AI Safety Research Lab

Dedicated research in responsible AI development, alignment, and safety to ensure beneficial artificial intelligence for humanity.

Core Safety Principles

Fundamental principles guiding our AI safety research and development

99.9% reliability

Ensuring AI systems perform reliably across diverse conditions and gracefully handle unexpected situations.

100% explainable

Developing AI systems that can explain their decisions and reasoning in human-understandable terms.

Zero breaches

Protecting AI systems from adversarial attacks while preserving user privacy and data security.

Critical areas of AI safety research for responsible AI development

Ensuring AI systems understand and pursue intended objectives while remaining aligned with human values.

Human Value Alignment

Reward Modeling Techniques

Human Preference Learning

Constitutional AI Methods

Developing AI systems that remain safe and reliable under adversarial conditions and distribution shifts.

Adversarial Training Methods

Uncertainty Quantification

Distribution Shift Detection

AI System Stress Testing

Research into understanding and explaining AI decision-making processes for transparency and accountability.

Mechanistic Interpretability

Concept Bottleneck Models

Attention Mechanism Analysis

Causal Intervention Methods

Comprehensive frameworks for evaluating and ensuring AI safety

Systematic methodology for identifying, analyzing, and mitigating potential risks in AI systems.

AI Threat Modeling

Failure Mode Analysis

Impact Assessment

Risk Mitigation Strategies

Standardized protocols for testing and validating AI safety measures across different applications.

Safety Benchmark Suites

Red Team Evaluations

Capability Assessment

Continuous Monitoring

Collaborate with us to ensure AI systems remain safe, beneficial, and aligned with human values.