Safety measures built into AI systems to prevent harmful, inappropriate, or off-topic outputs.
Safety measures built into AI systems to prevent harmful, inappropriate, or off-topic outputs. Can be implemented through training (RLHF), prompt engineering, output filtering, or external validation layers. Every major AI model ships with guardrails, though determined users often find ways around them.
The broad field studying how to build AI systems that are safe, reliable, and beneficial.
Systematically testing an AI system by trying to make it produce harmful, biased, or incorrect outputs.
A mathematical function applied to a neuron's output that introduces non-linearity into the network.
An optimization algorithm that combines the best parts of two other methods — AdaGrad and RMSProp.
Artificial General Intelligence.
The research field focused on making sure AI systems do what humans actually want them to do.
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