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Sustainability

Blog Posts

• Using Machine Learning to Increase Durability and Reduce Returns for Sports and Fashion Goods

  

  Ali Naeem and Alan Fortuny Sicart, January 27, 2024
• Circularity in Fashion, powered by AI

  

  Alan Fortuny Sicart, February 13, 2023
• The Green Last Mile Project: Accelerating Cargo-Bike Logistics in Cities

  

  Nicolas Collignon, October 11, 2022

Webinars

• Decarbonizing AI: The Good, the Bad, and the Ugly

  Dr. Noman Bashir (University of Massachusetts Amherst), July 28, 2023

Innovation Grants

• Towards greener last-mile operations: Supporting cargo-bike logistics through optimized routing of multi-modal urban delivery fleets

  Maria Astefanoaei (IT University of Copenhagen); Akash Srivastava (MIT-IBM Watson AI Lab, IBM Research); Kai Xu (University of Edinburgh); Nicolas Collignon (Kale Collective); Esben Sørig (Kale Collective); Soonmyeong Yoon (Kale Collective), 2022

Talks

• NeurIPS 2023
  • Fireside Chat: LLMs and their Implications for Climate Change

Workshop Papers

Venue

Title

NeurIPS 2023

How to Recycle: General Vision-Language Model without Task Tuning for Predicting Object Recyclability (Papers Track) Abstract and authors: (click to expand) Abstract: Waste segregation and recycling place a crucial role in fostering environmental sustainability. However, discerning the whether a material is recyclable or not poses a formidable challenge, primarily because of inadequate recycling guidelines to accommodate a diverse spectrum of objects and their varying conditions. We investigated the role of vision-language models in addressing this challenge. We curated a dataset consisting >1000 images across 11 disposal categories for optimal discarding and assessed the applicability of general vision-language models for recyclability classification. Our results show that Contrastive Language-Image Pre- training (CLIP) model, which is pretrained to understand the relationship between images and text, demonstrated remarkable performance in the zero-shot recycla- bility classification task, with an accuracy of 89%. Our results underscore the potential of general vision-language models in addressing real-world challenges, such as automated waste sorting, by harnessing the inherent associations between visual and textual information. Authors: Eliot Park (Harvard Medical School); Eddy Pan (Harvard Medical School); Shreya Johri (Harvard Medical School); Pranav Rajpurkar (Harvard Medical School)