Glossary term

Generative AI

What Is Generative AI?

Generative AI refers to a group of computer programs that can learn from a given body of data, such as images, text, code, or datasets, identify patterns in the data, and generate new content that resembles the input. There are currently two prominent frameworks for generative AI: Generative Adversarial Networks (GANs) and Generative Pre-trained Transformers (GPTs).

The GAN framework was developed by Ian Goodfellow in 2014, and pits two neural network sub-models against each other. A generator model is used to create new content, which a discriminator model classifies as either real or fake. The models compete against each other, continuously learning and improving until the discriminator model can’t tell the difference between the generator’s outputs and the real input examples.

The GPT framework is used for generative language modeling. GPT utilizes deep learning to generate novel, human-like language from any text input. GPT-4, OpenAI’s largest and most advanced large language model to date, assesses common word groupings and can understand when and where to use specific words to construct meaningful language that mimics human-created communications remarkably well. As a multimodal model, GPT-4 can respond to both text and image inputs, though its output is text only.

Why Is Generative AI Important?

Although it has been around since 1980, generative AI has seen massive leaps in performance over the past few years, allowing new applications to become possible and for the value of generative AI to increase exponentially. The latest generative AI technology, like GPT-4 and DALL-E 2, are machine learning algorithms that can produce new content totally independently. While it may not be able to surpass human creativity yet, generative AI can facilitate innovation, design, and engineering, dramatically accelerating human inventiveness and productivity. In fact, Gartner anticipates that when generative AI becomes widely adopted, it will contribute to a productivity revolution, leading to more efficient business operations, especially in the healthcare, manufacturing, media, entertainment, automotive, aerospace, and energy industries.

Generative AI Use Cases

Time-intensive tasks that would traditionally require high focus and attention can now be accelerated or even automated by leveraging generative AI. A sample of possible applications are as follows.

Text Generation

Text generation is where large language models like GPT-4 excel. With them, users can automatically draft blog posts, newsletters, product descriptions – virtually any kind of written content. Jasper, which utilizes GPT-4 along with a collection of other models, is designed specifically for business use cases and can receive a prompt, learn from publicly available or privately provided sources, and generate text documents for social media content, sales emails, or advertisements.  

Image Creation

Image-generating models such as OpenAI’s DALL-E 2 can replicate any artistic style or create entirely original images that are at times indistinguishable from human-generated art, all from a short text prompt. Image generators lend themselves to creating branding collateral, developing concept art, or quickly mocking up instructional materials.

Text Summarization

Summarizing long documents is one of the most sought-after uses of generative AI technology. There are a number of products that specialize in distilling large sections of text and highlight key information so users can read more efficiently, including Jasper. Other products, such as Wordtune, can also condense YouTube videos into text highlights, allowing watchers more time to think and respond. Quickly generated summaries are incredibly useful for creating email newsletters, media monitoring, or legal contract analysis.

Data Augmentation

Data augmentation is a process through which generative AI can augment a dataset by creating additional data points that mimic the properties of the original set. This practice is often used to improve the quality of datasets and is a common method for improving the performance of deep learning models.

Product Development

The iterative nature of a product’s development lifecycle makes it a prime application for generative AI, which can help at multiple stages in the process. During initial conceptualization, generative AI can rapidly convert the designer’s vision into wireframes or mockups and quickly test new configurations, materials, or structures. By providing parameters and goals for the design, generative AI can even come up with new ideas or original designs that engineers may have missed.

Curious to learn more about emerging technologies that have the potential to transform your enterprise? Check out our post on cutting-edge data science techniques to learn how you can leverage DSML to create a competitive advantage.

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