Generative AI has taken the world by storm, transforming how we create, consume, and interact with various forms of media. In this blog post, we'll dive into the different flavors of generative AI, including synthetic media, which includes image, video, text, and audio generation. We'll also discuss large language models (LLMs) and diffusion models, which are key components of generative AI technologies. With AI tools, in general, becoming easier to use and cheaper, the time is ripe for business users to leverage this technology to make a significant impact on their work and output.

What is generative AI and why does it matter?

Generative AI is a subset of artificial intelligence that can create new content based on training data. Generative AI is revolutionizing business, creating new value in sales, marketing, and other parts of the company, in every industry. Creating audio for voiceovers and producing text and images for marketing campaigns are just a couple of examples of how generative AI has revolutionized content creation.

Generative AI use cases

For instance, startups to enterprise businesses can harness the power of generative AI APIs to develop innovative applications, ranging from personalized marketing campaigns to virtual reality experiences. The potential of generative AI is vast, and as technology advances, we can expect it to play an even more significant role in shaping our digital landscape.

At G2, we are excited to be a trusted resource for all things generative AI. The first subcategory is Synthetic Media, and G2 will launch more categories in this rapidly evolving space in the upcoming months.

Synthetic media: the new frontier of content creation

Synthetic media encompasses any AI-generated media, including images, videos, texts, and audio. Some popular AI-powered tools in G2’s Synthetic Media category include AI generative art tools, photo generators, and drawing generators.     

Synthetic media types, encompassing images, videos, texts, and audio, offer a wide range of uses and applications across various industries. Some of the most common use cases for each media type include the following:

Text-to-text

AI-generated text finds its use in content creation, producing blog posts, news articles, and social media content to help businesses and individuals maintain a consistent online presence. This text can be generated using standalone platforms like ChatGPT, applications that have GPT 3 or GPT 4 built-in, and much more. Customer support benefits from AI-generated text through chatbots and virtual assistants that provide automated support, improving response times and customer satisfaction. AI-generated text is also employed in real-time translation tools, breaking language barriers and facilitating global communication. Creative writers and authors can use AI-generated text as a valuable tool for inspiration, plot suggestions, and even entire manuscripts.

Text-to-image

AI-generated images, which can be produced by tools like Midjourney and DALL·E 2, have applications in advertising where they can create visually striking and personalized advertisements for digital and print media. Artists and designers can use AI-generated images for innovative art pieces, blending traditional methods with cutting-edge technology. In gaming, developers can create realistic and immersive virtual environments, characters, and objects with AI-generated images. The fashion industry can also benefit from AI-generated images, using them to visualize new designs, fabrics, and patterns for rapid prototyping and iteration.

Text-to-video

Although generative AI for video is in a nascent phase, the possibilities are exciting. AI-generated videos can play a significant role in film and TV production, creating realistic visual effects, virtual sets, and even entire animated films, thus reducing production costs and time. In marketing, AI-generated videos enable the creation of personalized promotional content tailored to individual customer preferences and demographics. AI-generated educational videos cater to students' unique needs and learning styles by offering customized learning materials. 

Audio generation

AI-generated audio has various applications, including music production, where it can create unique compositions and explore new genres and styles. Podcasts and audiobooks benefit from AI-generated audio, producing high-quality, natural-sounding voiceovers for narrations. Voice assistants rely on AI-generated audio to understand and respond to user queries.

The future of code is generative

Generative AI is also revolutionizing the world of code development and creation. AI-powered tools, such as code completion assistants and automatic error detection systems, streamline the software development process, making it more efficient and accessible. By leveraging AI-generated code snippets and providing real-time suggestions, these tools help developers write cleaner and more efficient code and enable individuals with limited coding experience to participate in software development. The impact of generative AI on code development is poised to democratize access to technology and foster innovation in the software industry.

The possibilities are endless

It is important to note that this category is only the beginning. Generative AI cuts across various categories, supercharging content creation for sales, marketing, human resources, biotech, and more.

The technology behind generative AI is advancing fast with new technologies and methods such as large language models (LLMs) and diffusion models cropping up and making splashes, allowing creators to develop applications and create content quickly and efficiently.

Large language models (LLMs)

LLMs are artificial intelligence models trained on vast amounts of text data to understand and generate human-like text. These models, such as GPT-4 by OpenAI, can generate coherent and contextually relevant text based on user input.

The primary goal of LLMs is to create AI text generators that can understand and respond to natural language queries with human-like proficiency. LLMs have been used to develop chatbots, generate news articles, and even write entire novels.

Diffusion models

Diffusion models are a recent development in generative AI that focuses on creating realistic images, videos, and audio by simulating a diffusion process. Instead of relying on traditional generative techniques like generative adversarial networks (GANs), diffusion models use a denoising process to generate high-quality synthetic media.

Diffusion models gradually transform a noisy image, video, or audio into a clean, realistic version. They do this through a series of steps, where the AI "cleans up" the content bit by bit, removing noise and adding details at each step. The result is high-quality synthetic media that looks and sounds realistic, all while being generated in a simple and easy-to-understand process.

Diffusion models have shown great potential in generating AI art, with some AI-generated images being virtually indistinguishable from photographs taken by humans. As these models continue to develop, we can expect more realistic and higher-quality synthetic media in the near future.

Looking forward

Generative AI has opened up new possibilities for creativity and innovation across every industry. As we continue to explore the potential of technologies like LLMs and diffusion models, we can expect to see even more groundbreaking applications in the world of synthetic media. G2 believes generative AI will become more accessible to non-technical users and shake up business productivity and innovation more than any digital technology since the advent of the PC. 

Edited by Shanti S Nair