A deep dive into the Mark I Perceptron (circa 1957): the first hardware realization of Rosenblatt's learning rule, using a 20×20 cadmium sulfide camera, a plugboard for feature wiring, and manual weight adjustments. We'll explain how it classified binary categories, how learning shuffled weights, why it could only solve linearly separable problems, and how the XOR challenge contributed to the Perceptron Winter. Finally, we connect these early ideas to modern AI and vision systems, showing why this piece remains foundational.

Note:  This podcast was AI-generated, and sometimes AI can make mistakes.  Please double-check any critical information.

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