Op dinsdag 5 maart 2024 organiseerden jeugdprogramma Het Klokhuis en de ZB een interactieve Meet Up over Artificial Intelligence, oftewel AI. Dit gebeurde naar aanleiding van de vier Klokhuis... Show moreOp dinsdag 5 maart 2024 organiseerden jeugdprogramma Het Klokhuis en de ZB een interactieve Meet Up over Artificial Intelligence, oftewel AI. Dit gebeurde naar aanleiding van de vier Klokhuis-uitzendingen over dit onderwerp. Presentator Tirsa With sprak met gasten en leerlingen uit groep 6, 7 en 8 van scholen uit het hele land.Een Snapchat maken met een grappige filter, slimme stofzuigers of ChatGPT je spreekbeurt laten schrijven: Kunstmatige Intelligentie is overal. Maar wat is het precies en wat kunnen we er mee? Informaticus Maarten Lamers legde in de studio uit hoe machines zichzelf slimmer maken en wat het verschil is tussen ons menselijke brein en het computerbrein.Onderzoeker Oumaima Hajri vertelde waar we kunstmatige intelligentie allemaal tegenkomen. Ontzettend handig, maar het is volgens haar ook belangrijk om zelf kritisch te blijven nadenken. Waarom dat van belang is, legde ze uit tijdens de Meet Up.De derde gast was basisschooldocent Tim Vissers. Hij ontwikkelt ‘Futureproof’ lesmateriaal. Tim liet zien waar je Kunstmatige Intelligentie kunt tegenkomen in de klas. Tijdens de Meet Up daagde hij de leerlingen uit om zelf aan de slag te gaan met de AI-studio van Het Klokhuis. Show less
Inflammatory Bowel Diseases (IBD) such as Crohn’s disease (CD) and ulcerative colitis (UC) are chronic immunological digestive diseases with a progressive character and associated with significant... Show moreInflammatory Bowel Diseases (IBD) such as Crohn’s disease (CD) and ulcerative colitis (UC) are chronic immunological digestive diseases with a progressive character and associated with significant healthcare costs. Different solutions have been proposed such as innovation in care monitoring or implementation of electronic health (eHealth). IBD is one of many chronic diseases that could benefit from eHealth, adding smartphone applications to the toolbox for care management has the potential improve disease understanding, enhance medication adherence, improve patient-physician communications, and for earlier interventions by medical professionals when problems arise. Furthermore, the accessibility to Big Data and increased computational resources have paved the way for Artificial Intelligence (AI) to provide potential solutions for the management of prototypical complex diseases with advanced heterogeneity and alternating disease states, like IBD. In this thesis we assessed the current economic and psychosocial impact of IBD by assessing its effect on indirect costs, productivity and caregiving. Furthermore, we observed if we can proactively identify IBD patients’ needs using eHealth and Artificial Intelligence. Lastly, we analyze the impact of monitoring IBD patients using eHealth interventions in order to facilitate the delivery of high-value care. Show less
The optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial... Show moreThe optimization of algorithm (hyper-)parameters is crucial for achieving peak performance across a wide range of domains, ranging from deep neural networks to solvers for hard combinatorial problems. However, the proper evaluation of new algorithm configuration (AC) procedures (or configurators) is hindered by two key hurdles. First, AC scenarios are hard to set up, including the target algorithm to be optimized and the problem instances to be solved. Second, and even more significantly, they are computationally expensive: a single configurator run involves many costly runs of the target algorithm. Here, we propose a benchmarking approach that uses surrogate scenarios, which are computationally cheap while remaining close to the original AC scenarios. These surrogate scenarios approximate the response surface corresponding to true target algorithm performance using a regression model. In our experiments, we construct and evaluate surrogate scenarios for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems. We generalize previous work by building surrogates for AC scenarios with multiple problem instances, stochastic target algorithms and censored running time observations. We show that our surrogate scenarios capture overall important characteristics of the original AC scenarios from which they were derived, while being much easier to use and orders of magnitude cheaper to evaluate. Show less
Rizzini, M.; Fawcett, C.; Vallati, M.; Gerevini, A.E.; Hoos, H.H. 2017
Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques... Show moreCombining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning.Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation. Show less
Lindauer, M.; Hutter, F.; Hoos, H.H.; Schaub, T. 2017