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

Implementing AI

 

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9:00–12:30 Tuesday, 9 October 2018
Location: Buckingham Room - Palace Suite Level: Beginner
Secondary topics:  Computer Vision, Deep Learning tools
Mo Patel (Independent)
Average rating: *....
(1.50, 2 ratings)
Computer vision has led the artificial intelligence renaissance, and pushing it further forward is PyTorch, a flexible framework for training models. Mo Patel offers an overview of computer vision fundamentals and walks you through PyTorch code explanations for notable objection classification and object detection models. Read more.
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9:00–12:30 Tuesday, 9 October 2018
Location: Blenheim Room - Palace Suite Level: Intermediate
Secondary topics:  Deep Learning tools, Platforms and infrastructure
Denis Batalov (Amazon)
Join Denis Batalov for an overview of the Amazon SageMaker machine learning platform. Denis walks you through setting up an Amazon SageMaker notebook (a hosted Jupyter Notebook server), using a built-in SageMaker deep learning algorithm, and building your own neural network architecture using SageMaker's prebuilt TensorFlow containers. Read more.
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10:20–10:35 Wednesday, 10 October 2018
Location: King's Suite
Secondary topics:  Computer Vision, Edge computing and Hardware
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. Read more.
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11:05–11:45 Wednesday, 10 October 2018
Location: King's Suite - Balmoral Level: Intermediate
Secondary topics:  Deep Learning models, Ethics, Privacy, and Security, Text, Language, and Speech
Yishay Carmiel (IntelligentWire)
Average rating: *****
(5.00, 1 rating)
In recent years, there's been a quantum leap in the performance of AI, as deep learning made its mark in areas from speech recognition to machine translation and computer vision. However, as artificial intelligence becomes increasingly popular, data privacy issues also gain traction. Yishay Carmiel reviews these issues and explains how they impact the future of deep learning development. Read more.
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11:05–11:45 Wednesday, 10 October 2018
Location: King's Suite - Sandringham
Secondary topics:  Deep Learning tools, Edge computing and Hardware
Yangqing Jia (Facebook), Dmytro Dzhulgakov (Facebook)
Machine learning sits at the core of many essential products and services at Facebook. Yangqing Jia and Dmytro Dzhulgakov offer an overview of the hardware and software infrastructure that supports machine learning at global scale. Read more.
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11:05–11:45 Wednesday, 10 October 2018
Location: Westminster Suite Level: Intermediate
Secondary topics:  Deep Learning tools, Platforms and infrastructure
Jonathan Hung (LinkedIn), Keqiu Hu (LinkedIn), Anthony Hsu (LinkedIn)
Jonathan Hung, Keqiu Hu, and Anthony Hsu offer an overview of TensorFlow on YARN (TonY), a framework to natively run TensorFlow on Hadoop. TonY enables running TensorFlow distributed training as a new type of Hadoop application. Its native Hadoop connector, together with other features, aims to run TensorFlow jobs as reliably and flexibly as other first-class objects on Hadoop. Read more.
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11:05–11:45 Wednesday, 10 October 2018
Location: Windsor Suite
Secondary topics:  Edge computing and Hardware, Platforms and infrastructure
Nigel Toon (Graphcore)
Nigel Toon explains how scaling IPUs will increase the productivity of machine intelligence researchers everywhere. Join in to explore what can we do and expect from the field with vastly more compute. Read more.
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13:45–14:25 Wednesday, 10 October 2018
Location: King's Suite - Balmoral Level: Intermediate
Secondary topics:  Ethics, Privacy, and Security, Retail and e-commerce
Rupert Steffner (WUNDER)
Average rating: ***..
(3.00, 1 rating)
The increase in automated decision making, along with doubts in the quality of algorithmic decisions, has driven demand for transparency and accountability in AI. Rupert Steffner explains why the shift from black box to white box is a great opportunity to build AI models that create trust with the user and shares Sense-Infer-Act-Learn, a logical AI execution model to enable a more trustworthy AI. Read more.
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14:35–15:15 Wednesday, 10 October 2018
Location: King's Suite - Sandringham Level: Intermediate
Secondary topics:  Deep Learning models, Edge computing and Hardware
Bruno Fernandez-Ruiz details a unified network that jointly performs various mission-critical tasks in real time on a mobile environment, within the context of driving. Along the way, he outlines the challenges that emerge when training a single mobile network for multiple tasks, such as object detection, object attributes recognition, classification, and tracking. Read more.
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14:35–15:15 Wednesday, 10 October 2018
Location: Westminster Suite Level: Intermediate
Secondary topics:  Edge computing and Hardware
Shaoshan Liu (PerceptIn)
Shaoshan Liu explains how PerceptIn built the first FPGA-based computing system for autonomous driving. Read more.
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16:00–16:40 Wednesday, 10 October 2018
Location: King's Suite - Balmoral Level: Intermediate
Secondary topics:  Media, Marketing, Advertising, Text, Language, and Speech
Rahul Dodhia (Microsoft)
Artificial intelligence is mature enough to make substantial contributions to the legal industry. Rahul Dodhia offers an overview of an AI assistant that can perform routine tasks such as contract review and checking compliance with regulations at higher accuracy rates than legal professionals. Read more.
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16:00–16:40 Wednesday, 10 October 2018
Location: Windsor Suite Level: Beginner
Secondary topics:  Deep Learning models, Financial Services, Temporal data and time-series
Gaurav Chakravorty explains how recommender systems can be utilized for investment management and details how AI and deep learning are used in trading today. Read more.
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16:00–16:40 Wednesday, 10 October 2018
Location: Westminster Suite Level: Intermediate
Secondary topics:  Computer Vision, Deep Learning tools
Anmol Jagetia (Media.net)
Machine learning and object recognition have matured to the point that exciting applications are now possible. Anmol Jagetia demonstrates how to create a Pokédex that uses a camera phone to recognize the Pokémon it's looking at in real time. You'll see how to gather data, prepare your dataset, tune models, and deploy it to a mobile device, using the same tech that is used in self-driving cars. Read more.
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16:50–17:30 Wednesday, 10 October 2018
Location: King's Suite - Balmoral Level: Beginner
Secondary topics:  Computer Vision, Media, Marketing, Advertising, Text, Language, and Speech
Daniel Ecer (eLife Sciences Publications Ltd), Paul Shannon (eLife Sciences Publications Ltd)
eLife’s mission is to accelerate discovery and encourage responsible behaviors in science. Daniel Ecer and Paul Shannon detail eLife’s journey in using NLP, computer vision, and similarity algorithms to find more diverse peer reviewers, apply semantics to archive content, automate the submission process, and find insights into the sentiment of scholarly content. Read more.
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16:50–17:30 Wednesday, 10 October 2018
Location: King's Suite - Sandringham Level: Intermediate
Secondary topics:  Edge computing and Hardware
Kaz Sato (Google)
Average rating: *****
(5.00, 1 rating)
Kaz Sato offers an overview of ML Ops (DevOps for ML), sharing solutions and best practices for bringing ML into production service. You'll learn how to combine Apache Airflow, Kubeflow, and cloud services to build a data pipeline for continuous training and validation, version control, scalable serving, and ongoing monitoring and alerting. Read more.
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16:50–17:30 Wednesday, 10 October 2018
Location: Windsor Suite Level: Intermediate
Secondary topics:  Platforms and infrastructure, Retail and e-commerce, Text, Language, and Speech
Alan Nichol (Rasa)
Average rating: ****.
(4.50, 2 ratings)
Alan Nichol walks you through building fully machine learning-based voice and chatbots with the open source Rasa stack. Read more.
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11:05–11:45 Thursday, 11 October 2018
Location: King's Suite - Sandringham Level: Intermediate
Secondary topics:  Platforms and infrastructure, Retail and e-commerce
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. Read more.
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11:55–12:35 Thursday, 11 October 2018
Location: Park Suite Level: Intermediate
Secondary topics:  Deep Learning models, Platforms and infrastructure
The common perception of applying deep learning is that you take an open source or research model, train it on raw data, and deploy the result as a fully self-contained artifact. The reality is far more complex. Nick Pentreath shares lessons learned building a deep learning model exchange and discusses the future of standardized cross-framework deep learning model training and deployment. Read more.
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11:55–12:35 Thursday, 11 October 2018
Location: Hilton Meeting Room 3-6 Level: Intermediate
Secondary topics:  Temporal data and time-series
Aileen Nielsen (Skillman Consulting)
Average rating: *****
(5.00, 1 rating)
Deep learning for time series prediction has made rapid progress in the past few years, but performance still greatly lags that of other intelligence tasks. Aileen Nielsen offers an overview of the state of the art in 2018, covering the hottest new architectures, emerging best practices for RNN training, and long overdue standard metrics to measure and compete on neural network prediction. Read more.
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13:45–14:25 Thursday, 11 October 2018
Location: King's Suite - Sandringham Level: Beginner
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. Read more.
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13:45–14:25 Thursday, 11 October 2018
Location: Windsor Suite Level: Intermediate
Secondary topics:  Computer Vision, Deep Learning models, Retail and e-commerce
Florian Wilhelm (inovex GmbH)
Average rating: *****
(5.00, 1 rating)
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. Read more.
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13:45–14:25 Thursday, 11 October 2018
Location: Westminster Suite Level: Intermediate
Secondary topics:  Computer Vision, Deep Learning models, Text, Language, and Speech
Lars Hulstaert (Microsoft)
Transfer learning allows data scientists to leverage insights from large labeled datasets. The general idea of transfer learning is to use knowledge learned from tasks for which a lot of labeled data is available in settings where only little labelled data is available. Lars Hulstaert explains what transfer learning is and demonstrates how it can boost your NLP or CV pipelines. Read more.
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13:45–14:25 Thursday, 11 October 2018
Location: Hilton Meeting Room 3-6
Secondary topics:  Ethics, Privacy, and Security
Katharine Jarmul (KIProtect)
When you train a model on private data, how much of that information does the model retain? Katharine Jarmul reviews research on attacks against models to extract training data and expose potentially sensitive information. Katharine then shares potential defenses as well as best practices when training models using private or sensitive data. Read more.
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14:35–15:15 Thursday, 11 October 2018
Location: Windsor Suite Level: Advanced
Secondary topics:  Deep Learning models
On his journey to the top spot at Kaggle, Marios Michailidis noticed that many of the things he does to perform competitively in data challenges could be automated. Marios shares lessons learned from his Kaggle experience and shows how you can achieve competitive performance in predictive modeling tasks automatically, using H2O.ai’s Driverless AI—an AI that creates AI. Read more.
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16:00–16:40 Thursday, 11 October 2018
Location: King's Suite - Balmoral Level: Intermediate
Secondary topics:  Reinforcement Learning, Retail and e-commerce, Text, Language, and Speech
Dr. Sid J Reddy (Conversica)
Sid Reddy shows you how to avoid the hype and decide which use cases are the best for deep reinforcement learning. You'll explore the Markov decision process with conversational AI and learn how to set up the environment, states, agent actions, transition probabilities, reward functions, and end states. You'll also discover when to use end-to-end reinforcement learning. Read more.
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16:00–16:40 Thursday, 11 October 2018
Location: Windsor Suite Level: Beginner
Secondary topics:  Computer Vision, Deep Learning models, Deep Learning tools
Vanja Paunic (Microsoft), Patrick Buehler (Microsoft)
Average rating: **...
(2.00, 2 ratings)
Dramatic progress has been made in computer vision. Deep neural networks (DNNs) trained on millions of images can recognize thousands of different objects, and they can be customized to new use cases. Vanja Paunic and Patrick Buehler outline simple methods and tools that enable users to easily and quickly adapt Microsoft's state-of-the-art DNNs for use in their own computer vision solutions. Read more.
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16:00–16:40 Thursday, 11 October 2018
Location: Westminster Suite Level: Beginner
Secondary topics:  Edge computing and Hardware
Jameson Toole (Fritz)
Machine learning and AI models now outperform humans on many tasks. However, sending sensor data up to the cloud and back is too slow for many apps and autonomous machines. Jameson Toole explains why developers seeking to provide seamless user experiences must now move their models down to devices on the edge, where they can run faster, at lower cost, and with greater privacy. Read more.
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16:50–17:30 Thursday, 11 October 2018
Location: King's Suite - Sandringham Level: Beginner
Secondary topics:  Computer Vision
natalie fridman (ImageSat International (iSi))
Average rating: ****.
(4.00, 1 rating)
Detection of moving vessels with satellite sensors is a challenging problem. Satellite imagery is expensive, covers a very small area, and can be acquired only at predefined acquisition opportunities. Natalie Fridman dives into this challenging problem and shares ISI's AI-based solution along with successful examples of detecting maritime vessels with ISI's satellites. Read more.
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16:50–17:30 Thursday, 11 October 2018
Location: Windsor Suite Level: Intermediate
Secondary topics:  Platforms and infrastructure
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. Read more.
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16:50–17:30 Thursday, 11 October 2018
Location: Westminster Suite Level: Intermediate
Secondary topics:  Edge computing and Hardware, Platforms and infrastructure
Zhipeng Huang (Huawei)
Zhipeng Huang explains how resource representation (RR) works with various intermediate representation (IR) technologies to help achieve the democratization of AI. Read more.
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16:50–17:30 Thursday, 11 October 2018
Location: Park Suite Level: Beginner
Alasdair Allan (Babilim Light Industries)
Average rating: *****
(5.00, 1 rating)
Google's AIY Projects kits bring Google's machine learning algorithms to developers with limited experience in the field, allowing them to prototype machine learning applications and smart hardware more easily. Alasdair Allan walks you through setting up and building the kits and demonstrates how to use the kits' Python SDK for machine learning both in the cloud and locally on a Raspberry Pi. Read more.