What Does Generative AI Mean For Your Brand And What Does It Have To Do With The Future Of The Metaverse?
ChatGPT, Midjourney, Lensa AI, and deep fakes – Generative AI is transforming our content landscape. It’s prompting questions like what is art? How do AI copyrights work? Is it ethical to use generative AI for my job? Is Generative AI a fad or a useful tool for brands?
I, along with my co-founder Brandon Kaplan, Chief Innovation Officer at Journey, addressed these questions in a recent webinar we hosted about Generative AI and what it means for brands and what AI’s role is in the future of the Metavese.
Like many technologies, Generative AI is years in the making. Like how the iPhone revolutionized the consumer experience in 2006, OpenAI with it is user-friendly interfaces and APIs, will revolutionize consumer behavior in 2023 and beyond.
Artificial intelligence is a technology that can perform tasks that traditionally only humans could perform. “Generative AI refers to artificial intelligence that can generate novel content, rather than simply analyzing or acting on existing data,” said Brandon Kaplan. If you think of artificial intelligence as a pie, generative AI is one of the slices. Robots, deep learning, machine learning, and conversational artificial intelligence are all slices of the AI pie.
The generative AI pie graph is an example of the types of AI. Some artificial intelligence may overlap with others. This is not the whole pie, especially as AI evolves and we find new ways to use it.
ChatGPT, a sub-product of OpenAI, is the most well-known AI tool. It passed 1 million users in just 5 days. “GPT3, which is the currently available version, is trained on 175 billion parameters or data points. GPT4, which is coming up this year, likely trained on over 100 trillion parameters,” said Brandon Kaplan. Generative artificial intelligence is an abundance technology. “If used ethically and in the right ways, we can use these technologies to scale what we do and create better products and experiences,” Kaplan explained. You can use it for inspiration, break writer’s block, use AI for scale. Personalize generative AI by connecting it to different data sets to produce content and reduce friction.
Generative AI comes in all types of forms. Many generative AI uses are visual such as image, video, and avatar generation. Generative AI can also write text, summarize articles, and generate code. Audio is another area where generative AI thrives.
ChatGPT is the most well-known text generative AI. It lets coders build 3x as fast. You can try it for yourself. Ask ChatGPT to generate code, review it (or ask a friend) to see how it matches up. Even though ChatGPT can generate code or text quickly, it’s important to double check it. ChatGPT is a machine learning model. While it’s trained on a large data set, there are data biases in any model.
Generative image AIs can create art from scratch, modify pictures (like removing objects), or add objects to a picture. Types of image AIs include Midjourney and Lensa. Neural Radiance Fields (AI NeRF) are a new type of AI that produces 3D models from 2D pictures. NVIDIA created instant NeRFs that achieve more than 1,000x speedups in some cases. Their model requires seconds to train on a few dozen still photos (plus data on the camera angles) to render the resulting 3D scene within tens of milliseconds.
Pictory and Potion video dubbing, generate videos. Generative AI can create scenes from scratch or like Runway’s Gen-1 program, transform existing videos into new ones. They call it video-to-video that can be used for storyboarding, masking, rendering and however else people unleash their creativity with generative video AI.
Generative audio AI creates media from scratch using synthetic voices. “The audio space, the ability to create audio, to convert, to edit, to dub and translate audio is going to be the underlying media element of a lot of what we all create,” said Kaplan. In the future, we might be typing into virtual keyboards, but we’ll probably be doing a lot of voice.
