O'Reilly Artificial Intelligence Conference 2019 - London, United Kingdom

Table of contents

1. Keynotes
   1. Highlights from the Keynotes of Artificial Intelligence Conference, London 2019
   2. Building and deploying AI applications and systems at scale - Ben Lorica (O'Reilly), Roger Chen (Computable)
   3. The power of knowledge at scale - Alexis Crowell Helzer (Intel)
   4. For AI to thrive, failure is necessary: A practical guide (sponsored by IBM Watson) - Ritika Gunnar (IBM)
   5. Public policy and deep reinforcement learning on AWS - Emily Webber (Amazon Web Services)
   6. Unlocking data capital with AI (sponsored by Dell) - Arash Ghazanfari (Dell Technologies)
   7. Large-scale machine learning at Facebook: Implications of platform design on developer productivity - Kim Hazelwood (Facebook), Mohamed Fawzy (Facebook)
   8. Real-time AI for entity resolution - Jeff Jonas (Senzing)
   9. Machine learning challenges at LinkedIn: Spark, TensorFlow, and beyond - Zhe Zhang (LinkedIn)
   10. Start your engines: Making deep reinforcement learning accessible to all developers (sponsored by AWS) - Ian Massingham (Amazon Web Services)
   11. The quest for high-quality data - Ihab Ilyas (University of Waterloo)
   12. Accelerate with purpose - Walter Riviera (Intel)
   13. When to trust AI - Marta Kwiatkowska (Trinity College, University of Oxford)
   14. When flying is cheaper than standing still - Raffaello D’Andrea (Verity | ETH Zurich)
2. Sponsored
   1. How to deploy large-scale distributed data analytics and machine learning on containers (sponsored by HPE) - Thomas Phelan (HPE BlueData)
   2. AI growing pains: Platform considerations for moving from POC to large-scale deployments (sponsored by Dell Technologies) - Thomas Henson (Dell Technologies)
   3. Making reinforcement learning practical for real-world developers (sponsored by AWS) - Lyndon Leggate (Deep)
   4. Framing business problems as machine learning (sponsored by AWS) - Carlos Escapa (Amazon Web Services)
   5. Build, train, and deploy predictive maintenance models at industrial scale (sponsored by AWS) - Sergey Ermolin (Amazon Web Services)
   6. Autonomous ship: The Mayflower project (sponsored by IBM Watson) - Brett A Phaneuf (Submergence Group (US) and MSubs (UK))
   7. For AI to thrive, failure is necessary: A practical guide (sponsored by IBM Watson) - Ritika Gunnar (IBM)
   8. More info from your documents: AI handwriting recognition and automatic parsing (sponsored by AXA) - Ciprian Tomoiaga (AXA)
3. Models and Methods
   1. Principled tools for analyzing weight matrices of production-scale deep neural networks - Michael Mahoney (UC Berkeley)
   2. A pragmatic introduction to building NLP models - Julien Simon (Amazon Web Services)
   3. Concepts and tools for fairness, explainability, and robustness in machine learning - Ilya Feige (Faculty)
   4. Building differentially private machine learning models using TensorFlow - Chang Liu (Georgian Partners ), Ji Chao Zhang (Georgian Partners)
   5. Anomaly detection using deep learning to measure the quality of large datasets - Sridhar Alla (BlueWhale)
   6. Online evaluation of machine learning models - Ted Dunning (MapR)
   7. Anomaly detection in smart buildings using federated learning - Tuhin Sharma (Binaize Labs), Bargava Subramanian (Binaize Labs)
   8. NLP for healthcare: Feature engineering and model diagnostics - Manas Ranjan Kar (Episource)
   9. Adversarial network for natural language synthesis - Rajib Biswas (Ericsson)
   10. An artificial intelligence framework to counter international human trafficking - Tom Sabo (SAS)
   11. Audience projection of target consumers over multiple domains: A NER and Bayesian approach - Gianmario Spacagna (Helixa)
   12. Sequence to sequence (S2S) modeling for time series forecasting - Arun Kejariwal (Independent), Ira Cohen (Anodot)
   13. Introducing a new anomaly-detection algorithm (SR-CNN) inspired by computer vision - Qun Ying (Microsoft)
   14. A practical guide toward algorithmic bias and explainability in machine learning - Alejandro Saucedo (The Institute for Ethical AI Machine Learning)
4. AI Business Summit
   1. Executive Briefing: Business at the speed of AI - Bahman Bahmani (Rakuten)
   2. Executive Briefing: Optimizing for skill sets—Data engineers, data scientists, and analysts - Ted Malaska (Capital One)
   3. Executive Briefing: Designing and building responsible AI - Ariadna Font Llitjós (Twitter)
   4. Executive Briefing: From laggard to leader—Winning the AI race - Anastasia Kouvela (A.T. Kearney ), Bharath Thota (A.T. Kearney)
   5. Executive Briefing: Will you learn Chinese to advance in AI? - Charlotte Han (Independent)
   6. Fairness in AI: Applying deep learning to credit scoring - Martin Benson (Jaywing)
   7. The dangers of data leakage in production machine learning systems - Martin Goodson (Evolution AI)
   8. Make Alexa and Siri speak with each other: Toward a universal grammar in AI - Tobias Martens (Universal Namespace)
   9. Executive Briefing: A look at the future of online pricing and algorithm-led collusion - Rebecca Gu (Electron), Cris Lowery (Baringa Partners)
   10. Executive Briefing: Advances in privacy for machine learning systems - Katharine Jarmul (KIProtect)
   11. Executive Briefing: Why your AI initiative will fail - Umit Cakmak (IBM)
   12. Executive Briefing: Unpacking AutoML - Paco Nathan (derwen.ai)
   13. Executive Briefing: The black box—Interpretability, reproducibility, and data management - Mark Madsen (Teradata)
   14. Executive Briefing: How the growth of voice-based AI stands to blur the lines of big data - Andreas Kaltenbrunner (NTENT)
   15. Predicting the quality of life from satellite imagery - Ganes Kesari (Gramener), Soumya Ranjan (Gramener)
   16. Learning structural changes from text data - Weifeng Zhong (Mercatus Center at George Mason University)
5. Implementing AI
   1. Federated learning introduction and examples with TensorFlow Federated - Alex Ingerman (Google)
   2. Fighting cybercrime with AI - Carlos Rodrigues (Siemens)
   3. Containerized architectures for deep learning - Antje Barth (AWS)
   4. About Space Invaders and automated scaling - Michael Friedrich (Adobe), Stefanie Grunwald (Adobe)
   5. Developing a modern, open source machine learning pipeline with Kubeflow - Steve Flinter (Mastercard Labs), Ahmed Menshawy (Mastercard Labs)
   6. Deep learning with Horovod and Spark using GPUs and Docker containers - Thomas Phelan (HPE BlueData)
   7. Practical on-device AI and ML using Swift - Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee)
   8. Clue: Evaluate the impact of your new training pipeline on existing models in production - Bruno Wassermann (IBM Research)
   9. Measuring embedded machine learning - Alasdair Allan (Babilim Light Industries)
   10. Implementing an AI multicloud broker - Holger Kyas (Open Group, Helvetia Insurances, University of Applied Sciences)
   11. Building, teaching, and training simulations for machine learning with a game engine - Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee)
   12. Why biotech needs knowledge graph convolutional networks for discovery - James Fletcher (Grakn)
   13. Rethinking predictive maintenance - Zaid Tashman (Accenture Labs)
   14. Deploying machine learning models on the edge - Yan Zhang (Microsoft), Mathew Salvaris (Microsoft)
   15. Scaling machine learning at Careem - Ahmed Kamal (Careem)
   16. Azure AI reference architectures - Danielle Dean (iRobot), Wee Hyong Tok (Microsoft), Mathew Salvaris (Microsoft)
   17. Industrialized capsule networks for text analytics - Abhishek Kumar (Publicis Sapient)
   18. A data-driven approach to model the physics of superheated gas hitting a wall - Vignesh Gopakumar (United Kingdom Atomic Energy Authority)
   19. ROCm and Hopsworks for end-to-end deep learning pipelines - Jim Dowling (Logical Clocks), Ajit Mathews (AMD)
   20. Developing perception algorithms for autonomous vehicles - Adam Grzywaczewski (NVIDIA)
   21. Zero to hero with TensorFlow 2.0 - Laurence Moroney (Google)
   22. Creating smaller, faster, production-worthy mobile machine learning models - Jameson Toole (Fritz)
   23. Using ML for personalizing food search at Gojek - Jewel James (Gojek), Mudit Maheshwari (Gojek)
   24. Deep learning on mobile - Siddha Ganju (NVIDIA), Meher Kasam (Square)
   25. Trends to watch: How shifts in data structure and volume demand new approaches to AI compute - Alexis Crowell Helzer (Intel)
6. Interacting with AI
   1. The intersection of AI and HCI: Gamifying the latest artificial intelligence research - Casey Dugan (IBM Research), Zahra Ashktorab (IBM Research)
7. Case Studies
   1. Automating customer complaints classification in German - Adithya Hrushikesh (Vodafone )
   2. Executive Briefing: Fusing data and design - Tim Daines (QuantumBlack), Philip Pilgerstorfer (QuantumBlack)
   3. To arms: The battle against misinformation - Danielle Deibler (MarvelousAI)
   4. AI beyond the buzzword: Do it well or do it twice! - Walter Riviera (Intel)
   5. Improve the speed of ML innovations at LinkedIn - Zhe Zhang (LinkedIn)
8. Tutorials
   1. Scalable AI and reinforcement learning with Ray - Edward Oakes (UC Berkeley Electrical Engineering Computer Sciences), Peter Schafhalter (UC Berkeley RISELab), Kristian Hartikainen (UC Berkeley Robotics and AI Lab) - Part 1
   2. Scalable AI and reinforcement learning with Ray - Edward Oakes (UC Berkeley Electrical Engineering Computer Sciences), Peter Schafhalter (UC Berkeley RISELab), Kristian Hartikainen (UC Berkeley Robotics and AI Lab) - Part 2
   3. Text analytics 101: Deep learning and attention networks all the way to production - Akshay Kulkarni (Publicis Sapient), Pramod Singh (Publicis Sapient) - Part 1
   4. Text analytics 101: Deep learning and attention networks all the way to production - Akshay Kulkarni (Publicis Sapient), Pramod Singh (Publicis Sapient) - Part 2
   5. Herding cats: Product management in the machine learning era - Ira Cohen (Anodot), Arun Kejariwal (Independent) - Part 1
   6. Herding cats: Product management in the machine learning era - Ira Cohen (Anodot), Arun Kejariwal (Independent) - Part 2
   7. TFX: Production ML pipelines with TensorFlow - Robert Crowe (Google), Pedram Pejman (Google) - Part 1
   8. TFX: Production ML pipelines with TensorFlow - Robert Crowe (Google), Pedram Pejman (Google) - Part 2