Dear Members Informatics India Ltd and Ashoka University are pleased to announce the 7th Library Technology Conclave – LTC2023. Watching the new developments in the Artificial Intelligence (AI) domain and its increasing relevance to the LIS and its user community, we are happy to present the LTC2023 on a highly topical theme: EVOLVING LIBRARY ECOSYSTE IN THE AI and MACHINE LERNING LANDSCAPE. Date: 4-6 January 2023 Venue: Ashoka University, Delhi-NCR, Sonipat LTC2023 is will be coming with many new interesting innovative models of presentations by researchers & library practitioners alike. Save the dates and watch this forum for more announcements and the launch of the conclave website soon in the coming days. ABOUT THE CONCLAVE THEME In academic campuses around the world, libraries pioneered and provided leadership in the automation of functions and processes very early in the late 60s and 70s of the last century. But somewhere, the LIS community appears to have lagged behind in embracing machine learning (ML) in its knowledge organization and management functions. The ML bug is catching up now with LIS in a serious way with two recent reports, one by the Library of Congress (Machine Learning + Libraries: The Status of the Field, 2020) and another by OCLC (Responsible Operations: Data Science, Machine Learning, and AI in Libraries 2019). The industry serving the LIS community and its users is already thickly into the development of new tools based on ML and Deep Learning (DL) in areas like semantic search, voice-based search queries, recommender systems, automated classification & indexing, knowledge graph generation, new citation metrics models based on context-analysis, and so on. There are many examples of how AI can improve or even transform information discovery, and library management functions. * Reference Service in libraries is a prominent area that can undergo transformational changes with Alexa-like technologies applied to the digital collection of libraries. * In citation mapping, based on the actual content of papers, AI algorithms can create far better mapping systems of the actual research whereas the current mapping is largely limited to the network of researchers presented in the citation system. New citation metrics are emerging with the help of ML. Scite.AI, a new ML based citation database classifies every cited reference to identify whether a citing paper is supporting or disproving the cited paper which is done based on the analysis of the context of citation using Deep Learning techniques. * Plagiarism checking: Current models are based on similarity matching of text words. ML models can come up with more improved syntax and semantics based checking. Copying through the technique of paraphrasing are summarising can be detected by the emerging new ML based models. * Classification and indexing which are at the heart of precision and qualitative search and discovery can be accomplished at the full-text level. As is well known, the current Boolean based full-text search throws up too much of noise at the cost of relevance. Many developments are happening in the application of AI in Libraries. We will be presenting a position paper soon on AI in Libraries as a background note to the conference. N. V. Sathyanarayana | Chairman & Managing Director | Informatics India Ltd. | Bangalore 560004 |INDIA | | www.informaticsglobal.comhttp://www.informaticsglobal.com | https://LTC2022.org | -- This message has been scanned for viruses and dangerous content by MailScanner, and is believed to be clean.