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Hackathon HACk.DATA.KI.BOTS - Pitch "EK"

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Jeder kennt das Problem von großen Laufwerken, unstrukturierten bzw. ordnerbasierten Dateiablagen und das Problem des Auffindens von Dokumenten. Stellt man sich vor, man hat einen riesigen Ordner in den einfach alle Dateien, egal von welchem Typ, abgelegt und verwaltet werden. Man bekommt ein Dokument per E-Mail, legt dieses hier ab und die künstliche Intelligenz kümmert sich um die Analyse, Ablage und Auffinden des Dokuments. Um dies zu ermöglichen, kommen neuronale Netze und Machine Learning Algorithmen zum Einsatz, welche die Daten auslesen, erkenne, strukturieren und die logische Strukturierung aufbauen.

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Hackathon HACk.DATA.KI.BOTS - Pitch "EK"

  1. 1. Contact to experts Search preferred way of finding information Fast finding Accurate information Bio 29 years old Married Karlsruhe Scientist City University Large unstructured data lake Major research university Wishes & Needs Frank Schmidt Project leader Intuitive usage Large structured data
  2. 2. www.intranet.cityuniversity.edu Information Events Students Faculty & Staff News Academics Campus Life News REDWOOD SHORES, Calif. – The Cardinal closed out the Stanford Invitational Sunday morning with wins in the first and third varsity eight boats. Stanford clocked a time of 6:11.9 into the headwind, while the Hoyas finished with a time of 6:28.7. Stanford faculty, students and staff win grants to advance diversity Stanford has awarded 20 grants to support innovative initiatives that advance the diversity of the university Intranet CityUniversity Today Law & Policy MyUniversity School of Engineering City University
  3. 3. www.intranet.cityuniversity.edu Intranet Machine Learning Intranet City University Results Learning.docx Learn with products. Get to know how the world. .. Machine.xls 1+5+4 () . .. 1.docx http://leraningwiththings.edu/download/marz/fff/kkk/learning.docx http://leraningwiththings.edu/download/marz/fff/kkk/machine.xls http://leraningwiththings.edu/download/marz/fff/kkk/machine.doc Search Results .docx .pptx .xlsx .xls 40 20 20 01 Information Events Students Faculty & Staff News Academics Campus Life .pdf200
  4. 4. What can be achieved with COPS? – using a university as the data source
  5. 5. Extracting Knowledge www.cops.tech fffCognitive Organizational Platform Service
  6. 6. Real-World Problem • Lack of knowledge • Experts inside the company are not connected • Existing solutions cannot be found • … Online search has become the preferred way of finding information quickly and accurately Hierarchical (Formal) Holacratic (Informal) Organizational Structure
  7. 7. Collaboration before individual knowledge
  8. 8. Integration Intranet Search- engine User
  9. 9. Technological implementation NLP Type Tags Entities Classifi- cation Summary Image processing
  10. 10. Use Cases People Departments Business Units Information ExternalWiki Document Systems Who is an expert in ...? Which departments are working on …? Does the solution exists already in …? State of research? I don’t have information about …? Is the error documented? Was there already a similar project?
  11. 11. Summary Cognitive search engine is a leverage for efficiency • Enable people to work faster • Enable people to produce better quality • Inspire your employees to innovate and invent • Effect on workflows • Communication between people • user behavior through search logs • the average ranking of a clicked hit in the result list • the amount of users that are using the system • estimate the level of information your users are expecting Monitor
  12. 12. Fabian Retkowski - Informatik 2. Mastersemester Who are we? Jan Kielmann - Informatik 1. Mastersemester Sebastian Findeisen - Informationswirtschaft 4. Mastersemester Manuel Lang - Informatik 4. Mastersemester
  13. 13. Thank you!
  14. 14. www.intranet.cityuniversity.edu Intranet Feature Engineering Intranet City University Results FeatureEngineering-ProcessSupport.docx A common grievance of customers navigating any support process is the length of time it takes to resolve an issue. .... the company to think about the support issues riders and drivers might face while using their features by making support implications a critical section in feature engineering documents. http://leraningwiththings.edu/download/marz/fff/kkk/ FeatureEngineering-ProcessSupport.docx Contact Information Events Students Faculty & Staff News Academics Campus Life Chat 016328711917 Alexandre Alahi Feature Engineering ML expert CV Related Project Paper Chat 016328711957 Feature Engineering Algorithm expert Related Project Paper Date: 18.05.2017 Paper: High Class Research Rating: 10 Author: Alecandre Alahi FeatureEngineering-Process.pdf Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature engineering is fundamental to the application of machine learning, and is both difficult and expensive. The need for manual feature engineering can be obviated by automated ... Date: 18.11.2019 Paper: High Class Research Rating: 10 Author: Alecandre Alahi Matei Zaharia
  15. 15. Story time Search Engine Extracting Knowledge
  16. 16. Further features (knowledge vector) • Connecting people with lack of knowledge with • Domain experts • Relevant Publications • Several structured/unstructured sources • Extracted Knowledge out of other systems
  17. 17. Quellen - Bilder ▪ Mann auf der Leiter ▪ https://www.firestock.ru/muzhchina-s-binoklem-na-lestnitse-man-with-binoculars-on-the-stairs/ ▪ Frau mit Glühbirne ▪ https://www.christiancourier.com/articles/771-my-people-are-destroyed-for-a-lack-of-knowledge ▪ Straßenpfeiler ▪ http://cordantsolutions.com/opioid-overdose-statistics-vary/ ▪ Kopf ▪ https://www.businesswire.com/news/home/20150220005498/en/Report-Cognitive-Scale-Recognized-Independent-Research-Firm ▪ Mann mit Fernglas ▪ https://www.groupecho.com/en/corporate-validation.htm

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