Towards AI Conversing: FloodBot using Deep Learning Model Stacks

Talking to the electronic device and getting the required information at a minimal time has become today's norm. Although AI-powered conversational agents have percolated the commercial market, their use in a communal setting is still evolving. We postulate that the deployments of chatbots in disaster-prone areas can be beneficial to watch, monitor, and warn people during the crisis. Furthermore, the successful implementation of such technology can be life-saving. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques. In this work, we discuss our deployment of a real-time flood monitoring chatbot called FloodBot. We collect, annotate and visually parse images from potentially hazardous areas. We detect the flood conditions and identify objects in harm's way by stacking deep learning models such as a convolutional neural network (CNN), single-shot multi-box object detection (SSD). We then feed the image contents to a knowledge base of our artificially intelligent FloodBot and explore its AI-Conversing power using end to end memory network. We also showcase the power of cross-domain transfer learning and model fusion techniques.