The Artificial Intelligence Conference - London, UK 2018

Table of contents

1. Keynotes
   1. AI in production: The droids you’re looking for - Jonathan Ballon (Intel)
   2. The state of automation technologies - Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
   3. AI and machine learning at Amazon (sponsored by Amazon Web Services) - Ian Massingham (Amazon Web Services)
   4. Why we built a self-writing Wikipedia - Amy Heineike (Primer)
   5. Trust and transparency of AI for the enterprise (sponsored by IBM Watson)- Ruchir Puri (IBM)
   6. AI for a Better World - Ashok Srivastava (Intuit)
   7. Rethinking Software Engineering in the AI Era - Yangqing Jia (Facebook)
   8. Bringing AI into the enterprise: A functional approach to the technologies of intelligence - Kristian Hammond (Northwestern Computer Science)
   9. Fireside chat with Marc Warner and Rannia Leontaridi - Marc Warner (ASI), Louis Barson (BEIS)
   10. Deep learning at scale: A field manual - Jason Knight (Intel)
   11. Building artificial people: Endless possibilities and the dark side - Supasorn Suwajanakorn (Independent)
   12. The missing piece - Cassie Kozyrkov (Google)
   13. Notes from the frontier: Making AI work - Michael Chui (McKinsey Global Institute)
2. Sponsored
   1. Advanced machine learning with Amazon SageMaker (sponsored by Amazon Web Services) - Julien Simon (Amazon Web Services)
   2. The evolution of AI at the network edge: How silicon is paving a path for IoT innovation (sponsored by Intel AI) - Gary Brown (Intel)
   3. The AI in fail (sponsored by Teradata) - Christopher Hillman (Teradata)
3. Implementing AI
   1. Protecting your secrets - Katharine Jarmul (KIProtect)
   2. Scaling machine intelligence with IPUs - Nigel Toon (Graphcore)
   3. Applied Machine Learning at Facebook: An Infrastructure Perspective - Yangqing Jia (Facebook), Dmytro Dzhulgakov (Facebook)
   4. Architecting AI applications - Mikio Braun (Zalando SE)
   5. The use of recommender systems in the chief investment office: A case study - Gaurav Chakravorty (qplum)
   6. Sense-Infer-Act-Learn: A model for trustworthy AI - Rupert Steffner (WUNDER)
   7. Deep prediction: A year in review for deep learning for time series - Aileen Nielsen (Skillman Consulting)
   8. How to build privacy and security into deep learning models - Yishay Carmiel (IntelligentWire)
   9. H2O’s Driverless AI - Marios Michailidis (H2O.ai)
   10. AI for automation and influence in open science publishing - Daniel Ecer (eLife Sciences), Paul Shannon (eLife Sciences)
   11. Satellite detection of moving objects in a maritime environment - natalie fridman (ImageSat International (iSi))
   12. TonY: Native support of TensorFlow on Hadoop - Jonathan Hung (LinkedIn), Keqiu Hu (LinkedIn), Anthony Hsu (LinkedIn)
   13. Deprecating the state machine: Building conversational AI with the Rasa stack - Alan Nichol (Rasa)
   14. How machines learn to code: Machine learning on source code - Thomas Endres (TNG Technology Consulting), Samuel Hopstock (TNG Technology Consulting)
   15. The last mile on democratizing AI - Zhipeng Huang (Huawei)
   16. DragonFly+: An FPGA-based quad-camera visual SLAM system for autonomous vehicles - Shaoshan Liu (PerceptIn)
   17. Artificial intelligence at the edge - Jameson Toole (Fritz)
   18. Do-it-yourself artificial intelligence - Alasdair Allan (Babilim Light Industries)
4. TensorFlow at AI
   1. Building AI with TensorFlow: An overview (sponsored by Google) - Sandeep Gupta (Google), Edd Wilder-James (Google)
   2. Ready, set, go: Using TensorFlow to prototype, train, and productionalize your models (sponsored by Google) - Amit Patankar (Google)
   3. Tensor2Tensor (sponsored by Google) - Ryan Sepassi (Google)
   4. Frontiers of TensorFlow: Mathematics and music (sponsored by Google) - Joshua Dillon (Google Research), Wolff Dobson (Google)
   5. Cloud AutoML: Customize machine learning models with your own data (sponsored by Google) - Lucio Floretta (Google Cloud)
   6. Pragmatic ML development with scikit-learn and TensorFlow using Google ML Engine (sponsored by Google) - Zack Akil (Google)
   7. TensorFlow for JavaScript (sponsored by Google) - Daniel Smilkov (Google), Nikhil Thorat (Google)
   8. AutoGraph and distributed TensorFlow (sponsored by Google) - Brian Lee (Google Brain), Priya Gupta (Google)
   9. Acceleration with TPUs (sponsored by Google) - Thomas Norrie (Google)
   10. The future of ML is tiny. (sponsored by Google) - Pete Warden (Google)
   11. Machine learning in production with TensorFlow Extended (TFX) (sponsored by Google) - Kenny Song (Google), Quentin de Laroussilhe (Google)
   12. From zero to ML on Google Cloud Platform (sponsored by Google) - Sara Robinson (Google)
5. Impact of AI on Business and Society
   1. Unsupervised ML and fraud detection with deep neural networks - Giorgia Fortuna (Machine Learning Reply)
   2. What is ML Ops? Solutions and best practices for applying DevOps to production ML services - Kaz Sato (Google)
   3. Algorithms gone wild: Applying machine learning for insights into machine learning algorithms - Ira Cohen (Anodot)
   4. Design to architecture and code using deep learning: Implications for GUI development - Archisman Majumdar (Mphasis)
   5. AI in business forecasting: Lessons from building an intelligent cashflow engine - Johnnie Ball (Fluidly)
6. Models and Methods
   1. Federated learning - Ryan Micallef (Cloudera Fast Forward Labs)
   2. Harden and improve your deep learning models with targeted ensembles - Alan Mosca (nPlan | Birkbeck, University of London)
   3. Business forecasting using hybrid approach: A new forecasting method using deep learning and time series - Pasi Helenius (SAS), Larry Orimoloye (SAS)
   4. Forecasting at Uber: Machine learning approaches - Andrea Pasqua (Uber)
   5. Legal contract review by an artificial intelligence - Rahul Dodhia (Microsoft)
   6. How CLEVER is your neural network? Robustness evaluation against adversarial examples - Pin-Yu Chen (IBM Research AI)
   7. Multitask learning in PyTorch applied to news classification - Ryan Micallef (Cloudera Fast Forward Labs)
   8. AI and the challenges that remain - David Barber (UCL)
   9. Accelerating innovation through analogy mining - Dafna Shahaf (The Hebrew University of Jerusalem)
   10. Performance evaluation of GANs in a semisupervised OCR use case - Florian Wilhelm (iNovex)
   11. Democratizing deep learning through knowledge transfer - Lars Hulstaert (Microsoft)
   12. Natural language processing, understanding, and generation - Amy Heineike (Primer)
   13. Decentralized data markets for training AI models - Roger Chen (Computable Labs)
   14. Deep reinforcement learning: How to avoid the hype and make it work for you - Dr. Sid J Reddy (Conversica)
   15. Enabling traditional vision on specialized deep learning hardware - Paul Brasnett (Imagination Technologies )
   16. Building end-to-end computer vision solutions from pretrained deep learning models - Vanja Paunic (Microsoft), Patrick Buehler (Microsoft)
   17. Efficient neural network training on Intel Xeon-based supercomputers - Ananth Sankar (Intel), Valeriu Codreanu (SURFsara), Damian Podareanu (SURFsara), Colin Healy (Dell)
   18. Reinforcement Learning Coach - Gal Novik (Intel AI)
7. AI Business Summit
   1. Executive Briefing: Moving AI off your product roadmap and into your products - Ashok Srivastava (Intuit)
   2. A day in the life of a data scientist in an AI company - Francesca Lazzeri (Microsoft), Jaya Mathew (Microsoft)
   3. Executive Briefing: Best practices for human in the loop—The business case for active learning - Paco Nathan (derwen.ai)
   4. AI and financial crime - Martin Goodson (Evolution AI), Mark St. John Qualter (RBS)
   5. Executive Briefing: Organizational design for effective AI - Mariya Yao (Metamaven)
   6. Executive Briefing: How to augment sparse training sets with synthetic data - Daeil Kim (AI.Reverie)
   7. How AI is taking geospatial data from alternative to mainstream in finance - James Crawford (Orbital Insight)
   8. Executive Briefing: Who is going to make money in AI? Understanding the value chain of AI - Simon Greenman (Best Practice AI)
   9. Executive Briefing: Ethical AI—How to build products that customers will love and trust - Susan Etlinger (Altimeter Group)
   10. AI for counterterrorism - Marc Warner (ASI)
   11. Executive Briefing: Why designing for trust matters - Max Gadney (After the flood), Sabih Ali (After the Flood)
   12. Lessons learned building an open deep learning model exchange - Nick Pentreath (IBM)
   13. How artificial intelligence is changing advertising in China: A conversation with Bessie Lee and Ching Law - Bessie Lee (Withinlink), Ching Law (Tencent)
   14. Refining the Turing test in the quest for AI authenticity - Aileen Nielsen (Skillman Consulting)
   15. Executive Briefing: Putting a face onto AI—After Nadia, get ready for the digital human workforce - Marie Johnson (Centre for Digital Business Pty Ltd)
   16. Executive Briefing: What’s the value of an AI center of excellence (COE)? - Benjamin Wright-Jones (Microsoft), Simon Lidberg (Microsoft)
8. AI in the Enterprise
   1. The OS for AI: How microservices and serverless enable the next generation of machine intelligence - Diego Oppenheimer (Algorithmia)
   2. An ecosystem analysis of the AI industry using the case of autonomous driving - Weiyue Wu (University of Oxford)
   3. Lessons learned implementing AI for IoT globally at Panasonic - Christopher Nguyen (Arimo)
   4. Executive Briefing: Supporting digital business transformation through AI everywhere - Philip Carnelley (IDC)
   5. Machine learning at scale with Kubernetes - Christopher Cho (Google), David Sabater (Google)
   6. Personalizing the user experience and playlist consumption on Spotify - Mounia Lalmas (Spotify)
   7. Scaling deep learning on AWS using C5 instances with MXNet, TensorFlow, and BigDL: From the edge to the cloud - Gaurav Kaul (Amazon Web Services), Suneel Marthi (Amazon Web Services), Grigori Fursin (dividiti)
9. Interacting with AI
   1. On the road to artificial general intelligence - Danny Lange (Unity Technologies)
   2. Building a Pokédex to recognize Pokémon in real time using TensorFlow and object recognition - Anmol Jagetia (Media.net)
   3. The WSJ dynamic paywall - Chris Boyd (Dow Jones), John Wiley (The Wall Street Journal)
   4. PyTorch 1.0 - bringing research and production together - Dmytro Dzhulgakov (Facebook)
   5. Portability and performance in embedded deep learning: Can we have both? - Cormac Brick (www.intel.com)
   6. Building safe artificial intelligence with OpenMined - Andrew Trask (OpenMined)
10. Tutorials
    1. Image classification models in TensorFlow - Benoit Dherin (Google) - Part 1
    2. Image classification models in TensorFlow - Benoit Dherin (Google) - Part 2
    3. Image classification models in TensorFlow - Benoit Dherin (Google) - Part 3
    4. Image classification models in TensorFlow - Benoit Dherin (Google) - Part 4
    5. Building reinforcement learning applications with Ray - Richard Liaw (UC Berkeley RISELab), Eric Liang (UC Berkeley RISELab) - Part 1
    6. Building reinforcement learning applications with Ray - Richard Liaw (UC Berkeley RISELab), Eric Liang (UC Berkeley RISELab) - Part 2
    7. PyTorch: A flexible approach for computer vision models - Mo Patel (Independent) - Part 1
    8. PyTorch: A flexible approach for computer vision models - Mo Patel (Independent) - Part 2
    9. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 1
    10. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 2
    11. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 3
    12. Bringing AI into the enterprise - Kristian Hammond (Northwestern Computer Science) - Part 4
    13. Building deep learning applications with Amazon SageMaker - Denis Batalov (Amazon) - Part 1
    14. Building deep learning applications with Amazon SageMaker - Denis Batalov (Amazon) - Part 2
    15. Recurrent neural networks for time series forecasting - Yijing Chen (Microsoft), Dmitry Pechyoni (Microsoft), Angus Taylor (Microsoft), Vanja Paunic (Microsoft) - Part 1
    16. Recurrent neural networks for time series forecasting - Yijing Chen (Microsoft), Dmitry Pechyoni (Microsoft), Angus Taylor (Microsoft), Vanja Paunic (Microsoft) - Part 2