What is generative AI?

How does it work and what is its history?

Techopedia editor Margaret Rouse offers a comprehensive explanation of generative artificial intelligence (AI), describing it as “a broad label that’s used to describe any type of AI that can be used to create new text, images, video, audio, code or synthetic data.  While the term [is] often associated with ChatGPT and deep fakes, the technology was initially used to automate the repetitive processes used in digital image correction and digital audio correction”.¹

Generative AI includes learning algorithms that make predictions and algorithms that can leverage prompts to autonomously compose articles and generate images. “Therefore, because machine learning and deep learning are inherently focused on generative processes, they can be considered types of generative AI, too”.¹   

George Lawton notes that generative AI first begins with “a prompt in the form of a text, image, video design, [some] musical notes, or any input that the AI system can process, [followed by] various AI algorithms [that] return new content [such as essays, solutions to problems, or realistic fakes created from pictures or audio of a person] in response to the prompt”.²

Rouse states that early generative AI “required submitting data via an API or an otherwise complicated process, [requiring] developers [to] familiarize themselves with special tools and write applications using programming languages such as Python”.¹

Modern generative AI has a much more flexible user experience where ender users can input their requests using natural language instead of code. “Generative AI was introduced in the 1960s in chatbots. But it was not until 2014, with the introduction of GANs [that] generative AI could create convincingly authentic images, videos and audio of real people”.² GANs and variational autoencoders (VAE) are two common generative models for image and text creation.

Random noise can be leveraged by some generative AI models as an input to generate new outputs. To do this, the generative AI model “takes a random noise vector as input, passes it through the network and generates output that is similar to the training data. The new data can then be used as additional, synthetic training data for creative applications in art, music and text generation”.¹

Generative AI that is leveraged as a means of enhancing human creativity “can be categorized as a type of augmented artificial intelligence”.¹   

What are common generative AI applications?

With the immense capabilities that generative AI offers, it’s no surprise that there’s a myriad of different applications for end users looking to create text, images, videos, audio, code, and synthetic data.  Here are some examples of the most popular generative AI applications.

• ChatGPT – Possibly the most famous or infamous example of a generative AI application. Created by Open AI in December 2022, ChatGPT is an online AI chatbot where users prompt it with questions, and it responds by generating answers to those questions.
• The Lensa app – This application uses AI to transform your portrait-type photos into dynamic custom portraits. Created by Prisma Labs in 2018, Lensa allows users to create transform their selfies into that of a superhero, a rockstar, or a myriad of other templates.
  
• DALL·E 2 – An AI system where users input descriptions using plain language and it creates realistic images and art based on those descriptions.  Created by Open AI in April 2022, DALL·E 2 uses a diffusion model that generates higher quality images than the original Dall-E’s discrete variational autoencoder (dVAE).
  
• Copy.ai – An AI writing tool that leverages ML to create various types of text content. Released by Paul Yacoubian in October 2022, Copy.ai offers different tools depending on each users’ copywriting needs and can produce long-form web copy, emails, social media content, and more.
• Midjourney – An AI-based image generator program and service. First launched on 14 March 2022, Midjourney has been leveraged to generate award winning art, artwork used in children’s books, and images of public figures that have caused a lot of controversy.
  

What is the different between generative AI and traditional AI?

Bernard Marr writes that traditional AI, (aka narrow AI or weak AI) “focuses on performing a specific task intelligently [and] refers to systems designed to respond to a particular set of inputs”.³ These traditional AI systems can process data and make learned choices or predictions from that data.  Some of these systems function similarly to something like the IBM supercomputer Deep Blue.  They’re fed a considerable amount of data, in Deep Blue’s case chess specific data, and use it to either develop a game winning strategy or to respond to an opponent’s strategy. Other traditional AI systems operate similarly to Siri or Alexa, responding to and predicting the needs of a household, while others function more like recommendation engines for Google, Netflix, or Amazon. “AIs [that] have been trained to follow specific rules, do a particular job, and do it well, but they don’t create anything new”.³

Inversely, generative AI can create new things (text, art, music, videos, and more) from the plain language prompts that it receives. “Generative AI models are trained on a set of data and learn the underlying patterns to generate new data that mirrors the training set”.³  

How can generative AI benefit your business?

Generative AI is perhaps the most recognizable type of AI today. It has immense potential to help enterprises produce high quality content quickly, help users to innovate, creating new products, and offers avenues for improving customer service and communication. Generative AI models are commonly leveraged for creating visual or audio art, writing web content or essays, running web searches, and much more.

For enterprises looking to leverage generative AI tools, here are some of the benefits that your organization can hope to leverage:  

• Quickly create content – Arguably the most obvious and accessible of generative AI’s benefits, generative AI can be used to rapidly create content for anything from blogs, marketing newsletters, social media, and more. Jackie Wiles of Gartner reports that “by 2025, 30% of outbound marketing messages from large organizations will be synthetically generated, up from less than 2% in 2022”.⁴
• Offer a better customer experience – By utilizing human-like chatbots that contain a vast depth of product or services knowledge for handling anything from routine inquiries to resolving technical issues, it potentially eliminates any wait time that customers would otherwise have to spend waiting to talk to a human customer service or technical support agent. 
  
• Improve personalization – Machine learning algorithms can track and analyze an end user’s search history and purchase history, and then leverage that data to make targeted product recommendations or to produce content specifically for that end user. Generative AI can also be utilized when it comes to the onboarding and continuing education of employees, potentially creating customized lessons for each individual employees learning styles.
• Accelerate design cycles and produce new products and services – Generative AI can “accelerate development in industries such as pharmaceuticals where drug discovery can take a decade or more [and to] to launch products and shrink R&D timelines and budgets in the process”.⁵
• Streamline complex processes – ChatGPT and a variety of other machine learning models that can help developers with writing application code in JavaScript and other languages, and with debugging code.  Machine learning models can also be used with writing and analyzing content, such as analyzing a variety of models spread throughout the different sections of an enterprise. 
  
• Broad analysis and applications – Generative AI can be leveraged to analyze a large amount of data on a particular subject, then process that data using a model, and ultimately looking for high-level patterns that could be used for developing business strategies.

Resources

1. Generative AI, Margaret Rouse, Techopedia, 27 June 2023.
2. What is generative AI? Everything you need to know, George Lawton, TechTarget, 2023.
3. The Difference Between Generative AI And Traditional AI: An Easy Explanation For Anyone, Bernard Marr, Forbes, 24 July 2023.
4. Beyond ChatGPT: The Future of Generative AI for Enterprises, Jackie Wiles, Gartner, 26 January 2023.
5. 7 top generative AI benefits for business, John Moore, TechTarget, 19 April 2023.