Youyou et al. showed that from 70 likes the algorithm could predict the personality better than friends, from 150 likes better than family members and from 300 likes even better than the test person himself. However, the machine learning algorithm does not know the person better than the colleagues, the friends or the person themselves. The machine can "only", after sufficient "supervised learning" trials (iterations), determine the correlation between the click behaviour on Facebook and the scored Big5 factors better than individuals. Prediction replaces the Big5 questionnaire. But we are not getting closer to the personality of people than with the Big5 questionnaire. It is argued that - though data mining can help enormously - psychology remains a subject of the narrative in the end.
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Many attempts have been made to build an artificial brain. This paper aims to contribute to the conceptualization of an artificial learning system that functionally resembles an organic brain in a number of important neuropsychological aspects. Probably the techniques (algorithms) required are already available in various fields of artificial intelligence. However, the question is how to combine those techniques. The combination of truly autonomous learning, in which "accidental" findings (serendipity) can be used without supervision, with supervised learning from both the surrounding and previous knowledge, is still very challenging. In the event of changed circumstances, network models that can not utilize previously acquired knowledge must be completely reset, while in representation-driven networks, new formation will remain outside the scope, as we will argue. In this paper considerations to make artificial learning functionally similar to organic learning, and the type of algorithm that is necessary in the different hierarchical layers of the brain are discussed. To this end, algorithms are divided into two types: conditional algorithms (CA) and completely unsupervised learning. It is argued that in a conceptualisation of an artificial device that is functional similar to an organic learning system, both conditional learning (by applying CA’s), and non-conditional (supervised) learning must be applied.
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Background: Although physical activity (PA) has positive effects on health and well-being, physical inactivity is a worldwide problem. Mobile health interventions have been shown to be effective in promoting PA. Personalizing persuasive strategies improves intervention success and can be conducted using machine learning (ML). For PA, several studies have addressed personalized persuasive strategies without ML, whereas others have included personalization using ML without focusing on persuasive strategies. An overview of studies discussing ML to personalize persuasive strategies in PA-promoting interventions and corresponding categorizations could be helpful for such interventions to be designed in the future but is still missing. Objective: First, we aimed to provide an overview of implemented ML techniques to personalize persuasive strategies in mobile health interventions promoting PA. Moreover, we aimed to present a categorization overview as a starting point for applying ML techniques in this field. Methods: A scoping review was conducted based on the framework by Arksey and O’Malley and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria. Scopus, Web of Science, and PubMed were searched for studies that included ML to personalize persuasive strategies in interventions promoting PA. Papers were screened using the ASReview software. From the included papers, categorized by the research project they belonged to, we extracted data regarding general study information, target group, PA intervention, implemented technology, and study details. On the basis of the analysis of these data, a categorization overview was given. Results: In total, 40 papers belonging to 27 different projects were included. These papers could be categorized in 4 groups based on their dimension of personalization. Then, for each dimension, 1 or 2 persuasive strategy categories were found together with a type of ML. The overview resulted in a categorization consisting of 3 levels: dimension of personalization, persuasive strategy, and type of ML. When personalizing the timing of the messages, most projects implemented reinforcement learning to personalize the timing of reminders and supervised learning (SL) to personalize the timing of feedback, monitoring, and goal-setting messages. Regarding the content of the messages, most projects implemented SL to personalize PA suggestions and feedback or educational messages. For personalizing PA suggestions, SL can be implemented either alone or combined with a recommender system. Finally, reinforcement learning was mostly used to personalize the type of feedback messages. Conclusions: The overview of all implemented persuasive strategies and their corresponding ML methods is insightful for this interdisciplinary field. Moreover, it led to a categorization overview that provides insights into the design and development of personalized persuasive strategies to promote PA. In future papers, the categorization overview might be expanded with additional layers to specify ML methods or additional dimensions of personalization and persuasive strategies.
In het ziekenhuis kan elke fout een leven kosten. Zo kan al een kleine bereidingsfout bij het klaarmaken van intraveneuze medicijnen (IV) leiden tot levensbedreigende omstandigheden voor de patiënt. Bereiding van dit type medicijnen gebeurt in de apotheek en op de verpleegafdeling. Met name op de verpleegafdeling is het een drukke en onvoorspelbare setting. Wereldwijd komen in deze setting ernstige bereidingsfouten nog te frequent voor. Om deze menselijke fouten te reduceren, wordt in deze KIEM aanvraag een proof-of-concept ‘slim oog’ ontwikkeld die vlak voor de toediening detecteert of de juiste dosis aanwezig is, of het type medicijn correct is en geen vervuiling aanwezig is. Het slimme oog maakt gebruik van hyperspectrale technologie en artificial intelligence, en is een samenwerking tussen de Computer Vision & Data Science afdeling van NHL Stenden Hogeschool, de automatische medicijncontrole specialist ZiuZ, en het Tjongerschans ziekenhuis. De unieke combinatie tussen nieuwe AI-technieken, hyperspectrale techniek en de toepassing op intraveneuze medicijnen is voor dit consortium technisch nieuw, en is nog niet eerder ontwikkeld voor de toepassing aan het bed of in de medicijnkamer op de verpleegafdeling. De onvoorspelbare setting en de urgentie aan het bed maakt dit onderzoek technisch uitdagend. Tevens moet het uiteindelijke device klein en draagbaar en snel werkzaam zijn. Om de grote verscheidenheid aan mogelijke gebruik scenario's en menselijke fouten te vangen in het algoritme, wordt een door NHLS ontwikkelde simulatie procedure gevolgd: met nabootsing van de praktijksituatie in samenwerking met zorgverleners, met opzettelijke fouten, en computer gegenereerde beeldmanipulatie. Het project zal geïntegreerd worden in het onderwijs volgens de design-based methode, met teams bestaande uit domein experts, bedrijven, docent-onderzoekers en studenten. Het uiteindelijke doel is om met een proof-of-concept aan-het-bed demonstrator een groot consortium van ziekenhuizen, ontwikkelaars en eindgebruikers enthousiast te maken voor een groter vervolgproject.
The postdoc candidate, Giuliana Scuderi, will strengthen the connection between the research group Biobased Buildings (BB), (collaboration between Avans University of Applied Sciences and HZ University of Applied Sciences (HZ), and the Civil Engineering bachelor programme (CE) of HZ. The proposed research aims at deepening the knowledge about the mechanical properties of biobased materials for the application in the structural and infrastructural sectors. The research is relevant for the professional field, which is looking for safe and sustainable alternatives to traditional building materials (such as lignin asphalt, biobased panels for bridge constructions, etc.). The study of the mechanical behaviour of traditional materials (such as concrete and steel) is already part of the CE curriculum, but the ambition of this postdoc is that also BB principles are applied and visible. Therefore, from the first year of the programme, the postdoc will develop a biobased material science line and will facilitate applied research experiences for students, in collaboration with engineering and architectural companies, material producers and governmental bodies. Consequently, a new generation of environmentally sensitive civil engineers could be trained, as the labour market requires. The subject is broad and relevant for the future of our built environment, with possible connections with other fields of study, such as Architecture, Engineering, Economics and Chemistry. The project is also relevant for the National Science Agenda (NWA), being a crossover between the routes “Materialen – Made in Holland” and “Circulaire economie en grondstoffenefficiëntie”. The final products will be ready-to-use guidelines for the applications of biobased materials, a portfolio of applications and examples, and a new continuous learning line about biobased material science within the CE curriculum. The postdoc will be mentored and supervised by the Lector of the research group and by the study programme coordinator. The personnel policy and job function series of HZ facilitates the development opportunity.
The HAS professorship Future Food Systems is performing applied research with students and external partners to transform our food system towards a more sustainable state. In this research it is not only a question of what is needed to achieve this, but also how and with whom. The governance of our food system needs rethinking to get the transformative momentum going in a democratic and constructive manner. Building on the professorship’s research agenda and involvement in the transdisciplinary NWA research project, the postdoc will explore collective ownership and inclusive participation as two key governance concepts for food system transformation. This will be done in a participatory manner, by learning from and with innovative bottom-up initiatives and practitioners from the field. By doing so, the postdoc will gain valuable practical insights that can aid to new approaches and (policy) interventions which foster a sustainable and just food system in the Netherlands and beyond. A strong connection between research and education is created via the active research involvement of students from different study programs, supervised by the postdoc (Dr. B. van Helvoirt). The acquired knowledge is embedded in education by the postdoc by incorporating it into HAS study program curricula and courses. In addition, it will contribute to the further professional development of qualitative research skills among HAS students and staff. Through scientific, policy and popular publications, participation in (inter)national conferences and meetings with experts and practitioners, the exposure and network of the postdoc and HAS in the field of food systems and governance will be expanded. This will allow for the setting up of a continuous research effort on this topic within the professorship via follow-up research with knowledge institutes, civic society groups and partners from the professional field.
