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Machine Learning in Transport & Logistics | Nodes

The presentation from our recent event in Copenhagen, discussing ML in the transport and logistics sector. <br><br>www.nodesagency.com

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Machine Learning in Transport & Logistics | Nodes

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  1. Nodes event Machine Learning in Transport & Logistics

  2. Laus Breyen-Vinding - Director of Business Development & Strategy @Nodes Casper Rasmussen - CTO & Partner @Nodes & Christian Duffau-Rasmussen – Partner & Data Scientist hos NorthBridge Technologies Coffee and networking Thomas Prinds Meyer – Project Manager Business Innovations & Systems GCO @DSV Lars Holmquist – Smart Data @DFDS Casper Rasmussen - CTO & Partner @Nodes

  3. The vision of Monstar Lab is to change the world with technology.

  4. Nodes is hired to facilitate an on-going development and innovation process with quarterly innovation releases of the app.

  5. We worked with Fujitsu to facilitate a sprint around assistive technology. The sprint helped Fujitsu understand the on-boarding process of new starters, and gave them a number of ways to better that first experience.

  6. Based on the Sitecore Experience Platform, we have build a next level Sitecore solution with connected customer experiences.

  7. By taken a deep dive into the Careem services, setups and code we helped future proof the company’s infrastructure.

  8. Key takeaways today 1. Be able to separate buzz from relevance 2. Learn about the applications of ML 3. Get started with a practical process

  9. 1 6 Forecasting demand Inventory management 2 7 Reduce freight cost Predictive maintenance Fleet optimisation & autonomisation 3 8 Risk reduction 4 9 Supply chain efficiency Customer churn prediction Automating customer service 5 10 Control & surveillance

  10. Casper Rasmussen - CTO & Partner @Nodes Christian Duffau-Rasmussen – Partner & Data Scientist @NorthBridge Technologies

  11. CR: Intro til ML AI / ML buzzwords AI will be the best or worst thing ever for humanity -Elon Musk, Aug 2017, Twitter 18

  12. AI == Machine Learning + Business Logic

  13. January 2018 - Alibaba and Microsoft announces text comprehension at human level on Standfords SQuAD dataset. Model :82.65% Humans: 82.304%. Question: “What is the second most abundant element?” Correct answer: Helium. Attached text: “By mass, oxygen is the third-most abundant element in the universe, after hydrogen and helium.”

  14. May 2018 - Google Duplex service to allow an AI assistant to book appointments over the phone.

  15. Linear regression Logistic regression Machine Learning Statistics Signal processing Shallow Neural Nets t-test Deep Learning

  16. Machine Learning methods Reinforcement learning Supervised learning Unsupervised learning M. Riedmiller (2005)

  17. Machine Learning methods Reinforcement learning Supervised learning Unsupervised learning Google Deepmind August 2018 Nvidia December 2018

  18. Machine Learning methods - Deep Learning AI and Deep Learning -> synonyms ● A flexible class of mathematical models ● Deep Learning has revolutionized Machine Learning ●

  19. The Unreasonable Effectiveness of Deep Learning Convolutional Deep Nets Image Processing - Segmenting images into objects (Sub human) - OCR (Par humans) - Image classification (Par humans) Recurrent NN Word Vectors End-to-end neural models Natural Language Processing - Semantic text modelling (Near human) - Text generation (Sub Human) - Machine translation (Sub Human) Speech processing - Speech-to-text (Near Human) - Text-to-speech (Near Human)

  20. ML methods - How Deep Learning works y = w1· n1 + w2 · n2 + c w1 n1 y x y n2 w2 x

  21. ML methods - Deep Learning (demo) playground.tensorflow.org

  22. How pilot projects can look like Find points on t-shirts ● Image processing and Active Shape Modelling ● Cootes and Taylor (1995) ●

  23. How pilot projects can look like Automatic keyword tagging in product descriptions ● Conditional Random Fields ● Lafferty, McCallum and Pereira (2001) ●

  24. Available technologies Cloud solutions Open source for custom solutions Many more...

  25. Casper Rasmussen - CTO & Partner @Nodes

  26. “How many shippings of product X will happen within the next 2 weeks?” Result / Prediction Trained Model Structure data Data

  27. Getting started Data first approach 100 hours packages Validate predictions / results Select one or more targets Analyse data Train model Test model Problem first approach Validate predictions / results Strategy to get prototype data Gather Train and test model Identify pain prototyping data

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