Grok 3
S
Mechanistic Interpretability
Total Score (9.09/10)
Total Score Analysis: Impact (9.9/10) drives transparency breakthroughs, potentially solving core challenges like deception detection. Feasibility (9.5/10) leverages advanced tools, though full ASI interpretation remains challenging. Uniqueness (9.6/10) offers a distinct mechanistic focus. Scalability (9.6/10) grows with automation but may face complexity limits. Auditability (9.7/10) ensures oversight by design. Sustainability (9.6/10) advances with growing research interest. Pdoom (0.1/10) is negligible as it reduces risks. Cost (5.5/10) reflects high computational demands.
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)
Robustness and Reliability in ASI
Total Score (9.07/10)
Total Score Analysis: Impact (9.8/10) ensures dependable ASI systems, critical for safe deployment. Feasibility (9.7/10) reflects strong empirical progress in testing methodologies. Uniqueness (9.2/10) targets robustness distinctly. Scalability (9.6/10) applies widely with automation advances. Auditability (9.4/10) enables reliable checks. Sustainability (9.5/10) grows with industry adoption. Pdoom (0.3/10) is minimal. Cost (4.5/10) is moderate due to safety focus.
DeepMind's Robustness Research: Score (9.20/10)
Anthropic's Reliability Initiatives: Score (9.10/10)
OpenAI's Safety Testing: Score (9.00/10)
Scalable Oversight Mechanisms
Total Score (9.02/10)
Total Score Analysis: Impact (9.8/10) enables robust control over ASI, addressing scalable oversight challenges. Feasibility (9.6/10) integrates well with existing systems. Uniqueness (9.3/10) pioneers oversight methods. Scalability (9.6/10) excels broadly with adaptation. Auditability (9.4/10) ensures reliable monitoring. Sustainability (9.4/10) persists with research. Pdoom (0.3/10) is low. Cost (5.0/10) reflects complexity.
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)
Value Alignment Methods
Total Score (9.10/10)
Total Score Analysis: Impact (9.8/10) ensures ethical ASI, tackling core alignment challenges. Feasibility (9.5/10) advances with proven techniques like RLHF. Uniqueness (9.1/10) blends diverse approaches effectively. Scalability (9.4/10) applies widely across systems. Auditability (9.0/10) tracks alignment progress. Sustainability (9.2/10) maintains ethical standards. Pdoom (0.5/10) is low. Cost (4.0/10) is moderate.
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)
A
ASI Governance and Policy
Total Score (8.62/10)
Total Score Analysis: Impact (9.5/10) shapes global standards, critical for safe ASI deployment. Feasibility (9.0/10) benefits from recent international frameworks despite geopolitical challenges. Uniqueness (8.0/10) overlaps with other efforts but offers distinct frameworks. Scalability (9.5/10) spans nations with growing cooperation. Auditability (9.6/10) ensures compliance clarity. Sustainability (9.5/10) endures with institutional support. Pdoom (0.5/10) mitigates risks if successful. Cost (5.0/10) reflects complexity.
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)
ASI Safety Standards and Certification
Total Score (9.00/10)
Total Score Analysis: Impact (9.8/10) ensures broad safety across ASI systems. Feasibility (9.6/10) advances with increasing standardization efforts. Uniqueness (8.5/10) focuses on certification distinctly. Scalability (9.5/10) applies globally. Auditability (9.5/10) enforces compliance effectively. Sustainability (9.0/10) evolves with updates. Pdoom (0.5/10) reduces risks. Cost (4.5/10) reflects implementation effort.
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)
AI Safety Advocacy & Communication
Total Score (8.81/10)
Total Score Analysis: Impact (9.7/10) boosts awareness, enabling broader safety efforts. Feasibility (9.6/10) excels with digital outreach. Uniqueness (8.9/10) varies by approach. Scalability (9.6/10) reaches globally. Auditability (9.0/10) tracks engagement impact. Sustainability (9.3/10) grows with support. Pdoom (0.9/10) is low. Cost (2.5/10) is efficient.
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 for alignment. Feasibility (9.0/10) leverages collaboration across fields. Uniqueness (9.2/10) stands out with interdisciplinary methods. 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 needs.
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 critical expertise for alignment. Feasibility (9.5/10) uses established programs. Uniqueness (9.0/10) focuses on skill development. Scalability (9.4/10) expands globally. Auditability (9.4/10) tracks progress effectively. Sustainability (9.4/10) persists with demand. Pdoom (0.3/10) is low. Cost (4.0/10) is moderate.
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 essential research. Feasibility (9.6/10) grows with donor support. Uniqueness (8.7/10) overlaps with philanthropy but targets safety. Scalability (9.5/10) scales effectively. Auditability (9.5/10) tracks funding impact. Sustainability (9.5/10) rises with interest. Pdoom (0.3/10) is low. Cost (5.5/10) reflects scale.
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 (8.38/10)
Total Score Analysis: Impact (9.7/10) speeds safety solutions by accelerating research. Feasibility (8.0/10) relies on aligned AI, introducing dependency risks. Uniqueness (9.4/10) stands out with recursive AI use. Scalability (9.5/10) scales with computational power. Auditability (9.0/10) ensures iteration but hinges on AI transparency. Sustainability (9.4/10) supports ongoing research. Pdoom (1.0/10) reflects minor risks if assisting AI misaligns. Cost (4.5/10) reflects resource intensity.
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)
Cognitive Approaches to ASI Alignment
Total Score (8.62/10)
Total Score Analysis: Impact (9.8/10) offers novel solutions via cognitive insights. Feasibility (9.0/10) grows with interdisciplinary research. Uniqueness (9.5/10) leverages neuroscience uniquely. Scalability (9.2/10) fits ASI systems. Auditability (9.3/10) enhances oversight. Sustainability (9.0/10) needs continued focus. Pdoom (0.3/10) is low. Cost (5.0/10) reflects effort.
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, a key sub-problem. Feasibility (8.8/10) advances with formal tools. Uniqueness (9.2/10) offers rigorous verification. Scalability (9.0/10) fits complex systems. Auditability (9.5/10) excels in precision. Sustainability (8.8/10) continues with adoption. Pdoom (0.4/10) is low. Cost (5.5/10) reflects complexity.
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 across stakeholders. Feasibility (9.6/10) excels with digital platforms. Uniqueness (8.9/10) varies by delivery method. Scalability (9.5/10) reaches widely. Auditability (9.5/10) tracks educational outcomes. Sustainability (9.5/10) fosters networks. Pdoom (0.2/10) is low. Cost (3.0/10) is efficient.
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, a critical sub-problem. Feasibility (9.4/10) advances with technology. Uniqueness (9.1/10) targets runtime protection. Scalability (9.2/10) applies widely. Auditability (9.3/10) tracks dynamically. Sustainability (9.2/10) persists with development. Pdoom (0.4/10) is low. Cost (5.0/10) is moderate.
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 among ASI. Feasibility (9.4/10) uses simulations effectively. Uniqueness (9.2/10) targets cooperative behavior. Scalability (9.2/10) scales with systems. Auditability (9.3/10) tracks interactions. Sustainability (9.2/10) persists with research. Pdoom (0.5/10) is low. Cost (5.0/10) is moderate.
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) identifies vulnerabilities proactively. Feasibility (8.5/10) leverages expertise effectively. Uniqueness (9.2/10) targets risk assessment. Scalability (9.3/10) grows with testing scope. Auditability (9.4/10) tracks flaws. Sustainability (9.3/10) persists with need. Pdoom (0.4/10) is low. Cost (5.0/10) justifies outcomes.
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 via hybrid reasoning. Feasibility (8.5/10) is promising with current progress. Uniqueness (9.5/10) combines neural and symbolic uniquely. Scalability (8.5/10) fits systems with adaptation. Auditability (9.0/10) boosts transparency. Sustainability (8.5/10) needs ongoing research. Pdoom (0.5/10) is low. Cost (5.0/10) is moderate.
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 accuracy. Feasibility (8.0/10) faces practical challenges. Uniqueness (9.0/10) offers specific verification methods. Scalability (9.0/10) applies broadly. Auditability (9.5/10) requires high precision. Sustainability (9.0/10) persists with refinement. Pdoom (0.5/10) is low. Cost (5.5/10) reflects effort.
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 fundamentally. Feasibility (9.3/10) advances with mathematical rigor. Uniqueness (9.5/10) tackles unique decision-making issues. Scalability (8.7/10) applies gradually to ASI. Auditability (9.5/10) ensures clarity. Sustainability (9.3/10) thrives with focus. Pdoom (0.5/10) is low. Cost (5.0/10) is moderate.
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 during learning. Feasibility (9.4/10) uses simulations effectively. Uniqueness (9.3/10) prioritizes safe exploration. Scalability (9.1/10) applies to training broadly. Auditability (9.2/10) tracks safely. Sustainability (9.2/10) refines with tech. Pdoom (0.5/10) is low. Cost (5.0/10) is moderate.
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 effectively. Feasibility (8.0/10) requires interdisciplinary effort. Uniqueness (9.2/10) focuses on future safety. Scalability (8.8/10) applies globally over time. Auditability (8.0/10) tracks progress with difficulty. Sustainability (9.3/10) ensures long-term focus. Pdoom (0.7/10) reduces risks. Cost (5.0/10) reflects broad needs.
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 safety metrics. Feasibility (9.3/10) grows with data availability. Uniqueness (8.7/10) focuses on evaluation distinctly. Scalability (8.9/10) applies across ASI systems. Auditability (9.3/10) excels in measurement. Sustainability (8.5/10) needs regular updates. Pdoom (0.7/10) is low. Cost (5.0/10) is moderate.
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 effectively. Feasibility (9.5/10) grows with robust methods. Uniqueness (8.8/10) targets adversarial protection. Scalability (9.2/10) adapts broadly. Auditability (9.1/10) ensures reliability. Sustainability (8.9/10) requires upkeep. Pdoom (0.5/10) is low. Cost (5.5/10) is moderate.
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, enhancing safety. Feasibility (9.4/10) advances with system design. Uniqueness (9.1/10) focuses on capability bounds. Scalability (9.0/10) applies to various systems. Auditability (9.3/10) tracks limits effectively. Sustainability (9.0/10) persists with refinement. Pdoom (0.6/10) is low. Cost (5.0/10) is moderate.
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 via correctability. Feasibility (8.4/10) progresses with theoretical work. Uniqueness (8.9/10) targets corrigibility uniquely. Scalability (8.9/10) applies broadly. Auditability (8.4/10) ensures clarity with effort. Sustainability (8.9/10) persists with focus. Pdoom (0.5/10) is low. Cost (4.5/10) is moderate.
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 goal alignment issues. Feasibility (7.9/10) advances with ongoing research. Uniqueness (9.1/10) addresses specific risks. Scalability (8.9/10) applies to systems broadly. Auditability (7.9/10) remains theoretical. Sustainability (8.9/10) continues with effort. Pdoom (0.4/10) is low. Cost (5.0/10) reflects complexity.
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 causal 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 with progress. Pdoom (0.5/10) is low. Cost (5.0/10) is moderate.
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 (8.21/10)
Total Score Analysis: Impact (9.0/10) builds trust and aids alignment. Feasibility (8.5/10) advances with research. Uniqueness (8.5/10) targets explainability distinctly. 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) is moderate.
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 implementation. Uniqueness (8.5/10) targets operational safety. Scalability (9.2/10) is key for deployment. Auditability (9.0/10) allows monitoring. Sustainability (8.8/10) requires focus. Pdoom (0.6/10) is low. Cost (5.5/10) is notable.
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 human-ASI interaction. Feasibility (8.5/10) needs interdisciplinary effort. Uniqueness (8.0/10) focuses on collaborative 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) is moderate.
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 with simulations. Uniqueness (9.0/10) offers virtual testing distinctly. Scalability (9.0/10) grows with compute power. Auditability (9.0/10) allows detailed analysis. Sustainability (9.0/10) persists with tech advances. Pdoom (0.5/10) is low. Cost (5.5/10) is moderate.
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 under uncertainty. Feasibility (8.0/10) is promising with research. Uniqueness (8.5/10) targets uncertainty distinctly. Scalability (9.0/10) integrates broadly. Auditability (8.5/10) allows monitoring. Sustainability (9.0/10) evolves with progress. Pdoom (0.5/10) is low. Cost (5.5/10) is moderate.
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 for safety. Feasibility (9.0/10) leverages open-source communities. Uniqueness (7.0/10) is method-based, not conceptually unique. Scalability (9.5/10) reaches globally. Auditability (9.5/10) ensures transparency. Sustainability (9.0/10) thrives on community support. Pdoom (1.0/10) is low but carries dual-use risks. Cost (4.0/10) is efficient.
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 via debate. Feasibility (8.5/10) advances with research progress. Uniqueness (9.5/10) is distinct in approach. Scalability (9.5/10) fits ASI systems. Auditability (9.0/10) allows oversight. Sustainability (9.0/10) persists with development. Pdoom (0.3/10) is low. Cost (5.0/10) is moderate.
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)
Global Ethical Consensus for ASI
Total Score (7.62/10)
Total Score Analysis: Impact (9.5/10) shapes ASI ethics globally. Feasibility (7.5/10) faces diplomatic challenges. Uniqueness (8.5/10) targets ethical consensus. Scalability (9.0/10) applies globally. Auditability (8.0/10) monitors agreements. Sustainability (9.0/10) ensures ethical focus. Pdoom (1.0/10) reduces risks. Cost (5.5/10) reflects effort.
IEEE Ethically Aligned Design: Score (8.00/10)
Asilomar AI Principles: Score (7.90/10)
Montreal Declaration: Score (7.85/10)
Inverse Reinforcement Learning for ASI
Total Score (7.60/10)
Total Score Analysis: Impact (9.5/10) addresses value learning effectively. Feasibility (7.5/10) is promising but unproven at ASI scale. Uniqueness (8.5/10) is specific to IRL. Scalability (9.0/10) applies broadly. Auditability (8.0/10) inspects reward models. Sustainability (8.5/10) needs further learning. Pdoom (0.5/10) is low. Cost (6.0/10) reflects computational needs.
Cooperative IRL (CIRL): Score (8.00/10)
DeepMind's IRL Research: Score (7.90/10)
Stanford's Value Learning Project: Score (7.80/10)
Data Curation for AI Alignment
Total Score (7.64/10)
Total Score Analysis: Impact (8.5/10) shapes ASI behavior via data. Feasibility (9.0/10) uses established data practices. Uniqueness (7.0/10) overlaps with other methods. Scalability (9.0/10) fits large datasets. Auditability (9.0/10) allows inspection. Sustainability (8.0/10) needs ongoing curation. Pdoom (1.0/10) reduces risks slightly. Cost (5.0/10) is moderate.
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)
ASI Security and Integrity
Total Score (8.10/10)
Total Score Analysis: Impact (9.5/10) secures ASI operations from threats. Feasibility (8.5/10) advances with security tech. Uniqueness (9.0/10) targets integrity distinctly. Scalability (9.0/10) applies widely. Auditability (9.0/10) allows checks. Sustainability (9.0/10) maintains security focus. Pdoom (0.5/10) is low. Cost (5.5/10) reflects needs.
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 among ASI systems. Feasibility (8.0/10) needs advanced multi-agent work. Uniqueness (8.5/10) targets multi-agent challenges. Scalability (9.0/10) fits large systems. Auditability (8.5/10) allows monitoring. Sustainability (8.5/10) persists with research. Pdoom (0.7/10) is low. Cost (5.5/10) is moderate.
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 applications. Feasibility (9.0/10) leverages current healthcare tech. Uniqueness (8.5/10) targets healthcare specifically. Scalability (9.0/10) applies to medical systems. Auditability (9.0/10) ensures compliance. Sustainability (9.0/10) persists with healthcare needs. Pdoom (0.5/10) is low. Cost (5.0/10) reflects specialization.
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 in critical systems. Feasibility (8.8/10) builds on existing tech. Uniqueness (8.5/10) targets autonomous safety. Scalability (9.0/10) applies to vehicles and beyond. Auditability (9.0/10) allows checks. Sustainability (9.0/10) persists with demand. Pdoom (0.5/10) is low. Cost (5.5/10) reflects complexity.
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 comprehensively. Feasibility (8.5/10) uses participatory methods effectively. 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 long-term. Pdoom (0.5/10) is low. Cost (5.0/10) reflects engagement efforts.
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
Moral Uncertainty in AI Alignment
Total Score (7.49/10)
Total Score Analysis: Impact (9.0/10) handles value conflicts effectively but doesn't directly solve core technical challenges. Feasibility (7.5/10) advances with philosophical and computational progress, yet practical implementation remains uncertain. Uniqueness (8.5/10) targets a neglected aspect of alignment. Scalability (9.0/10) applies broadly across ASI systems. Auditability (7.0/10) is limited by subjective evaluation needs. Sustainability (9.0/10) remains relevant with ongoing ethical debates. Pdoom (0.5/10) is low as it aims to mitigate risks. Cost (5.0/10) is moderate, requiring interdisciplinary research.
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)
Behavioral Economics in AI Alignment
Total Score (7.48/10)
Total Score Analysis: Impact (8.5/10) improves interaction design but is peripheral to core alignment problems. Feasibility (8.0/10) builds on established research, though ASI-specific applications are unproven. Uniqueness (8.5/10) leverages distinct behavioral insights. Scalability (8.5/10) applies to human-AI interfaces widely. Auditability (8.0/10) allows empirical testing. Sustainability (8.0/10) evolves with behavioral science. Pdoom (0.5/10) is low, with minimal risk increase. Cost (5.0/10) is moderate, requiring interdisciplinary effort.
Nudge Theory for AI Design: Score (7.80/10)
Behavioral Reward Modeling: Score (7.60/10)
Cognitive Bias Mitigation: Score (7.70/10)
Hardware-Based AI Safety
Total Score (7.45/10)
Total Score Analysis: Impact (9.0/10) enforces safety at a foundational level, though not a complete solution. Feasibility (7.5/10) advances with hardware tech but faces integration challenges. Uniqueness (9.5/10) offers a rare hardware-centric approach. Scalability (8.5/10) standardizes across systems with adoption. Auditability (8.0/10) allows physical verification. Sustainability (8.0/10) persists with tech development. Pdoom (0.5/10) is low, reducing risks. Cost (7.0/10) is high due to specialized hardware needs.
Trusted Execution Environments: Score (7.60/10)
Hardware Anomaly Detection: Score (7.40/10)
Secure AI Processing Units: Score (7.30/10)
Organizational Safety Practices
Total Score (7.40/10)
Total Score Analysis: Impact (9.0/10) shapes safe ASI development practices, though indirect. Feasibility (8.0/10) requires cultural shifts, achievable with effort. Uniqueness (7.5/10) overlaps with governance but focuses on internal culture. Scalability (9.0/10) applies across organizations. Auditability (7.0/10) is challenging due to internal variability. Sustainability (9.0/10) maintains safety focus. Pdoom (1.0/10) slightly increases if poorly implemented. Cost (5.5/10) reflects training and policy costs.
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 (7.43/10)
Total Score Analysis: Impact (9.5/10) aligns with idealized human values, a high-leverage goal. Feasibility (7.0/10) is theoretical, lacking clear implementation paths. Uniqueness (9.0/10) offers a distinct philosophical approach. Scalability (9.0/10) fits advanced ASI conceptually. Auditability (6.0/10) is subjective and hard to verify. Sustainability (9.0/10) focuses on long-term alignment. Pdoom (0.5/10) is low, aiming to reduce risks. Cost (5.5/10) reflects theoretical research needs.
MIRI's CEV Research: Score (7.80/10)
FHI Value Extrapolation: Score (7.70/10)
Value Inference Models: Score (7.60/10)
C
Ontological Safety in ASI
Total Score (5.80/10)
Total Score Analysis: Impact (7.0/10) prevents misinterpretations but addresses a niche sub-problem rather than core alignment issues. Feasibility (5.0/10) is moderate, with theoretical challenges and limited empirical progress. Uniqueness (9.0/10) offers distinct conceptual insights. Scalability (8.0/10) applies to advanced systems conceptually. Auditability (6.0/10) is difficult due to its abstract nature. Sustainability (7.0/10) requires ongoing theoretical research. Pdoom (1.0/10) reduces specific risks but carries opportunity costs. Cost (6.5/10) reflects specialized research needs.
MIRI Ontological Crisis Research: Score (6.00/10)
FHI Conceptual Alignment: Score (5.90/10)
Category Theory for ASI: Score (5.80/10)
Recursive Self-Improvement Safety
Total Score (5.90/10)
Total Score Analysis: Impact (8.0/10) is critical for maintaining alignment during self-improvement, a key sub-problem. Feasibility (5.0/10) is low due to its theoretical nature and lack of practical solutions. Uniqueness (9.0/10) targets a specific, underexplored challenge. Scalability (8.0/10) fits self-improving systems conceptually. Auditability (6.0/10) is challenging due to complexity. Sustainability (9.0/10) focuses on long-term safety research. Pdoom (3.0/10) reflects inherent risks if unsolved. Cost (5.0/10) is moderate, requiring theoretical effort.
MIRI's Tiling Agents: Score (6.10/10)
Self-Improvement Safety Models: Score (6.00/10)
FHI Recursive Safety: Score (5.90/10)
Recursive Self-Improvement Safety
Total Score (5.90/10)
Total Score Analysis: Impact (8.0/10) is critical for maintaining alignment during self-improvement, a key sub-problem. Feasibility (5.0/10) is low due to its theoretical nature and lack of practical solutions. Uniqueness (9.0/10) targets a specific, underexplored challenge. Scalability (8.0/10) fits self-improving systems conceptually. Auditability (6.0/10) is challenging due to complexity. Sustainability (9.0/10) focuses on long-term safety research. Pdoom (3.0/10) reflects inherent risks if unsolved. Cost (5.0/10) is moderate, requiring theoretical effort.
MIRI's Tiling Agents: Score (6.10/10)
Self-Improvement Safety Models: Score (6.00/10)
FHI Recursive Safety: Score (5.90/10)
Ontological Safety in ASI
Total Score (5.80/10)
Total Score Analysis: Impact (7.0/10) prevents misinterpretations but addresses a niche sub-problem rather than core alignment issues. Feasibility (5.0/10) is moderate, with theoretical challenges and limited empirical progress. Uniqueness (9.0/10) offers distinct conceptual insights. Scalability (8.0/10) applies to advanced systems conceptually. Auditability (6.0/10) is difficult due to its abstract nature. Sustainability (7.0/10) requires ongoing theoretical research. Pdoom (1.0/10) reduces specific risks but carries opportunity costs. Cost (6.5/10) reflects specialized research needs.
MIRI Ontological Crisis Research: Score (6.00/10)
FHI Conceptual Alignment: Score (5.90/10)
Category Theory for ASI: Score (5.80/10)
D
ASI and Anthropology
Total Score (4.47/10)
Total Score Analysis: Impact (6.0/10) aids value alignment culturally but is secondary to technical solutions. Feasibility (5.0/10) requires interdisciplinary effort with uncertain ASI applicability. Uniqueness (9.0/10) provides rare cultural insights. Scalability (8.5/10) spans diverse societies conceptually. Auditability (7.5/10) tracks qualitative data. Sustainability (8.0/10) remains relevant culturally. Pdoom (1.0/10) is low but carries opportunity costs. Cost (5.5/10) reflects research demands.
Cultural AI Alignment Project: Score (7.50/10)
Anthropological Value Studies: Score (7.40/10)
FHI Cultural Alignment Research: Score (7.30/10)
Evolutionary Algorithms for ASI Alignment
Total Score (4.42/10)
Total Score Analysis: Impact (5.0/10) offers speculative robust solutions, lacking direct relevance to core challenges. Feasibility (6.0/10) is moderate but unproven for ASI complexity. Uniqueness (9.0/10) provides a distinct evolutionary approach. Scalability (8.0/10) applies theoretically. Auditability (8.0/10) benefits from simulations. Sustainability (8.0/10) evolves with compute. Pdoom (0.5/10) is low. Cost (5.0/10) leverages existing tools.
Evolutionary Strategies for Safety: Score (5.80/10)
Co-Evolution of ASI and Values: Score (5.70/10)
Genetic Algorithms for ASI Safety: Score (5.60/10)
Quantum Computing for ASI Alignment
Total Score (4.33/10)
Total Score Analysis: Impact (8.0/10) could revolutionize alignment if successful, but relevance is speculative. Feasibility (3.0/10) is low due to nascent quantum tech and unclear applicability. Uniqueness (9.5/10) explores a novel paradigm. Scalability (9.0/10) could handle complexity if realized. Auditability (6.0/10) is challenging due to quantum nature. Sustainability (7.0/10) depends on tech progress. Pdoom (0.5/10) is low. Cost (8.0/10) is high.
Quantum Algorithms for Alignment: Score (5.50/10)
Quantum ML for Interpretability: Score (5.40/10)
Quantum Simulations: Score (5.30/10)
Control Theory for AI Alignment
Total Score (4.40/10)
Total Score Analysis: Impact (6.0/10) ensures stability but lacks direct ASI relevance. Feasibility (5.0/10) needs significant adaptation from traditional control. Uniqueness (8.5/10) offers distinct methods. Scalability (8.5/10) fits complex systems theoretically. Auditability (8.5/10) allows monitoring conceptually. Sustainability (8.5/10) persists with research. Pdoom (0.5/10) is low. Cost (6.0/10) reflects interdisciplinary needs.
Feedback Control for ASI: Score (8.00/10)
Control Theory Research Group: Score (7.90/10)
DeepMind's Control Applications: Score (7.80/10)
E
Public Engagement for ASI Alignment
Total Score (2.90/10)
Total Score Analysis: Impact (4.0/10) has limited direct effect on technical alignment, often misdirecting focus. Feasibility (8.5/10) uses platforms easily but lacks depth. Uniqueness (7.5/10) complements advocacy but isn’t novel. Scalability (9.0/10) reaches globally but superficially. Auditability (8.5/10) tracks engagement, not outcomes. Sustainability (8.0/10) needs continuous effort. Pdoom (6.0/10) increases via misinformation risks. Cost (4.5/10) is moderate.
ASI Safety Town Halls: Score (7.90/10)
Crowdsourced Alignment Surveys: Score (7.80/10)
ASI Educational Campaigns: Score (7.70/10)
AI Alignment Prizes
Total Score (2.85/10)
Total Score Analysis: Impact (4.0/10) spurs innovation superficially, not core solutions. Feasibility (6.0/10) uses competition but lacks focus. Uniqueness (8.0/10) targets prizes distinctly. Scalability (9.0/10) reaches globally but ineffectively. Auditability (8.5/10) tracks entries, not impact. Sustainability (8.0/10) depends on funding. Pdoom (5.0/10) diverts resources. Cost (4.0/10) is efficient but misdirected.
ASI Safety Competition: Score (7.85/10)
FLI AI Safety Prizes: Score (7.80/10)
Alignment Challenge Prizes: Score (7.75/10)
Differential Technological Development
Total Score (2.80/10)
Total Score Analysis: Impact (5.0/10) prioritizes safety conceptually but lacks practical leverage. Feasibility (8.6/10) needs unrealistic coordination. Uniqueness (9.1/10) focuses on sequencing. Scalability (8.4/10) applies globally in theory. Auditability (8.7/10) tracks priorities with difficulty. Sustainability (8.7/10) lasts conceptually. Pdoom (6.0/10) increases via capability acceleration risks. Cost (5.5/10) reflects planning.
F
Naive Alignment Assumptions
Total Score (1.00/10)
Total Score Analysis: Impact (1.0/10) offers little benefit, misaligned with core challenges. Feasibility (10.0/10) is easy but ineffective. Uniqueness (2.0/10) is common among flawed ideas. Scalability (1.0/10) fails to address complexity. Auditability (1.0/10) is unverifiable. Sustainability (1.0/10) collapses under scrutiny. Pdoom (9.0/10) increases risk significantly. Cost (1.0/10) is low but irrelevant.
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, neglecting alignment. Feasibility (10.0/10) is trivially achievable but dangerous. Uniqueness (2.0/10) is common among reckless efforts. Scalability (1.0/10) amplifies risks. Auditability (1.0/10) is poor without safety focus. Sustainability (1.0/10) is unsustainable. Pdoom (9.5/10) is extremely high. Cost (2.0/10) varies but is irrelevant.
Unregulated ASI Research Labs: Score (1.20/10)
Competitive AI Arms Races: Score (1.15/10)
Ignoring Alignment Research: Score (1.10/10)
Unrestricted Open-Source ASI Development
Total Score (1.30/10)
Total Score Analysis: Impact (2.0/10) provides negligible alignment benefit due to lack of safety focus. Feasibility (9.0/10) is high as open-sourcing is straightforward, yet risky. Uniqueness (3.0/10) is common among capability-focused efforts. Scalability (2.0/10) amplifies risks without control. Auditability (2.0/10) is poor due to decentralized nature. Sustainability (2.0/10) fails under misuse potential. Pdoom (9.0/10) significantly increases existential risk via uncontrolled proliferation. Cost (3.0/10) varies but is relatively low.
Ignoring Alignment Research Altogether
Total Score (0.33/10)
Total Score Analysis: Impact (0.5/10) is negligible, offering no alignment progress. Feasibility (10.0/10) is trivially achievable but catastrophic. Uniqueness (1.0/10) is common among reckless actors. Scalability (0.5/10) exacerbates risks exponentially. Auditability (0.5/10) is impossible without safety focus. Sustainability (0.5/10) collapses under consequences. Pdoom (10.0/10) maximizes existential risk by neglecting safety. Cost (1.0/10) is minimal but irrelevant.
Promoting ASI Development for Economic Gain Without Safety Considerations
Total Score (1.00/10)
Total Score Analysis: Impact (1.0/10) is negligible for alignment, focusing on capability over safety. Feasibility (10.0/10) is easy but dangerous. Uniqueness (2.0/10) is common among profit-driven efforts. Scalability (1.0/10) increases risk without mitigation. Auditability (1.0/10) is poor due to lack of safety focus. Sustainability (1.0/10) is unsustainable long-term. Pdoom (9.5/10) is high, prioritizing economics over safety. Cost (2.0/10) varies but is irrelevant.
Corporate ASI Initiatives Without Safety Protocols: Score (1.10/10)
Government-Funded ASI Projects Ignoring Alignment: Score (1.05/10)
Startups Racing to ASI Without Safety Measures: Score (1.00/10)