{"id":8302,"date":"2020-05-27T08:53:43","date_gmt":"2020-05-27T15:53:43","guid":{"rendered":"http:\/\/cm-edgetun.pages.dev\/garage-en-us\/?p=8302"},"modified":"2020-09-24T15:37:29","modified_gmt":"2020-09-24T22:37:29","slug":"with-new-garage-project-trove-people-can-contribute-photos-to-help-developers-build-ai-models","status":"publish","type":"post","link":"https://cm-edgetun.pages.dev/en-us/garage\/blog\/2020\/05\/with-new-garage-project-trove-people-can-contribute-photos-to-help-developers-build-ai-models\/","title":{"rendered":"With new Garage project Trove, people can contribute photos to help developers build AI models"},"content":{"rendered":"

Sep 23, 2020 Update<\/strong> Trove is now open to try on Android and a new web app! To learn more, read the full story on the Garage Blog<\/a>.<\/p>\n

Every day, developers and researchers are finding creative ways to leverage AI to augment human intelligence and solve tough problems. Whether they\u2019re training a computer vision model that can spot endangered snow leopards<\/a> or help us do our business expenses more easily when we scan pictures of receipts, they need a lot of quality pictures to do it. Developers usually crowd source these large batches of pictures by enlisting the help of gig workers to submit photos, but often, these calls for photos feel like a black box. Participants have little insight into why they\u2019re submitting a photo and can feel like their time was lost when their submissions are rejected without explanation. At the same time, developers can find that these sourcing projects take a long time to complete due to lower quality and less diverse inputs.<\/p>\n

We\u2019re excited to announce that Trove, a Microsoft Garage project<\/a>, is exploring a solution that can enhance the experience and agency for both parties. Trove is a marketplace app that allows people to contribute photos to AI projects that developers can then use to train machine learning models. Interested parties can request an invite to join the experiment as a contributor<\/a> or developer<\/a>. Trove is currently accepting a small number of participants in the United States<\/strong> on both Android and iOS.<\/p>\n

A marketplace that puts transparency and choice first<\/h3>\n

Today, most data collection is passive, with many people unaware that their data is being collected or not making a real-time, active choice to contribute their information. And even those who contribute more directly to model training projects are often not provided the greater context and purpose of the project; there\u2019s little to no feedback loop to correct and align data submissions to better fit the needs of project.<\/p>\n

For people who rely on this data gig work as an important source of income, this rejection experience can leave them feeling frustrated and without any agency to contribute better submissions and a higher return on their time investment. With machine learning being a critical step in unlocking advancements from speech to image recognition, there\u2019s an important opportunity to increase the quality of data, while making sure that contributors have the clarity and choice they need to participate in the process.<\/p>\n

The Trove team has found a way to overcome these tough tradeoffs in a marketplace solution that emphasizes greater communication, context, and feedback between developers and project participants. \u201cThere\u2019s a better way we can do this. You can have the transparency of how your data is being used and actually want to opt in to contribute to these projects and advance science and AI,\u201d shares Krishnan Raghupathi, the Senior Program Manager for Trove. \u201cWe\u2019d love to see this become a community where people are a key part of the project.\u201d<\/p>\n

To read more about key features and how Trove works for developers and contributors, check it out on the Garage Workbench<\/strong><\/a>.<\/p>\n

\"\"<\/p>\n

Aspiring to higher quality data and increased contributor agency<\/h3>\n

The team behind Trove was originally inspired by thought leaders exploring how we can embrace the need for a large volume of data to enable AI advancements, while providing more agency to contributors and recognizing the value of their data. \u201cWe wanted to explore these concepts through something concrete,\u201d shared Christian Liensberger, the lead Principal Program Manager on the project. \u201cWe decided to form an incubation team and build something that could show how things could be different.\u201d<\/p>\n

In creating Trove, the incubation team had to think through principles that would guide them as they brought such an experience to life. They believe that the best framework to produce the higher quality data needed to train these AI models involves connecting content creators to AI developers more directly. Trove was built with a design and approach that focuses on four core principles:<\/p>\n