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26 Jan 2022 / 17:30 - 19:00
Knowledge Intensive AI: beyond the data-driven approach
The abundance of data and the recent advances in deep neural networks (e.g. Transformer models) allowed for the creation of data-driven AI systems/models with new emergent capabilities that produced impressive results across many different domains and tasks in the last years.
But, data-driven only approaches are prone to critical limitations that can impose critical risks when deployed in real-case scenarios. In this master class, we will discuss the main characteristics and differences of data-driven and knowledge-intensive AI approaches, when knowledge-intensive AI approaches should be chosen, how to incorporate knowledge in modern AI approaches and why knowledge helps improve the capabilities of data-driven approaches.
Estevam Rafael Hruschka,
Estevam Hruschka is the Head of Research at Megagon Labs
Estevam Hruschka is the Head of Research at Megagon Labs (the research arm of Recruit Holdings - https://recruit-holdings.com/en/) in Silicon Valley, California, where he leads research and innovation efforts that support Recruit Holdings subsidiaries (such as Indeed.com, Glassdoor.com and many others), and also contribute to push the state-of-the-art (with papers published in top-tier conferences) in Artificial Intelligence, Machine Learning, Natural Language Processing (and Understanding), Knowledge Graphs, Knowledge Representation, Data Management and Databases. He is also adjunct professor at the Machine Learning department of Carnegie Mellon University (USA).
Prior to Megagon Labs, Estevam was co-leader of the Read The Web project where he helped create the first Never-Ending Learning System (http://rtw.ml.cmu.edu) in the history of Computer Science and Artificial Intelligence. Also, he was associate professor of computer science at the Federal University of Sao Carlos (Brazil) and Visiting Professor at Carnegie Mellon University (Pittsburgh, PA).
Between 2017 and 2020, he was with Amazon in Seattle, WA, where (among some other projects) he helped building Alexa Search Team from scratch, and helped Alexa to get smarter everyday by reading the Web.