AI News, BOOK REVIEW: Artificial Intelligence/Neural Networks/Introduction

Artificial Intelligence/Neural Networks/Introduction

brain is a jelly-like structure made of grey matter which is not rigid and could not be dissected for examination under a microscope, until in 1873, Camillo Golgi developed a staining technique that allowed the observation of the elements that make up the brain.

Like the conventional procedures used by many in programming, a neuron has three basic functions: it takes signals from other neurons as input, processes them and sends a signal to other neurons.

With the lack of information available on neural networks as such, Warren McCulloch and Walter Pitts sat down together in 1943 to try and explain the workings of the brain demonstrating how individual neurons can communicate with others in a network.

Largely based on the feedback theory by Norbert Wiener, their paper on this atomic level of psychology enthralled Marvin Minsky and Dean Edmonds so much as to build the first ever neural network in 1951 out of three hundred vacuum tubes and a surplus automatic pilot from a B-24 bomber[1]

Depending on whether the neuron incorporates the learning mechanism or not, neural learning rules can be as simple as adding weight to a synapse each time it fires, and gradually degrading those weights over time, as in the earliest learning rules, Delta rules that accelerate the learning by applying a delta value according to some error function in a back propagation network, to Pre-synaptic/Post-synaptic rules based on biochemistry of the synapse and the firing process.

Today there are literally hundreds of different models, that all call themselves neural networks, even if some of them no longer have models of nerves, or no longer actually require networks to achieve similar effects.

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Video from Coursera - University of Toronto - Course: Neural Networks for Machine Learning:

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