Volkswagen Foundation AI research Grant for the Chair of Prof. Hinz
The Volkswagen Foundation has given the chair the opportunity to create a research proposal for a more extensive AI project. With this in mind, the foundation is funding the chair with an increased amount of money, which can be used to finance two additional employees and research equipment, among other things.
The application for the advanced research project "ML2MT" deals with the question of how people can benefit from AI not only as decision support systems in the short term, but also learn from the systems in the long term in order to improve their own skills. In particular and as a first step, this research idea is investigated in the area of medicine and healthcare. We are curious to see how the research on this topic and especially this project will develop further!
AI Research Grant of the BMWi for the Chair of Prof. Hinz
The project "ForeSight" praised by the BMWi, and in particular the sub-project of Prof. Hinz, has now received the grant notice. Due to its far-reaching implications for the economy, society and business, the project was initially found to be a winner in the AI competition and ultimately eligible for funding. The project aims at the implementation and research of an AI platform in the field of smart living, which is to fundamentally change the future of living and the industry built around it.
The subproject of Prof. Hinz focuses in particular on the socio-economic factors, human AI interaction, and the development of AI-based, context-specific services. We congratulate Prof. Hinz on his funding and are looking forward to the results of his research!
Project ForeSight wins BMWi KI innovation contest
The winners of the KI-Innovationswettbewerb des Bundesministerium für Wirtschaft und Energie (BMWi) were announced (further information can be found in the BMWi press release).
In addition to 15 other platform projects, the ForeSight project, in which our professorship will be involved, was also awarded.
The goal of ForeSight is to establish an AI-based platform for the development of innovative Smart Living services in the residential environment, especially on the basis of AI-based technologies. The aim is to create a new, connecting ecosystem for manufacturers, vendors and users from different industries that will also help overcome today's interoperability problems when using components from different vendors.
We are delighted by the award and look forward to the collaboration starting in 2020.
Prof. Dr. Oliver Hinz joins the Editorial Board for MAKE
Prof. Dr. Oliver Hinz has joined the Editorial Board of the journal Machine Learning & Knowledge Extraction (MAKE). MAKE is an open-access journal focusing on machine learning and its applications. More information about MAKE can be found here.
Best Paper Award for Katharina Keller at UbiComp 2019
Katharina Keller and her co-authors won the Best Paper Award at UbiComp 2019 as part of the 4th Workshop on Ubiquitous Personal Assistance.
Together with Kim Valerie Carl, Hendrik Jöntgen, Benjamin M. Abdel-Karim, Prof. Dr. Max Mühlhäuser and Prof. Dr. Oliver Hinz, she examines the following aspects in the paper "K.I.T.T., Where Are You? - Why Smart Assistance Systems in Cars Enrich People's Lives" examines possible factors influencing the intention to use smart assistance systems in cars.
Completion of the research project ENTOURAGE
The BMWI-funded research project ENTOURAGE, in which an open ecosystem for intelligent, secure and trustworthy assistance systems on the Internet of Things was developed, will be concluded on 11.09.2019 at the IAA.
Visitors from outside the project are welcome, but are requested to register in advance, otherwise no access to the exhibition grounds can be guaranteed. Further information can be found on the project website.
Exciting calls for papers for the research area AI
We would like to inform you about these two exciting calls for papers for the research area AI for which Professor Hinz is holding a track chair and guest editor role respectively.
You can find additional information by clicking on their title.
Machine learning is revolutionizing our ability to leverage data and tackle challenging applications such as machine translation, recommender systems, fraud detection, and medical diagnosis. Interactive learning supports these advances by casting learning as a dialogue between a model and one or more users, who may play the role of teachers, targets, and judges of the model being learned, and who can also learn as being part of the teaching-learning loop. Interactive learning encompasses different styles of interaction, from acquiring label and ranking information as in active learning and preference elicitation to exchanging structured supervision as in imitation learning. This allows the model to produce personalized predictions and recommendations and handle data-scarce settings by only querying for informative, targeted supervision. Interaction is also required for establishing a mutual understanding between the model and the user, and in turn directability and trust.
Designing successful interactive learning schemes requires to solve a number of key challenges like minimizing the cognitive cost for the user while optimizing query informativeness, devising effective interaction protocols based on different types of queries (membership, ranking, search, explanation, etc.), producing optimal questions by explicitly and efficiently capturing the uncertainty of the model, distributing the load of query answering across multiple teachers with heterogeneous abilities, designing or estimating realistic models of user behavior, increasing tolerance to noise and actively guiding the user toward providing better and more robust supervision, and, more generally, automatically discovering the user's expertise level and adapting the interaction accordingly. Such an interaction is likely to help make such systems more transparent and the results more explainable.
Only then interactive learning will unlock unprecedented opportunities for both scientific research and commercial exploitation in Artificial Intelligence, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, Bioinformatics, Agriculture, Social Web, Finance, e-Commerce, and Design, among other domains.
This special issue aims at surveying established research in interactive learning, as well as overviewing recent advances on algorithms, models and effective process design around humans in the loop.
If you are interested in contributing (technical reports, system descriptions, project reports, survey articles, discussions and dissertation abstracts) to this special issue, please contact one of the guest editors before the submission deadline.
Submission deadline: 1st of September 2019 Reviews / resubmission: November 2019
The advancement of machine learning algorithms, the availability of large amounts of data to feed these algorithms, and increasing processing power have enabled the successful application of artificial intelligence (AI) capabilities in the field. Among those are speech recognition, natural language processing, computer vision, predictive analytics, and robotics.
Developers can now draw upon these capabilities to improve existing systems (e.g., machine translation) or innovate new technologies (e.g., smart speaker). We refer to information systems incorporating AI capabilities as AI-based systems. AI-based systems affect the relationship between humans and machines in solving tasks. AI-based systems can assist humans in an increasing number of tasks or even solve them autonomously. Furthermore, AI capabilities allow systems to appear more humanly and thus create anthropomorphic perceptions (i.e., by interacting via speech). AI-based systems offer opportunities for businesses to become more efficient or to innovate new products and services. However, AI-based systems need to be designed and implemented carefully to avoid unwanted drawbacks such as discrimination through bias in automated decision making. These developments open up a wide array of questions at the individual, organizational and societal level. This track wants to contribute towards answering these questions and provide insights regarding the best possible design and use of AI-based systems. The track is open for contributions to the topic from all theoretical and methodological angles. We also welcome work regarding the use of AI capabilities for research purposes and the impact of AI on information system discipline.
Covered areas include, but are not limited to:
·Hybrid and Augmented Intelligence
·AI-based Assistance Systems
·Development, Design, and Implementation of AI-based Systems
·(Over)Trust in AI-based Systems
·Explainability and Transparency of AI-based Systems
·Anthropomorphic Systems and Perceptions of Humanness
·Business, Managerial, and Strategic Implications of AI
·AI-based Data Analytics and Decision Making
·AI-based Methods in IS Research
·Relationship between AI and IS Research
·Downsides of AI-based Systems: Bias, Discrimination, and Aversion
Differentiation is the buzzword to assert oneself as a smaller cloud provider against the big hyperscaler. An economic and technological outlook from Professor Hinz of the Goethe University in Frankfurt in the run-up to the "Cloud 2018 Technology & Services Conference" in September: cloudcomputing-insider.de
Continous availability harms concentration
Very interesting article on heise.de about smartphone usage after work and its consequences. Even our project Sandra is mentioned in the article.
The role of hyperscalers
The public cloud is firmly in control of hyperscalers. This endagers dependences and a "vendor-lockin". Oliver Hinz is of the mind that smaller providers can occupy niches.
In the context of Hosting & Service Provider (HSP) Summit 2018 (17. - 18.05 in Frankfurt), Prof. Dr. Oliver Hinz will expound economic and technical future prospects of the ASA-Service-Model.
Interview with Prof. Dr. Oliver Hinz
The cloud market is faces technological and economic changes. In an interview in the forefront of the Hosting & Service Provider Summits 2018 Prof. Dr. Oliver Hinz reveals for what provider need to be prepared.
Interview with Prof. Dr. Oliver Hinz about the current Facebook data affair
In a current article in Meinungsbarometer Prof. Dr. Oliver Hinz talks about the facebook data affair.
We wish all students, colleagues and staff of the Goethe University a Merry Christmas and a happy new year 2018! Please note, from December 27 - 29, the university is closed. We'll be back January 2nd onwards. - Your Chair of Information Systems and Information Management.
Li, Xitong / Grahl, Joern / Hinz, Oliver (2021): "How Do Recommender Systems Lead to Consumer Purchases? A Causal Mediation Analysis of a Field Experiment", Information Systems Research, forthcoming.
Hinz, Oliver / Hill, Shawndra / Sharma, Amit (2021): "Second Screening – The Influence of Concurrent TV Consumption on Online Shopping Behavior", Information Systems Research, conditionally accepted.
Weiler, Michael / Stolz, Simon / Lanz, Andreas / Schlereth, Christian / Hinz, Oliver (2021): "Social Capital Accumulation Through Social Media Networks: Evidence from a Randomized Field Experiment and Individual-Level Panel Data", Management Information Systems Quarterly (MISQ), forthcoming.