City of Boston

Mentors: Student: Sonnet

Using machine learning, this project will develop an image processing pipeline that takes a user submitted image from Boston’s 311 app and helps the resident submit a corresponding non-emergency city request. By automating this process, this service can be accessible and more intuitive for a wider range of audiences, who may not have English as their first language or be well-versed with using technology, while also consuming less of the city’s resources due to erroneous submissions. Curating a dataset from the previously submitted 311 system photos, I will prepare a large image collection that will then be used to develop a CNN for image classification with the TensorFlow library. Then, I will develop a large language model trained on past service requests that can help draft a service request for the user that is customized to the specific situation at hand. These models will be saved, compressed, then deployed to an accessible API endpoint.

Mentors: Student: Mira Yu

Historically, my city's 311 app has only been offered in English, but 37.4% of my fellow Bostonians don't speak English. All residents deserve access to our city's resources, and I'm excited to help address this need. I'll be fine-tuning a bidirectional machine translation model on City of Boston-specific phrases reflecting regional dialects unique to Spanish, Vietnamese, Chinese, and Haitian Creole-speaking communities of Boston.

Mentors: Student: Hengxu Li

This proposal aims to revolutionize the City of Boston's 311 service request system by integrating AI-based image recognition. The current process, while functional, can be cumbersome and prone to misclassification, leading to inefficient allocation of city resources. By enabling residents to submit requests through a simple photo, analyzed and categorized by an AI model, the system becomes more accessible, efficient, and user-friendly. The project will involve building and training an AI model using historical 311 data, developing a high-performance API for the model, and ensuring seamless integration with web and app interfaces for a broad user base. The deliverables include a trained AI model, a robust API, and a prototype app interface ready for city-wide deployment after testing and optimization.