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Predicting Earthquakes A Week In Advance To Save Lives And Billions
Hosted in collaboration with Peking University and Capgemini's AETA Project, this Al Challenge aims to leverage data collected via a novel 3-part sensory system and a machine-learning-based algorithm. To solve this, AETA hosted an Al Challenge on DPhi. As a result, 900+ data scientists participated, and they came up with an Al model which can predict earthquakes with an 87% daily accuracy.
Empowering an enterprise to safeguard global NFT trade with AI
bitsCrunch GmbH is a deep-tech startup backed by Coinbase and several notable companies. bitsCrunch products secure the global trade of NFTs by preventing wash trading and forgery. Their team was looking to hire data scientists across various levels and decided to leverage DPhi’s AI Challenges. Eventually, 340+ data scientists participated in their first ever AI challenge out of which 19 data scientists were shortlisted and the top 7 teams were awarded.
Enabling recommendations for 100 million+ users.
Trell is a social media app with over 100 million+ downloads. They wanted to build a solid AI team and crowdsource innovative ML innovation to build better recommender systems for the newsfeed of their app. DPhi hosted their first-ever AI challenge with the problem statement of optimizing the newsfeed algorithm through given real-world datasets. Eventually, Trell saw participation from 2000+ data scientists and 267 shortlisted ML model submissions.
Helping global agriculture by classifying pests
Backed by the UN, World Data League aims to solve high impact challenges. To crowdsource innovation from our community, DPhi collaborated with them and conducted an AI challenge with a problem statement that affects global agro produce and supply chain. Eventually, data scientists from all over the world competed to build a novel model with 88.9% accuracy on the test dataset. The team that developed it has reached the World Semis of the ongoing prestigious AI challenge.
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