEEE Ethically Aligned Design: Score (8.00/10)

Asilomar AI Principles: Score (7.90/10)

Montreal Declaration: Score (7.85/10)

Moral Uncertainty in AI Alignment

Total Score (7.52/10)

Total Score Analysis: Impact (9.0/10) handles value conflicts. Feasibility (7.5/10) is advancing. Uniqueness (8.5/10) targets uncertainty. Scalability (9.0/10) applies broadly. Auditability (7.0/10) needs evaluation. Sustainability (9.0/10) remains relevant. Pdoom (0.5/10) is low. Cost (5.0/10) is moderate.

Description: Navigating moral uncertainty in ASI.

CHAI's Moral Uncertainty Research: Score (8.00/10)

FHI's Moral Uncertainty in AI: Score (7.90/10)

Moral Decision Frameworks: Score (7.85/10)

Inverse Reinforcement Learning for ASI

Total Score (7.60/10)

Total Score Analysis: Impact (9.5/10) addresses value learning. Feasibility (7.5/10) is promising. Uniqueness (8.5/10) is specific. Scalability (9.0/10) applies broadly. Auditability (8.0/10) inspects rewards. Sustainability (8.5/10) needs learning. Pdoom (0.5/10) is low. Cost (6.0/10) is computational.

Description: Inferring values from behavior for ASI alignment.

Cooperative IRL (CIRL): Score (8.00/10)

DeepMind's IRL Research: Score (7.90/10)

Stanford's Value Learning Project: Score (7.80/10)

Behavioral Economics in AI Alignment

Total Score (7.50/10)

Total Score Analysis: Impact (8.5/10) improves interaction. Feasibility (8.0/10) builds on research. Uniqueness (8.5/10) uses behavioral insights. Scalability (8.5/10) applies widely. Auditability (8.0/10) allows testing. Sustainability (8.0/10) evolves. Pdoom (0.5/10) is low. Cost (5.0/10) is moderate.

Description: Using behavioral economics for ASI alignment.

Nudge Theory for AI Design: Score (7.80/10)

Behavioral Reward Modeling: Score (7.60/10)

Cognitive Bias Mitigation: Score (7.70/10)

Economic Incentives for AI Alignment

Total Score (7.57/10)

Total Score Analysis: Impact (8.5/10) offers frameworks. Feasibility (8.0/10) needs collaboration. Uniqueness (8.5/10) uses economics. Scalability (9.0/10) handles systems. Auditability (8.0/10) allows analysis. Sustainability (8.5/10) endures. Pdoom (1.0/10) is low. Cost (5.5/10) is moderate.

Description: Designing economic incentives for ASI alignment.

Windfall Clause (FHI): Score (7.80/10)

Mechanism Design for AI Safety: Score (7.70/10)

AI Alignment via Bargaining: Score (7.60/10)

Hardware-Based AI Safety

Total Score (7.52/10)

Total Score Analysis: Impact (9.0/10) enforces safety. Feasibility (7.5/10) advances with tech. Uniqueness (9.5/10) is distinct. Scalability (8.5/10) standardizes. Auditability (8.0/10) allows checks. Sustainability (8.0/10) persists. Pdoom (0.5/10) is low. Cost (7.0/10) is high.

Description: Enforcing ASI safety via hardware.

Trusted Execution Environments: Score (7.60/10)

Hardware Anomaly Detection: Score (7.40/10)

Secure AI Processing Units: Score (7.30/10)

Data Curation for AI Alignment

Total Score (7.64/10)

Total Score Analysis: Impact (8.5/10) shapes ASI behavior. Feasibility (9.0/10) uses data practices. Uniqueness (7.0/10) overlaps methods. Scalability (9.0/10) fits large datasets. Auditability (9.0/10) allows inspection. Sustainability (8.0/10) needs curation. Pdoom (1.0/10) reduces risks. Cost (5.0/10) is moderate.

Description: Curating data for ASI alignment.

OpenAI's Data Curation Efforts: Score (8.00/10)

Anthropic's Data Selection: Score (7.90/10)

Google's Data Curation: Score (7.80/10)

Control Theory for AI Alignment

Total Score (7.60/10)

Total Score Analysis: Impact (9.0/10) ensures stability. Feasibility (8.0/10) needs interdisciplinary work. Uniqueness (8.5/10) offers distinct methods. Scalability (8.5/10) fits complex systems. Auditability (8.5/10) allows monitoring. Sustainability (8.5/10) persists. Pdoom (0.5/10) is low. Cost (6.0/10) is moderate.

Description: Using control theory for ASI safety.

Feedback Control for ASI: Score (8.00/10)

Control Theory Research Group: Score (7.90/10)

DeepMind's Control Applications: Score (7.80/10)

Organizational Safety Practices

Total Score (7.50/10)

Total Score Analysis: Impact (9.0/10) shapes ASI development. Feasibility (8.0/10) needs cultural shifts. Uniqueness (7.5/10) overlaps governance. Scalability (9.0/10) applies widely. Auditability (7.0/10) is challenging. Sustainability (9.0/10) maintains practices. Pdoom (1.0/10) reduces risks. Cost (5.5/10) is moderate.

Description: Prioritizing ASI safety in organizations.

Anthropic's Safety-First Culture: Score (7.70/10)

OpenAI's Safety Governance: Score (7.60/10)

Microsoft's Responsible AI: Score (7.50/10)

Coherent Extrapolated Volition

Total Score (7.50/10)

Total Score Analysis: Impact (9.5/10) aligns with idealized values. Feasibility (7.0/10) is theoretical. Uniqueness (9.0/10) is distinct. Scalability (9.0/10) fits advanced ASI. Auditability (6.0/10) is subjective. Sustainability (9.0/10) focuses long-term. Pdoom (0.5/10) is low. Cost (5.5/10) reflects research.

Description: Aligning ASI with extrapolated human values.

MIRI's CEV Research: Score (7.80/10)

FHI Value Extrapolation: Score (7.70/10)

Value Inference Models: Score (7.60/10)

Public Engagement for ASI Alignment

Total Score (7.55/10)

Total Score Analysis: Impact (8.0/10) ensures societal alignment. Feasibility (8.5/10) uses platforms. Uniqueness (7.5/10) complements advocacy. Scalability (9.0/10) reaches globally. Auditability (8.5/10) tracks engagement. Sustainability (8.0/10) needs effort. Pdoom (0.5/10) is low. Cost (4.5/10) is moderate.

Description: Engaging public in ASI alignment decisions.

ASI Safety Town Halls: Score (7.90/10)

Crowdsourced Alignment Surveys: Score (7.80/10)

ASI Educational Campaigns: Score (7.70/10)

ASI Security and Integrity

Total Score (8.10/10)

Total Score Analysis: Impact (9.5/10) secures ASI operations. Feasibility (8.5/10) advances with tech. Uniqueness (9.0/10) targets security. Scalability (9.0/10) applies widely. Auditability (9.0/10) allows checks. Sustainability (9.0/10) maintains security. Pdoom (0.5/10) is low. Cost (5.5/10) reflects needs.

Description: Securing ASI from threats and failures.

DARPA's Assured Autonomy: Score (8.50/10)

NIST AI Security Group: Score (8.30/10)

OpenMined's PySyft: Score (8.20/10)

Multi-Agent Alignment Strategies

Total Score (7.67/10)

Total Score Analysis: Impact (9.0/10) ensures coordination. Feasibility (8.0/10) needs advanced work. Uniqueness (8.5/10) targets multi-agent issues. Scalability (9.0/10) fits large systems. Auditability (8.5/10) allows monitoring. Sustainability (8.5/10) persists. Pdoom (0.7/10) is low. Cost (5.5/10) is moderate.

Description: Coordinating multiple ASI systems for alignment.

DeepMind's Multi-Agent RL: Score (8.00/10)

FHI's Cooperative AI Program: Score (7.90/10)

Stanford Multi-Agent Lab: Score (7.80/10)

AI Safety in Healthcare

Total Score (8.42/10)

Total Score Analysis: Impact (9.8/10) ensures safe medical AI. Feasibility (9.0/10) leverages current tech. Uniqueness (8.5/10) targets healthcare. Scalability (9.0/10) applies to systems. Auditability (9.0/10) ensures compliance. Sustainability (9.0/10) persists with need. Pdoom (0.5/10) is low. Cost (5.0/10) reflects specialization.

Description: Ensuring AI safety in healthcare applications.

Safe AI for Medical Diagnosis: Score (8.60/10)

Ethical AI in Patient Care: Score (8.50/10)

DeepMind Health Safety: Score (8.40/10)

AI Safety in Autonomous Systems

Total Score (8.37/10)

Total Score Analysis: Impact (9.7/10) ensures safe autonomy. Feasibility (8.8/10) builds on tech. Uniqueness (8.5/10) targets autonomy. Scalability (9.0/10) applies to vehicles. Auditability (9.0/10) allows checks. Sustainability (9.0/10) persists. Pdoom (0.5/10) is low. Cost (5.5/10) reflects complexity.

Description: Ensuring safety in autonomous ASI systems.

Safe Autonomous Vehicle Control: Score (8.60/10)

Tesla Autopilot Safety: Score (8.50/10)

Mobileye Safety Systems: Score (8.40/10)

Inclusive Value Alignment

Total Score (8.20/10)

Total Score Analysis: Impact (9.5/10) addresses value diversity. Feasibility (8.5/10) uses participatory methods. Uniqueness (8.0/10) focuses on inclusivity. Scalability (9.0/10) applies globally. Auditability (8.5/10) tracks representation. Sustainability (9.0/10) fosters equity. Pdoom (0.5/10) is low. Cost (5.0/10) reflects engagement.

Description: Ensuring ASI aligns with diverse human values.

Participatory Alignment Project: Score (8.50/10)

Cross-Cultural AI Ethics Initiative: Score (8.40/10)

Global Values Aggregation Platform: Score (8.30/10)

B

Quantum Computing for ASI Alignment

Total Score (6.69/10)

Total Score Analysis: Impact (9.5/10) could revolutionize alignment. Feasibility (6.5/10) is early-stage. Uniqueness (9.5/10) is unique. Scalability (9.0/10) handles complexity. Auditability (6.0/10) is challenging. Sustainability (7.0/10) depends on tech. Pdoom (0.5/10) is low. Cost (8.0/10) is high.

Description: Using quantum computing for ASI alignment.

Quantum Algorithms for Alignment: Score (7.00/10)

Quantum ML for Interpretability: Score (6.95/10)

Quantum Simulations: Score (6.90/10)

Ontological Safety in ASI

Total Score (6.57/10)

Total Score Analysis: Impact (9.0/10) prevents misinterpretations. Feasibility (7.0/10) is promising. Uniqueness (9.0/10) is distinct. Scalability (8.0/10) applies to systems. Auditability (6.0/10) is challenging. Sustainability (7.0/10) needs research. Pdoom (1.0/10) reduces risks. Cost (6.5/10) is high.

Description: Ensuring ASI understands human concepts.

MIRI Ontological Crisis Research: Score (7.00/10)

FHI Conceptual Alignment: Score (6.80/10)

Category Theory for ASI: Score (6.50/10)

Recursive Self-Improvement Safety

Total Score (6.60/10)

Total Score Analysis: Impact (9.5/10) is critical for safety. Feasibility (7.0/10) is theoretical. Uniqueness (9.0/10) targets specific issues. Scalability (8.0/10) fits self-improving systems. Auditability (6.0/10) is difficult. Sustainability (9.0/10) is long-term. Pdoom (3.0/10) reflects risks. Cost (5.0/10) is moderate.

Description: Maintaining alignment during ASI self-improvement.

MIRI's Tiling Agents: Score (8.00/10)

Self-Improvement Safety Models: Score (7.95/10)

FHI Recursive Safety: Score (7.90/10)

Evolutionary Algorithms for ASI Alignment

Total Score (7.35/10)

Total Score Analysis: Impact (8.5/10) offers robust solutions. Feasibility (7.0/10) is theoretical. Uniqueness (9.0/10) is distinct. Scalability (8.0/10) fits systems. Auditability (8.0/10) uses simulations. Sustainability (8.0/10) evolves. Pdoom (0.5/10) is low. Cost (5.0/10) is moderate.

Description: Guiding ASI alignment with evolutionary principles.

Evolutionary Strategies for Safety: Score (7.60/10)

Co-Evolution of ASI and Values: Score (7.55/10)

Genetic Algorithms for ASI Safety: Score (7.50/10)

ASI and Anthropology

Total Score (7.18/10)

Total Score Analysis: Impact (8.5/10) aids global alignment. Feasibility (7.0/10) needs interdisciplinary work. Uniqueness (9.0/10) offers cultural insights. Scalability (8.5/10) spans societies. Auditability (7.5/10) tracks studies. Sustainability (8.0/10) remains relevant. Pdoom (1.0/10) is low. Cost (5.5/10) is moderate.

Description: Studying human cultures for ASI alignment.

Cultural AI Alignment Project: Score (7.50/10)

Anthropological Value Studies: Score (7.40/10)

FHI Cultural Alignment Research: Score (7.30/10)

C

D

E

F

Naive Alignment Assumptions

Total Score (1.00/10)

Total Score Analysis: Impact (1.0/10) offers little benefit. Feasibility (10.0/10) is easy but ineffective. Uniqueness (2.0/10) is common. Scalability (1.0/10) doesn't scale. Auditability (1.0/10) is hard to verify. Sustainability (1.0/10) is not sustainable. Pdoom (9.0/10) increases risk. Cost (1.0/10) is low but irrelevant.

Description: Approaches based on incorrect or oversimplified beliefs about ASI alignment.

Market-Driven Alignment: Belief that economic incentives will naturally lead to aligned ASI. Score (1.10/10)

Technological Determinism: Assuming ASI will inherently be beneficial. Score (1.05/10)

Anthropomorphic Alignment: Expecting ASI to share human values by default. Score (1.00/10)

Reckless Capability Acceleration

Total Score (1.00/10)

Total Score Analysis: Impact (1.0/10) is harmful. Feasibility (10.0/10) is easy but dangerous. Uniqueness (2.0/10) is unfortunately common. Scalability (1.0/10) increases risk. Auditability (1.0/10) is poor. Sustainability (1.0/10) is not sustainable. Pdoom (9.5/10) is high. Cost (2.0/10) varies.

Description: Pursuing rapid ASI development without safety measures.

Unregulated ASI Research Labs: Score (1.20/10)

Competitive AI Arms Races: Score (1.15/10)

Ignoring Alignment Research: Score (1.10/10)