Running AI models directly on local devices (phones, laptops, IoT devices) instead of in the cloud.
Running AI models directly on local devices (phones, laptops, IoT devices) instead of in the cloud. Offers faster response times, better privacy, and offline capability. Apple Intelligence, on-device Gemini Nano, and quantized LLMs are examples. Requires model compression to fit hardware constraints.
Reducing the precision of a model's numerical values — for example, from 32-bit to 4-bit numbers.
Running a trained model to make predictions on new data.
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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