By now, you've probably heard the term "generative AI" used to describe new artificial intelligence tools. You may even be familiar with devices that use generative AI (or GenAI) to create new content, such as ChatGPT and DALL-E. 

But even if you're aware of the rise of GenAI, it can sometimes take time to discern the differences between generative AI and older forms. The evolution of artificial intelligence is happening so quickly that it can be hard to keep up—which is why we've put together this handy explainer. 

Let's look at generative AI and how it's different from other types of AI. We'll also look at the effects of the widespread use of AI that creates content.

What is generative AI (GenAI)?

McKinsey and Company define GenAI as "algorithms that you can use to create new content, including audio, code, images, text, simulations, and videos." 

In recent years, AI algorithms that are based on machine learning have become much more powerful. This means you can use them more creatively to make new things based on the user's request. This is called "generative AI." 

What are some examples of generative AI?

Today, examples of generative AI applications abound. ChatGPT is a generative AI that can generate new text simply by being fed a natural-language prompt. As a fun test of ChatGPT's capabilities, you can ask it to provide you with a recipe for chocolate chip cookies, for example, or request that it write you a song in the tradition of your favorite artist. 

DALL-E is a generative AI algorithm that can generate fresh visual images based on text prompts. Simply asking DALL-E to create a picture of a rhinoceros having coffee at a cafe will yield startlingly accurate results. There are many other generative AI apps as well. 

Generative AI vs. AI

It is essential to understand that this form of AI fundamentally differs from older AI in several ways.

The applications of traditional AI are generally limited to repetitive and analytic tasks, which don't require creative capability. These tasks include finding patterns, classifying data, optimizing analytics, and making decisions based on large datasets. 

On the other hand, it has all the machine learning power to take data as a prompt and create new content based on what the algorithm "understands" about the prompt. As a result, it can produce unique content of nearly any kind, including images, deep fakes, text content, video, and more. 

What are the limitations?

Because generative AI can make almost any content, you can use it in bad ways and make big mistakes. Researchers have pointed out that ChatGPT, in particular, regularly generates false and misleading information—including fake hyperlinks and made-up sources—in response to user prompts. 

Knowing this, it's clear that this technology is compelling, but its use comes with significant risks. Businesses considering deploying generative AI tools as part of their tech stack should stay up-to-date on the limitations and concerns associated with GenAI and be sure they use the tech in a way that doesn't leave them vulnerable to undue risk.

How it's being used

The age of AI has just begun. New, ever-more powerful tools leveraging these advanced algorithms are just around the corner. Moving forward, each new iteration of this form of AI will require organizations to consider the benefits and risks associated with the technology and how it can be helpful for their bottom line. 

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