Presented By O’Reilly and Intel AI
Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY

Schedule: Implementing AI sessions

9:00am–12:30pm Monday, April 30, 2018
Location: Sutton South
Amy Unruh (Google)
Average rating: **...
(2.75, 4 ratings)
Amy Unruh walks you through training a machine learning system using popular open source library TensorFlow, starting from conceptual overviews and building all the way up to complex classifiers. Along the way, you'll gain insight into deep learning and how it can be applied to complex problems in science and industry. Read more.
9:00am–12:30pm Monday, April 30, 2018
Location: Nassau East/West
Bruno Goncalves (Data For Science, Inc)
Average rating: ***..
(3.80, 5 ratings)
Bruno Gonçalves explores word2vec and its variations, discussing the main concepts and algorithms behind the neural network architecture used in word2vec and the word2vec reference implementation in TensorFlow. Bruno then presents a bird's-eye view of the emerging field of "anything"-2vec methods that use variations of the word2vec neural network architecture. Read more.
9:00am–12:30pm Monday, April 30, 2018
Location: Regent Parlor
Ashwin Vijayakumar gives you a hands-on overview of Intel's Movidius Neural Compute Stick, a miniature deep learning hardware development platform that you can use to prototype, tune, and validate your AI programs (specifically deep neural networks). Read more.
1:40pm–5:10pm Monday, April 30, 2018
Location: Nassau East/West
Mo Patel (Independent)
Average rating: ***..
(3.67, 3 ratings)
Computer vision has led the artificial intelligence renaissance, and pushing it further forward is PyTorch, a flexible framework for training models. Mo Patel and Neejole Patel offer an overview of computer vision fundamentals and walk you through PyTorch code explanations for notable objection classification and object detection models. Read more.
1:40pm–5:10pm Monday, April 30, 2018
Location: Sutton South
Greg Werner (3Blades), N C
Average rating: ****.
(4.00, 2 ratings)
Greg Werner walks you through using MXNet and TensorFlow to train deep learning models and deploy them using the leading serverless compute services in the market: AWS Lambda, Google Cloud Functions, and Azure Functions. You'll also learn how to monitor and iterate upon trained models for continued success using standard development and operations tools. Read more.
1:40pm–5:10pm Monday, April 30, 2018
Location: Regent Parlor
Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley), Ion Stoica (UC Berkeley)
Average rating: ***..
(3.00, 1 rating)
Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the art RL algorithms. Read more.
11:05am–11:45am Tuesday, May 1, 2018
Location: Grand Ballroom West
Srinivasa Karlapalem demonstrates an approach for high-throughput single-shot multibox object detection (SSD) on edge devices using FPGAs, specifically for surveillance. Read more.
11:05am–11:45am Tuesday, May 1, 2018
Location: Sutton North/Center
William Benton (Red Hat)
Intelligent applications learn from data to provide improved functionality to users. William Benton examines the confluence of two development revolutions: almost every exciting new application today is intelligent, and developers are increasingly deploying their work on container application platforms. Join William to learn how these two revolutions benefit one another. Read more.
11:05am–11:45am Tuesday, May 1, 2018
Location: Nassau East/West
Scott Zoldi (FICO)
Average rating: ****.
(4.75, 4 ratings)
Scott Zoldi discusses innovations in explainable AI, such as Reason Reporter, which explains the workings of neural network models used to detect fraudulent payment card transactions in real time, and offers a comparative study with local interpretable model-agnostic explanations (LIME) that demonstrates why the former are better at providing explanations. Read more.
11:55am–12:35pm Tuesday, May 1, 2018
Location: Grand Ballroom West
Average rating: ****.
(4.00, 3 ratings)
Forecasting the long-term values of time series data is crucial for planning. But how do you make use of a recurrent neural network when you want to compute an accurate long-term forecast? How can you capture short- and long-term seasonality or discover small patterns from the data that generate the big picture? Mustafa Kabul shares a scalable technique addressing these questions. Read more.
11:55am–12:35pm Tuesday, May 1, 2018
Location: Sutton North/Center
Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
Average rating: *****
(5.00, 1 rating)
Deep learning has fueled the emergence of many practical applications and experiences. Meanwhile, container technologies have been maturing, allowing organizations to simplify the development and deployment of applications in various environments. Join Wee Hyong and Danielle Dean as they walk you through using the Cognitive Toolkit (CNTK) with Kubernetes clusters. Read more.
1:45pm–2:25pm Tuesday, May 1, 2018
Location: Grand Ballroom East
Kaarthik Sivashanmugam (Microsoft), Wee Hyong Tok (Microsoft)
Kaarthik Sivashanmugam and Wee Hyong Tok share recommendations to address the common challenges in enabling scalable and efficient distributed DNN training and the lessons learned in building and operating a large-scale training infrastructure. Read more.
1:45pm–2:25pm Tuesday, May 1, 2018
Location: Grand Ballroom West
Manas Ranjan Kar (Episource)
Episource is building a scalable NLP engine to help summarize medical charts and extract medical coding opportunities and their dependencies to recommend best possible ICD10 codes. Manas Ranjan Kar offers an overview of the wide variety of deep learning algorithms involved and the complex in-house training-data creation exercises that were required to make it work. Read more.
1:45pm–2:25pm Tuesday, May 1, 2018
Location: Nassau East/West
Aurélien Géron (Kiwisoft)
Average rating: ***..
(3.83, 6 ratings)
The stock market is well known to be extremely random, making investment decisions difficult, but deep learning can help. Drawing on a concrete financial use case, Aurélien Géron explains how LSTM networks can be used for forecasting. Read more.
1:45pm–2:25pm Tuesday, May 1, 2018
Location: Concourse A
Jamie Irza (Raytheon)
Average rating: ***..
(3.00, 1 rating)
Activity-based intelligence (ABI) is the art and science of understanding normal patterns of life to enhance the ability of a system to detect anomalous behavior (e.g., to identify cases of credit card fraud). Jamie Irza demonstrates how machine learning can be used to implement ABI for detecting threatening behavior from unmanned aerial systems, commonly known as drones. Read more.
1:45pm–2:25pm Tuesday, May 1, 2018
Location: Morgan
Jacob Graham (Intel), Mallika Fernandes (Accenture)
Average rating: ***..
(3.50, 2 ratings)
In manufacturing, software development, and aerospace, tech-op teams need to make critical decisions on the spot with very little information. In this session, presented by Intel Saffron, the speakers share actual use cases of cognitive AI-based applications helping technical professionals make more confident decisions to solve the pressing issues in their day-to-day work. Read more.
2:35pm–3:15pm Tuesday, May 1, 2018
Location: Sutton North/Center
Wadkar Sameer (Comcast NBCUniversal), Nabeel Sarwar (Comcast NBCUniversal)
Average rating: ***..
(3.50, 2 ratings)
Sameer Wadkar and Nabeel Sarwar explain how to seamlessly integrate model development and model deployment processes to enable rapid turnaround times from model development to model operationalization in high-velocity data streaming environments. Read more.
2:35pm–3:15pm Tuesday, May 1, 2018
Location: Sutton South
Jan Neumann (Comcast), Dominique Izbicki (Comcast)
Average rating: *****
(5.00, 2 ratings)
Jan Neumann and Jeanine Heck explain how Comcast uses deep learning to build virtual assistants that allow its customers to contact the company with questions or concerns and how it uses contextual information about customers and systems in a reinforcement learning framework to identify the best actions that answer these customers' questions or resolve their concerns. Read more.
2:35pm–3:15pm Tuesday, May 1, 2018
Location: Nassau East/West
Ambika Sukla (Morgan Stanley)
Average rating: ****.
(4.00, 2 ratings)
Financial econometric models are usually handcrafted using a combination of statistical methods, stochastic calculus, and dynamic programming techniques. Ambika Sukla explains how recent advancements in AI can help simplify financial model building by carefully replacing complex mathematics with a data-driven incremental learning approach. Read more.
2:35pm–3:15pm Tuesday, May 1, 2018
Location: Concourse A
Yacin Nadji (Georgia Institute of Technology)
Average rating: *****
(5.00, 2 ratings)
The adversarial nature of security makes applying machine learning complicated. If attackers can evade signatures and heuristics, what is stopping them from evading ML models? Yacin Nadji evaluates, breaks, and fixes a deployed network-based ML detector that uses graph clustering. While the attacks are specific to graph clustering, the lessons learned apply to all ML systems in security. Read more.
4:00pm–4:40pm Tuesday, May 1, 2018
Location: Grand Ballroom West
Julie Zhu (Optum), Dima Rekesh (Optum)
Julie Zhu and Dima Rekesh share a deep learning approach for imputing a medical condition based on a multiyear history of prescriptions filled by an individual, using Python and Keras. Read more.
4:00pm–4:40pm Tuesday, May 1, 2018
Location: Sutton North/Center
Zhenxiao Luo (Uber)
Average rating: ***..
(3.00, 1 rating)
From determining the most convenient rider pickup points to predicting the fastest routes, Uber uses data-driven machine learning to create seamless trip experiences. Zhenxiao Luo explains how Uber tackles data caching in large-scale machine learning, exploring Uber's machine learning architecture, how Uber uses big data to power machine learning, and how to use data caching to speed up AI jobs. Read more.
4:00pm–4:40pm Tuesday, May 1, 2018
Location: Concourse A
Joshua Patterson (NVIDIA), Aaron Sant-Miller (Booz Allen Hamilton)
Average rating: *****
(5.00, 1 rating)
Drawing on NVIDIA’s system for detecting anomalies on various NVIDIA platforms, Joshua Patterson and Aaron Sant-Miller explain how to bootstrap a deep learning framework to detect risk and threats in operational production systems, using best-of-breed GPU-accelerated open source tools. Read more.
4:50pm–5:30pm Tuesday, May 1, 2018
Location: Sutton North/Center
Chris Benson (Practical AI)
Average rating: ***..
(3.00, 2 ratings)
Deep learning is the driving force behind the current AI revolution and will impact every industry on the planet. However, success requires an AI strategy. Chris Benson walks you through creating a strategy for delivering deep learning into production and explores how deep learning is integrated into a modern enterprise architecture. Read more.
4:50pm–5:30pm Tuesday, May 1, 2018
Location: Nassau East/West
Average rating: ****.
(4.00, 2 ratings)
Historically, the consumer loan industry has restricted itself to using relatively simple machine learning models and techniques to accept or deny loan applicants. However, more powerful (but also more complicated) methods can significantly improve business outcomes. Sean Kamkar shares a framework for evaluating, explaining, and managing these more complex methods. Read more.
4:50pm–5:30pm Tuesday, May 1, 2018
Location: Beekman Parlor
Alan Nichol (Rasa)
Fortune 500 companies are building conversational AI in-house to create a competitive edge. Alan Nichol shares a case study of a successful customer acquisition chatbot built by a large corporation and demonstrates how to build a useful, engaging conversational AI bot based entirely on machine learning using Rasa NLU and Rasa Core, the leading open source libraries for building conversational AI. Read more.
11:05am–11:45am Wednesday, May 2, 2018
Location: Grand Ballroom West
Mark Hammond (Microsoft)
Average rating: **...
(2.00, 2 ratings)
Reinforcement learning is a powerful machine learning technique for solving problems in dynamic and adaptive environments. Mark Hammond dives into two real-world case studies and demonstrates how to build and deploy deep reinforcement learning models for industrial applications. Read more.
11:05am–11:45am Wednesday, May 2, 2018
Location: Sutton North/Center
Xiaoyong Zhu (Microsoft)
Average rating: ***..
(3.00, 1 rating)
Superresolution is a process for obtaining one or more high-resolution images from one or more low-resolution observations. Xiaoyong Zhu shares the latest academic progress in superresolution using deep learning and explains how it can be applied in various industries, including healthcare. Along the way, Xiaoyong demonstrates how the training can be done in a distributed fashion in the cloud. Read more.
11:55am–12:35pm Wednesday, May 2, 2018
Location: Grand Ballroom West
Tim Kraska (MIT)
Average rating: *****
(5.00, 2 ratings)
Tim Kraska explains how fundamental data structures can be enhanced using machine learning with wide-reaching implications even beyond indexes, arguing that all existing index structures can be replaced with other types of models, including deep learning models (i.e., learned indexes). Read more.
11:55am–12:35pm Wednesday, May 2, 2018
Location: Nassau East/West
Stephanie Kim (Algorithmia)
Stephanie Kim discusses the basics of facial recognition and the importance of having diverse datasets when building out a model. Along the way, she explores racial bias in datasets using real-world examples and shares a use case for developing an OpenFace model for a celebrity look-alike app. Read more.
11:55am–12:35pm Wednesday, May 2, 2018
Location: Morgan
Harsh Kumar (Intel)
Harsh Kumar explains one way the energy industry is using AI and computer vision for security surveillance: a video analytics solution that can be optimized for the functional safety of workers in the loading and unloading zone of an oil and gas offshore rig. Read more.
1:45pm–2:25pm Wednesday, May 2, 2018
Location: Grand Ballroom West
Average rating: ***..
(3.00, 1 rating)
New technologies have the potential to revolutionize the aviation industry. Airports in particular are perfect candidates for AI and machine learning concepts. Carolina Sanchez Hernandez discusses how National Aviation Technical Services (NATS) is collaborating with several companies and institutes to change the way that data is captured and processed to transform airport operations. Read more.
1:45pm–2:25pm Wednesday, May 2, 2018
Location: Sutton North/Center
Anand Rao (PwC)
There are many enterprise AI use cases for automation and operational decision making, but when it comes to strategic decision making—especially for new products or when entering new markets—there are very few successful use cases. Anand Rao presents four successful use cases on gamifying strategy and applying agent-based simulation in the auto, payments, medical devices, and airlines industries. Read more.
1:45pm–2:25pm Wednesday, May 2, 2018
Location: Concourse A
Kaz Sato (Google)
Average rating: ****.
(4.00, 2 ratings)
TensorFlow Lite—TensorFlow’s lightweight solution for Android, iOS, and embedded devices—enables on-device machine learning inference with low latency and a small binary size. Kazunori Sato walks you through using TensorFlow Lite, helping you overcome the challenges to bring the latest AI technology to production mobile apps and embedded systems. Read more.
2:35pm–3:15pm Wednesday, May 2, 2018
Location: Grand Ballroom East
Mridu Narang (Microsoft)
Average rating: ***..
(3.00, 3 ratings)
In a world of information overload and manipulation, knowledge acquisition techniques are expected to provide instant, precise, and succinct answers. Question-answering (QnA) systems must serve answers with high accuracy and be backed by strong verification techniques. Mridu Narang offers an overview of the challenges of and approaches taken by large-scale QnA systems. Read more.
2:35pm–3:15pm Wednesday, May 2, 2018
Location: Grand Ballroom West
Yulia Tell (Intel), Maurice Nsabimana (World Bank Development Data Group)
Yulia Tell and Maurice Nsabimana walk you through getting started with BigDL and explain how to write a deep learning application that leverages Spark to train image recognition models at scale. Along the way, Yulia and Maurice detail a collaborative project to design and train large-scale deep learning models using crowdsourced images from around the world. Read more.
2:35pm–3:15pm Wednesday, May 2, 2018
Location: Sutton North/Center
Murali Kaundinya (Independent)
Murali Kaundinya outlines an InnerSource model to curate and operationalize machine learning and deep learning algorithms with a common workflow and engaging user experience. Focusing on patterns and practices, Murali then shares lessons learned implementing four enterprise scale use cases: optical character recognition, release engineering, virtual customer assistants, and data unification. Read more.
2:35pm–3:15pm Wednesday, May 2, 2018
Location: Morgan
Dr. Sid J. Reddy (Conversica)
Sid Reddy shares Conversica's artificial intelligence approach to creating, deploying, and continuously improving an automated sales assistant that engages in a genuinely human conversation at scale with every one of an organization’s sales leads. Read more.
4:00pm–4:40pm Wednesday, May 2, 2018
Location: Nassau East/West
Pramit Choudhary (h2o.ai)
Average rating: ****.
(4.67, 3 ratings)
Predicting the target label for computer vision machine learning problems is not enough. You must also understand the why, what, and how of the categorization process. Pramit Choudhary offers an overview of ways to faithfully interpret and evaluate deep neural network models, including CNN image models to understand the impact of salient features in driving categorization. Read more.
4:00pm–4:40pm Wednesday, May 2, 2018
Location: Concourse A
Alasdair Allan (Babilim Light Industries)
Average rating: ***..
(3.00, 1 rating)
The 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 explains how to set up and build the kits and how to use the Python SDK to use machine learning both in the cloud and locally on the Raspberry Pi. Read more.
4:00pm–4:40pm Wednesday, May 2, 2018
Location: Beekman Parlor
Moses Guttmann (Seematics)
Average rating: *****
(5.00, 1 rating)
One of the most important aspects of deep learning is the quality and quantity of the data used in the learning process. Moses Guttmann explores the problem and offers approaches to solve it. Read more.
4:50pm–5:30pm Wednesday, May 2, 2018
Location: Grand Ballroom East
Raghav Ramesh (DoorDash)
Average rating: *****
(5.00, 1 rating)
DoorDash is a last-mile delivery platform, and its logistics engine powers fulfillment of every delivery on its three-sided marketplace of consumers, Dashers, and merchants. Raghav Ramesh highlights AI techniques used by DoorDash to enhance efficiency and quality in its marketplace and provides a framework for how AI can augment core operations research problems like the vehicle routing problem. Read more.
4:50pm–5:30pm Wednesday, May 2, 2018
Location: Grand Ballroom West
Chris Watkins (Commonwealth Scientific and Industrial Research Organisation)
The achievement of human-level accuracy in image classification through the use of modern AI algorithms has renewed interest in its application to automated protein crystallization imaging. Christopher Watkins explores the development of the deep tech pipeline required for the robust operation of an online classification system in CSIRO's GPU cluster and shares lessons learned along the way. Read more.
4:50pm–5:30pm Wednesday, May 2, 2018
Location: Sutton North/Center
Rupert Steffner (WUNDER)
The road to real-world AI is long and winding. All we've heard from reputable experts turned out to be true, including the need for better data, a new UX, and new ways of learning. To help you along the way, Rupert Steffner highlights lessons learned implementing cognitive AI applications to help consumers find the products they love. Read more.
4:50pm–5:30pm Wednesday, May 2, 2018
Location: Regent Parlor
Ian Beaver (Verint - Next IT), Cynthia Freeman (Next IT)
Average rating: ****.
(4.60, 5 ratings)
Conversation is emerging as the next great human-machine interface. Ian Beaver and Cynthia Freeman outline the challenges faced by the AI industry to relate to humans in the way they relate to each other and highlight findings from a recent study to demonstrate relational strategies used by humans in conversation and explain how virtual assistants must evolve to communicate effectively. Read more.
4:50pm–5:30pm Wednesday, May 2, 2018
Location: Concourse A
Jorge Silva (SAS)
Average rating: ****.
(4.00, 2 ratings)
Recommender systems suffer from concept drift and scarcity of informative ratings. Jorge Silva explains how SAS uses a Bayesian approach to tackle both problems by making the learning process online and active. Active learning prioritizes the most informative users and items by quantifying uncertainty in a principled, probabilistic framework. Read more.