Generative AI and digital twins enable the rapid creation and testing of new content from a variety of multimodal inputs. Inputs and outputs of generative AI models can include text, images, video, audio, animation, 3D models, and other types of data. Alongside generative AI, digital twins provide a virtual canvas where creative concepts, assets, and environments can be modeled, tested, and evolved in real time.
With generative AI, startups and large organizations can immediately extract knowledge from their proprietary datasets. For example, you can build custom applications that speed up content generation for in-house creative teams or end customers. This can include summarizing source materials for creating new visuals or generating on-brand videos that suit your business’s narrative.
Digital twins—virtual representations of creative assets, environments, or processes—complement generative AI content creation by providing a dynamic environment for simulation, testing, and optimization. By mirroring real or conceptual objects in a digital space, digital twins allow teams to visualize, iterate, and refine content before it is finalized.
Streamlining the creative process is one key benefit. Generative AI also provides rich information to grasp underlying patterns that exist in your datasets and operations. Businesses can augment training data to reduce model bias and simulate complex scenarios. This competitive advantage fuels new opportunities to enhance content workflows, improve decision-making, and boost team efficiency in today’s fast-paced, evolving market.
Generative AI tools powered by large language models (LLMs) show tremendous potential to transform business. To derive maximum business value, enterprises need models customized to extract insights and generate content specific to their business needs. Customizing LLMs can be an expensive, time-consuming process that requires deep technical expertise and full-stack technology investments.
For a faster, more cost-effective path to customized generative AI, enterprises are getting started with pretrained foundation models. Rather than starting from scratch, these models provide a base for enterprises to build on top of, expediting development and fine-tuning cycles while reducing costs of running and maintaining generative AI applications in production.
Startups and enterprises looking to build custom generative AI models to generate context-relevant content can employ the NVIDIA AI Foundry service.