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Auflistung nach Schlagwort "Artificial intelligence"

1 - 10 von 49
  • Zeitschriftenartikel
    AI Startup Business Models
    (Business & Information Systems Engineering: Vol. 64, No. 1, 2022) Weber, Michael; Beutter, Moritz; Weking, Jörg; Böhm, Markus; Krcmar, Helmut
    We currently observe the rapid emergence of startups that use Artificial Intelligence (AI) as part of their business model. While recent research suggests that AI startups employ novel or different business models, one could argue that AI technology has been used in business models for a long time already—questioning the novelty of those business models. Therefore, this study investigates how AI startup business models potentially differ from common IT-related business models. First, a business model taxonomy of AI startups is developed from a sample of 100 AI startups and four archetypal business model patterns are derived: AI-charged Product/Service Provider, AI Development Facilitator, Data Analytics Provider, and Deep Tech Researcher. Second, drawing on this descriptive analysis, three distinctive aspects of AI startup business models are discussed: (1) new value propositions through AI capabilities, (2) different roles of data for value creation, and (3) the impact of AI technology on the overall business logic. This study contributes to our fundamental understanding of AI startup business models by identifying their key characteristics, common instantiations, and distinctive aspects. Furthermore, this study proposes promising directions for future entrepreneurship research. For practice, the taxonomy and patterns serve as structured tools to support entrepreneurial action.
  • Zeitschriftenartikel
    AI-Enhanced Hybrid Decision Management
    (Business & Information Systems Engineering: Vol. 65, No. 2, 2023) Bork, Dominik; Ali, Syed Juned; Dinev, Georgi Milenov
    The Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach.
  • Zeitschriftenartikel
    Der Artificial Intelligence Act – eine Praxisanalyse am Beispiel von Gesichtserkennungssoftware
    (HMD Praxis der Wirtschaftsinformatik: Vol. 59, No. 2, 2022) Wudel, Alexandra; Schulz, Michael
    Der Einsatz Künstlicher Intelligenz (KI) eröffnet ein Spannungsfeld zwischen Innovationspotentialen und Regulierungsdrang. Auf harmonisierte Richtlinien zur Regulierung dieser Technologie kann derzeit jedoch nicht zurückgegriffen werden. Gleichzeitig kommt es zu Diskriminierungsfällen, durch die der Bedarf an entsprechenden Gesetzen deutlich wird. Ein politischer Meilenstein in der Entwicklung eines Regelwerks kommt aus der Europäischen Union. Seit dem 21.04.2021 liegt der Artificial Intelligence Act der Europäischen Kommission vor. Der Gesetzesvorschlag versucht einen Spagat, um Innovationen weiter zu fördern und gleichzeitig ethische Dimensionen wie Nicht-Diskriminierung zu regulieren. Dieser Artikel stellt eine Statusanalyse von KI-Systemen in der Strafverfolgung in Europa im Vergleich zu den USA vor. Dieser soll herausstellen inwieweit derzeit ein Unterschied im Einsatz von KI aufgrund der geografischen Lage existiert und welche Chancen ebenso wie Herausforderungen ein globales Regelwerk für den ethischen Einsatz von KI aus europäischer Perspektive birgt. Da die Anwendung von Gesichtserkennungssoftware im Bereich der Strafverfolgung weit verbreitet ist, kann durch diese Untersuchung dringend notwendiger Handlungsbedarf der Gesetzgebung erkannt werden. The use of Artificial Intelligence (AI) opens up a field of tension between innovation potential and the need for regulation. However, harmonized guidelines for regulating this technology do currently not exist. At the same time, there are cases of discrimination in which the need for appropriate laws becomes clear. A political milestone in the development of a set of rules comes from the European Union. The Artificial Intelligence Act of the European Commission has been in place since April 21, 2021. The proposed law attempts a balancing act to further promote innovation and at the same time to regulate ethical dimensions such as non-discrimination. This article presents a status analysis of AI systems in law enforcement in Europe compared to the USA. This comparison is intended to highlight the extent to which there is currently a difference between the use of AI due to the geographical location and what opportunities and challenges a global set of rules for the ethical use of AI offers from a European perspective. Since the use of facial recognition software is widespread in the area of law enforcement, this investigation can identify the urgent need for legislative action.
  • Zeitschriftenartikel
    Artificial Intelligence as a Service
    (Business & Information Systems Engineering: Vol. 63, No. 4, 2021) Lins, Sebastian; Pandl, Konstantin D.; Teigeler, Heiner; Thiebes, Scott; Bayer, Calvin; Sunyaev, Ali
  • Zeitschriftenartikel
    Auswirkungen der Medizinprodukteverordnung auf ML-Lösungen in Schweizer Spitälern
    (HMD Praxis der Wirtschaftsinformatik: Vol. 61, No. 2, 2024) Russ, Christian; Stalder, Philipp H.; Rufinatscha, Stefanie; Pimentel, Tibor; Geissmann, Lukas
    Künstliche Intelligenz (KI) ist schon länger in den Spitälern direkt und indirekt präsent. Oftmals ist KI für Arbeitsplatzfunktionen im Bürobereich wie z. B. in Spracherkennungssoftware verfügbar, teilweise auch in Personal- und Ressourcen-Optimierungssoftware. Das Spektrum reicht speziell im medizinischen Bereich von datengetriebenen Analysen und Informationsunterstützungssystemen bis hin zur Generierung von Diagnose- und Therapievorschlägen für das medizinische Personal. Jedoch sind vielen Akteuren in den Spitälern der Umfang und die Auswirkung von KI-Technologien gar nicht wirklich bewusst. Noch weniger bekannt sind dabei die regulatorischen Vorgaben in Kombination mit dem Einsatz von Maschinellem Lernen (ML). Basierend auf einer repräsentativen Befragung von allgemeinen Spitälern in der Schweiz wurde der aktuelle Stand der KI-Nutzung erhoben. Auf dieser Basis werden die Anforderungen an ML-Systeme in Bezug auf die Medizinprodukteverordnung und deren Auswirkung in Hinblick auf den konformen Einsatz von medizinischer Software analysiert. Wir präsentieren einen Vorschlag, wie ML-Systeme besser mit den Regulatorien in Einklang gebracht werden können. Im Ausblick wird auf die möglichen Grenzen und Notwendigkeiten für zukünftige Weiterentwicklungen eingegangen. Artificial intelligence (AI) has been present in hospitals directly and indirectly for some time. Often AI is available for workplace functions in the office area, such as speech recognition software, and in some cases also in personnel and resource optimization software. In the medical field, specifically, the spectrum ranges from data-driven analyses and information support systems to the generation of diagnostic and therapeutic suggestions for medical personnel. However, many players in hospitals are not aware of the scope and impact of AI technologies. What is not well known are the regulatory requirements in combination with the use of machine learning (ML). Based on a representative survey of general hospitals in Switzerland, the current state of AI usage was determined. On this basis, we analyze the requirements for ML systems with respect to the Medical Device Regulation and their impact with respect to the compliant use of medical software. We present a proposal on how ML systems can be brought more in line with regulations. In the concluding outlook, the possible limitations and necessities for future developments are discussed.
  • Zeitschriftenartikel
    Chatbot – Der digitale Helfer im Unternehmen: Praxisbeispiele der Schweizerischen Post
    (HMD Praxis der Wirtschaftsinformatik: Vol. 55, No. 4, 2018) Stucki, Toni; D’Onofrio, Sara; Portmann, Edy
    Chatbots gewinnen an Bedeutung. Sie ermöglichen uns Menschen, in natürlicher Sprache mit Computersystemen zu kommunizieren. Im einfachsten Fall extrahiert der Chatbot aus der Äusserung eines Benutzers dessen Intention, fragt fehlende Informationen in einer Wissensbank ab und bereitet dem Benutzer eine Antwort auf. Somit stellen Chatbots eine Schnittstelle zwischen Informationen und Nutzern dar. In einem Unternehmen können dadurch mehrere Vorteile generiert werden. Der Wissensfluss kann sowohl innerhalb des Unternehmens als auch in der Interaktion mit dem Kunden optimiert werden. Benutzer, seien es nun Mitarbeiter oder Kunden, erhalten von Chatbots schnell und in gleichbleibend hoher Qualität die gesuchten Informationen. Damit der menschliche Benutzer den sprechenden oder schreibenden Chatbot akzeptiert, sollte dieser angemessen kommunizieren. Was dies bedeutet, wie dies möglich wird und welche Potentiale Chatbots bieten, soll dieser Artikel anhand Praxisbeispielen der Schweizerischen Post diskutieren. Die kritische Reflexion der gewonnenen Erkenntnisse runden den Artikel ab. Chatbots are becoming increasingly important. They enable us humans to communicate with computer systems in natural language. In the simplest case, the chatbot extracts a user’s intention from his or her expression, asks for missing information in a knowledge base and prepares an answer for the user. Chatbots represent an interface between information and users. Several advantages can thus be generated in a company. The knowledge flow can be optimized within the company and in interaction with the customers. Users, whether they are employees or customers, quickly receive the information they are looking for from chatbots in consistently high quality. To accept the talking or writing chatbot, it should communicate appropriately. This article will discuss what this means, how this becomes possible and what potentials chatbots can offer, based on practical examples from Swiss Post. The article ends with a critical reflection on the lessons learned.
  • Zeitschriftenartikel
    Cognition, Interaction, Design
    (KI - Künstliche Intelligenz: Vol. 31, No. 4, 2017) Bhatt, Mehul; Cutting, James; Levin, Daniel; Lewis, Clayton
    This transcript documents select parts of discussions on the confluence of cognition, interaction, design, and human behaviour studies. The interview and related events were held as part of the CoDesign 2017 Roundtable (Bhatt in CoDesign 2017—The Bremen Summer of Cognition and Design/CoDesign Roundtable. University of Bremen, Bremen, 2017) at the University of Bremen (Germany) in June 2017. The Q/A sessions were moderated by Mehul Bhatt (University of Bremen, Germany., and Örebro University, Sweden) and Daniel Levin (Vanderbilt University, USA). Daniel Levin served in a dual role: as co-moderator of the discussion, as well as interviewee. The transcript is published as part of a KI Journal special issue on “Semantic Interpretation of Multi-Modal Human Behaviour Data” (Bhatt and Kersting in Special Issue on: Semantic Interpretation of Multimodal Human Behaviour Data, Artif Intell, 2017).
  • Zeitschriftenartikel
    COMBI: Artificial Intelligence for Computer-Based Forensic Analysis of Persons
    (KI - Künstliche Intelligenz: Vol. 36, No. 2, 2022) Becker, Sven; Heuschkel, Marie; Richter, Sabine; Labudde, Dirk
    During the prosecution process the primary objective is to prove criminal offences to the correct perpetrator to convict them with legal effect. However, in reality this may often be difficult to achieve. Suppose a suspect has been identified and is accused of a bank robbery. Due to the location of the crime, it can be assumed that there is sufficient image and video surveillance footage available, having recorded the perpetrator at the crime scene. Depending on the surveillance system used, there could be even high-resolution material available. In short, optimal conditions seem to be in place for further investigations, especially as far as the identification of the perpetrator and the collection of evidence of their participation in the crime are concerned. However, perpetrators usually act using some kind of concealment to hide their identity. In most cases, they disguise their faces and even their gait. Conventional investigation approaches and methods such as facial recognition and gait analysis then quickly reach their limits. For this reason, an approach based on anthropometric person-specific digital skeletons, so-called rigs, that is being researched by the COMBI research project is presented in this publication. Using these rigs, it should be possible to assign known identities, comparable to suspects, to unknown identities, comparable to perpetrators. The aim of the COMBI research project is to study the anthropometric pattern as a biometric identifier as well as to make it feasible for the standardised application in the taking of evidence by the police and prosecution. The approach is intended to present computer-aided opportunities for the identification of perpetrators that can support already established procedures.
  • Zeitschriftenartikel
    Constraint Based World Modeling for Multi Agent Systems in Dynamic Environments
    (KI - Künstliche Intelligenz: Vol. 24, No. 4, 2010) Göhring, Daniel
    Mobile autonomous robotics is a young and complex field of research. Since the world is uncertain and since robots can only gain partial information about it, probabilistic navigation algorithms became popular whenever a robot has to localize itself or surrounding objects. Furthermore, cooperative exploration and localization approaches have become very relevant lately, as robots begin to act not just alone but in groups. Within my thesis I analyze, how information can be exchanged between robots in order to improve their world model. Therefore I examine how communication of spatial percept-relations can help to improve the accuracy of the world model, in particular when the robots are poorly self-localized. First, percept-relations are being used to increase the modeling accuracy in static situations, later the approach is extended to moving objects. After focussing on suitable sensory data for communication, in the second part I present a Bayesian modeling approach, using constraint satisfaction techniques for complex belief functions. Constraint based localization methods will be analyzed in order to have a group of robots efficiently localized and to model their environment. The presented algorithms were implemented and tested within the RoboCup Standard Platform League (SPL).
  • Zeitschriftenartikel
    Datenmarktplätze für Künstliche Intelligenz im Gesundheitswesen: Potenziale, Herausforderungen und Strategien zur Bewältigung
    (HMD Praxis der Wirtschaftsinformatik: Vol. 59, No. 6, 2022) Guse, Richard; Thiebes, Scott; Hennel, Phil; Rosenkranz, Christoph; Sunyaev, Ali
    Das Training von Künstliche Intelligenz (KI)-Modellen, die auf maschinellem Lernen (ML) beruhen, erfordert eine große Menge qualitativ hochwertiger Daten. Besonders im Gesundheitswesen mit seinen hochsensiblen Daten und hohen Anforderungen an den Datenschutz besitzen einzelne Akteur:innen oft jedoch nicht ausreichend hochwertige Daten. Datenmarktplätze für KI zielen darauf ab, dieses Problem zu lösen, indem sie Datenanbieter und Datenkonsumenten miteinander verbinden und den Handel von Daten ermöglichen. Allerdings haben sich Datenmarktplätze im Gesundheitswesen, trotz erster technischer Konzepte und einiger Pilotprojekte, bisher noch nicht erfolgreich durchsetzen können. Im Rahmen der vorliegenden Studie wurden daher Interviews mit einer Reihe von relevanten Expert:innen und Akteur:innen durchgeführt, um Potenziale, Herausforderungen und mögliche Strategien zur Bewältigung der Herausforderungen zu identifizieren. Die Ergebnisse der Studie verdeutlichen anhand der drei Dimensionen des Technology-Organization-Environment-Frameworks spezifische Potenziale von Datenmarktplätzen für KI im Gesundheitswesen, aber gleichzeitig auch eine Reihe von Herausforderungen, die es zu adressieren gilt. Die erarbeiteten Bewältigungsstrategien liefern hierbei erste Ansätze zur Beseitigung der identifizierten Herausforderungen, zeigen jedoch auch die Notwendigkeit der weiteren Forschung auf diesem Gebiet auf. Training artificial intelligence (AI) models requires a large amount of high-quality data. However, especially in healthcare with its highly sensitive data and high privacy requirements, individual stakeholders often do not own sufficient high-quality data. Data marketplaces for AI aim to solve this problem by connecting data providers and data consumers and enabling data trading. However, despite initial technical concepts and some pilot projects, data marketplaces have not yet been successful in the healthcare sector. Within this study, expert interviews were therefore conducted with a number of relevant experts and stakeholders to identify potentials, challenges and possible strategies for overcoming the challenges. Based on the three dimensions of the technology, organization and environment framework, the results of the study highlight specific potentials of data marketplaces for AI in healthcare, but at the same time also a number of challenges that need to be addressed. The mitigation strategies developed here provide initial approaches for eliminating the challenges identified, but also highlight the need for further research in this area.