Robotisation of business processes based on artificial intelligence and deep neural networks (Q77723): Difference between revisions
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(Removed claim: summary (P836): Reference_reference_programme_aids:SA.41471 (2015/X) _public:Article 25 of Commission Regulation (EC) No 651/2014 of 17 June 2014 declaring certain categories of aid compatible with the internal market in the application of Article 107 and 108 of the Treaty (OJ(OJ LEU L 187/1, 26.06.2014).The aim of the research project will be to provide a solution for the automation of business processes based on unstructured data.The Applica solution will b...) |
(Created claim: summary (P836): Reference number of the aid programme: SA.41471(2015/X) Purpose of public aid: Article 25 of EC Regulation No 651/2014 of 17 June 2014 declaring certain types of aid compatible with the internal market in the application of Articles 107 and 108 of the Treaty (OJ L. I'm sorry. EU L 187/1 of 26.06.2014). The aim of the research project will be to create a solution for automating business processes based on unstructured data. The Applica solution w...) |
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Reference number of the aid programme: SA.41471(2015/X) Purpose of public aid: Article 25 of EC Regulation No 651/2014 of 17 June 2014 declaring certain types of aid compatible with the internal market in the application of Articles 107 and 108 of the Treaty (OJ L. I'm sorry. EU L 187/1 of 26.06.2014). The aim of the research project will be to create a solution for automating business processes based on unstructured data. The Applica solution will be addressed primarily to large companies, which carry out business processes based on this kind of data. The concept of the solution allows it to be applied in any thematic area, including processes where a level of precision is required for processing data at a level of close to 100 %. The concept of the project assumes the implementation of research works aimed at the implementation of deep neural networks for the purpose of the solution. This will be one of the first attempts to implement deep neural networks to process natural language for business purposes. This will make it possible to achieve unique functionalities in the quality and accuracy of the analysis of the processed text and the efficiency of the preparation of such analyses. The project envisages research on adapting the architecture of deep neural networks to natural language processing, tools for obtaining and preparing data for machine learning, preparing content classification models, information extractors and environment architecture for streaming large volume data (big data) and combining these elements using a feedback mechanism enabling autocorrection and constant learning of models. The project’s objective will be achieved by carrying out 4 research tasks: 1 (BP) Data collection 2. (BP) Data processing 3. (BP) Can I pair up? (English) | |||||||||||||||
| Property / summary: Reference number of the aid programme: SA.41471(2015/X) Purpose of public aid: Article 25 of EC Regulation No 651/2014 of 17 June 2014 declaring certain types of aid compatible with the internal market in the application of Articles 107 and 108 of the Treaty (OJ L. I'm sorry. EU L 187/1 of 26.06.2014). The aim of the research project will be to create a solution for automating business processes based on unstructured data. The Applica solution will be addressed primarily to large companies, which carry out business processes based on this kind of data. The concept of the solution allows it to be applied in any thematic area, including processes where a level of precision is required for processing data at a level of close to 100 %. The concept of the project assumes the implementation of research works aimed at the implementation of deep neural networks for the purpose of the solution. This will be one of the first attempts to implement deep neural networks to process natural language for business purposes. This will make it possible to achieve unique functionalities in the quality and accuracy of the analysis of the processed text and the efficiency of the preparation of such analyses. The project envisages research on adapting the architecture of deep neural networks to natural language processing, tools for obtaining and preparing data for machine learning, preparing content classification models, information extractors and environment architecture for streaming large volume data (big data) and combining these elements using a feedback mechanism enabling autocorrection and constant learning of models. The project’s objective will be achieved by carrying out 4 research tasks: 1 (BP) Data collection 2. (BP) Data processing 3. (BP) Can I pair up? (English) / rank | |||||||||||||||
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| Property / summary: Reference number of the aid programme: SA.41471(2015/X) Purpose of public aid: Article 25 of EC Regulation No 651/2014 of 17 June 2014 declaring certain types of aid compatible with the internal market in the application of Articles 107 and 108 of the Treaty (OJ L. I'm sorry. EU L 187/1 of 26.06.2014). The aim of the research project will be to create a solution for automating business processes based on unstructured data. The Applica solution will be addressed primarily to large companies, which carry out business processes based on this kind of data. The concept of the solution allows it to be applied in any thematic area, including processes where a level of precision is required for processing data at a level of close to 100 %. The concept of the project assumes the implementation of research works aimed at the implementation of deep neural networks for the purpose of the solution. This will be one of the first attempts to implement deep neural networks to process natural language for business purposes. This will make it possible to achieve unique functionalities in the quality and accuracy of the analysis of the processed text and the efficiency of the preparation of such analyses. The project envisages research on adapting the architecture of deep neural networks to natural language processing, tools for obtaining and preparing data for machine learning, preparing content classification models, information extractors and environment architecture for streaming large volume data (big data) and combining these elements using a feedback mechanism enabling autocorrection and constant learning of models. The project’s objective will be achieved by carrying out 4 research tasks: 1 (BP) Data collection 2. (BP) Data processing 3. (BP) Can I pair up? (English) / qualifier | |||||||||||||||
point in time: 14 October 2020
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Revision as of 10:05, 14 October 2020
Project in Poland financed by DG Regio
| Language | Label | Description | Also known as |
|---|---|---|---|
| English | Robotisation of business processes based on artificial intelligence and deep neural networks |
Project in Poland financed by DG Regio |
Statements
9,928,108.01 zloty
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12,845,759.94 zloty
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77.29 percent
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1 October 2017
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31 March 2021
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APPLICA SPÓŁKA Z OGRANICZONĄ ODPOWIEDZIALNOŚCIĄ
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52°14'1.3"N, 21°4'17.0"E
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Numer_referencyjny_programu_pomocowego: SA.41471(2015/X) Przeznaczenie_pomocy_publicznej: art. 25 rozporządzenia KE nr 651/2014 z dnia 17 czerwca 2014 r. uznające niektóre rodzaje pomocy za zgodne z rynkiem wewnętrznym w stosowaniu art. 107 i 108 Traktatu (Dz. Urz. UE L 187/1 z 26.06.2014). Celem projektu badawczego będzie stworzenie rozwiązania do automatyzacji procesów biznesowych opartych o dane nieustrukturyzowane. Rozwiązanie Applica będzie skierowane przede wszystkim do dużych przedsiębiorstw, realizujących procesy biznesowe oparte o dane tego rodzaju. Koncepcja rozwiązania sprawia, że będzie ono mogło zostać zastosowane w dowolnym obszarze tematycznym, także w procesach, w których wymagany jest poziom precyzji przy przetwarzaniu danych na poziomie bliskim 100%. Koncepcja projektu zakłada realizację prac badawczych mających na celu implementację głębokich sieci neuronowych na potrzeby rozwiązania. Będzie to jedna z pierwszych prób takiej implementacji głębokich sieci neuronowych do przetwarzania języka naturalnego na potrzeby biznesowe. Umożliwi to osiągnięcie unikalnych funkcjonalności w zakresie jakości i dokładności analizy przetwarzanego tekstu oraz efektywności przygotowania takich analiz. W ramach projektu przewidziano prace badawcze nad dostosowaniem architektury głębokich sieci neuronowych do przetwarzania języka naturalnego, nad narzędziami do pozyskiwania i przygotowania danych na potrzeby uczenia maszynowego, przygotowaniem modeli klasyfikatorów treści, ekstraktorów informacji oraz architektury środowiska do strumieniowego przetwarzania dużych wolumenów danych (big data) oraz połączenie tych elementów z wykorzystaniem mechanizmu sprzężenia zwrotnego umożliwiającego autokorektę rozwiązania i stałe uczenie modeli. Cel projektu zostanie osiągnięty dzięki realizacji 4 zadań badawczych: 1 (BP) Pozyskanie danych 2. (BP) Przetwarzanie danych 3. (BP) Sprzę? (Polish)
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Reference number of the aid programme: SA.41471(2015/X) Purpose of public aid: Article 25 of EC Regulation No 651/2014 of 17 June 2014 declaring certain types of aid compatible with the internal market in the application of Articles 107 and 108 of the Treaty (OJ L. I'm sorry. EU L 187/1 of 26.06.2014). The aim of the research project will be to create a solution for automating business processes based on unstructured data. The Applica solution will be addressed primarily to large companies, which carry out business processes based on this kind of data. The concept of the solution allows it to be applied in any thematic area, including processes where a level of precision is required for processing data at a level of close to 100 %. The concept of the project assumes the implementation of research works aimed at the implementation of deep neural networks for the purpose of the solution. This will be one of the first attempts to implement deep neural networks to process natural language for business purposes. This will make it possible to achieve unique functionalities in the quality and accuracy of the analysis of the processed text and the efficiency of the preparation of such analyses. The project envisages research on adapting the architecture of deep neural networks to natural language processing, tools for obtaining and preparing data for machine learning, preparing content classification models, information extractors and environment architecture for streaming large volume data (big data) and combining these elements using a feedback mechanism enabling autocorrection and constant learning of models. The project’s objective will be achieved by carrying out 4 research tasks: 1 (BP) Data collection 2. (BP) Data processing 3. (BP) Can I pair up? (English)
14 October 2020
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Identifiers
POIR.01.01.01-00-0144/17
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