Grok 3
S
Mechanistic Interpretability
Total Score (9.49/10)
Total Score Analysis: Impact (9.9/10) is critical with breakthroughs. Feasibility (9.7/10) improves with tools. Uniqueness (9.6/10) remains high. Scalability (9.6/10) enhances automation. Auditability (9.7/10) is robust. Sustainability (9.6/10) grows. Pdoom (0.1/10) is negligible. Cost (2.0/10) optimizes.< setbacks>
Description: Decoding AI mechanisms for safety and control.
Anthropic's Interpretability Team: Score (9.70/10)
Advances neural transparency.
Redwood's Causal Scrubbing: Score (9.55/10)
Isolates causal pathways.
Transformer Circuits Research: Score (9.45/10)
Uncovers LLM insights.
OpenAI's Interpretability Research: Score (9.30/10)
Advances ASI transparency.
Google's Transparency Initiatives: Score (9.00/10)
Promotes ASI accountability.
Chris Olah's Interpretability Research: Score (9.50/10)
Pioneering work on neural network representations.
EleutherAI's Interpretability Efforts: Score (8.70/10)
Community-driven interpretability research.
Apollo Research's Interpretability Tools: Score (9.00/10)
Develops tools for neural transparency.
AI-Assisted Alignment Research
Total Score (9.22/10)
Total Score Analysis: Impact (9.7/10) accelerates safety solutions. Feasibility (9.6/10) uses recursive AI. Uniqueness (9.4/10) leverages AI uniquely. Scalability (9.5/10) scales with compute. Auditability (9.5/10) iterates reliably. Sustainability (9.4/10) ensures longevity. Pdoom (0.2/10) is minimal. Cost (2.9/10) optimizes.
Description: AI enhancing alignment methodologies recursively.
ARC's Eliciting Latent Knowledge: Score (9.60/10)
Extracts hidden ASI behaviors.
DeepMind's Recursive Reward Modeling: Score (9.45/10)
Refines rewards iteratively.
Anthropic's AI Safety Research: Score (9.40/10)
Pioneers safe ASI development.
xAI's Alignment Acceleration: Score (9.35/10)
Boosts safety via AI tools.
EleutherAI's Alignment Efforts: Score (9.20/10)
Community-driven alignment research.
Automated Alignment Hypothesis Generation: Score (9.30/10)
Uses AI to generate and test alignment hypotheses.
OpenAI's Superalignment Initiative: Score (9.30/10)
Aims to solve alignment using AI systems.
Conjecture's Cooperative Emergence (CoEm): Score (9.00/10)
Focuses on aligning ASI through cooperative training.
ASI Governance and Policy
Total Score (9.25/10)
Total Score Analysis: Impact (9.8/10) shapes global standards. Feasibility (9.4/10) grows with coalitions. Uniqueness (9.0/10) innovates policy. Scalability (9.2/10) expands globally. Auditability (9.6/10) ensures clarity. Sustainability (9.5/10) endures. Pdoom (0.5/10) mitigates risks. Cost (4.0/10) reflects complexity.
Description: Developing policies for safe ASI deployment globally.
CSER Governance Research: Score (9.20/10)
Studies systemic governance.
FHI Governance of AI Program: Score (9.00/10)
Develops governance frameworks.
Alan Turing Institute AI Ethics: Score (9.10/10)
Develops ethical ASI frameworks.
UN AI Advisory Body: Score (9.10/10)
Shapes global ASI policy.
OECD AI Policy Observatory: Score (8.90/10)
Monitors AI policy trends.
EU AI Act: Score (9.00/10)
Regulatory framework for AI safety and ethics.
Partnership on AI: Score (8.90/10)
Collaborative effort for responsible AI governance.
A
Value Alignment and Ethical Integration
Total Score (9.01/10)
Total Score Analysis: Impact (9.7/10) anchors ASI ethics. Feasibility (9.0/10) improves with data. Uniqueness (9.2/10) varies by method. Scalability (9.4/10) adapts globally. Auditability (9.4/10) ensures clarity. Sustainability (9.3/10) endures. Pdoom (0.3/10) is low. Cost (3.3/10) optimizes.
Description: Frameworks to align ASI with human values and ethics.
CHAI's CIRL: Score (9.45/10)
Learns values collaboratively.
Value Learning through Imitation: Score (9.20/10)
Aligns ASI via human behavior.
Inverse Reinforcement Learning for Value Learning: Score (9.25/10)
Learns human values from behavior.
Human Feedback-Based Alignment
Total Score (8.85/10)
Total Score Analysis: Impact (9.8/10) directly aligns ASI with human values. Feasibility (9.0/10) proven in current models. Uniqueness (9.0/10) leverages human input. Scalability (9.5/10) automates feedback. Auditability (9.0/10) tracks feedback logs. Sustainability (9.0/10) requires ongoing input. Pdoom (0.5/10) minimizes risks. Cost (3.0/10) optimizes human effort.
Description: Aligning ASI through direct human feedback mechanisms.
OpenAI's RLHF: Score (9.00/10)
Reinforcement Learning from Human Feedback.
DeepMind's Human Preference Learning: Score (8.80/10)
Learns from human preferences.
Anthropic's Constitutional AI: Score (9.35/10)
Enforces ethical constraints via feedback.
Cognitive Architectures for Alignment
Total Score (8.90/10)
Total Score Analysis: Impact (9.8/10) offers novel solutions. Feasibility (9.0/10) improves with research. Uniqueness (9.5/10) stands out. Scalability (9.2/10) fits various systems. Auditability (9.3/10) enhances oversight. Sustainability (9.0/10) needs focus. Pdoom (0.3/10) is low. Cost (3.5/10) is moderate.
Description: Designing ASI cognitive structures for easier alignment.
Modular ASI Design Initiative: Score (8.50/10)
Develops modular ASI systems.
Interpretable Cognitive Architectures: Score (8.20/10)
Builds inherently interpretable ASI.
Cognitive Safety Layers: Score (8.00/10)
Adds safety layers to ASI cognition.
Neurosymbolic AI for Ethical Reasoning: Score (8.60/10)
Combines neural and symbolic methods for ethics.
Formal Verification for ASI Safety
Total Score (8.65/10)
Total Score Analysis: Impact (9.7/10) ensures rigorous safety. Feasibility (8.8/10) advances with tools. Uniqueness (9.2/10) offers verification. Scalability (9.0/10) applies broadly. Auditability (9.5/10) excels. Sustainability (8.8/10) continues. Pdoom (0.4/10) is low. Cost (4.5/10) reflects complexity.
Description: Applying formal methods to verify ASI safety.
Verified ASI Systems Project: Score (8.70/10)
Verifies ASI systems formally.
Formal Safety Proofs for ASI: Score (8.40/10)
Develops safety proofs for ASI.
Automated Verification Tools: Score (8.30/10)
Builds tools for ASI verification.
DeepMind Formal Methods: Score (7.90/10)
Applies formal methods to ASI safety.
Formal Specification of ASI: Score (7.95/10)
Defines rigorous ASI behavior specifications.
Scalable Oversight Mechanisms
Total Score (9.07/10)
Total Score Analysis: Impact (9.7/10) ensures robust control. Feasibility (9.6/10) integrates effectively. Uniqueness (9.3/10) pioneers oversight. Scalability (9.5/10) excels broadly. Auditability (9.4/10) is reliable. Sustainability (9.4/10) sustains. Pdoom (0.3/10) is low. Cost (3.9/10) justifies impact.
Description: Monitoring and controlling advanced ASI systems.
ARC's Scalable Oversight: Score (9.35/10)
Oversees superintelligent ASI.
DeepMind's Oversight Research: Score (9.20/10)
Scales human-AI supervision.
Human-in-the-Loop Systems: Score (9.15/10)
Integrates human feedback.
Strategic AI Safety Funding
Total Score (9.08/10)
Total Score Analysis: Impact (9.7/10) fuels critical research. Feasibility (9.6/10) grows with donors. Uniqueness (8.7/10) overlaps philanthropy. Scalability (9.5/10) scales effectively. Auditability (9.5/10) tracks precisely. Sustainability (9.5/10) rises. Pdoom (0.3/10) is low. Cost (5.0/10) reflects scale.
Description: Funding pivotal ASI alignment efforts.
Open Philanthropy: Score (9.15/10)
Funds diverse safety initiatives.
Future of Life Institute: Score (9.00/10)
Supports innovative projects.
Longview Philanthropy AI Grants: Score (8.95/10)
Funds long-term safety research.
Survival and Flourishing Fund: Score (8.80/10)
Funds AI safety and existential risk reduction.
AI Safety Red Teaming
Total Score (9.03/10)
Total Score Analysis: Impact (9.6/10) uncovers vulnerabilities. Feasibility (9.5/10) leverages expertise. Uniqueness (9.2/10) identifies risks. Scalability (9.3/10) grows effectively. Auditability (9.4/10) tracks flaws. Sustainability (9.3/10) persists. Pdoom (0.4/10) is low. Cost (4.1/10) justifies outcomes.
Description: Proactively testing ASI for vulnerabilities.
Redwood's Red Teaming: Score (9.15/10)
Stress-tests ASI safety.
Adversarial Testing for LLMs: Score (9.00/10)
Probes LLMs for weaknesses.
Robustness Challenges: Score (8.95/10)
Tests ASI under adversity.
OpenAI's Red Teaming Efforts: Score (8.90/10)
Conducts red teaming for model safety.
AI Safety Talent Development
Total Score (9.13/10)
Total Score Analysis: Impact (9.6/10) builds critical expertise. Feasibility (9.5/10) leverages 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 (3.3/10) moderates.
Description: Cultivating skilled ASI alignment researchers.
ML Safety at Oxford: Score (9.15/10)
Trains alignment researchers.
AI Safety Camp: Score (9.05/10)
Fosters new talent.
ML Safety Scholars Program: Score (8.80/10)
Mentors future experts.
Comprehensive AI Safety Education
Total Score (8.98/10)
Total Score Analysis: Impact (9.6/10) builds global 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 effectively. Sustainability (9.5/10) fosters networks. Pdoom (0.2/10) is low. Cost (0.7/10) is efficient.
Description: Educating stakeholders in ASI safety principles.
Alignment Forum: Score (9.05/10)
Hosts safety discourse.
AI Safety YouTube Channels: Score (8.75/10)
Explains safety concepts.
AI Alignment Newsletter: Score (8.70/10)
Summarizes alignment updates.
Runtime Safety Mechanisms
Total Score (8.98/10)
Total Score Analysis: Impact (9.5/10) ensures real-time safety. Feasibility (9.4/10) advances with tech. Uniqueness (9.1/10) focuses on 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 (4.0/10) moderates.
Description: Real-time monitoring and intervention for ASI safety.
Anthropic's Runtime Safety: Score (9.10/10)
Monitors ASI in real-time.
Real-Time Monitoring Systems: Score (8.95/10)
Detects anomalies dynamically.
Anomaly Detection in ASI: Score (8.90/10)
Identifies unsafe patterns.
Cooperative AI Systems
Total Score (8.98/10)
Total Score Analysis: Impact (9.5/10) fosters safe coordination. Feasibility (9.4/10) leverages simulations. Uniqueness (9.2/10) addresses 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 (3.9/10) moderates.
Description: Designing ASI for safe, cooperative behavior.
DeepMind's Cooperative AI: Score (9.10/10)
Studies cooperative ASI behavior.
Multi-Agent RL for Cooperation: Score (8.85/10)
Trains ASI for cooperative tasks.
Game Theory for ASI Coordination: Score (8.80/10)
Applies game theory to safety.
AI Safety Advocacy & Communication
Total Score (9.11/10)
Total Score Analysis: Impact (9.7/10) raises vital 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) strengthens. Pdoom (0.9/10) is low. Cost ( gauge (1.0/10) is efficient.
Description: Raising ASI risk awareness among stakeholders.
FLI Advocacy & Communication: Score (9.15/10)
Promotes safety awareness.
AI Safety Podcasts: Score (8.90/10)
Discusses alignment challenges.
Public Awareness Campaigns: Score (8.85/10)
Educates on ASI risks broadly.
B
AI Ethics and Fairness
Total Score (8.10/10)
Total Score Analysis: Impact (9.5/10) ensures societal acceptance. Feasibility (8.5/10) progresses with research. Uniqueness (8.0/10) overlaps with alignment. Scalability (9.0/10) applies broadly. Auditability (8.0/10) allows checks. Sustainability (9.0/10) maintains standards. Pdoom (1.0/10) is low. Cost (4.0/10) is moderate.
Description: Ensuring ASI systems are fair and ethical.
Algorithmic Fairness Research: Score (8.20/10)
Develops fair ML algorithms.
Ethical AI Guidelines: Score (8.15/10)
Establishes ethical standards.
Fairness in Machine Learning: Score (8.10/10)
Focuses on ML fairness.
Neuro-Symbolic AI for Alignment
Total Score (8.40/10)
Total Score Analysis: Impact (9.5/10) offers novel solutions. Feasibility (8.5/10) is early but promising. Uniqueness (9.5/10) stands out. Scalability (8.5/10) fits various systems. Auditability (9.0/10) boosts transparency. Sustainability (8.5/10) needs research. Pdoom (0.5/10) is low. Cost (4.0/10) moderates.
Description: Combining neural and symbolic reasoning for ASI control.
Neuro-Symbolic Program Synthesis: Score (8.50/10)
Synthesizes interpretable programs.
Hybrid AI Models for Safety: Score (8.40/10)
Builds safe hybrid systems.
Symbolic Reasoning in DL: Score (8.30/10)
Enhances ASI reasoning safety.
Human-AI Value Alignment Verification
Total Score (8.35/10)
Total Score Analysis: Impact (9.7/10) builds trust. Feasibility (8.0/10) is tough but key. Uniqueness (9.0/10) targets verification. Scalability (8.5/10) fits broadly. Auditability (9.5/10) ensures rigor. Sustainability (8.5/10) needs updates. Pdoom (0.3/10) is low. Cost (4.5/10) is notable.
Description: Verifying ASI alignment with human values.
Value Alignment Testing Suites: Score (8.40/10)
Tests alignment comprehensively.
Ethical Scenario Simulations: Score (8.35/10)
Simulates value alignment.
Alignment Verification Protocols: Score (8.30/10)
Establishes verification standards.
Agent Foundations Research
Total Score (8.83/10)
Total Score Analysis: Impact (9.6/10) 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 (3.1/10) moderates.
Description: Formalizing ASI decision-making foundations.
Decision Theory for ASI: Score (8.85/10)
Refines ASI decision frameworks.
Logical Uncertainty: Score (8.80/10)
Addresses reasoning uncertainty.
MIRI Embedded Agency: Score (8.75/10)
Explores embedded decision theory.
Safe Exploration Research
Total Score (8.78/10)
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 (3.5/10) moderates.
Description: Ensuring ASI learns safely without harm.
Constrained Exploration in RL: Score (8.75/10)
Bounds exploration safely.
Safe Policy Optimization: Score (8.70/10)
Optimizes with safety constraints.
ETH Zurich Safe AI Lab: Score (8.65/10)
Advances safe exploration.
Existential Risk Mitigation Strategies
Total Score (8.58/10)
Total Score Analysis: Impact (9.6/10) targets x-risks. Feasibility (9.0/10) grows interdisciplinarily. Uniqueness (9.4/10) focuses on x-risk. Scalability (8.8/10) applies broadly. Auditability (9.1/10) tracks progress. Sustainability (9.1/10) lasts. Pdoom (0.8/10) reduces risk. Cost (3.7/10) moderates.
Description: Preventing ASI-related existential catastrophes.
ASI Risk Scenarios Analysis: Score (8.55/10)
Models potential ASI risks.
Long-Term Safety Planning: Score (8.50/10)
Plans sustained ASI safety.
GCRI ASI Focus: Score (8.45/10)
Assesses risk reduction.
AI Safety Benchmarking & Evaluation
Total Score (8.38/10)
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 (3.7/10) moderates.
Description: Standardized benchmarks for ASI safety.
Safety Benchmarks for LMs: Score (8.35/10)
Evaluates LLM safety metrics.
Robustness Evaluation Metrics: Score (8.30/10)
Measures ASI robustness.
HELM Framework: Score (8.25/10)
Benchmarks safety comprehensively.
Adversarial Robustness Research
Total Score (8.53/10)
Total Score Analysis: Impact (9.5/10) mitigates attack risks. Feasibility (9.5/10) grows with methods. Uniqueness (8.8/10) focuses on 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 (3.7/10) moderates.
Description: Strengthening ASI against adversarial attacks.
Certified Defenses: Score (8.45/10)
Ensures robust defenses.
Adversarial Training Techniques: Score (8.40/10)
Improves ASI resilience.
Redwood's Adversarial Training: Score (8.35/10)
Builds resilient systems.
AI Capability Control
Total Score (8.73/10)
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 (3.4/10) moderates.
Description: Designing ASI with capability limits.
Capability Bounding Mechanisms: Score (8.65/10)
Restricts ASI capabilities safely.
Operational Limits in ASI: Score (8.60/10)
Defines safe boundaries.
OpenAI's Controlled ASI: Score (8.55/10)
Limits operational scope.
Corrigibility Research
Total Score (8.43/10)
Total Score Analysis: Impact (9.4/10) addresses safety issues. Feasibility (8.4/10) progresses theoretically. Uniqueness (8.9/10) focuses on 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 (3.6/10) moderates.
Description: Developing ASI that can be corrected or shut down.
Shutdown Problem Solutions: Score (8.40/10)
Solves safe shutdown issues.
Interruptible Agents: Score (8.35/10)
Designs interruptible ASI.
MIRI's Corrigibility Research: Score (8.30/10)
Builds corrigible frameworks.
Inner Alignment Research
Total Score (8.28/10)
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 (4.1/10) reflects complexity.
Description: Ensuring ASI optimizes intended objectives.
Mesa-Optimization Prevention: Score (8.40/10)
Prevents unintended optimization.
Objective Robustness Techniques: Score (8.35/10)
Ensures goal alignment.
Reward Tampering Research: Score (8.30/10)
Prevents reward manipulation.
Causal Approaches to AI Alignment
Total Score (8.46/10)
Total Score Analysis: Impact (9.4/10) enhances control via causality. Feasibility (8.4/10) advances 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) continues. Pdoom (0.5/10) is low. Cost (4.1/10) reflects needs.
Description: Using causal models for safe ASI decisions.
Causal Influence Diagrams: Score (8.40/10)
Models causal safety impacts.
Incentive Design via Causality: Score (8.35/10)
Designs safe incentives.
FHI Causal Research: Score (8.30/10)
Explores causal inference.
AI Transparency and Explainability
Total Score (8.27/10)
Total Score Analysis: Impact (9.0/10) builds trust. Feasibility (8.5/10) advances with research. Uniqueness (8.5/10) focuses on 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 (4.0/10) moderates.
Description: Making ASI decisions transparent and understandable.
Explainable AI Techniques: Score (8.25/10)
Develops interpretable models.
Interpretable Machine Learning: Score (8.20/10)
Enhances model transparency.
OpenAI's Explainability: Score (8.15/10)
Works on interpretable ASI.
AI Safety in Deployment and Operations
Total Score (8.32/10)
Total Score Analysis: Impact (9.2/10) affects real-world safety. Feasibility (8.8/10) needs practical work. Uniqueness (8.5/10) focuses on 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 (4.5/10) is notable.
Description: Ensuring safe ASI deployment and operations.
Deployment Safety Protocols: Score (8.15/10)
Secures ASI deployment.
Operational Risk Management: Score (8.10/10)
Manages operational risks.
AI Incident Database: Score (8.05/10)
Logs failures for insights.
Human-AI Collaboration and Interface Design
Total Score (8.15/10)
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 (4.0/10) moderates.
Description: Designing safe human-ASI interaction systems.
Collaborative AI Systems: Score (8.15/10)
Builds cooperative interfaces.
User-Centric AI Design: Score (8.10/10)
Focuses on human-AI usability.
MIT CSAIL Collaboration: Score (8.05/10)
Develops teamwork interfaces.
AI Alignment via Debate and Amplification
Total Score (8.25/10)
Total Score Analysis: Impact (9.7/10) enhances oversight. Feasibility (8.5/10) progresses with research. Uniqueness (9.0/10) offers distinct methods. Scalability (9.0/10) applies broadly. Auditability (8.0/10) is measurable. Sustainability (9.0/10) persists. Pdoom (1.0/10) reduces risks. Cost (4.0/10) moderates.
Description: Using debate and amplification for ASI alignment.
Debate as a Training Signal: Score (8.35/10)
Trains ASI via debate.
Amplification for Alignment: Score (8.30/10)
Amplifies human oversight.
C
Differential Technological Development
Total Score (7.98/10)
Total Score Analysis: Impact (9.2/10) prioritizes safe progress. Feasibility (8.6/10) depends on 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 (4.2/10) reflects planning.
Description: Prioritizing safe ASI tech development.
Tech Prioritization Frameworks: Score (8.05/10)
Prioritizes safe tech paths.
Safe Development Pathways: Score (8.00/10)
Sequences ASI progress safely.
FHI Differential Tech: Score (7.95/10)
Studies development prioritization.
AI Alignment Prizes
Total Score (7.85/10)
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 (2.0/10) is efficient.
Description: Competitions incentivizing ASI alignment solutions.
ASI Safety Competition: Score (7.85/10)
Promotes safe ASI innovation.
Alignment Innovation Awards: Score (7.80/10)
Rewards alignment breakthroughs.
Alignment Challenge Prizes: Score (7.75/10)
Funds alignment solutions.
ASI Safety in Multi-Agent Systems
Total Score (8.02/10)
Total Score Analysis: Impact (9.2/10) ensures safe interactions. Feasibility (8.0/10) is complex. Uniqueness (8.7/10) addresses multi-agent dynamics. Scalability (9.0/10) fits large systems. Auditability (8.5/10) is challenging. Sustainability (8.5/10) needs work. Pdoom (0.7/10) is low. Cost (4.5/10) is significant.
Description: Ensuring safe multi-ASI interactions.
Cooperative Multi-Agent Systems: Score (8.25/10)
Designs cooperative protocols.
Multi-Agent Coordination: Score (8.20/10)
Coordinates ASI safely.
FHI Cooperative AI: Score (8.15/10)
Explores cooperation frameworks.
Long-Term ASI Safety and Planning
Total Score (7.88/10)
Total Score Analysis: Impact (9.5/10) addresses x-risks. Feasibility (7.5/10) is speculative. Uniqueness (9.0/10) focuses on future. Scalability (8.5/10) fits long-term scenarios. Auditability (7.0/10) is tough. Sustainability (9.5/10) is inherent. Pdoom (0.8/10) reduces risks. Cost (3.5/10) moderates.
Description: Ensuring ASI alignment over long periods.
ASI Macrostrategy Research: Score (8.45/10)
Studies long-term ASI paths.
Long-Term Impact Assessments: Score (8.40/10)
Assesses sustained safety.
Long-Term Future Fund: Score (8.35/10)
Funds long-term safety.
AI Boxing and Containment Strategies
Total Score (7.45/10)
Total Score Analysis: Impact (9.5/10) prevents catastrophes. Feasibility (7.0/10) is tough for ASI. Uniqueness (9.0/10) targets containment. Scalability (7.5/10) needs tailoring. Auditability (9.0/10) allows testing. Sustainability (8.0/10) evolves. Pdoom (1.0/10) reduces risk. Cost (6.0/10) is high.
Description: Containing ASI to prevent unintended consequences.
Logical Containment Methods: Score (7.55/10)
Uses logical containment.
Physical Isolation Techniques: Score (7.50/10)
Isolates ASI physically.
ASI Alignment in Multi-Stakeholder Scenarios
Total Score (7.60/10)
Total Score Analysis: Impact (9.0/10) tackles complex alignment. Feasibility (7.5/10) is challenging. Uniqueness (8.5/10) focuses on stakeholders. Scalability (8.5/10) applies broadly. Auditability (8.0/10) allows oversight. Sustainability (8.0/10) needs work. Pdoom (1.0/10) reduces risks. Cost (4.0/10) moderates.
Description: Aligning ASI with multiple, conflicting human values.
Multi-Value Alignment Framework: Score (8.60/10)
Develops multi-stakeholder frameworks.
Stakeholder Negotiation Protocols: Score (8.30/10)
Creates negotiation protocols.
Conflict Resolution in Alignment: Score (8.10/10)
Addresses alignment conflicts.
Recursive Self-Improvement Safety
Total Score (7.38/10)
Total Score Analysis: Impact (9.5/10) is crucial for safety. Feasibility (7.0/10) is theoretical. Uniqueness (9.0/10) addresses specific challenges. Scalability (8.0/10) applies to self-improving systems. Auditability (6.0/10) is difficult. Sustainability (9.0/10) is long-term. Pdoom (1.0/10) reduces risks. Cost (4.0/10) is moderate.
Description: Ensuring ASI maintains alignment during recursive self-improvement.
MIRI's Tiling Agents Research: Score (8.00/10)
Studies agents that can create improved versions while preserving goals.