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A Primer on AI for Architects with Anthony Alford
The InfoQ Podcast
Embrace the Power of Classifiers and Weights
Enumeration data types function as classifiers, providing a probability for each possible output class, such as identifying a dog or a cat. The ideal scenario is to have the correct classification close to 100% probability, while incorrect classifications are minimized. This principle extends to text, where a vocabulary is represented through a probability distribution. Large language models, like GPT-4, are trained on extensive datasets presumed to encompass a significant portion of internet content, which informs the probability outputs. Deep learning models utilize neural networks, fundamentally operating through matrix multiplication, where input tensors represent matrices that interact with model weights to produce weighted sums of input values.
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