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.
So, in light of these developments, how should social scientists think differently about people, the economy, and society? And how should the engineers who write these algorithms handle the social and ethical dilemmas their creations pose? "These are the kinds of questions you can't answer with just the technical solutions," says Dashun Wang, an associate professor of management and organizations at Kellogg. "These are fundamentally interdisciplinary issues." Indeed, economists seeking to predict how automation will impact the labor market need to understand which skills machines are best suited to perform.
Saravanan Muthiayah, professor of Information Technology, Multimedia University, Malaysia, who was the chief guest, highlighted the current trends and explained the thrust areas in the field of Computer Science and Engineering and Information Technology. Inaugurating the conference, N. Krishnamohan, Registrar (in-charge), highlighted the special features of newly introduced programmes such as B.E.-CSE (Artificial Intelligence & Machine Learning) and B.E.-CSE (Big Data Analytics). K. Raghukandan, Dean, faculty of engineering and technology, explained the rapid growth and impact of the recent developments in Computer Science and Information Technology. More than 100 research articles from various recent research areas such as artificial intelligence, machine learning, big data analytics, internet of things, cyber security, block chain technology, face recognition, computer networks, speech processing, image processing and so on were presented during the meet. Scientists and research scholars from across the country presented their research in the areas of computing and information technology.
The European Commission has chosen Time Machine as one of the six proposals retained for preparing large-scale research initiatives to be strategically developed in the next decade. Time Machine foresees to design and implement advanced new digitisation and Artificial Intelligence (AI) technologies to mine Europe's vast cultural heritage, providing fair and free access to information that will support future scientific and technological developments in Europe. The Time Machine Project, which involves FAU as well as several other institutions, will create advanced AI technologies to make sense of vast amounts of information from complex historical data sets. This will enable the transformation of fragmented data – with content ranging from medieval manuscripts and historical objects to smartphone and satellite images – into useable knowledge for industry. In essence, a large-scale computing and digitisation infrastructure will map Europe's entire social, cultural and geographical evolution.
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.