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Saul: Towards Declarative Learning Based Programming.

Parisa Kordjamshidi1, Dan Roth1, Hao Wu1

  • 1University of Illinois at Urbana-Champaign.

IJCAI : Proceedings of the Conference
|December 5, 2015
PubMed
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We introduce Saul, a new probabilistic programming language designed for AI development. Saul simplifies AI systems by handling complex data and enabling abstraction, improving model integration and reasoning.

Area of Science:

  • Artificial Intelligence
  • Programming Languages
  • Machine Learning

Background:

  • Existing programming languages struggle with messy data and high-level abstraction for AI development.
  • Seamless integration of trainable models and robust theoretical foundations are lacking.

Purpose of the Study:

  • Introduce Saul, a novel object-functional programming language in Scala.
  • Address shortcomings in AI system development, focusing on data interaction and abstraction.
  • Facilitate easier development of AI applications with complex data.

Main Methods:

  • Saul allows learning, naming, and manipulating abstractions over relational data.
  • It supports seamless incorporation of probabilistic or discriminative trainable components.
  • Inference over trainable models is provided for composition and decision-making.

Related Experiment Videos

Main Results:

  • Saul is built on a declaratively defined relational data model.
  • It utilizes piecewise learned factor graphs with declarative learning and inference objectives.
  • Inference is supported over probabilistic models enhanced with knowledge-based constraints.

Conclusions:

  • Saul offers a robust framework for AI development, simplifying interaction with complex data.
  • The language facilitates relational feature engineering and structured output prediction.
  • Saul enhances the development of AI systems by providing abstraction and flexible model integration.