How AI-Generated Models Can Create New Content and Experiences

AI models can transform data into insights that drive AI-generated models and business operations. These scalable, accessible solutions can also create new content and experiences for customers.

Emerging technologies like large language models (LLMs) and neural radiance fields enable teams to transform text and images at scale. The technology can also support 3D modeling, drug development and digital twins.

Text

Generative AI models create patterns found in images, sounds, software code, molecules, 3D designs and other data types. Recent progress in learning representations allows them to efficiently iterate on useful variations.

While generative AI models can produce a variety of credible writing on demand, they often lack a personal touch and emotional depth. They also tend to overuse certain phrases and vocabulary that they learned during training, resulting in articles with an overly “keyword-stuffed” feel.

Nevertheless, generative AI can help organizations save time and resources. It can turn a research paper into a well-written and largely correct essay in seconds, for example. And, it can quickly summarize a lengthy medical research report for a business leader’s review. To get there, though, these tools need a lot of data to train on. For instance, OpenAI’s large language model was trained on 45 terabytes of text—equivalent to a quarter of the Library of Congress. This isn’t something that garden-variety start-ups can afford to do.

Images

Fashion brands use AI models to showcase clothing, which can save them money by reducing the need for model fees and photoshoots. These digital models can also help customers visualize how clothes fit and look, which enhances the online shopping experience and increases customer confidence in making purchases.

AI art generators, such as OpenAI’s DALL-E and the popular Midjourney, produce visuals based on prompts that can include text or images. The generated imagery can look like sketches, paintings or photos.

AI-generated models can help reduce data augmentation costs by creating synthetic data that addresses corners and exceptions that natural (non-labeled) data might miss. This capability spans all modalities and is used in applications such as image recognition, medical coding, DNA sequencing, weather forecasting and natural disaster prediction. ML and regression models are often used to perform this function.

Videos

When it comes to AI-generated videos, the public marvels at the technological prowess that can create lifelike creations. But experts also ring alarm bells about their potential to spread misinformation and erode trust in society.

In the case of generative models, the technology learns to replicate human features from extensive data sets of images and video clips. These include everything from facial expressions to subtle changes in voice modulation over time.

Using these AI generators feels pretty intuitive, and it’s easy to see how this tech can be useful for brands. For instance, Synthesia lets you upload a photo to generate a video with an AI avatar. While these avatars are incredibly realistic, most viewers can identify them as AI-generated by their mannerisms and articulation. They also tend to stay on screen a little too long (video pacing needs work). The software is free, but plans start at $22/month.

Audio

Creating high-quality music or sound effects can be an extremely challenging task for AI models. Generative AI models can do this through text prompts like “Play calming violin music” or “Simulate sound of knocking on door.”

The model then analyzes these audio waveforms or spectrograms and finds patterns. It then creates new sounds based on those patterns. These sounds can be incredibly realistic and detailed, like the sound of a dripping faucet or the voice of a person in deep conversation.

This type of generative AI is popular in the field of eLearning and infotainment. Companies use Generative AI to generate content that’s personalized and engaging for their target audience. The Generative AI technology also allows for a seamless translation of text into voice and sound.