Generative AI: What Is It & How Does It Work?

Unless you’ve had your head in the sand for the last several months, you’ve probably noticed your friends and colleagues talking about ChatGPT, or programs with similar functions.

ChatGPT and programs like it are examples of something called Generative AI. Depending on who you ask, you might have the impression that Generative AI is a fascinating application of advanced technology. Or, you might see it as a dystopian development that’s going to threaten the jobs of everyone you know. No matter where you stand, what’s clear is that Generative AI is going to have a profound impact on the future of business and information.

In this article, we’ll give you a broad primer on Generative AI, including what it is and how it works. We’ll also take a look at the history of Generative AI, some Generative AI examples, and Generative AI’s impact on business.

What Is Generative AI?

First let’s start by asking: what is Generative AI? The meaning of Generative AI is some kind of program or application that allows users to create totally new content based on different kinds of input. The input—and output—can take the form of text, images, audio, video, 3D models, and other forms of data.

How Generative AI Works

Now let’s try to understand how Generative AI works. Broadly speaking, Generative AI is a form of machine learning, which uses technology called a neural network to look for patterns within existing data. Those patterns are then used to produce new content that conforms to certain parameters set by the user.

Advances in computing power as well as large language models (LLMs) have increased AI’s power to rapidly process unprecedentedly massive data sets. This has enabled significant breakthroughs in Generative AI’s ability to produce meaningful outputs driven by the patterns and structures the model identifies in its data inputs.

History of Generative AI

In order to better understand the function and potential impacts of Generative AI, it’s important to learn the history of Generative AI. In its most basic form, Generative AI has actually been around for decades. Computer scientists began experimenting with artificial intelligence and even producing chatbots as early as the 1960s. However, these early models were quite primitive. They were limited by computing power and the relative lack of sophistication of early models for working with large enough data sets. It’s proven difficult for these early models to generate realistic and meaningful outputs.

In recent decades, the power of Generative AI has taken many leaps forward. Scientists have been developing increasingly sophisticated machine learning models and neural networks capable of modeling and predicting data with increasing accuracy. By the late 2000s, this process had come to be known as “deep learning,” and it offered applications in processing images, videos, texts, and more.

In 2014, computer scientists developed something called generative adversarial networks (GANS), which represented a major advance in the development of Generative AI. GANs enabled neural networks not only to process and predict data, but to generate new data, including text, images, audio, and video.

Generative AI Examples

Now let’s look at some of the most salient Generative AI examples.

The Generative AI example that’s been on everyone’s mind for the last year is ChatGPT. ChatGPT is a next-generation chatbot, developed by OpenAI, that draws from several generations of large learning models, the most recent of which is GPT-4. ChatGPT enables remarkably human-like interactions between users and the model. Beyond conversation, however, ChatGPT enables users to design highly specific inputs that can lead to very sophisticated text-based outputs. For example, users can instruct ChatGPT to generate essays with specific parameters regarding length, style, language, and detail.

Another popular Generative AI example is Dall-E, which was also developed by OpenAI. Like ChatGPT, Dall-E enables users to provide a highly specific set of inputs. The difference, however, is that instead of text, Dall-E generates novel digital images. These images can be photorealistic depictions of specific content, or they can mimic paintings and other forms of animation, with their style and content dictated by the user.

Generative AI Impact on Business

Whether Generative AI is utopian or dystopian, what’s clear is that it’s going to have a profound impact on the future of business and media. Let’s examine some possible Generative AI impacts on business.

Improved Efficiency

Generative AI gives users the power to create new forms of content with unprecedented speed and sophistication. Applied properly, this can give employees the ability to improve the efficiency with which they complete their tasks, enabling businesses to operate more efficiently as a whole.

Content Creation

Generative AI can serve as an extremely powerful content creation tool. By leveraging the power of Generative AI, companies may be able to produce more sophisticated content at greater scale than was previously possible.

Personalized Experiences

By leveraging the power of artificial intelligence and automation, Generative AI can allow businesses to create increasingly personalized customer experiences. In the old days, businesses were limited in the forms of content, products, and services they could provide—which also meant they were limited in the customers they could attract. Now, businesses may be able to use Generative AI to tailor their offerings to appeal to every customer individually, leading to greater customer attraction, engagement, and retention.

New Revenue Streams

Generative AI itself is a next-generation tool that businesses can offer to users on a subscription-, advertisement-, or freemium-based model. The historically rapid adoption of Dall-E and ChatGPT has resulted in OpenAI’s valuation rising to over $30 billion, and other companies are following suit by developing similar products.

Human Capital Replacement

One highly debated impact of Generative AI on business is its potential for replacing human capital. By mimicking human creativity, Generative AI threatens worker security by potentially giving businesses the power to replace human work with automation. This may have the immediate impact of increasing profits for businesses while severely damaging the labor market for workers. The long-term effects of Generative AI on human capital remain to be seen.

Copyright Infringement

Another major threat to businesses is Generative AI’s potential for infringing on copyrighted material. Many Generative AI models pull data from a variety of sources, which may include copyrighted material. This could lead to outputs that either directly infringe on copyrights, or else operate in a sort of gray area that may be hard to litigate.

Security Threats

Generative AI also poses many potential security threats. For example, Generative AI can be used to convincingly mimic human communication, which can lead to phishing attacks. Generative AI can also be used to create so-called “deep fakes,” which mimic authentic images, audio, and visual content. Deep fakes can be used to harm an individual’s or a company’s public image and even create legal trouble.

Conclusion

Whether you’re one of those people who’s excited or horrified by the advent of Generative AI, one thing’s for sure: the technology is here to stay, and it has the power to disrupt just about every aspect of doing business. Generative AI may lead to many positive impacts on businesses, including by empowering the efficiency and effectiveness of content creation. Conversely, Generative AI—like any tool—can be leveraged for destructive and predatory purposes. While Generative AI is sure to have a profound impact on business and culture in the coming years, its exact impacts remain to be seen.

 

Additional Resources:

• What Is Big Data? Examples, Benefits, & Challenges
• What Is Predictive Analytics
• Top Technology Consulting Firms