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
Sinds de opkomst van AI vraagt men zich af of computers ook creatief kunnen zijn, en steeds vaker zien we voorbeelden van zogenaamde “artificial creativity”. Computers componeren muziek, bedenken... Show moreSinds de opkomst van AI vraagt men zich af of computers ook creatief kunnen zijn, en steeds vaker zien we voorbeelden van zogenaamde “artificial creativity”. Computers componeren muziek, bedenken recepten, maken visuele kunstwerken en tappen zelfs moppen. Maar kunnen we creativiteit wel in een computerprogramma vatten? En hoe dan? Maarten Lamers bespreekt wat computers op dit vlak eigenlijk kunnen, en net zo belangrijk: wat niet. Aan de hand van klassieke en actuele voorbeelden van “creatieve” computers wordt duidelijk hoe deze systemen werken, en wat dit met AI te maken heeft. Zo wordt het eenvoudiger om te begrijpen wat er in de media over creatieve AI geschreven wordt, zonder technisch diepgaande kennis.Deze lezing is georganiseerd in samenwerking met Studium Generale en vindt plaats tijdens de Art & Science Week van het Leiden European City of Science programma. Show less
According to Chiao in his contribution to this book, the desirability of the use of AI in sentencing should be evaluated by comparing computers to the status quo ante, rather than to an unrealistic... Show moreAccording to Chiao in his contribution to this book, the desirability of the use of AI in sentencing should be evaluated by comparing computers to the status quo ante, rather than to an unrealistic, and in any case unrealized, ideal. Although we agree that changes to the legal process such as adopting algorithmic sentencing methods can be beneficial when the change is an incremental improvement over the status quo, in order to assess whether the change is an improvement, we need to know what this “ideal” is toward which improvements are aimed. Therefore, the question whether AI is better at making sentencing decisions than human judges is approached differently in this chapter. We compare human with AI judges by evaluating the extent to which they are able to make a legitimate sentencing decision: Is legitimacy better achieved by machine than by human judges? 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
