https://www.dagstuhl.de/20091

23. – 28. Februar 2020, Dagstuhl-Seminar 20091

SE4ML - Software Engineering for AI-ML-based Systems

Organisatoren

Kristian Kersting (TU Darmstadt, DE)
Miryung Kim (UCLA, US)
Guy Van den Broeck (UCLA, US)
Thomas Zimmermann (Microsoft Corporation – Redmond, US)

Auskunft zu diesem Dagstuhl-Seminar erteilen

Susanne Bach-Bernhard zu administrativen Fragen

Michael Gerke zu wissenschaftlichen Fragen

Dokumente

Programm des Dagstuhl-Seminars (Hochladen)

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Motivation

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.

The goal of this Dagstuhl Seminar is to bring 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. The issues addressed in the seminar will include the following:

  1. What challenges do people building AI-ML-based systems face?
  2. How do we re-think software development tools such as debugging, testing, and verification tools for complex AI-ML-based systems?
  3. How do we reason about correctness, explainability, repeatability, traceability, and fairness, while building AI-ML pipeline?
  4. What are innovative paradigms that seamlessly embed, reuse, and chain models, while abstracting away most low-level details?

The topics of the seminar address pressing demands from industry; the research questions are very relevant for practical software systems development that leverages artificial intelligence (AI) and machine learning (ML). In 2016, companies invested $26–39 billion in AI and McKinsey predicts that investments will be growing over the next few years. Any AI- and 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. Ideas brainstormed in the seminar will contribute to a new suite of ML-relevant software development tools such as debuggers, testers and verification tools that increase developer productivity in building complex AI systems. Furthermore, we will also discuss new innovative AI and ML abstractions that improve programmability in designing intelligent systems.

Motivation text license
  Creative Commons BY 3.0 DE
  Kristian Kersting, Miryung Kim, Guy Van den Broeck, and Thomas Zimmermann

Classification

  • Artificial Intelligence / Robotics
  • Programming Languages / Compiler
  • Software Engineering

Keywords

  • Correctness / explainability / traceability / fairness for ML
  • Debugging/ testing / verification for ML systems
  • Data scientist productivity

Dokumentation

In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.

 

Download Übersichtsflyer (PDF).

Publikationen

Es besteht weiterhin die Möglichkeit, eine umfassende Kollektion begutachteter Arbeiten in der Reihe Dagstuhl Follow-Ups zu publizieren.

Dagstuhl's Impact

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