The Artificial Intelligence Conference - London, UK 2018

Video description

An executive briefing predicting the winners in AI (who will make money? who has the best AI optimized chipsets?), a deep dive tutorial into the Amazon SageMaker machine learning platform, and a keynote imagining the endless possibilities (and dark side) of building artificial people by the computer vision expert responsible for the totally synthesized Obama speech video. These three talks represent just a fraction of the presentations delivered by the world's brightest minds in AI and machine learning at the Artificial Intelligence Conference London 2018. This video compilation is a complete recording of the best keynote addresses, tutorials, and technical sessions delivered at this fascinating event.

Highlights include:

  • The AI Business Summit: executive briefings and technical sessions covering the most promising and important developments in AI for business, including Kristian Hammond's (Northwestern Computer Science) practical walk-through on how to bring AI into your company; Bessie Lee (Withinlink) and Ching Law (Tencent) on how AI is changing advertising in China; and Simon Greenman's (Best Practice AI) look at AI's moneymakers.
  • TensorFlow at the AI Conference: sessions led by engineers from the Google Cloud Platform and TensorFlow teams covering topics such as image classification models in TensorFlow, distributed training, TF Lite, TensorFlow.js, AutoML, CMLE, TPUs, and Kubeflow.
  • Hours of in-depth AI tutorials including the UC Berkeley RiseLab team on building reinforcement learning applications with Ray; the Microsoft Cloud AI Group on how to apply recurrent neural networks to time series forecasting; and former Think Big Analytics AI practice director Mo Patel on computer vision and PyTorch.
  • Keynotes covering the challenges of AI from Supasorn Suwajanakorn (Independent), Ruchir Puri (IBM), Jonathan Ballon (Intel), Ashok Srivastava (Intuit), and others.
  • Implementing AI sessions, including Graphcore's founder Nigel Toon look at Intelligence Processing Units (the super-smart chips of the future); Rasa's CTO Alan Nichol on building conversational AI bots using the Rasa stack; and IntelligentWire's Yishay Carmiel on how to build privacy and security into deep learning models.
  • Models and Methods sessions, including Amy Heineike's (Primer) review of NLP algorithms; Alan Mosca (University of London) on how to harden and improve deep learning models with targeted ensembles; and Vitaly Kuznetsov (Google) and Zelda Mariet (MIT) on sequence-to-sequence modeling versus classical time series modeling.
  • Impact of AI on Business and Society sessions, including Marc Warner (ASI) on using AI to combat terrorism; Mark St. John Qualter (Royal Bank of Scotland) on using AI to spot financial crimes; and Mounia Lalmas (Spotify) on using AI to personalize the user experience.
  • AI in the Enterprise sessions, including Christine Foster (The Alan Turing Institute) and Rakshit Kapoor (HSBC) on the benefits of the Turing-HSBC partnership; John Wiley (Wall Street Journel) on the AI driven WSJ paywall; and Christopher Nguyen (Arimo) on AI and IoT at Panasonic.

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 (
    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 (
    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 (
    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 (
    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

Product information

  • Title: The Artificial Intelligence Conference - London, UK 2018
  • Author(s): O'Reilly Media, Inc.
  • Release date: October 2018
  • Publisher(s): O'Reilly Media, Inc.
  • ISBN: 9781492025887