Machine Learning applications in the Social Sciences and the Humanities

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Machine Learning, a recurrent and obvious topic in Science and Technology, will also radically change the way research is carried out in the Social Sciences and the Humanities in a near future. A close cooperation between SSH scholars and computer scientists could have a huge impact on both SSH and STEM (Science, Technology, Engineering and Mathematics) related research topics. On the one hand, social scientists and humanities scholars may not be able to design and implement themselves the machine learning algorithms they need for their research. The role of a linguist, a historian or a social scientist should be thus to help computer scientists outperform current machine learning models by offering them theoretical approaches both could adapt together to improve their accuracy. This conference, organised by the Social Sciences and Humanities Working Group of the Coimbra Group, will explore the practical possibilities Machine Learning offers to selected research fields within SSH, particularly linguistics, literature, musicology, and sociology.


Doing text analytics for Digital Humanities and Social Sciences with CLARIN (LDK tutorial), Galway 2017

VideoLectures.NET

Text is a basic material, a primary data layer, in many areas of humanities and social sciences. If we want to move forward with the agenda that the fields of digital humanities and computational social sciences are projecting, it is vital to bring together the technical areas that deal with automated text processing, and scholars in the humanities and social sciences. Much progress has been made in the last two decades in text analytics, a field that draws on recent advances in computational linguistics, information retrieval and machine learning. By now we know what to expect from basic tools, such as named entity recognition. To foster new areas of research, it is necessary to not only understand what is out there in terms of proven technologies and infrastructures such as CLARIN, but also how the developers of text analytics can work with researchers in the humanities and social sciences to understand the challenges in each other's field better.


Computing and artificial intelligence: Humanistic perspectives from MIT

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The MIT Stephen A. Schwarzman College of Computing (SCC) will reorient the Institute to bring the power of computing and artificial intelligence to all fields at MIT, and to allow the future of computing and AI to be shaped by all MIT disciplines. To support ongoing planning for the new college, Dean Melissa Nobles invited faculty from all 14 of MIT's humanistic disciplines in the School of Humanities, Arts, and Social Sciences to respond to two questions: As Nobles says in her foreword to the series, "Together, the following responses to these two questions offer something of a guidebook to the myriad, productive ways that technical, humanistic, and scientific fields can join forces at MIT, and elsewhere, to further human and planetary well-being." The following excerpts highlight faculty responses, with links to full commentaries. The excerpts are sequenced by fields in the following order: the humanities, arts, and social sciences. "The advent of artificial intelligence presents our species with an historic opportunity -- disguised as an existential challenge: Can we stay human in the age of AI? In fact, can we grow in humanity, can we shape a more humane, more just, and sustainable world? With a sense of promise and urgency, we are embarked at MIT on an accelerated effort to more fully integrate the technical and humanistic forms of discovery in our curriculum and research, and in our habits of mind and action."


Research Data Scientist/Research Software Engineer - ai-jobs.net

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Launched in November 2015, the Alan Turing Institute is the national institute for data science and artificial intelligence. Our mission is to make great leaps in research to change the world for the better. The Institute is headquartered at The British Library, and brings together researchers from a range of disciplines – mathematics, statistics, computer science, engineering and social sciences, – from thirteen leading universities and industry partners. The permanent research staff of the institute's Research Engineering Group work to realise cutting edge research as professionally usable software tools and to apply these to address real-world data science and modelling challenges. The group's staff are research software engineers and data scientists.


CLARIN - Common Language Resources and Technology Infrastructure

VideoLectures.NET

CLARIN makes digital language resources available to scholars, researchers, students and citizen-scientists from all disciplines, especially in the humanities and social sciences, through single sign-on access. CLARIN offers long-term solutions and technology services for deploying, connecting, analyzing and sustaining digital language data and tools. CLARIN supports scholars who want to engage in cutting edge data-driven research, contributing to a truly multilingual European Research Area.