{"id":191977,"date":"2025-01-07T06:51:00","date_gmt":"2025-01-07T05:51:00","guid":{"rendered":"https:\/\/liora.io\/en\/?p=191977"},"modified":"2026-02-06T07:51:08","modified_gmt":"2026-02-06T06:51:08","slug":"all-about-artificial-neural-networks","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/all-about-artificial-neural-networks","title":{"rendered":"What are Artificial Neural Networks?"},"content":{"rendered":"Artificial neural networks are at the core of the recent remarkable advancements in artificial intelligence. How do they function and what lends them their remarkable strength?<\/b>\n\nThe mathematical concept of artificial neural networks (ANN)<\/b> was first introduced in 1943 by two researchers from the University of Chicago, Warren McCullough, and Walter Pitts. In an article for the journal Brain Theory<\/i>, they presented a theory that focused on the neuron as a fundamental element in responding to external stimuli.\n\nIt wasn’t until 1957 that the first prototype utilizing ANNs was developed\u2014the Perceptron by Frank Rosenblatt<\/b>. It aimed to perform recognition tasks using a repetitive learning algorithm. However, at the time, computers were not sufficiently powerful to process the vast amounts of data required for effective real-world application. This limitation was highlighted by Marvin Minsky and Seymour Papert in their book Perceptrons<\/i> (1969), where they pointed out the constraints of neural networks. Consequently, research in this area paused for nearly two decades.\n\nA significant breakthrough occurred in 1986 when David Rumelhart, Geoffrey Hinton, and Ronald Williams published a paper on backpropagation, a learning technique that rekindled interest in neural networks.\n\nHowever, it wasn’t until the advent of Big Data<\/a> and massively parallel computers that neural networks received adequate processing power. A milestone was achieved in 2012 when Alex Krizhevsky’s team won the ImageNet competition, which focused on image recognition.\n\n