Impact Driven AI applications — Session 2 at USF
By fast.ai/Data Institute students
It was great to see enthusiastic audience at last week’s USF seminar — the second part of the Impact Driven AI applications driven by the Data Institute/ Fast.ai students. We had a hall filled with about 160 participants! This session is part of the series which started as a discussion among a small group of students during the advanced Deep learning course offered by fast.ai earlier this year.
It is exciting to see the community growing and working together in the pursuit of leveraging Deep Learning in not just shiny cool projects but the ones which drive tremendous value around the globe by solving important and immediate problems. And we are thrilled to announce we are taking this forward with ImpactAI.org, a hands-on think tank to explore new domains and build AI driven products where they are most needed. Please watch the video to learn more about the journey so far. This is an open call for all AI/ ML practitioners who are interested in driving tremendous social value. Please reach out at sravya[at]impactai.org or anjana[at]impactai.org
I would like to take this moment to acknowledge and thank all collaborators who played an important role in making this possible: Janardhan Shetty, Anjana Prabhakar, Matthew Kleinsmith, Jeremy Howard, Rachel Thomas, USF — Data Institute, Xinxin Li, Samar Haider, Sara Hooker, Brookie Guzder, Yannet Interian, Kirsten Keihl, Mindi Mysliwiec, Leslie Blakeman
Brief summary of the talks, slide decks and video
Sravya Tirukkovalur shares details about the first project at ImpactAI.org, where the team is using satellite imagery to assess structural damage after an earthquake, speeding up a critical piece in the relief coordination.
Brookie Guzder-Williams talks about “Forma 250: Near real-time detection of tree cover loss through satellite imagery”
Xinxin works with Xeed, a wearable technology company, to develop a system for Parkinson’s patient therapy management. She speaks about her work on applying deep learning to Parkinson’s disease therapeutic research, the core of which is a machine learning model trained with clinical studies data. This new system would enable a doctor to gauge patients’ symptoms, such as tremors and dyskinetic, via sensor data collected out of the clinic, rather than relying on written diaries or interviews with patient caregivers.
Update:
We checked after the talk and the statistic on number of radiologists mentioned in the talk is inaccurate. Apologies.
