Generative Adversarial Networks: How Can AI Learn So Much While Its IQ Remains Zero?

A short while ago we wrote about why it is a good idea to get friendly with AI before it arrives in your everyday work environment. We introduced the text-to-art algorithm Midjourney – as an easy and entertaining partner for this venture. But how do these algorithms work? How can they translate our words (Superman and Wonderwoman in Starbucks) into (more or less) fitting images? And how is it possible that while these models get better and better, their IQ remains 0? Superman and Wonderwoman in Starbucks – by Midjourney Welcome to the wonderful world of Generative Adversarial Networks or GANs for short. These are a type of A.I. (a neural network to be precise) that can generate data from scratch. And although this topic is quite complicated, it is easy to understand the basic principle of how they learn.  The famous case of the forger and the art detective Imagine a Talented Youngster who decides to make his fortune by forging Picasso. He has a severe disadvantage though: although he has read about the painter, he has never seen any of his paintings. On the upside, he has an excellent style guide that includes the important characteristics of the painter’s works, like the colours he often used, his typical brushstroke techniques and the topic of his paintings. And he has a superpower: he paints with incredible speed, generating tons of new pictures every day.  Then there is the Art Detective, who knows a good deal about Picasso’s pa...
Source: The Medical Futurist - Category: Information Technology Authors: Tags: TMF Artificial Intelligence in Medicine AI algorithm deep learning GAN Generative Adversarial Networks Source Type: blogs