Perceptron Mark I

Training consumed around 6.9 × 10⁵ FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.

None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.

The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.

It has a parameter count of 1K. "Figure 4.8 Illustration of the Mark 1 perceptron hardware. The photograph on the left shows how the inputs were obtained using a simple camera system in which an input scene, in this case a printed character, was illuminated by powerful lights, and an image focussed onto a 20 × 20 array of cadmium sulphide photocells, giving a primitive 400 pixel image. The perceptron also had a patch board, shown in the middle photograph, which allowed different configurations of input features to be tried. Often these were wired up at random to demonstrate the ability of the perceptron to learn without the need for precise wiring, in contrast to a modern digital computer. The photograph on the right shows one of the racks of adaptive weights. Each weight was implemented using a rotary variable resistor, also called a potentiometer, driven by an electric motor thereby allowing the value of the weight to be adjusted automatically by the learning algorithm." source: Bishop, Christopher M. (2006). Pattern Recognition and Machine Learning The Perceptron had a 400-pixel visual input and 1000 neurons in the hidden layer. https://twitter.com/DiegoKuonen/status/1130352233223262208. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.

It was published by Cornell Aeronautical Laboratory,Cornell University, based in United States of America, an organisation categorised as academia,Academia.

It was published in January 1957. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.

It works in the domain of Other, and is recorded as handling the task of binary classification. These are the areas it was designed around; they describe intent rather than a hard boundary.