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Schmidt, F.A.: Crowdproduktion von Trainingsdaten : zur Rolle von Online-Arbeit beim Trainieren autonomer Fahrzeuge (2019)
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- Abstract
- Seit 2017 gibt es einen starken Anstieg in der Nachfrage nach hochpräzisen Trainingsdaten für die KI-Modelle der Automobilindustrie. Ohne große Mengen dieser Daten ist das ehrgeizige Ziel des autonomen Fahrens nicht zu erreichen. Damit aus selbstlernenden Algorithmen selbstlenkende Fahrzeuge werden können, braucht es allerdings zunächst viel Handarbeit, die von Crowds auf der ganzen Welt geleistet wird. Sie bringen den lernenden Maschinen das Hören, das Sehen und das umsichtige Fahren bei.Auf der Kundenseite für crowdproduzierte Trainingsdaten drängen Dutzende gut finanzierte Firmen in den entstehenden Markt für das autonome Fahren. Mit den großen Automarken (OEMs) und deren etablierten Zulieferern konkurrieren nun auch große Hardware- und Softwarefirmen wie Intel und Nvidia, Google und Apple, die bisher wenig mit Autos zu tun hatten; hinzu kommen zahlreiche risikokapitalfinanzierte Start-ups. 2018 verfügten bereits 55 Firmen über eine Lizenz zum Testen eigener autonomer Fahrzeu-ge in Kalifornien. Tendenz steigend. Sie alle sind für das Funktionieren ihrer Algorithmen auf präzise beschriftete - "annotierte" - Trainingsdaten angewiesen. Sie brauchen Millionen von Fotos aus dem Straßenverkehr, bei denen jedes abgebildete Pixel einer Szene semantisch einem Objekt zugeordnet wurde. Fahrbahnmarkierungen, Fahrzeuge, und Fußgänger müssen trennscharf voneinander abgegrenzt und mit Zusatzinformationen versehen werden, damit im Zuge des maschinellen Lernens daraus Regeln abgeleitet und Softwaremodelle entwickelt werden können.
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Loth, K.; Grunewald, F.: Ideen zu einer gemeinsamen Sacherschliessung (1996)
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- Date
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Grazia Colonia; Dimmler, E.; Dresel, R.; Messner, C.; Krobath, A.; Petz, S.; Sypien, M.; Boxen, P. van; Harders, M.; Heuer, D.; Jordans, I.; Juchem, K.; Linnertz, M.; Mittelhuber, I.; Schwammel, S.; Schlögl, C.; Stock, W.G.: Informationswissenschaftliche Zeitschriften in szientometrischer Analyse (2002)
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- Series
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- 22. 2.2019 9:26:20
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- Pages
- 29 S
