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AI workloads inside databases
Guy Van den Broeck · Alexander Ratner · Benjamin Moseley · Konstantinos Karanasos · Parisa Kordjamshidi · Molham Aref · Arun Kumar

Mon Dec 13 12:45 PM -- 01:59 PM (PST) @ None

In this panel we will discuss the following topics

  • How close is the industry to creating AI native databases?
  • How fast is research progressing in the field of bringing AI and databases close
  • SQL is the most popular language in databases and pytorch/tensorflow are very popular in AI. Can they converge or we need a new language?
  • Can existing databases be modified to host AI algorithms or we need to work from scratch?
  • From a systems perspective is the GPU adoption of databases an opportunity to implement AI algorithms
  • AI models are approaching the ~1TB and they contain Trillions of parameters. Are databaases ready to host/manage these type of models?

Author Information

Guy Van den Broeck (UCLA)

I am an Assistant Professor and Samueli Fellow at UCLA, in the Computer Science Department, where I direct the Statistical and Relational Artificial Intelligence (StarAI) lab. My research interests are in Machine Learning (Statistical Relational Learning, Tractable Learning), Knowledge Representation and Reasoning (Graphical Models, Lifted Probabilistic Inference, Knowledge Compilation), Applications of Probabilistic Reasoning and Learning (Probabilistic Programming, Probabilistic Databases), and Artificial Intelligence in general.

Alexander Ratner (Stanford University)
Benjamin Moseley (Carnegie Mellon University)
Konstantinos Karanasos (Microsoft)
Parisa Kordjamshidi (Michigan State University)

Parisa Kordjamshidi is an assistant professor of Computer Science & Engineering at Michigan State University. Her research interests are machine learning, natural language processing, and declarative learning-based programming. She has worked on the extraction of formal semantics and structured representations from natural language. She obtained NSF CAREER award on 2019. She is leading a project supported by Office of Naval research to perform basic research and develop a declarative learning-based programming framework for integration of domain knowledge into statistical/neural learning. She is a member of Editorial board of Journal of Artificial Intelligence Research (JAIR), a member of Editorial Board of Machine Learning and Artificial Intelligence, part of the journal of Frontiers in Artificial Intelligence and Frontiers in Big Data. She has published papers, organized international workshops and served as a (senior) program committee member or area chair of conferences such as IJCAI, AAAI, ACL, EMNLP, COLING, ECAI and a member of organizing committee of EMNLP-2021, ECML-PKDD-2019 and NAACL-2018 conferences.

Molham Aref (RelationalAI)
Arun Kumar (UC San Diego)

Arun Kumar is an Associate Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute and an HDSI Faculty Fellow at the University of California, San Diego. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics.

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