A Dozen Times Artificial Intelligence Startled The World

The best uses of Generative Models and how they work.

Generative Adversarial Networks (GANs) are some of the most fascinating ways to “teach” computers to do human tasks.

We’ve always heard that competition can boost performance, but now GANs are taking “learning from Competition” to an industrial scale.

Generative Adversarial Networks are defined by AI entities (Neural Networks) that compete with each other to get better at their respective tasks.

Imagine a Malware bot competing against a Security bot, each relentlessly trying to execute its own objective (e.g. Invade VS Protect), and in this process, becoming better and better at its respective task.

First coined by Ian Goodfellow from the University of Montreal, GANs have recently shown us the power of “Unsupervised Learning” due to their widespread success.

So how do GANs work?

A GAN has two competing neural network models. One of the network takes noise as input and generates samples (called the generator). The other network (the discriminator) receives samples from both the generator and the training data and is able to distinguish between the two sources.

These two networks play a continuous game. In it, the generator learns to produce new samples to fool the discriminator who in turn gets better at distinguishing generated data from real data.

These two networks are trained simultaneously, and after millions of rounds of “play” the generated samples become indistinguishable from the real data.

In simple terms the generator is like a forger trying to produce some counterfeit material, and the discriminator is like the police trying to detect the forged items.

Since the entire process is automated and limited only by available computation power, GANs can accomplish extraordinary feats.

Below are some of the coolest GAN applications in action.

1. A look into a machine’s imagination.

Google’s Deep Dream creates psychedelic images.

Researchers at Google Brain have developed a way to visually represent what their neural network, GoogleNet, “thinks” of as the essence of objects.

Using this method, the GAN produced some images that can be described as psychedelic in nature.

These dream-like hallucinogenic images are the byproduct of deliberately over-processed images through an image classifying entity. The system involved in creating them has been dubbed ‘Deep Dream’.