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pub:teaching:courses:rnd [2018/04/24 06:19] – sbk | pub:teaching:courses:rnd [2018/05/30 10:10] – [2018-04-25] sbk |
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- 2018-03-28 [[pub:teaching:courses:rnd#section20180328|Richard Lucas]] TedxKazimierz | - 2018-03-28 [[pub:teaching:courses:rnd#section20180328|Richard Lucas]] TedxKazimierz |
- 2018-04-25 [[pub:teaching:courses:rnd#section20180425|SKK S.A. - IoT i sztuczna inteligencja – jak poskromić technologię]] | - 2018-04-25 [[pub:teaching:courses:rnd#section20180425|SKK S.A. - IoT i sztuczna inteligencja – jak poskromić technologię]] |
| - 2018-06-06 [[pub:teaching:courses:rnd#section20180606|Philip Morris International - Artificial Intelligence at PMI - Automating Business Processes in a Corporate Environment]] |
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==== Jesień 2017 - dodatkowe spotkania ==== | ==== Jesień 2017 - dodatkowe spotkania ==== |
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===== Spotkania RnD 2018 ===== | ===== Spotkania RnD 2018 ===== |
| ==== 2018-06-06 ==== |
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| **Temat**: |
| //Artificial Intelligence at PMI - Automating Business Processes in a Corporate Environment// |
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| **Speakers**: |
| - Debmalya Biswas, Lead Enterprise Architect |
| - Nicolas Correa, Manager IS |
| - Mateusz Lugowski, Solution Architecture Intern |
| - Vladyslav Melnyk, Solution Architect |
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| **Contents:** |
| - **Team Introduction** |
| - **Experiment #1 - 'Deep Learning based Natural Language Search'** \\ In this talk, we will provide details of a Deep Learning based solution (DrQA) for Natural Language Search over factory machine manuals. The proposed architecture allows identifying the precise answer span corresponding to a query, which improves over the paragraph level answers enabled by pure statistical approaches. |
| - **Experiment #2 - 'Planogram - Shelf Compliance at Point of Sale'** \\ In this talk we will provide details of a Deep Learning based solution for shelf compliance image recognition use-case at PMI Points-of-Sales (PoS). The experimental architecture consists of two stages, first one detects all positions on the shelf (empty positions, our and non PMI products). The second one is executing classification of detected positions and provides a type of each. System finds all PMI products and their types, non-PMI products, empty slots, counts them and verifies if everything is placed correctly due to PMI requirements. |
| - **Experiment #3 - 'Helpdesk Ticket Classification'** \\ In this talk we will provide details about neural network based solution for helpdesk ticket classification. The experimental network architecture consists of a mix of fully connected layers on top of number of keyword occurrences and a stack of one dimensional convolutions over word vector embeddings for the whole text. |
| - **Discussion & Closing** |
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| **Speakers’ Biograms** |
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| {{:pub:teaching:courses:pmi_biswas.png?50 |}}**Dr. Debmalya Biswas** is a Lead Enterprise Architect in the Information Services group at PMI, based in Lausanne, Switzerland. He has over 15 years’ experience working on a variety of technology areas, such as, AI, Big Data, Semantic Web, Information Retrieval, and Data Privacy. During this period, he has worked for leading technology companies such as Nokia, SAP, Oracle, Swisscom, Infosys, and the Swiss startup Iprova. He is the (co-)author of around 40 patents and 30 articles published in leading scientific conferences and journals. He holds a PhD in Computer Science from INRIA, France. |
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| {{:pub:teaching:courses:pmi_correa.png?50 |}}**Nicolas Correa** is a Lead ISE (Information Services Expert) in Artificial Intelligence, based in Krakow, Poland. He holds a Bachelor's degree of Computer Science at UADE (Universidad Argentina De la Empresa), with over 10 years of experience working at different multinational companies from different industries (Pricewaterhouse&Coopers, IBM, Avaya), providing a solid professional experience, mainly in Project Management by executing Telecoms and IT related projects supporting Leaf, Agronomy, ITS&P (Illicit Trade Strategy & Prevention), and Operations. |
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| {{:pub:teaching:courses:pmi_lugowski.png?50 |}}**Mateusz Lugowski** is an AI Solution Architect Intern in the Infromation Services group at PMI, based in Krakow, Poland. He has finished a BSc in Control Systems and Robotics at the AGH University of Science and Technology, and is pursuing a Master's degree in the same subject. Although his professional experience in the AI domain is quite short, he has been a hobbyist for some time, and applies his knowledge in commercial use cases in his current position. |
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| {{:pub:teaching:courses:pmi_melnyk.png?50 |}}**Vladyslav Melnyk** is an AI Solution Architect in the Information Services group at PMI, based in Krakow, Poland. He has finished a Masters Computer Engineering degree in Lviv Polytechnic University in 2017. He has experience in providing AI solutions for logistics domain, he implemented various Deep Learning models and algorithms for Image Recognition and Computer Vision tasks. Beside this, he is very enthusiastic in all AI opportunities, likes to implement, extend, and apply latest research in deep learning. |
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| **About PMI** |
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| Philip Morris International is a leading international tobacco company, with a diverse workforce of around 81,000 people. Our products are the choice of 150 million consumers worldwide, and for those who choose to continue to smoke, we will continue to offer them the best quality products. But that’s not where our vision for smokers ends. |
| We’re dedicated to doing something very dramatic – replacing cigarettes with the smoke-free products that we’re developing and selling. That’s why we have a total of over 400 dedicated scientists, engineers, and technicians developing less harmful alternatives to cigarettes at our two Research & Development sites in Switzerland and Singapore. It’s the biggest shift in our history. |
| This company transformation triggers demand for innovative IT solutions such as Artificial Intelligence & Machine Learning that will help us automate our business processes, releasing time & energy of our employees to focus on generating value to our customers and implementing our strategy towards smoke-free future. |
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| Key facts about PMI: |
| * 81,000 employees around the world |
| * 400+ R&D scientists, engineers and technicians working on smoke-free products |
| * 180+ markets where our products are sold |
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| Further information: |
| * [[http://www.pmi.com|]] |
| * [[http://www.pmiscience.com|]] |
| * [[http://www.pmicareers.com|]] |
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