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 Level: Advanced
Secondary topics:  Machine Learning
Average rating: *****
(5.00, 4 ratings)
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 surveys 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 Level: Intermediate
Secondary topics:  Cloud, Deep Learning
Joseph Spisak (Amazon), Sunil Mallya (Amazon Web Services)
Joseph Spisak and Sunil Mallya 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: Murray Hill E/W Level: Advanced
Secondary topics:  Cloud, Deep Learning
Yufeng Guo (Google), Amy Unruh (Google)
Average rating: **...
(2.00, 2 ratings)
Amy Unruh and Yufeng Guo walk you through training and deploying a machine learning system using TensorFlow, a popular open source library. Amy and Yufeng begin by giving an overview of TensorFlow and demonstrating some fun, already-trained TensorFlow models. Then, they show how to build a simple classifier in TensorFlow, before introducing some more complex classifier 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)
Average rating: **...
(2.67, 3 ratings)
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 Level: Beginner
Secondary topics:  Deep Learning
Laura Graesser (New York University)
Average rating: *****
(5.00, 1 rating)
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: Sutton Center Level: Beginner
Secondary topics:  Deep Learning
Yiheng Wang (Intel), Jennie Wang (Intel)
Average rating: **...
(2.50, 2 ratings)
Yiheng Wang and Jennie Wang offer 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 and Jennie demonstrate how to develop with BigDL and share some practical use cases. Read more.
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1:30pm5:00pm Tuesday, June 27, 2017
Location: Murray Hill E/W Level: Intermediate
Secondary topics:  Deep Learning, Machine Learning
Arthur Juliani (Unity Technologies)
Average rating: *****
(5.00, 2 ratings)
Recently, computers have been able to learn to play Atari games, Go, and first-person shooters at a superhuman level. Underlying all these accomplishments is deep reinforcement learning. Arthur Juliani offers a deep dive into reinforcement learning, 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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10:10am10:30am Wednesday, June 28, 2017
Location: Grand Ballroom
Secondary topics:  Deep Learning, Machine Learning
Average rating: ****.
(4.56, 9 ratings)
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)
Average rating: ****.
(4.25, 4 ratings)
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)
Average rating: ***..
(3.20, 5 ratings)
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: Murray Hill E/W Level: Intermediate
Risto Miikkulainen (Sentient.ai)
Average rating: ****.
(4.50, 2 ratings)
Risto Miikkulainen explains how to use massively distributed evolutionary algorithms to evolve the actual architectures of deep networks. Read more.
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11:05am11:45am Wednesday, June 28, 2017
Location: Gramercy East/West Level: Intermediate
Secondary topics:  Deep Learning, Financial services
Eric Greene (Think Big Analytics)
Average rating: **...
(2.67, 3 ratings)
Eric Greene compares different approaches to creating models that predict payment amounts, time, and recipient for recurring expenses such as rent, loans, utilities, and services, outlining the data requirements, feature modeling, and neural network architectures that work best, as well as common issues in training and deploying deep learning networks. Read more.
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11:05am11:45am Wednesday, June 28, 2017
Location: Grand Ballroom West
Secondary topics:  Deep Learning
Richard Socher (Salesforce)
Average rating: ****.
(4.60, 5 ratings)
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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11:55am12:35pm Wednesday, June 28, 2017
Location: Murray Hill E/W Level: Beginner
Secondary topics:  Machine Learning
Ben Vigoda (Gamalon)
Average rating: ***..
(3.00, 2 ratings)
Ben Vigoda introduces a new approach to machine learning called idea learning—teaching with ideas instead of labeled data—and demonstrates use cases with state-of-the-art performance in data applications involving structuring of product information, customer feedback, and AI/digital assistant requests. Read more.
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11:55am12:35pm Wednesday, June 28, 2017
Location: Beekman Level: Beginner
Christoph Peylo (Bosch Center for Artificial Intelligence)
Average rating: ***..
(3.00, 3 ratings)
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 still 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)
Average rating: **...
(2.00, 1 rating)
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)
Average rating: *****
(5.00, 2 ratings)
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: Murray Hill E/W Level: Beginner
Secondary topics:  Machine Learning
Matthew Taylor (Numenta)
Average rating: ****.
(4.00, 1 rating)
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.)
Average rating: **...
(2.00, 1 rating)
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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4:00pm4:40pm Wednesday, June 28, 2017
Location: Sutton North Level: Beginner
Secondary topics:  Hardware, IoT and its applications
Shaoshan Liu (PerceptIn)
Average rating: *****
(5.00, 1 rating)
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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4:00pm4:40pm Wednesday, June 28, 2017
Location: Grand Ballroom West Level: Intermediate
Secondary topics:  Cloud, Deep Learning
Yufeng Guo (Google)
Average rating: ****.
(4.00, 2 ratings)
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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4:50pm5:30pm 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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4:50pm5:30pm Wednesday, June 28, 2017
Location: Gramercy East/West Level: Intermediate
Secondary topics:  Deep Learning, Financial services
Thomas Wiecki (Quantopian)
Average rating: ***..
(3.67, 3 ratings)
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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11:05am11:45am Thursday, June 29, 2017
Location: Beekman Level: Beginner
Alberto Rizzoli (Aipoly)
Average rating: *****
(5.00, 1 rating)
Alberto Rizzoli explains how Aipoly began running convolutional neural networks locally on smartphones, eventually reaching a level of performance that made it a better option than cloud services, in the process unlocking new possibilities for making phones contextually aware. 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)
Average rating: ****.
(4.00, 1 rating)
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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11:05am11:45am Thursday, June 29, 2017
Location: Sutton Center/North Level: Intermediate
Secondary topics:  Machine Learning
Adam Marcus (B12)
Average rating: *****
(5.00, 2 ratings)
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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11:55am12:35pm Thursday, June 29, 2017
Location: Sutton Center/North Level: Intermediate
Secondary topics:  Machine Learning, Natural Language
Jason Laska (Clara Labs, Inc.)
Average rating: *****
(5.00, 1 rating)
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: Beekman 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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11:55am12:35pm Thursday, June 29, 2017
Location: Grand Ballroom West Level: Beginner
Secondary topics:  Deep Learning, Media
Soumith Chintala (Facebook)
Average rating: ***..
(3.00, 3 ratings)
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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1:45pm2:25pm Thursday, June 29, 2017
Location: Sutton Center/North Level: Intermediate
Secondary topics:  IoT and its applications, Machine Learning
Mark Hammond (Bonsai)
Average rating: ****.
(4.00, 1 rating)
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 of Matroid's pipeline, which uses TensorFlow, Kubernetes, and Amazon Web Services. Read more.
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1:45pm2:25pm Thursday, June 29, 2017
Location: Beekman Level: Beginner
Secondary topics:  Hardware, IoT and its applications, Machine Learning
Xiaofan Xu (Intel), Cormac Brick (Intel)
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. Cormac Brick and Xiaofan Xu explore the problem of the data wall and offer a solution: synthetic datasets. 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 state-of-the-art DNNs for use in your own computer vision solutions. 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 then 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)
Average rating: *....
(1.33, 3 ratings)
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: 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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4:00pm4:40pm Thursday, June 29, 2017
Location: Sutton South/Regent Parlor Level: Beginner
Rakesh Chada (x.ai)
Rakesh Chada introduces x.ai's Amy, an AI assistant that schedules meetings via email. Rakesh discusses Amy's architecture and the various challenges the team faced during its design and shares several machine learning approaches for intent classification. Rakesh concludes by exploring a novel method for error optimization in a conversational agent that exploits customer error tolerance. Read more.
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4:00pm4:40pm Thursday, June 29, 2017
Location: Beekman Level: Intermediate
Suman Roy (Betaworks)
Machine learning is empowering, but a critical drawback in the current ecosystem is the lack of tactical verification tools that can guarantee its fidelity in real-world applications. Suman Roy explores the tools and best practices during training, implementation, and postdeployment that can help explain what exactly we are teaching these machines. 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), Shahin Sefati (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 Shahin Sefati 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: 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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4:50pm5:30pm Thursday, June 29, 2017
Location: Sutton South/Regent Parlor Level: Intermediate
Secondary topics:  Machine Learning, Natural Language
Jonathan Mugan (DeepGrammar)
Average rating: *****
(5.00, 1 rating)
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.
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4:50pm5:30pm Thursday, June 29, 2017
Location: Grand Ballroom West Level: Beginner
Lindsey Zuloaga (HireVue)
Lindsey Zuloaga explains how machine learning from video interviews is disrupting the human resources space, bringing top candidates to the attention of recruiters and drastically reducing the time and energy companies spend finding and assessing potential employees. Read more.