Speaker
Description
The integration of multimodal artificial intelligence (AI) in warehouses monitoring offers substantial improvements in efficiency, accuracy, and safety. This approach leverages diverse data sources, including visual, and speech sensors, to provide comprehensive monitoring capabilities. Key challenges include the fusion of heterogeneous data streams, which requires sophisticated algorithms to interpret and integrate diverse inputs effectively. The development and training of AI models that can accurately analyse multimodal data are resource-intensive, demanding significant computational power and extensive datasets. Real-time processing is essential for prompt decision-making and incident response, yet achieving this remains a technical challenge due to the high volume of data and complexity of the models. Additionally, ensuring system robustness and reliability in varying warehouse environments is critical. We present here use cases where Multimodal AI has been successfully applied to demonstrate ability to deal with variabilities to monitor warehouses with unpredictable changes.
| Type of presentation | Talk |
|---|---|
| Classification | Both methodology and application |
| Keywords | AI, Industry, Logistics, Automation |