November 8, 2017
UPenn Professor and UC Berkeley PHD candidate are the second recipients of the $1 million challenge
SAN FRANCISCO, Nov. 8, 2017 /PRNewswire/ — CrowdFlower, the essential human-in-the-loop Artificial Intelligence platform for data science and machine learning teams, today announced the second-round winners of its $1 million “AI for Everyone” Challenge. For this round, the winners ideas focused on improving medical literature and understanding literary style through the use of machine learning and better training data.
This second round of awards comes on the heels of the first two winners, announced last month. This leaves 4 additional awards to be won through the “AI for Everyone” challenge which launched in May. The program enables companies, organizations or individuals using AI to solve critical problems in their industry of choice.
“The AI for Everyone Challenge was created to enable trailblazers to follow their passion in applying AI to solve problems across a wide range of fields and industries,” said Robin Bordoli, CEO of CrowdFlower. “Ani and Sarah deserve this honor as they encapsulate what this challenge was created for – original and bold solutions leveraging AI to make the world a better place. The fields of medicine and literature shall be improved by their innovative approaches.”
Ani Nenkova is an associate professor at the University of Pennsylvania who’s winning proposal is centered around medical literature and crowdsourcing a database of annotations that will allow for quick and easy searches, content review and abstracts of evidence based medicine. Currently, the process of reviewing medical literature is timely and cumbersome. With the use of AI and machine learning, Ani hopes to automate steps in the review process and modernize the system.
Ani will work in tandem with CrowdFlower’s Human-in-the-Loop platform to identify important sentences and phrases within clinical trials that will result in an output of easily digestible abstracts, reviews and summaries of the literature. This organization of the literature and database will help doctors and medical professionals source pertinent information easily and effectively.
The second recipient, Sarah Sterman is a PhD student at the University of California, Berkeley where she and her team at LiterAIry, under the guidance of the Hybrid Ecologies Lab at UC Berkeley, are looking at human creativity through the lens of AI. Literary style is currently studied through word choice, rhythm, grammar but is subjective and time consuming. They hope to develop a system through the use of AI for identifying features that influence the human perception of style across individual authors, works, genres and eras.
With the help of CrowdFlower, they will be able to think across different literary verticals and outside of the current usage of AI as it relates to plagiarism detection. Once a baseline of features that affect human perceptions of style has been established they hope to help create new technologies that enhance search and communication across many different fields, including predictive text assistants and web plugins that enable search through style.
Finalists were selected by a group of distinguished judges including members of CrowdFlower’s Scientific Advisory Board: Barney Pell, founder at Moon Express; Pete Warden, Staff Research Engineer at Google; Monica Rogati, independent data science advisor; Adrian Weller, Senior Research Fellow at the University of Cambridge; Jack Clark, Director of Strategy and Communications at OpenAI and Lukas Biewald, founder at CrowdFlower. Selection is based on the innovation of the project, its importance to the advancement of AI and the overall potential impact of the proposed initiative.
Applications for the next wave of winners is currently open. Interested parties can apply for the CrowdFlower “AI for Everyone” Challenge at https://www.crowdflower.com/ai-for-everyone/.
To learn more about CrowdFlower visit www.crowdflower.com
CrowdFlower is the essential human-in-the-loop AI platform for data science and machine learning teams. The CrowdFlower software platform trains, tests, and tunes machine learning models to make AI work in the real world. CrowdFlower’s technology and expertise supports a wide range of use cases including autonomous vehicles, intelligent personal assistants, medical image labeling, consumer product identification, content categorization, customer support ticket classification, social data insight, CRM data enrichment, product categorization, and search relevance.
Headquartered in the Mission District in San Francisco and backed by Canvas Ventures, Trinity Ventures, Industry Ventures, Microsoft Ventures, and Salesforce Ventures, CrowdFlower serves Fortune 500 and fast-growing data-driven organizations across a wide variety of industries. For more information, visit www.crowdflower.com