AI safety refers to the research and development of artificial intelligence systems that are reliably helpful, harmless, and honest.

The key goals of AI safety include:

- Helpful - AI systems should be beneficial to humanity, acting as useful assistants that enhance our capabilities and improve lives.

- Harmless - AIs should not present undue risks or cause harm, whether physical, psychological, economic, or societal. Safety measures must be taken to minimize harmful accidents or misuse.

- Honest - AI systems should provide truthful information and have transparent reasoning that humans can understand and audit. Deception or intentionally misleading users should be avoided.

- Robustness - AI algorithms must be stable, avoid unintended side effects, and handle new situations gracefully without failing dangerously.

- Fairness - Removing dangerous bias from AI while enhancing diversity, inclusivity and justice.

- Security - Preventing malicious actors from misusing or hijacking AI technologies.

- Privacy - Limiting unnecessary data collection and ensuring private information is protected.

- Coordination - Aligning complex intelligent systems to act in concert rather than conflict.

AI safety research strives to address these challenges through technical solutions like formal verification, uncertainty modeling, robustness testing, explainability methods, alignment techniques, and secure computing.

Following ethical AI principles is also crucial.

The goal is to create AI that benefits humanity safely and responsibly.