From generating content and code, creating images and videos, and testing algorithms with synthetic data, generative AI is a force multiplier enabling leaps in productivity and creativity for nearly every industry – and particularly in transportation, where it’s streamlining workflows and driving new business.
Much like when early iPhone app developers began using GPS, accelerometers and other sensors to create mobile applications, AI developers now can tap foundation models to build new experiences and capabilities. The process involves looking at vehicles across the industry, whether existing or historic. Then, with a great deal of human curation, some blend of popular designs and fresh inspirations based on a company’s stylings emerge. That forms the basis for artists’ 2D hand-drawn sketches that are then recreated as 3D models and clay prototypes.
In particular, when looking for “scrap” design elements, generative AI models can be trained on an automaker’s portfolio as well as vehicles industrywide, assisting this workflow. This can happen first by fine-tuning a small dataset of images with transfer learning, and then by tapping into Nvidia TAO Toolkit. Or it might require a more robust dataset of some 100-million images, depending on the requirements of the generative AI model.
When building manufacturing facilities, planning in simulation before launching into production helps to reduce costly change orders that can shut down factory lines.Generative AI is also making inroads in marketing and retail sales departments across many industries worldwide. These teams are expected to see a productivity lift from generative AI of more than $950-billion, according to a McKinsey report.