The practice of developing and deploying AI systems with careful attention to fairness, transparency, safety, privacy, and social impact.
The practice of developing and deploying AI systems with careful attention to fairness, transparency, safety, privacy, and social impact. Includes regular audits, bias testing, clear documentation, and meaningful human oversight. Increasingly required by regulation and demanded by enterprise customers.
The practice of developing AI systems that are fair, transparent, accountable, and respect human rights.
The broad field studying how to build AI systems that are safe, reliable, and beneficial.
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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