Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI

Andreas Holzinger, Peter Kieseberg, Edgar Weippl, A Min Tjoa

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

In this short editorial we present some thoughts on present and future trends in Artificial Intelligence (AI) generally, and Machine Learning (ML) specifically. Due to the huge ongoing success in machine learning, particularly in statistical learning from big data, there is rising interest of academia, industry and the public in this field. Industry is investing heavily in AI, and spin-offs and start-ups are emerging on an unprecedented rate. The European Union is allocating a lot of additional funding into AI research grants, and various institutions are calling for a joint European AI research institute. Even universities are taking AI/ML into their curricula and strategic plans. Finally, even the people on the street talk about it, and if grandma knows what her grandson is doing in his new start-up, then the time is ripe: We are reaching a new AI spring. However, as fantastic current approaches seem to be, there are still huge problems to be solved: the best performing models lack transparency, hence are considered to be black boxes. The general and worldwide trends in privacy, data protection, safety and security make such black box solutions difficult to use in practice. Specifically in Europe, where the new General Data Protection Regulation (GDPR) came into effect on May, 28, 2018 which affects everybody (right of explanation). Consequently, a previous niche field for many years, explainable AI, explodes in importance. For the future, we envision a fruitful marriage between classic logical approaches (ontologies) with statistical approaches which may lead to context-adaptive systems (stochastic ontologies) that might work similar as the human brain.
Original languageEnglish
Title of host publicationMachine Learning and Knowledge Extraction. CD-MAKE 2018
PublisherSpringer International
Pages1-8
ISBN (Print)978-3-319-99739-1
DOIs
Publication statusPublished - 2018
Event2018 International Cross Domain Conference for Machine Learning & Knowledge - Hamburg, Germany
Duration: 27 Aug 201830 Aug 2018

Publication series

NameLecture Notes in Computer Science
Volume11015

Conference

Conference2018 International Cross Domain Conference for Machine Learning & Knowledge
Abbreviated titleCD-MAKE 2018
CountryGermany
CityHamburg
Period27/08/1830/08/18

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Keywords

  • Explainable AI
  • Machine learning

ASJC Scopus subject areas

  • Artificial Intelligence

Fields of Expertise

  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Review

Cite this

Holzinger, A., Kieseberg, P., Weippl, E., & Tjoa, A. M. (2018). Current Advances, Trends and Challenges of Machine Learning and Knowledge Extraction: From Machine Learning to Explainable AI. In Machine Learning and Knowledge Extraction. CD-MAKE 2018 (pp. 1-8). (Lecture Notes in Computer Science; Vol. 11015). Springer International. https://doi.org/10.1007/978-3-319-99740-7_1