Io-Tahoe, a pioneer in Smart Data Discovery and AI-Driven Data Catalog products, in its efforts to continue to transform the data discovery market, today announced it has been named a Leader in the use of artificial intelligence (AI) and machine learning (ML) for data management in a new research report and decision guide from Enterprise Management Associates (EMA). The research report, which names Io-Tahoe a Leader, says companies which deploy AI-enabled analytics and data management solutions can potentially save up to $5,000,000 a year. EMA research also finds that they can create more value through enhancements such as increased speed of innovation; the report claims that 83 per cent of the companies surveyed are already seeing cost savings, along with a significant reduction in annual person-hours required to complete analysis of the data. "AI enablement signifies a major shift from passive to active use of metadata," said John Santaferraro, EMA's Research Director, Analytics, Business Intelligence, and Data Management. "The passive use of metadata focused on definitions and documentation, while the active use of metadata focuses on the delivery of services, such as data cataloguing, data governance, data discovery, and master data services."
Over 30 presentations will be given at the 2019 meeting. This year the meeting is focused on how you can successfully leverage the power of AI and machine learning by utilising high-quality big data in order to revolutionise your drug discovery and development process. Talks will explore implementation strategies and advances in the convergence of AI, informatics and data management for early drug discovery. "A good overview of what other pharmaceutical companies are doing with respect to data management, AI etc. in research" "Information from those developing and implementing data management systems" "The fact this was a scientific rather than very commercial conference" "The ability to network was very important. Once again this hugely successful senior level R&D event is co-located with the 4th Medicinal Chemistry Summit, which also explores machine learning and AI in Drug Discovery.
Over 60 researchers from 10 countries took part in the Third Knowledge Discovery in Databases (KDD) Workshop, held during the Eleventh National Conference on Artificial Intelligence in Washington, D.C. A major trend evident at the workshop was the transition to applications in the core KDD area of discovery of relatively simple patterns in relational databases; the most successful applications are appearing in the areas of greatest need, where the databases are so large that manual analysis is impossible. Progress has been facilitated by the availability of commercial KDD tools for both generic discovery and domain-specific applications such as marketing. At the same time, progress has been slowed by problems such as lack of statistical rigor, overabundance of patterns, and poor integration. Besides applications, the main themes of this workshop were (1) the discovery of dependencies and models and (2) integrated and interactive KDD systems.
After a decade of fundamental interdisciplinary research in machine learning, the spadework in this field has been done; the 1990s should see the widespread exploitation of knowledge discovery as an aid to assembling knowledge bases. The contributors to the AAAI Press book Knowledge Discovery in Databases were excited at the potential benefits of this research. The editors hope that some of this excitement will communicate itself to "AI Magazine readers of this article.