We're witnessing a transition in AI. Narrow systems able to execute specific tasks in a single domain are giving way to broad AI that learns more generally and can work across domains and problems. This shift is being bolstered by foundation models, which are trained on large, unlabeled datasets and fine-tuned for an array of applications. They are transforming how we compute at scale. But we need more innovation in hardware and infrastructure to power and deploy this next generation of AI models. Continuing to rely on conventional infrastructure and processors is simply not scalable — or sustainable.
By invitation only, researchers attended the forum event held at the T.J. Watson Research Center in NY to discuss what's next in foundation models and the challenges in designing AI hardware to support complex and multi-modal workloads. The forum included presentations from industry leaders, interactive demos by IBM experts, and a poster session showcasing research from leading universities.
Role: Design lead
Designed at IBM Research
All designs were based on images of an AI hardware wafer we photographed under a microscope.
Since the chips are inherently square, we used that as our main holding shape. The square was juxtaposed with an organic crop of the chip that could be animated for screen and used to create numerous variations for print.
For digital applications, we colorized the photographs with layers of color and blending modes. Because of resolution challenges, we used a simpler, vector representation for print applications.
The vectorized chip, square and text were combined in various ways to create environmental graphics applied throughout the T.J. Watson Research Center.