Put AI to work
June 26-27, 2017: Training
June 27-29, 2017: Tutorials & Conference
New York, NY

Schedule: Implementing AI sessions

You’ve decided that AI will help your organization, and you’re ready to get started. These sessions highlight the latest in tools, frameworks, algorithms, and approaches in building practical AI technology.

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9:00am12:30pm Tuesday, June 27, 2017
Location: Sutton North (Sponsored) Level: Advanced
Secondary topics:  Machine Learning
Probabilistic inference, a widely used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain or incomplete, is central to big data analytics to robotics and AI. Vikash Mansinghka and Richard Tibbetts survey the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts. Read more.
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9:00am12:30pm Tuesday, June 27, 2017
Location: Sutton Center (Sponsored) Level: Intermediate
Secondary topics:  Cloud, Deep Learning
Joseph Spisak (Amazon)
Joseph Spisak and Anima Anandkumar offer an introduction to the powerful and scalable deep learning framework Apache MXNet. You'll gain hands-on experience using Apache MXNet with preconfigured Deep Learning AMIs and CloudFormation Templates to help speed your development and leave able to quickly spin up AWS GPU clusters to train at record speeds. Read more.
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9:00am12:30pm Tuesday, June 27, 2017
Location: Sutton South/Regent Parlor Level: Advanced
Secondary topics:  Cloud, Deep Learning
Yufeng Guo (Google), Amy Unruh (Google)
TensorFlow is an increasingly popular open source machine intelligence library that is especially well suited for deep learning. Google Cloud Machine Learning (CloudML) lets you do distributed training and serving at scale. Yufeng Guo and Amy Unruh offer an introduction to TensorFlow concepts and walk you through using CloudML to do distributed training and scalable serving of your models. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Location: Beekman Level: Intermediate
Secondary topics:  Deep Learning, Machine Learning
Anusua Trivedi (Microsoft), Barbara Stortz (Microsoft), Patrick Buehler (Microsoft)
Anusua Trivedi, Barbara Stortz, and Patrick Buehler offer an overview of the Microsoft Cognitive Toolkit, which is native on both Windows and Linux and offers a flexible symbolic graph, a friendly Python API, and almost linear scalability across multi-GPU systems and multiple machines. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Location: Sutton North (Sponsored) Level: Intermediate
Secondary topics:  Deep Learning, Machine Learning
Arthur Juliani (Unity Technologies)
Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments has been deep reinforcement learning. Arthur Juliani covers RL from the basics using lookup tables and GridWorld all the way to solving complex 3D tasks with deep neural networks. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Location: Sutton South/Regent Parlor Level: Beginner
Secondary topics:  Deep Learning
Laura Graesser (New York University)
Laura Graesser offers a hands-on introduction to neural networks using the popular Python library Keras, focusing on building intuition for the core components of a neural network and what it means for a network to “learn.” You'll also get the opportunity to build and train your own network. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Location: Murray Hill E/W Level: Beginner
Secondary topics:  Deep Learning
Yiheng Wang (Intel)
Yiheng Wang offers an overview of BigDL, a distributed deep learning library on Apache Spark that helps users easily integrate most advanced deep learning algorithms (CNN, RNN, etc.) into popular big data platforms. Yiheng demonstates how to develop with BigDL and shares some practical use cases. Read more.
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10:10am10:30am Wednesday, June 28, 2017
Location: Grand Ballroom
Secondary topics:  Deep Learning, Machine Learning
Josh Tenenbaum explains how to build machines that learn and think like people. Read more.
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11:05am11:45am Wednesday, June 28, 2017
Location: Sutton South/Regent Parlor
Secondary topics:  Financial services, Natural Language
Jennifer Chu-Carroll (Elemental Cognition)
Why is reading comprehension hard? Jennifer Chu-Carroll offers an overview of current approaches, explaining where they fall short and what our ultimate expectations should be. Read more.
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11:05am11:45am Wednesday, June 28, 2017
Location: Beekman Level: Non-technical
Jana Eggers (Nara Logics)
AI has infinite possibilities, but to be adopted by businesses beyond R&D, these solutions must show results. The challenge is that AI often presents new opportunities that aren't easily quantified. Jana Eggers shares lessons learned while taking AI from ideas to results-delivering production solutions at various organizations, including Global 500 enterprises, tech companies, and nonprofits. Read more.
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11:05am11:45am Wednesday, June 28, 2017
Location: Grand Ballroom West
Secondary topics:  Deep Learning
Richard Socher (Salesforce)
Deep learning has made great progress in a variety of language tasks. However, there are still many practical and theoretical problems and limitations. Richard Socher shares some solutions. Read more.
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11:05am11:45am Wednesday, June 28, 2017
Location: Gramercy East/West Level: Intermediate
Secondary topics:  Deep Learning, Financial services
Eric Greene (Wells Fargo Digital Innovation Labs)
Next-generation payment applications will soon be intelligent enough to automatically schedule payments for customers effortlessly. Eric Greene explores how Wells Fargo AI Labs developed accurate predictive models leveraging deep learning and terabytes of transaction history data that propose payment amounts, time, and recipient for recurring expenses such as rent, loans, utilities, and services. Read more.
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11:05am11:45am Wednesday, June 28, 2017
Location: Murray Hill E/W Level: Intermediate
Risto Miikkulainen (Sentient.ai)
Risto Miikkulainen explains how to use massively distributed evolutionary algorithms to evolve the actual architectures of deep networks. Read more.
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11:55am12:35pm Wednesday, June 28, 2017
Location: Murray Hill E/W Level: Beginner
Secondary topics:  Machine Learning
Ben Vigoda (Gamalon)
Ben Vigoda offers an overview of Bayesian program synthesis (BPS), outlines the significant advantages it provides over deep learning technologies, and explains how it removes some of the biggest obstacles preventing AI from being adopted in the enterprise. Read more.
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11:55am12:35pm Wednesday, June 28, 2017
Location: Beekman Level: Beginner
Christoph Peylo (Bosch Center for Artificial Intelligence)
Generating commercial value from AI in a highly sophisticated industrial environment is a challenge. So far, AI accomplishments in this field stem mostly from marketing rather than systematic application to product lifecycles. Christoph Peylo shares examples of meaningful commercial IoT deployments and discusses obstacles that have to be overcome. Read more.
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1:45pm2:25pm Wednesday, June 28, 2017
Location: Grand Ballroom West Level: Intermediate
Secondary topics:  Cloud, Deep Learning
Guy Ernest (Amazon Web Services)
AWS is democratizing AI, helping you build deep learning systems in any scale, in any team size and skill, and for every use case. Guy Ernest discusses the state of deep learning, the tools that can take advantage of its power, and best practices for building successful businesses in the cloud, including data handling, models learning, deployment, and integration to other parts of the business. Read more.
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2:35pm3:15pm Wednesday, June 28, 2017
Location: Murray Hill E/W Level: Intermediate
Secondary topics:  Machine Learning
Philipp Moritz (UC Berkeley), Robert Nishihara (UC Berkeley)
AI applications are increasingly dynamic and interactive and work in real time. These properties impose new requirements on the distributed systems that support them. Philipp Moritz and Robert Nishihara offer an overview of Ray, a new system designed to support these emerging applications. Read more.
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4:00pm4:40pm Wednesday, June 28, 2017
Location: Grand Ballroom West Level: Intermediate
Secondary topics:  Cloud, Deep Learning
Yufeng Guo (Google)
Moving the heavy lifting of machine learning to the cloud is a great way to get large speed-ups. Yufeng Guo walks you through this process in detail so that you'll be ready to scale your own training and prediction services. Read more.
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4:00pm4:40pm Wednesday, June 28, 2017
Location: Murray Hill E/W Level: Beginner
Secondary topics:  Machine Learning
Matthew Taylor (Numenta)
Today's wave of AI technology is still being driven by the ANN neuron pioneered decades ago. Hierarchical temporal memory (HTM) is a realistic biologically constrained model of the pyramidal neuron reflecting today's most recent neocortical research. Matthew Taylor offers an overview of core HTM concepts, including sparse distributed representations, spatial pooling, and temporal memory. Read more.
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4:00pm4:40pm Wednesday, June 28, 2017
Location: Gramercy East/West Level: Intermediate
Secondary topics:  Hardware, Machine Learning
Qirong Ho (Petuum, Inc.)
Petuum, Inc. builds software that lets enterprises develop AI solutions in multiple programming languages and deploy them at scale and with high performance to internal, private computing resources that include a heterogeneous mix of workstations, clusters, CPUs, and GPUs. Qirong Ho outlines the architectural design choices and technical foundation needed to achieve these targets. Read more.
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4:50pm5:30pm Wednesday, June 28, 2017
Location: Gramercy East/West Level: Intermediate
Secondary topics:  Deep Learning, Financial services
Thomas Wiecki (Quantopian)
Expressing neural networks as a Bayesian model naturally instills uncertainty in its predictions. Thomas Wiecki demonstrates how to embed deep learning in the probabilistic programming framework PyMC3 to address uncertainty and nonstationarity. Read more.
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4:50pm5:30pm Wednesday, June 28, 2017
Location: Grand Ballroom West Level: Beginner
Sharan Narang (Baidu)
Artificial intelligence has had a tremendous impact on various applications at Baidu, including speech recognition and autonomous driving, although the performance requirements for all of these applications are very different. Sharan Narang outlines the challenges in inference for deep learning models and different workloads and performance requirements for various applications. Read more.
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11:05am11:45am Thursday, June 29, 2017
Location: Beekman Level: Intermediate
Secondary topics:  Machine Learning
Adam Marcus (B12)
AI has a way to go before it replaces the jobs we know today. But long before AI automates away jobs, it will elevate expertise. B12 is building infrastructure that celebrates humans where they’re best while bringing machines in for the rest. Adam Marcus offers an overview of human-assisted AI and demonstrates how it is already changing creative (and fundamentally human) fields like design. Read more.
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11:05am11:45am Thursday, June 29, 2017
Location: Sutton South/Regent Parlor Level: Beginner
Secondary topics:  Machine Learning, Transportation and Logistics, Vision
Matt Shobe (Mighty AI)
Autonomous vehicles must recognize objects in context, no matter the weather, time of day, or season. What does a cat in the road look like on a sunny summer day? How about on a snow-covered road at night? Matt Shobe shares lessons Mighty AI has learned while creating a training dataset for autonomous driving, including workflow tips and guidance for engineers building computer vision models. Read more.
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11:55am12:35pm Thursday, June 29, 2017
Location: Grand Ballroom West Level: Beginner
Secondary topics:  Deep Learning, Media
Soumith Chintala (Facebook)
Soumith Chintala discusses paradigm shifts in cutting-edge AI research and applications such as self-driving cars, robots, and game playing. Read more.
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11:55am12:35pm Thursday, June 29, 2017
Location: Beekman Level: Intermediate
Secondary topics:  Machine Learning, Natural Language
Jason Laska (Clara Labs, Inc.)
Clara Labs is fusing machine learning (ML) with distributed human labor for natural language tasks. The result is a virtuous cycle: ML predictions improve workers’ efficiency, and workers help improve prediction models. Jason Laska explores the challenges of building a real-time(ish) knowledge workforce, how to integrate automation, and key strategies Clara Labs learned that enable scale. Read more.
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11:55am12:35pm Thursday, June 29, 2017
Location: Sutton South/Regent Parlor Level: Beginner
Secondary topics:  Hardware, IoT and its applications
Shaoshan Liu (PerceptIn)
It is imperative to make high-profile technologies like AI affordable in order for these technologies to proliferate and to benefit the general public. Shaoshan Liu discusses PerceptIn's road to affordable AI-capable products. Read more.
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1:45pm2:25pm Thursday, June 29, 2017
Location: Grand Ballroom West Level: Beginner
Secondary topics:  Deep Learning, Vision
Timothy Hazen (Microsoft)
Dramatic progress has been made in computer vision: deep neural networks (DNNs) trained on tens of millions of images can now recognize thousands of different object types. These DNNs can also be easily customized to new use cases. Timothy Hazen shares simple methods and tools that enable you to adapt Microsoft's start-of-the-art DNNs for use in your own computer vision solutions. Read more.
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1:45pm2:25pm Thursday, June 29, 2017
Location: Beekman Level: Intermediate
Secondary topics:  IoT and its applications, Machine Learning
Mark Hammond (Bonsai)
As interactive and autonomous systems make their way into nearly every aspect of our lives, it is crucial to gain more trust in intelligent systems. Mark Hammond explores the latest techniques and research in building explainable AI systems. Join in to learn approaches for building explainability into control and optimization tasks, including robotics, manufacturing and logistics. Read more.
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1:45pm2:25pm Thursday, June 29, 2017
Location: Sutton South/Regent Parlor Level: Intermediate
Secondary topics:  Cloud, Deep Learning, Vision
Reza Zadeh (Stanford | Matroid)
Providing customized computer vision solutions to a large number of users is a challenge. Matroid allows the creation and serving of computer vision models and algorithms, model sharing between users, and serving infrastructure at scale. Reza Zadeh offers an overview o Matroid's pipeline, which uses TensorFlow, Kubernetes, and Amazon Web Services. Read more.
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2:35pm3:15pm Thursday, June 29, 2017
Location: Sutton South/Regent Parlor Level: Intermediate
Secondary topics:  Cloud, Deep Learning
Joseph Bradley (Databricks), Xiangrui Meng (Databricks)
Joseph Bradley and Xiangrui Meng share best practices for integrating popular deep learning libraries with Apache Spark, covering cluster setup, data ingest, configuring clusters, and monitoring jobs. Joseph and Xiangrui will demonstrate these techniques using Google’s TensorFlow library. Read more.
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2:35pm3:15pm Thursday, June 29, 2017
Location: Grand Ballroom West Level: Intermediate
Secondary topics:  Cloud, Deep Learning, Machine Learning, Vision
Matt Zeiler (Clarifai)
AI-powered machine-learning technologies bring a higher and more complex level of technical debt to applications. Matt Zeiler shares best practices for companies hoping to build AI into their businesses and explores how machine learning increases technical debt, the key contributors, and how to avoid or reduce technical debt related to machine learning. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Location: Grand Ballroom West Level: Intermediate
Secondary topics:  Cloud, Deep Learning, Vision
Yonghua Lin (IBM Research)
Yonghua Lin leads a deep dive into AI Vision, a deep learning system from IBM for image and video analysis in both edge and cloud environments, exploring its system design, performance optimization, and large-scale capability for training and inference. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Location: Gramercy East/West Level: Intermediate
Secondary topics:  Deep Learning, Media, Natural Language, Speech and Voice, User interface and experience
Jan Neumann (Comcast), Ferhan Ture (Comcast), Oliver Jojic (Comcast)
AI plays an essential role in creating the Comcast X1 entertainment experience and is how millions of its customers access their content on the TV. Jan Neumann, Ferhan Ture, and Oliver Jojic explain how AI enables Comcast to understand what you are looking for when you talk to the X1 voice remote and how Comcast scaled the voice interface to answer millions of voice queries every single night. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Location: Sutton South/Regent Parlor Level: Beginner
Secondary topics:  Deep Learning, Hardware, IoT and its applications
Michael B. Henry (Mythic)
Breakthroughs in deep learning and new analog-domain computation methods to deploy trained neural networks will deliver exciting new capabilities. Michael B. Henry explains why the combination of human-like levels of recognition and massive computation capabilities in a tiny package will enable products with true awareness and understanding of the user and environment. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Location: Murray Hill E/W Level: Intermediate
Secondary topics:  Deep Learning, Fashion, Retail and e-commerce, Vision
Pau Carré (Gilt)
Pau Carré explains how Gilt is reshaping the fashion industry by leveraging the power of deep learning and GPUs to automatically detect similar products and identify facets in dresses. Read more.
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4:50pm5:30pm Thursday, June 29, 2017
Location: Grand Ballroom West Level: Beginner
Secondary topics:  Hardware, IoT and its applications, Machine Learning
Xiaofan Xu (Intel), Cormac Brick (Intel, Movidius Group)
Data is the “oxygen” of the AI revolution, but access to data on a large scale remains a luxury of an elite group of tech companies, effectively creating a “data wall” blocking smaller companies. David Moloney and Xiaofan Xu explore the problem of the data wall and offer a solution: synthetic datasets. Read more.
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4:50pm5:30pm Thursday, June 29, 2017
Location: Sutton South/Regent Parlor Level: Intermediate
Secondary topics:  Machine Learning, Natural Language
Jonathan Mugan (DeepGrammar)
Jonathan Mugan surveys the field of natural language processing (NLP), both from a symbolic and a subsymbolic perspective, arguing that the current limitations of NLP stem from computers having a lack of grounded understanding of our world. Jonathan then outlines ways that computers can achieve that understanding. Read more.