What is AI Safety?
The field of research focused on ensuring AI systems behave as intended and do not cause harm.
Why It Matters
As AI becomes more capable, safety research helps ensure these systems remain beneficial and controllable.
Real-World Example
Building safeguards so an AI assistant refuses to provide instructions for dangerous activities.
“Understanding terms like AI Safety matters because it helps you have better conversations with developers and make smarter decisions about your software. You do not need to be technical. You just need to know enough to ask the right questions.”
Related Terms
AI Alignment
The challenge of ensuring AI systems pursue goals that match human values and intentions.
Bias in AI
When an AI system produces unfair or skewed results because of imbalances in its training data or design.
Red Teaming
Deliberately trying to find flaws, vulnerabilities, or harmful outputs in an AI system before deployment.
Constitutional AI
An approach to AI training where the model is given a set of principles to self-evaluate and improve its own responses.
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Related Terms
Bias in AI
When an AI system produces unfair or skewed results because of imbalances in its training data or design.
AI Alignment
The challenge of ensuring AI systems pursue goals that match human values and intentions.
Constitutional AI
An approach to AI training where the model is given a set of principles to self-evaluate and improve its own responses.
Red Teaming
Deliberately trying to find flaws, vulnerabilities, or harmful outputs in an AI system before deployment.
Large Language Model (LLM)
An AI system trained on massive amounts of text that can understand and generate human language.
Transformer
A type of AI architecture that processes text by paying attention to relationships between all words at once, rather...