Towards explainable artificial intelligence (XAI)
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2020Copyright
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2000-luvun aikana tekoälysovellukset ovat saavuttaneet erinomaisen suorituskyvyn useissa eri tehtävissä. Suuret datajoukot, kasvava laskennallinen teho sekä yhä monimutkaisemmat koneoppimismallit ovat mahdollistaneet sen. Valitettavasti nämä monimutkaiset mallit ovat usein vain mustia laatikoita ihmiskäyttäjille ja käyttäjällä
on vaikeuksia ymmärtää ja luottaa tekoälysysteemin lopputuloksiin. Selittävän tekoälyn osa-alueella on ollut suuri määrä tutkimusta sellaisten menetelmien kehittämiseksi, jotka lisäisivät tekoälysysteemien selittävyyttä. Tämä opinnäytetyö sisältää sekä kirjallisuuskatsauksen selittävän tekoälyn tutkimuksesta että kokeilun, jossa kartoitettiin yksinkertaisilla tekoälymenetelmillä ECR-ionilähteen optimaalisia parametreja maksimaaliselle ionisuihkun intensiteetille. In the 21st century, the applications of artificial intelligence (AI) have achieved great performance in various tasks. Large datasets, increasing computational power and more complex machine learning models have made it possible. Unfortunately, these complex models are often only black boxes to human users and the user has difficulties to understand and trust the outcomes of AI systems. There has been a great amount of research in the field
of explainable artificial intelligence (XAI) to develop methods that increase the explainability of AI systems. In addition to a literature review of the research in XAI, the present thesis includes a small project in which the parameters of an ECR ion source have been surveyed via simple machine learning methods in order to find the optimal parameters for the maximal ion beam intensity.
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