In this paper, we report on the initial results of an explorative study that aims to investigate the occurrence of cognitive biases when designers use generative AI in the ideation phase of a creative design process. When observing current AI models utilised as creative design tools, potential negative impacts on creativity can be identified, namely deepening already existing cognitive biases but also introducing new ones that might not have been present before. Within our study, we analysed the emergence of several cognitive biases and the possible appearance of a negative synergy when designers use generative AI tools in a creative ideation process. Additionally, we identified a new potential bias that emerges from interacting with AI tools, namely prompt bias.
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The concept of biodiversity, which usually serves as a shorthand to refer to the diversity of life on Earth at different levels (ecosystems, species, genes), was coined in the 1980s by conservation biologists worried over the degradation of ecosystems and the loss of species, and willing to make a case for the protection of nature – while avoiding this “politically loaded” term (Takacs, 1996). Since then, the concept has been embedded in the work of the Convention on Biological Diversity (CBD, established in 1992) and of the Intergovernmental science-policy Platform on Biodiversity and Ecosystem Services (IPBES, aka ‘the IPCC for biodiversity’, established in 2012). While the concept has gained policy traction, it is still unclear to which extent it has captured the public imagination. Biodiversity loss has not triggered the same amount of attention or controversy as climate change globally (with some exceptions). This project, titled Prompting for biodiversity, investigates how this issue is mediated by generative visual AI, directing attention to both how ‘biodiversity’ is known and imagined by AI and to how this may shape public ideas around biodiversity loss and living with other species.
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poster voor de EuSoMII Annual Meeting in Pisa, Italië in oktober 2023. PURPOSE & LEARNING OBJECTIVE Artificial Intelligence (AI) technologies are gaining popularity for their ability to autonomously perform tasks and mimic human reasoning [1, 2]. Especially within the medical industry, the implementation of AI solutions has seen an increasing pace [3]. However, the field of radiology is not yet transformed with the promised value of AI, as knowledge on the effective use and implementation of AI is falling behind due to a number of causes: 1) Reactive/passive modes of learning are dominant 2) Existing developments are fragmented 3) Lack of expertise and differing perspectives 4) Lack of effective learning space Learning communities can help overcome these problems and address the complexities that come with human-technology configurations [4]. As the impact of a technology is dependent on its social management and implementation processes [5], our research question then becomes: How do we design, configure, and manage a Learning Community to maximize the impact of AI solutions in medicine?
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Our unilateral diet has resulted in a deficiency of specific elements/components needed for well-functioning of the human body. Especially the element magnesium is low in our processed food and results in neuronal and muscular malfunctioning, problems in bone heath/strength, and increased chances of diabetes, depression and cardiovascular diseases. Furthermore, it has also been recognized that magnesium plays an important role in cognitive functioning (impairment and enhancement), especially for people suffering from neurodegenerative diseases (Parkinson disease, Alzheimer, etc). Recently, it has been reported that magnesium addition positively effects sleep and calmness (anti-stress). In order to increase the bioavailability of magnesium cations, organic acids such as citrate, glycerophosphate and glycinate are often used as counterions. However, the magnesium supplements that are currently on the market still suffer from low bio-availability and often do not enter the brain significantly.The preparation of dual/multiple ligands of magnesium in which the organic acid not only functions as a carrier but also has synergistically/complementary biological effects is widely unexplored and needs further development. As a result, there is a strong need for dual/multiple magnesium supplements that are non-toxic, stable, prepared via an economically and ecologically attractive route, resulting in high bioavailability of magnesium in vivo, preferably positively influencing cognition/concentration
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
Circular Economy is a novel disruptive paradigm redefining sustainability in the hospitality industry and addressing the environmental challenges set by this fast-growing impactful industry. To address these challenges, the creation of further knowledge on circular economy and its applications in the hospitality sector is fundamental, together with providing hoteliers and restaurateurs with proper skills and knowhow to tackle such challenges. Drawing on a on going pilot project on Circular Economy in Hotels in Amsterdam, the Friesland hospitality sector and the Professorship of Sustainability in Hospitality and Tourism at NHL Stenden University of Applied Sciences have set out to develop an innovative learning experimental environment in which Friesland hoteliers and restaurateurs can develop further knowledge and identify - together with students, researchers, and experts – possible key actions and strategies to implement regenerative circular processes of material up-cycling. To which extent this learning community of the Northern Netherlands contributes to develop wider knowledge on circular economy in hospitality and to identify, implement, and test innovative regenerative circular actions will be evaluated.
Lectorate, part of HAS green academy
