3,278 research outputs found
Emma Bell Miles journal, 1908-1911
No full textJournal authored by Walden's Ridge naturalist, artist, and author Emma Bell Miles from 1908 May 24 to 1911 April 25
Emma Bell Miles journal, 1911-1914
No full textJournal authored by Walden's Ridge naturalist, artist, and author Emma Bell Miles from 1911 January 9 to 1914 May 3
Emma Bell Miles journal, 1915-1918
No full textJournal authored by Walden's Ridge naturalist, artist, and author Emma Bell Miles from 1915 November 11 to 1918 August 8
Emma Bell Miles journal, 1915-1918
No full textJournal authored by Walden's Ridge naturalist, artist, and author Emma Bell Miles from 1915 November 11 to 1918 August 8
Emma Bell Miles journal, 1911-1914
No full textJournal authored by Walden's Ridge naturalist, artist, and author Emma Bell Miles from 1911 January 9 to 1914 May 3
Emma Bell Miles journal, 1908-1911
No full textJournal authored by Walden's Ridge naturalist, artist, and author Emma Bell Miles from 1908 May 24 to 1911 April 25
Emma Bell Miles journal, 1915
No full textJournal authored by Walden's Ridge naturalist, artist, and author Emma Bell Miles from 1915 June 15 to 1915 September 22. The journal also includes newspaper clippings of Miles' Fountain Square Conversation column authored for the Chattanooga News
Emma Bell Miles journal, 1915
No full textJournal authored by Walden's Ridge naturalist, artist, and author Emma Bell Miles from 1915 June 15 to 1915 September 22. The journal also includes newspaper clippings of Miles' Fountain Square Conversation column authored for the Chattanooga News
Evaluating the accuracy of data collection on mobile phones: A study of forms, SMS, and voice
No full textWhile mobile phones have found broad application in reporting health, financial, and environmental data, there has been little study of the possible errors incurred during mobile data collection. This paper provides the first (to our knowledge) quantitative evaluation of data entry accuracy on mobile phones in a resource-poor setting. Via a study of 13 users in Gujarat, India, we evaluated three user interfaces: 1) electronic forms, containing numeric fields and multiple-choice menus, 2) SMS, where users enter delimited text messages according to printed cue cards, and 3) voice, where users call an operator and dictate the data in real-time. Our results indicate error rates (per datum entered) of 4.2% for electronic forms, 4.8% for SMS, and 0.45% for voice. These results caused us to migrate our own initiative (a tuberculosis treatment program in rural India) from electronic forms to voice, in order to avoid errors on critical health data. While our study has some limitations, including varied backgrounds and training of participants, it suggests that some care is needed in deploying electronic interfaces in resource-poor settings. Further, it raises the possibility of using voice as a low-tech, high-accuracy, and cost-effective interface for mobile data collection.Massachusetts Institute of Technology. Public Service Cente
Human-Focused Reinforcement Learning
No full textPresented on February 27, 2020 at 12:00 p.m. in the Technology Square Research Building, Banquet Hall.Emma Brunskill is an assistant professor in the Computer Science Department at Stanford University where she leads the AI for Human Impact group. Her work focuses on reinforcement learning in high stakes scenarios--how can an agent learn from experience to make good decisions when experience is costly or risky, such as in educational software, healthcare decision making, or people-facing applications.Runtime: 56:32 minutesThere is increasing excitement about reinforcement learning--a subarea of machine learning for enabling an agent to learn to make good decisions. Yet numerous questions and challenges remain for reinforcement learning to help support progress in applications that involve interacting with people, like education, consumer marketing and healthcare. I will discuss our work on some of the technical challenges that arise in this pursuit, including sample efficiency, counterfactual reasoning, robustness, and applications to health and education
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