Document Open Access Logo

SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091)

Authors Kristian Kersting, Miryung Kim, Guy Van den Broeck, Thomas Zimmermann and all authors of the abstracts in this report



PDF
Thumbnail PDF

File

DagRep.10.2.76.pdf
  • Filesize: 7.89 MB
  • 12 pages

Document Identifiers

Author Details

Kristian Kersting
  • TU Darmstadt, DE
Miryung Kim
  • UCLA, US
Guy Van den Broeck
  • UCLA, US
Thomas Zimmermann
  • Microsoft Corporation - Redmond, US
and all authors of the abstracts in this report

Cite AsGet BibTex

Kristian Kersting, Miryung Kim, Guy Van den Broeck, and Thomas Zimmermann. SE4ML - Software Engineering for AI-ML-based Systems (Dagstuhl Seminar 20091). In Dagstuhl Reports, Volume 10, Issue 2, pp. 76-87, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2020)
https://doi.org/10.4230/DagRep.10.2.76

Abstract

Multiple research disciplines, from cognitive sciences to biology, finance, physics, and the social sciences, as well as many companies, believe that data-driven and intelligent solutions are necessary. Unfortunately, current artificial intelligence (AI) and machine learning (ML) technologies are not sufficiently democratized - building complex AI and ML systems requires deep expertise in computer science and extensive programming skills to work with various machine reasoning and learning techniques at a rather low level of abstraction. It also requires extensive trial and error exploration for model selection, data cleaning, feature selection, and parameter tuning. Moreover, there is a lack of theoretical understanding that could be used to abstract away these subtleties. Conventional programming languages and software engineering paradigms have also not been designed to address challenges faced by AI and ML practitioners. In 2016, companies invested $26–39 billion in AI and McKinsey predicts that investments will be growing over the next few years. Any AI/ML-based systems will need to be built, tested, and maintained, yet there is a lack of established engineering practices in industry for such systems because they are fundamentally different from traditional software systems. This Dagstuhl Seminar brought together two rather disjoint communities together, software engineering and programming languages (PL/SE) and artificial intelligence and machine learning (AI-ML) to discuss open problems on how to improve the productivity of data scientists, software engineers, and AI-ML practitioners in industry.

Subject Classification

ACM Subject Classification
  • Software and its engineering
  • Computing methodologies → Artificial intelligence
  • Computing methodologies → Machine learning
Keywords
  • correctness / explainability / traceability / fairness for ml
  • data scientist productivity
  • debugging/ testing / verification for ml systems

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail