That are now changing the creativity and technology industry.

Generative AI may be described as a branch of machine learning that involves the generation of new data in the form of images, text, sound, or samples that, in some measure, resemble the training sample characteristics.

The recent advance in Generative AI has been quite aggressive within the last decade because of the growth in neural networks as well as GANs.

During the earlier part of the 2010s, neural networks were showing favorable outputs on discriminative tasks, including image classification. However, the big issue was the generation of new content through the algorithmic means.

Pre-existing techniques like autoencoders were used to reconstruct available samples of images, but they were powerless in producing realistic samples.

A major breakthrough was however achieved in 2014 with the introduction of generative adversarial networks (GANs) by Ian Goodfellow.

As we know, GANs work where there are two neural networks, which include the generator neural network, which creates new content, and the discriminator neural network, which tries to decide about whether the generated content is original or fake.

In this minimax game, the two networks are in opposition, and the generator is encouraged to produce better and better fake outputs in a bid to fool the discriminator.

The very first GAN models can only produce pictures of objects with a very small size and in black & white. Nonetheless, these were quickly succeeded by improvements in computing capabilities and methods drawn from neural networks.

By as early as 2017, GANs’ capability in image generation was enhanced and became capable of producing high-resolution color images of faces, artwork, and photorealistic indoor scenes.

The generated images in many cases looked like real images from the perception of human beings as they observed the images being generated.

The text generation through the neural network also got a further boost in the year 2019 with the GPT-2 model by OpenAI, along with GPT-3 in the year 2020. These language models developed by the websites use large heaps of text data to produce enormously realistic text on any topic of the modeler’s preference.

Most of the initial work in GANs was mostly focused on image generation. Albeit, the recent developments in GAN architectures allow for broader utilization and are capable of generating video, audio, as well as 3D content.

For instance, StyleGAN, which was introduced in 2019 allows one to manipulate attributes such as position, age, or mood of the synthetic faces. It also shows that using VQGAN, one can generate images that look like the ones shown below from short text inputs.

Other GANs can also generate slow motion videos, Beethoven’s symphonies, or even molecules having a specific chemical composition. In view of the fact that neural networks, and especially GANs, are generative models, there arises new possibilities and risks of improper usage of artificial intelligence.

As mentioned earlier, generative models have the capacity to move forward many fields for media, education, and several sciences. But the same technology is creating deepfakes, AI-synthesised media for spreading more fake news, scams, and the like.

Nevertheless, tackling those societal impacts with additional investigation in the areas of watermarking, provenance tracking, and bias minimization is still under progress.

If the underlying machinery becomes capable of compute and data storage, more extensive models and datasets would be attainable, thereby creating further generative potential of the AI. And maybe in the near future, when there will be multimodal generative models available to create clearly defined responses in such areas as image, text, and voice.

There are still several ways to achieve exceptional results in generative AI, such as reinforcement learning and energy-based models.

Here, the change observed in the last decade threatens the generative AI’s future. Nevertheless, beginning with neural networks and proceeding to GAN and even beyond, the progress in this field is already changing the content generation in various industries.