Glossary term

Generative AI

As businesses continue to search for ways to accelerate growth and gain a competitive edge, AI seems like an obvious solution. However, AI refers to a broad spectrum of algorithms, all of which have different strengths in terms of how they best generate value.

Algorithms that automate repetitive tasks, identify anomalies, or even target advertising are commonplace in the world of business today, but until now we’ve been forced to rely solely on humans for creative work.

In 2021, Gartner listed Generative AI as one of 12 top strategic technology trends for 2022, crediting its “potential to create new forms of creative content” as its greatest potential value to businesses.

In this post, we’ll discuss what generative AI is, why it’s so important right now, and how it can help your organization rise above your competitors.

What is Generative AI?

In its broadest sense, Generative AI refers to a group of algorithms that can learn from data artifacts, such as images, text, code, or datasets. From there, it identifies patterns in the data and generates completely original content that resembles the input. There are currently two prominent frameworks for generative AI: Generative Adversarial Networks and Generative Pre-trained Transformers, or GANs and GPTs for short.

The GAN framework was developed by Ian Goodfellow in 2014, and pits two neural network sub-models against each other in the form of a zero-sum game. A generator model is used to output new content, which a discriminator model classifies as either real or fake. Unsupervised, the models can train themselves 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 original, human-like language from any text input.

OpenAI’s GPT-3 model demonstrates the culmination of research into this technique. Trained on over 175 billion parameters, GPT-3 assesses common word groupings and can understand when and where to use specific words to construct meaningful language that mimics how humans would communicate.

Why is Generative AI Important?

Unlike traditional machine learning, generative AI demonstrates technology’s first ability to algorithmically produce original and creative content. While it may not be able to surpass human creativity yet, generative AI can aid in creative disciplines to accelerate innovation, design, and engineering, which are typically super time-consuming pursuits.

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.

Generative AI Use Cases

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

Text Summarization

Generative AI can be used to condense large sections of text and highlight key information in an efficient manner, which is incredibly useful for creating email newsletters, media monitoring, or legal contract analysis.

Newsletters can be created automatically by inputting relevant sources into the generative model, which generates concise and original summaries of articles for your followers to read. Alternatively, you can use text summarization to take a break from your overloaded email inbox, and instead review automated, concise descriptions of each message you receive so that you can save time and prioritize your work more effectively.

Text Generation

Text generation is practical for automatically drafting blog posts, written content, or product descriptions. Jasper, a version of GPT-3 designed specifically for marketing, 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 Generation

Models such as OpenAI’s DALL-E 2, an image generating model based off the foundation of GPT-3, can replicate any style of art or create entirely original images that are indistinguishable from real pictures. Given any short text prompt, DALL-E 2 can generate nearly any image, lending itself to branding, concept art, or quickly creating instructional materials.

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 products development lifecycle makes it a prime application for generative AI, which can help at multiple steps in the process. During initial conceptualization, generative AI can rapidly convert the designer’s vision into tangible images and mockups and quickly test new shapes, 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.

Wrapping Up

As the market for generative AI matures, it will have a profound impact on business’ creative workflows and content generation. 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.

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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