At the Center for AI Value, we’re at the cutting edge of multimodal data modeling, unlocking the hidden potential of diverse data types to drive insights and innovation.
What is Multimodal Data Modeling?
Multimodal data modeling involves the integration and analysis of multiple types of data, such as text, images, and numerical data, to generate more comprehensive insights than single-mode data analysis.
Why It Matters
- Richer Insights: Combining multiple data types provides a more complete picture of complex phenomena.
- Improved Predictions: Multimodal models often outperform single-mode models in predictive tasks.
- Cross-functional Applications: The methodology is relevant in various business functions.
Our Research
We are conducting groundbreaking research using multimodal data to e.g., predict customer value, social media impact, or advertisement success. Furthermore, we are working on further developing the currently limited capabilities of transformer-based AI in the area of numerical data.
Applications
Multimodal data modeling has potential applications across various industries:
- Retail: Enhancing customer segmentation and personalization based on e.g., the pictures of the products customers bought
- Healthcare: Improving diagnosis by combining imaging data with patient records
- Manufacturing: Optimizing processes by integrating sensor data with production metrics
- Robotics: Combining generative AI with multimodal sensor data
