Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY
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.)
Ashwin Vijayakumar (Intel)
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).
Yacin Nadji (Georgia Institute of Technology)
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.
Chris Benson (Lockheed Martin)
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.
Kristian Hammond (Northwestern Computer Science)
Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making.
Mary Beth Ainsworth (SAS)
Comprehensive and sustainable wildlife monitoring technologies are key to maintaining biodiversity. Mary Beth Ainsworth offers an overview of SAS deep learning and computer vision capabilities that can rapidly analyze animal footprints to help map wildlife presence and scale conservation efforts around the world.
Sergey Ermolin (Intel)
Sergey Ermolin details the latest features, real-world use cases, and what's in store for 2018 for BigDL on Intel Xeon processor-based data center and cloud deployments.
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.
Erika Menezes (Microsoft),
Serina Kaye (Microsoft)
Erika Menezes and Serina Kaye share a data science process for music synthesis, including preprocessing, model architecture, training, and prediction, using Microsoft’s Azure Machine Learning.
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.
Radhika Dutt (Radical Product),
Geordie Kaytes (Fresh Tilled Soil),
Nidhi Aggarwal (Radical Product)
AI is a powerful tool, but often companies get more excited about their technology than in the customer value they’re creating. Radhika Dutt, Geordie Kaytes, and Nidhi Aggarwal share a framework for building customer-centered AI products. You'll learn how to craft a far-reaching vision and strategy centered around customer needs and balance that vision with the day-to-day needs of your company.
Paul Nemitz (European Commission)
The rise of AI has shown the importance of implementing the basic rules of democracy, human rights, and the rule of law into the innovation process and the programs of artificial intelligence by design and default. Paul Nemitz outlines justice-oriented AI development processes and shares a model for globally sustainable development and deployment of artificial intelligence in the future.
Stephen Piron (DeepLearni.ng)
Is your enterprise striving to build AI applications that produce transformative business value? Stephen Piron shares real-world examples of AI applications that are evolving the way enterprises work from the ground up as well as a framework for enterprise leaders to use to ensure their team’s AI initiatives lay the foundation for genuine business impact.
John Sumser (TwoColorHat)
AI and its related subtechnologies are being introduced into operational decision making throughout the enterprise. The most promising and risky experiments involve the way people are selected and utilized, but the use of AI in HR raises the specter of software product liability. John Sumser offers an overview of the available use case solutions and the accompanying ethical issues.
Kathryn Hume (integrate.ai)
Large enterprises struggle to apply deep learning and other machine learning technologies successfully because they lack the mindset, processes, or culture for an AI-first world. AI requires a radical shift. Kathryn Hume explores common failure models that hinder enterprise success and shares a framework for building an AI-first enterprise culture.
John Lewin (Microsoft)
Great AI products are more than technology; they are built on a clear (computationally tractable) model of customer success. Getting that model right can be more challenging than building the AI models themselves; and getting it wrong is very expensive. Shane Lewin outlines common pitfalls in defining AI products and explains how to organize teams to solve them.
Ian Beaver (Verint),
Cynthia Freeman (Next IT)
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.
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.
Srinivasa Karlapalem (Intel)
Srinivasa Karlapalem demonstrates an approach for high-throughput single-shot multibox object detection (SSD) on edge devices using FPGAs, specifically for surveillance.
Jacob Graham (Intel),
Mallika Fernandes (Accenture)
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.
Raghav Ramesh (DoorDash)
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.
Fiaz Mohamed (Intel AI Products Group),
Justin Herz (Warner Bros.)
In this fireside chat, Justin Herz and Fiaz Mohammed discuss how artificial intelligence can improve content discovery and monetization. In collaboration with Intel AI technologies, Warner Bros. is just scratching the surface of what’s possible.
Fiaz Mohamed (Intel AI Products Group)
The Intel AI portfolio includes hardware and software solutions that span use cases and edge-to-cloud implementations, rooted in extensive expertise in data science and research. Fiaz Mohamed explains how Intel AI solves today’s business problems and how Intel’s partner ecosystem is accelerating the adoption of solutions built on Intel technology.
Wadkar Sameer (Comcast NBCUniversal),
Nabeel Sarwar (Comcast NBCUniversal)
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.
Pramit Choudhary (h2o.ai)
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.
Thomas Reardon (CTRL-Labs)
Thomas Reardon offers an overview of brain-machine interface (BMI) technology and shares CTRL-Labs's transformative and noninvasive neural interface approach. Along the way, he discusses the near-term opportunities for practical applications that will soon revolutionize daily life and the industries they touch.
Aurélien Geron (Kiwisoft)
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.
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.
Dan Mbanga (Amazon Web Services)
For more than 20 years, Amazon has invested in experimenting and deploying AI at scale. Dan Mbanga explores how accelerating AI experimentation has influenced innovations such as Amazon Alexa, Prime Air, and Go and how developers and data scientists from startups to large-scale enterprises have benefited from this innovation.
Nick Pentreath (IBM)
In the last few years, RNNs have achieved significant success in modeling time series and sequence data, in particular within the speech, language, and text domains. Recently, these techniques have been begun to be applied to session-based recommendation tasks, with very promising results. Nick Pentreath explores the latest research advances in this domain, as well as practical applications.
Xiaoyong Zhu (Microsoft)
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.
Meihong Wang (facebook)
Everyone's Facebook news feed experience is unique and highly personalized. Meihong Wang explains how Facebook solves the personalization problem with machine learning techniques and offers an overview of its large-scale machine learning system that models every user and delivers them the most relevant content in real time.
David C Martin (IBM Watson)
David Martin explores cognitive function in conjunction with edge computing and IoT sensors and actuators for eldercare scenarios—specifically the identification of individuals, daily activity monitoring, and aberration detection performed on-premises using HomeAssistant, the Intu open source project, and IBM's Watson cognitive services.
Zoubin Ghahramani (Uber | University of Cambridge)
Zoubin Ghahramani discusses fundamental concepts and recent advances in artificial intelligence, highlighting research on the frontiers of deep learning, probabilistic programming, Bayesian optimization, and AI for data science. Zoubin concludes by considering the societal implications of this work.
Dario Gil (IBM)
The extraordinary progress in AI over the last few years has been enabled, in part, by modern advancements in computing. Dario Gil explores state-of-the-art computing for AI as it exists today as well as an innovation that will lead us into the decades to come: quantum computing for AI.
Ben Lorica (O'Reilly),
Roger Chen (Computable)
Keynote by program chairs Ben Lorica and Roger Chen
Neeyanth Kopparapu (Girls Computing League)
With the improvement of medical devices in the technological era, doctors have access to an enormous amount of unharnessed medical data. Artificial Intelligence is a tool that can be used to process this data to solve problems that are considered hard or impossible as a doctor. These AI tools is what Neeyanth used to help the field of diagnostics enter the digital age.
Abhijit Deshpande (Digitate)
We live in a world of constantly changing business environments across various business units, limited end-to-end visibility, and high alerts. Abhijit Deshpande details how to use machine learning to identify root causes of problems in minutes instead of hours or days to free up valuable time by automating routine tasks without scripting or preprogramming.
Jennifer Marsman (Microsoft)
Food production needs to double by 2050 to feed the world’s growing population. Jennifer Marsman details a solution that uses sensors in the soil, aerial imagery from drones, machine learning, and networking research in television whitespaces and discusses the AI for Earth grant program, which supports similar work in the areas of clean water, agriculture, biodiversity, and climate change.
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