TY - CHAP A1 - Kordon, Ulrich A1 - Wolff, Matthias A1 - Tschöpe, Constanze ED - Gerlach, Gerald T1 - Mustererkennung für Sensorsignale Y1 - 2009 SN - 978-3-941298-55-2 SP - 69 EP - 78 PB - TUDpress CY - Dresden ER - TY - CHAP A1 - Wolff, Matthias A1 - Hoffmann, Rüdiger ED - Esposito, Anna T1 - An Approach to Intelligent Signal Processing T2 - Behavioral Cognitive Systems Y1 - 2012 SP - 1 EP - 18 PB - Springer CY - Berlin [u.a.] ER - TY - CHAP A1 - Wolff, Matthias A1 - Tschöpe, Constanze A1 - Römer, Ronald ED - Mehnert, Dieter ED - Kordon, Ulrich ED - Wolff, Matthias T1 - Quo vadis, UASR? T2 - Systemtheorie Signalverarbeitung Sprachtechnologie Y1 - 2013 SN - 978-3-944331-19-5 SP - 276 EP - 285 PB - TUDpress CY - Dresden ER - TY - CHAP A1 - Hoffmann, Rüdiger A1 - Eichner, Matthias A1 - Kordon, Ulrich A1 - Tschöpe, Constanze A1 - Wolff, Matthias ED - Hentschel, Christian T1 - Anwendung von Spracherkennungsalgorithmen auf nichtsprachliche akustische Signale T2 - Sprachsignalverarbeitung : Analyse und Anwendungen ; zum 65. Geburtstag von Klaus Fellbaum Y1 - 2007 SN - 978-3-940046-02-4 SP - 46 EP - 57 PB - TUDpress CY - Dresden ER - TY - CHAP A1 - Hoffmann, Rüdiger A1 - Eichner, Matthias A1 - Wolff, Matthias ED - Braun, Angelika ED - Masthoff, Herbert R. T1 - Inverse Probleme in der Sprachsignalverarbeitung T2 - Phonetics and its Applications Y1 - 2002 SN - 3-515-08094-5 SP - 420 EP - 434 PB - Steiner CY - Stuttgart ER - TY - CHAP A1 - Eichner, Matthias A1 - Wolff, Matthias A1 - Hoffmann, Rüdiger ED - Wahlster, Wolfgang T1 - Data driven generation of pronunciation dictionaries T2 - Verbmobil - Foundations of speech-to-speech translation Y1 - 2000 SN - 978-3-662-04230-4 SP - 95 EP - 105 PB - Springer CY - Berlin [u.a.] ER - TY - CHAP A1 - Westendorf, C.-M. A1 - Wolff, Matthias ED - Schröder, Bernhard ED - Lenders, W. ED - Hess, W. ED - Portele, T. T1 - Automatische Generierung von Aussprachewörterbüchern aus Signaldaten T2 - Computer, Linguistik und Phonetik zwischen Sprache und Sprechen Y1 - 1998 SN - 3-631-33844-9 SN - 0-8204-3571-6 SP - 213 EP - 225 PB - Lang CY - Frankfurt am Main [u.a.] ER - TY - CHAP A1 - Schmitt, Ingo A1 - Wirsching, Günther A1 - Wolff, Matthias ED - Khrennikov, Andrei ED - Melucci, Massimo ED - Bourama, Toni T1 - Quantum-Based Modelling of Database States T2 - Quantum-Like Models for Information Retrieval and Decision-Making Y1 - 2019 UR - https://www.springer.com/de/book/9783030259129 SN - 978-3-030-25912-9 SN - 978-3-030-25913-6 U6 - https://doi.org/10.1007/978-3-030-25913-6 SP - 115 EP - 127 PB - Springer Nature Switzerland AG, Springer International Publishing CY - Cham ET - 1. Auflage ER - TY - CHAP A1 - Kraljevski, Ivan A1 - Tschöpe, Constanze A1 - Wolff, Matthias ED - Klimczak, Peter ED - Petersen, Christer T1 - Limits and Prospects of Big Data and Small Data Approaches in AI Applications T2 - AI - Limits and Prospects of Artificial Intelligence N2 - The renaissance of artificial intelligence (AI) in the last decade can be credited to several factors, but chief among these is the ever-increasing availability and miniaturization of computational resources. This process has contributed to the rise of ubiquitous computing via popularizing smart devices and the Internet of Things in everyday life. In turn, this has resulted in the generation of increasingly enormous amounts of data. The tech giants are harvesting and storing data on their clients’ behavior and, at the same time, introducing concerns about data privacy and protection. Suddenly, such an abundance of data and computing power, which was unimaginable a few decades ago, has caused a revival of old and the invention of new machine learning paradigms, like Deep Learning. Artificial intelligence has undergone a technological breakthrough in various fields, achieving better than human performance in many areas (such as vision, board games etc.). More complex tasks require more sophisticated algorithms that need more and more data. It has often been said that data is becoming a resource that is "more valuable than oil"; however, not all data is equally available and obtainable. Big data can be described by using the "four Vs"; data with immense velocity, volume, variety, and low veracity. In contrast, small data do not possess any of those qualities; they are limited in size and nature and are observed or produced in a controlled manner. Big data, along with powerful computing and storage resources, allow “black box” AI algorithms for various problems previously deemed unsolvable. One could create AI applications even without the underlying expert knowledge, assuming there are enough data and the right tools available (e.g. end-to-end speech recognition and generation, image and object recognition). There are numerous fields in science, industry and everyday life where AI has vast potential. However, due to the lack of big data, application is not straightforward or even possible. A good example is AI in medicine, where an AI system is intended to assist physicians in diagnosing and treating rare or previously never observed conditions, and there is no or an insufficient amount of data for reliable AI deployment. Both big and small data concepts have limitations and prospects for different fields of application. This paper will try to identify and present them by giving real-world examples in various AI fields. Y1 - 2023 UR - https://www.transcript-verlag.de/chunk_detail_seite.php?doi=10.14361%2F9783839457320-006 SN - 978-3-8376-5732-6 U6 - https://doi.org/10.14361/9783839457320-006 SP - 115 EP - 142 PB - transcript Verlag CY - Bielefeld ER - TY - CHAP A1 - Klimczak, Peter A1 - Wirsching, Günther A1 - Wolff, Matthias ED - Klimczak, Peter ED - Petersen, Christer T1 - Kondome verschlimmern nur das Problem: Eine formale Semantik-Analyse T2 - Popestar. Der Papst und die Medien KW - Papst KW - Medien Y1 - 2017 SN - 978-3-86599-254-3 SP - 295 EP - 312 PB - Kadmos CY - Berlin ER -