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Presentatie Bart Verheij

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Presentatie van het Datacongres ''data science voor maatschappelijke uitdagingen'' op 22 november 2018

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Presentatie Bart Verheij

  1. 1. Groningen Centre for Data Science and Systems Complexity (DSSC) Prof. dr. Bart Verheij Head of department Artificial Intelligence Bernoulli Institute of Mathematics, Computer Science and Artificial Intelligence, University of Groningen www.ai.rug.nl/~verheij
  2. 2.  Mission: understand and design complex systems and processes through very large data  70+ researchers  2 MSc specializations  H2020 awarded PhD Programme (10 PhD students)
  3. 3.  Trained, generic algorithms that can overcome the fragmentation of domain expertise and solve families of problems to support decision making in various disciplines/applications  Theoretically well-founded algorithms that combine data, knowledge and reasoning and are tested in real world applications  Application of appropriate amounts of data/computing from a variety of sources to control complex systems – with the aim to apply just-enough- data  Social, explainable, ethical AI  New methods to design algorithms for monitoring and control, which work in the presence of highly uncertain models or even in absence of them, while providing analytical certificates of their correctness  Demonstration of these new algorithms’ effectiveness on real complex systems which model critical infrastructures, including power systems, transportation networks, energy systems  Methods to manage the complexity of systems engineering especially in the high-tech industry
  4. 4.  Trained, generic algorithms that can overcome the fragmentation of domain expertise and solve families of problems to support decision making in various disciplines/applications  Theoretically well-founded algorithms that combine data, knowledge and reasoning and are tested in real world applications  Application of appropriate amounts of data/computing from a variety of sources to control complex systems – with the aim to apply just-enough- data  Social, explainable, ethical AI  New methods to design algorithms for monitoring and control, which work in the presence of highly uncertain models or even in absence of them, while providing analytical certificates of their correctness  Demonstration of these new algorithms’ effectiveness on real complex systems which model critical infrastructures, including power systems, transportation networks, energy systems  Methods to manage the complexity of systems engineering especially in the high-tech industry
  5. 5. Kennissystemen Artikel 6:162 lid 1 Hij die jegens een ander een onrechtmatige daad pleegt, welke hem kan worden toegerekend, is verplicht de schade die de ander dientengevolge lijdt, te vergoeden. ALS schade EN onrechtmatig EN toerekenbaar EN causaal-verband DAN schadevergoedingsplicht
  6. 6. Datasystemen
  7. 7. The two faces of Artificial Intelligence Expert systems Business rules Open data IBM’s Deep Blue Complex structure Knowledge tech Foundation: logic Explainability Adaptive systems Machine learning Big data IBM’s Watson Adaptive structure Data tech Foundation: probability theory Scalability
  8. 8. Goede kunstmatige intelligentie Goede antwoorden Goede redenen Goede keuzes
  9. 9. Data Kennis
  10. 10. jurix2018.ai.rug.nl
  11. 11. Workshops at the JURIX conference in Groningen (December 12-14)  AICOL - Artificial Intelligence and the Complexity of Legal Systems  TeReCom - Technologies for Regulatory Compliance  XAILA - EXplainable AI in Law  LeDAH - Legal Data Analytics Hackathon  ManyLaws Project  LDA2018 – Legal Data Analysis  Legal Design as Academic Discipline: Foundations, Methodology, Applications  Doctoral Consortium
  12. 12.  Mission: understand and design complex systems and processes through very large data  70+ researchers  2 MSc specializations  H2020 awarded PhD Programme (10 PhD students)

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