AI Safety Principles
Building trust in intelligent systems by ensuring robustness, reliability, and ethical alignment.
Robustness
Ensuring AI systems remain reliable under uncertainty through adversarial training and stress testing.
Safety Alignment
Ensuring AI systems remain aligned with human values through reinforcement learning from human feedback and preference learning.
Safety Technologies
Dynamic monitoring systems detect dangerous behaviors and enforce safety constraints during model execution.
Rigorous testing protocols that emulate adversarial attacks to identify vulnerabilities in AI systems.
Safety Standards
ISO 23894
Compliance with ISO standards for Trustworthy AI to ensure human-centric machine learning.
AI Safety Framework
Implementation of Google's AI Safety Framework to ensure safe model scaling practices.
Red Team Results
Independent third-party audits confirming our safety measures can withstand adversarial stress.
Current Safety Projects
Ongoing research and implementation initiatives that are pushing the boundaries of AI system safety and reliability.
Predictive modeling of catastrophic risk in large language models during deployment.
Enterprise-ready safety governance platform combining human oversight with automated constraints.