Generative AI

Introduction

Generative AI is a class of artificial intelligence that can generate new content and ideas. It has been described as "an AI that can create something new," but it's not quite there yet. Generative AI can generate images, sounds and even text. In this article, we'll explore what generative AI is, how it works and some examples where it's already being used in the real world.

What is Generative AI

Generative AI is an AI that can create new content. This means it can be used to create images, videos, music, text and even code. It could also be used to create ideas or concepts that aren't yet possible with traditional methods of creating original content (like writing).

What is does?

Generative AI is a technique that uses deep learning algorithms to generate images and videos. It can be used to create art, film, music and more.

The basic idea behind generative AI is that you have an input (such as a photograph) and you want it to produce something else—an output (like another photograph). In other words, you're asking the machine "How would I make this into something new?"

The result is often a combination of randomness and creativity.

This approach was first used to create art in the early 1990s by researchers at the University of Toronto. They fed a computer thousands of images and asked it to learn what features in an image were important. Then they asked it to generate images with similar features. At first, these were just random blobs and splotches—but as the program learned more about how humans see things, it started making increasingly realistic pictures.

In the years since, generative AI has been used to create everything from images and videos that look like paintings by Vincent van Gogh and Jackson Pollock to music inspired by Bach or Mozart. It's even been used to generate new video games.

Most recently, researchers at Google's AI lab announced that they've created a generative model that can create realistic images of human faces. The results are surprisingly good.

The researchers used a method called "inceptionism" to train their model. They took a large set of images and broke them down into their component parts—objects, faces, and so on. Then they had the computer try to predict which combinations of those pieces would yield new images that looked like something humans would recognize as real.

DALL-E?

DALL-E is a generative AI that can draw anything. This is why it's called "generative" AI, because DALL-E uses transformer architecture to create its images. Transformer architecture was developed in 2014 by a team at the University of Montreal, who named their method GANs (for "generative adversarial networks"). Since then, they've been working on developing generative versus discriminative models that are better suited for training deep neural networks to produce realistic images.

Generative Adversarial Networks

·??????GANs were developed in 2014 by a team at the University of Montreal—the same group behind DeepMind—and they've been working on developing more advanced versions since then: recurrent GANs; stacked multi-scale GANs; variational autoencoders; encoder-decoder architectures.

·??????The basic idea behind GANs is that they have two networks working against each other: a generator and a discriminator. The generator tries to produce images that are indistinguishable from real ones; the discriminator tries to tell the difference between real and fake data. Once you train both of these networks on lots of data, they start feeding off each other in an attempt to improve their work (with some help from human guidance).

·??????The generator learns how to mimic real images; the discriminator learns how to identify fake ones. It's an ingenious approach, and it has produced some of the best-looking generative models yet—including this one that can change faces in real time.

·??????GANs are also capable of generating extremely realistic images. When trained on a dataset of photographs, they can create new images that look like real-world shots without any prior knowledge about how the world works. We're not just talking about some blurry photos here and there; these GANs produce images so complex and detailed that it's hard to tell them apart from the real thing unless you zoom in really close or stare at them for hours.

·??????The following video shows what happens when you train a GAN on photos of celebrities and then give it an image of a random person. It starts by generating an image that looks like the celebrity (left), then gradually morphs into someone else (right):

·??????This approach is particularly fascinating because it shows how generative models can learn to replicate the visual style of a particular artist or photographer. In this case, the generator learns how to render people in the same way that portrait photographers do—by studying a large number of images and learning how they're put together.

Stable Diffusion

Stable diffusion is a generative AI technique that uses an autoencoder to create images. The image generator goes through two stages: the Image Information Creator and the Image Decoder.

The Image Information Creator

• The Image Information Creator runs for multiple steps to generate image information. This is the steps parameter in Stable Diffusion interfaces and libraries which often defaults to 50 or 100. The image information creator works completely in the??latent?space. This property makes it faster than previous diffusion models that worked in pixel space. In technical terms, this component is made up of a UNet neural network and a scheduling algorithm. The word “diffusion” describes what happens in this component. It is the step by step processing of information that leads to a high-quality image being generated in the end (by the next stage, the image decoder).

The image decoder

• The image decoder paints a picture from the information it got from the information creator. It runs only once at the end of the process to produce the final pixel image. The decoders use the received data as input for creating their own outputs; these outputs are then fed back into yet another set of neurons called "Inputs." These inputs then generate new versions of themselves—and so forth.

The future

Generative AI will be used in many applications and could become an essential part of our lives. Generative AI can also be used by companies to develop new designs for products in the future—and then produce those designs on demand. One of the sectors where Generative AI could have another big impact is on healthcare: by applying generative algorithms to medical data, researchers could use machine learning tools like neural networks or deep learning models to analyze how different combinations of medications affect different people differently (and maybe even discover new treatments). This type of research could help save lives—and make sure we're taking care of ourselves when there's no one else around!

Conclusion

Generative AI is a promising technology that has the potential to create new products, services and even entire industries. The current state of the field is still far from being able to provide these services at scale, but it's clear that this will change with time as more people get involved in research efforts around generative systems. For now though, we should keep an eye out for how these technologies could evolve over the next few years so that eventually they become useful tools instead of just science fiction.