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