One of the most promising generative AI use cases is accelerating drug discovery and research. Generative AI can create novel protein sequences with specific properties for designing antibodies, enzymes, vaccines, and gene therapy.

Healthcare and life sciences companies use generative AI tools to design synthetic gene sequences for synthetic biology and metabolic engineering applications. For example, they can create new biosynthetic pathways or optimize gene expression for biomanufacturing purposes.

Generative AI tools also create synthetic patient and healthcare data. This data can be useful for training AI models, simulating clinical trials, or studying rare diseases without access to large real-world datasets.

Read more about Generative AI in Healthcare & Life Sciences on AWS

Automotive companies use generative AI technology for many purposes, from engineering to in-vehicle experiences and customer service. For instance, they optimize the design of mechanical parts to reduce drag in vehicle designs or adapt the design of personal assistants.

Auto companies use generative AI tools to deliver better customer service by providing quick responses to the most common customer questions. Generative AI creates new materials, chips, and part designs to optimize manufacturing processes and reduce costs.

Another generative AI use case is synthesizing data to test applications. This is especially helpful for data not often included in testing datasets (such as defects or edge cases).

Read more about Generative AI for Automotive on AWS

Read more about Generative AI in Manufacturing on AWS

Generative AI use cases in telecommunication focus on reinventing the customer experience defined by the cumulative interactions of subscribers across all touchpoints of the customer journey.

For instance, telecommunication organizations apply generative AI to improve customer service with live human-like conversational agents. They reinvent customer relationships with personalized one-to-one sales assistants. They also optimize network performance by analyzing network data to recommend fixes. 

Read more about Generative AI for Telecom on AWS

From animations and scripts to full-length movies, generative AI models produce novel content at a fraction of the cost and time of traditional production.

Other generative AI use cases in the industry include:

• Artists can complement and enhance their albums with AI-generated music to create whole new experiences.
• Media organizations use generative AI to improve their audience experiences by offering personalized content and ads to grow revenues.
• Gaming companies use generative AI to create new games and allow players to build avatars.

In 2017, a further shift in AI research occurred with the introduction of transformers. Transformers seamlessly integrated the encoder-and-decoder architecture with an attention mechanism. They streamlined the training process of language models with exceptional efficiency and versatility. Notable models like GPT emerged as foundational models capable of pretraining on extensive corpora of raw text and fine-tuning for diverse tasks.

Transformers changed what was possible for natural language processing. They empowered generative capabilities for tasks ranging from translation and summarization to answering questions.

Many generative AI models continue to make significant strides and have found cross-industry applications. Recent innovations focus on refining models to work with proprietary data. Researchers also want to create text, images, videos, and speech that are more and more human-like.

The generative adversarial network (GAN) is another generative AI model that builds upon the diffusion model’s concept.

GANs work by training two neural networks in a competitive manner. The first network, known as the generator, generates fake data samples by adding random noise. The second network called the discriminator, tries to distinguish between real data and the fake data produced by the generator. 

During training, the generator continually improves its ability to create realistic data while the discriminator becomes better at telling real from fake. This adversarial process continues until the generator produces data that is so convincing that the discriminator can't differentiate it from real data.

GANs are widely used in generating realistic images, style transfer, and data augmentation tasks.

Variational autoencoders (VAEs) learn a compact representation of data called latent space. The latent space is a mathematical representation of the data. You can think of it as a unique code representing the data based on all its attributes. For example, if studying faces, the latent space contains numbers representing eye shape, nose shape, cheekbones, and ears.

VAEs use two neural networks—the encoder and the decoder. The encoder neural network maps the input data to a mean and variance for each dimension of the latent space. It generates a random sample from a Gaussian (normal) distribution. This sample is a point in the latent space and represents a compressed, simplified version of the input data.

The decoder neural network takes this sampled point from the latent space and reconstructs it back into data that resembles the original input. Mathematical functions are used to measure how well the reconstructed data matches the original data.

The transformer-based generative AI model builds upon the encoder and decoder concepts of VAEs. Transformer-based models add more layers to the encoder to improve performance on text-based tasks like comprehension, translation, and creative writing.

Transformer-based models use a self-attention mechanism. They weigh the importance of different parts of an input sequence when processing each element in the sequence.

Another key feature is that these AI models implement contextual embeddings. The encoding of a sequence element depends not only on the element itself but also on its context within the sequence.

How transformer-based models work

To understand how transformer-based models work, imagine a sentence as a sequence of words.

Self-attention helps the model focus on the relevant words as it processes each word. The transformer-based generative model employs multiple encoder layers called attention heads to capture different types of relationships between words. Each head learns to attend to different parts of the input sequence, allowing the model to simultaneously consider various aspects of the data.

Each layer also refines the contextual embeddings, making them more informative and capturing everything from grammar syntax to complex semantic meanings.