Presented By O’Reilly and Intel AI
Put AI to work
8-9 Oct 2018: Training
9-11 Oct 2018: Tutorials & Conference
London, UK

Speaker slides & video

Presentation slides will be made available after the session has concluded and the speaker has given us the files. Check back if you don't see the file you're looking for—it might be available later! (However, please note some speakers choose not to share their presentations.)

Francesca Lazzeri (Microsoft), Jaya Mathew (Microsoft)
With the growing buzz around data science, many professionals want to learn how to become a data scientist—the role Harvard Business Review called the "sexiest job of the 21st century." Francesca Lazzeri and Jaya Mathew explain what it takes to become a data scientist and how artificial intelligence solutions have started to reinvent businesses.
Ian Massingham (Amazon Web Services)
Ian Massingham discusses the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding, and explains how you can use easily accessible services from AWS to include AI features within your applications or build your own custom ML models for your own specific AI use cases.
Ashok Srivastava (Intuit)
Industry buzz sometimes focuses on an AI future with dire unintended consequences for humanity. Ashok Srivastava draws on his cross-industry experience to paint an encouraging picture of how AI can solve big problems with people, data, and technology to benefit society.
Jonathan Ballon (Intel)
Artificial intelligence in the future, at least represented in science fiction, can learn, interpret, and take action based on data analysis. AI in production is the present, a present that feels decidedly futuristic. Jonathan Ballon explains why Intel’s leading portfolio of AI and computer vision edge technology will drive advances that improve how we work and live.
Mikio Braun (Zalando SE)
Mikio Braun looks back on the past 20 years of machine learning research to explore aspects of artificial intelligence. He then turns to current examples like autonomous cars and chatbots, putting together a mental model for a reference architecture for artificial intelligence systems.
Christine Foster (The Alan Turing Institute), Rakshit Kapoor (HSBC)
In 2016, the Alan Turing Institute, the UK’s new national institute for data science and AI, announced a funded strategic multiyear research partnership with HSBC. Christine Foster and Rakshit Kapoor share insights and use cases that emerged while making this ambitious and innovative cross-sector partnership work.
Kristian Hammond (Northwestern Computer Science)
Kristian Hammond walks you through an approach to bring AI into the enterprise, based on the functional, business aspects of AI technologies. Kristian maps out simple rules, useful metrics, and where AI should live in the org chart, laying out the route you should follow to make good on the promise of the technologies of intelligence.
Supasorn Suwajanakorn (VISTEC (Vidyasirimedhi Institute of Science and Technology))
Supasorn Suwajanakorn discusses the possibilities and the dark side of building artificial people.
Business forecasting generally employs machine learning methods for longer and nonlinear use cases and econometrics approaches for linear trends. Pasi Helenius and Larry Orimoloye outline a hybrid approach that combines deep learning and econometrics. This method is particularly useful in areas such as competitive event (CE) forecasting (e.g., in sports events political events).
Jason Knight (Intel)
Jason Knight offers an overview of the state of the field for scaling training and inference across distributed systems from a practitioner's point of view. Along the way, Jason dives deep into available tools, resources, and venues for getting started without having to go it alone.
Paul Brasnett (Imagination Technologies )
In recent years, we’ve seen a shift from traditional vision algorithms to deep neural network algorithms. While many companies expect to move to deep learning for some or all of their algorithms, they may have a significant investment in classical vision. Paul Brasnett explains how to express and adapt a classical vision algorithm to become a trainable DNN.
Paco Nathan (derwen.ai)
Deep learning works well when you have large labeled datasets, but not every team has those assets. Paco Nathan offers an overview of active learning, an ML variant that incorporates human-in-the-loop computing. Active learning focuses input from human experts, leveraging intelligence already in the system, and provides systematic ways to explore and exploit uncertainty in your data.
Simon Greenman (Best Practice AI)
We're experiencing an AI gold rush. Tech giants, corporations, startups, and governments are investing billions, and headlines about AI have reached fever pitch. It's dizzying to keep track of the latest AI developments and claims. Join Simon Greenman to learn who can and who will make money in this gold rush—and who will become economic casualties along the way.
Marc Warner (ASI), Louis Barson (BEIS)
Fireside chat with Marc Warner and Louis Barson
Bessie Lee (Withinlink), Ching Law (Tencent)
Advertising in China is on the frontline of AI adoption and innovation. Join Bessie Lee and Ching Law for a conversation on how AI is changing advertising. You'll hear how China's white-hot AI advertising applications can serve as roadmaps and spark ideas in other industries and how companies like Tencent are improving performance by leveraging AI technology.
Pin-Yu Chen (IBM Research AI)
Neural networks are particularly vulnerable to adversarial inputs. Carefully designed perturbations can lead a well-trained model to misbehave, raising new concerns about safety-critical and security-critical applications. Pin-Yu Chen offers an overview of CLEVER, a comprehensive robustness measure that can be used to assess the robustness of any neural network classifiers.
Thomas Endres (TNG Technology Consulting), Samuel Hopstock (TNG Technology Consulting)
Thomas Endres and Samuel Hopstock demonstrate how to apply machine learning techniques on a program's source code, covering problems you may encounter, how to get enough relevant training data, how to encode the source code as a feature vector so that it can be processed mathematically, what machine learning algorithms to use, and more.
Christopher Nguyen shares lessons learned implementing multiple AI commercial projects at Panasonic. Along the way, Christopher discusses a number of use cases at various stages of implementation maturity and explains what AI really means today in enterprise products, where the key opportunities are, their impact, and key success factors in the adoption of AI across the enterprise.
Christopher Cho (Google), David Sabater (Google)
Christopher Cho details how to leverage Kubernetes and the mighty Kubernetes APIs to build a complete deep learning pipeline, from data ingestion and aggregation to preprocessing and ML training to serving. Along the way, Christopher covers Kubeflow, a Google open source solution for managing machine learning with TensorFlow in a portable, scalable manner.
Michael Chui (McKinsey Global Institute)
Drawing on the McKinsey Global Institute's groundbreaking research, Michael Chui explores commonly asked questions relating to AI and its impact on work. Michael also previews new research showing that despite the rapid pace of AI adoption, much foundational work in enterprises remains to be done to capture value at scale.
Florian Wilhelm (inovex GmbH)
Even in the age of big data, labeled data is a scarce resource in many machine learning use cases. Florian Wilhelm evaluates generative adversarial networks (GANs) when used to extract information from vehicle registrations under a varying amount of labeled data, compares the performance with supervised learning techniques, and demonstrates a significant improvement when using unlabeled data.
Dmytro Dzhulgakov (Facebook)
Dmytro Dzhulgakov explores PyTorch 1.0, from its start as a popular deep learning framework for flexible research to its evolution into an end-to-end platform for building and deploying AI models at production scale.
Gal Novik (Intel AI)
Gal Novik offers an overview of Reinforcement Learning Coach, an open source Python library that models the interaction between an agent and an environment in a modular way, making it easy for researchers to implement new reinforcement learning algorithms and for data scientists to integrate additional simulation environments modeling their business problems.
Yangqing Jia (Facebook)
Yangqing Jia shares a series of examples to illustrate the uniqueness of AI software and its connections to conventional computer science wisdom. Yangqing then discusses future software engineering principles for AI compute.
Zhipeng Huang (Huawei)
Zhipeng Huang explains how resource representation (RR) works with various intermediate representation (IR) technologies to help achieve the democratization of AI.
Cassie Kozyrkov (Google)
Why do businesses fail at machine learning despite its tremendous potential and the excitement it generates? Is the answer always in data, algorithms, and infrastructure, or is there a subtler problem? Will things improve in the near future? Cassie Kozyrkov shares lessons learned at Google and explains what they mean for applied data science.
Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)
What technologies are ready for adoption, and how should companies and organizations evaluate automation technologies? Ben Lorica and Roger Chen highlight recent trends in data, compute, and machine learning.
Gaurav Chakravorty explains how recommender systems can be utilized for investment management and details how AI and deep learning are used in trading today.
Amy Heineike (Primer)
Human-generated knowledge bases like Wikipedia have excellent precision but poor recall. Amy Heineike explains how Primer created a self-updating knowledge base that can track factual claims in unstructured text and describe what it learns in human-readable text.