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

Speakers

New speakers are added regularly. Please check back to see the latest updates to the agenda.

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Brad Abrams is the group product manager for the Google Assistant platform. Previously, Brad was the product unit manager of the Application Framework team at Microsoft. Brad is the author of several books from Addison-Wesley, including Framework Design Guidelines. He holds a BS in computer science from North Carolina State University.

Presentations

Building conversational experiences Session

Brad Abrams explores the latest design and development techniques for building natural language interfaces and draws on the Google Assistant, Actions on Google, and API.AI as examples to explore conversational UI best practices.

Bjorn Austraat is the global leader for cognitive visioning and strategy for IBM Watson’s Client Value and Transformation team. Bjorn has extensive experience partnering with senior management teams in Fortune 500 companies to develop and implement fully integrated cognitive business and technology solutions that drive innovation, profitability, and growth. His background ranges from enterprise-level deployment of cognitive technologies, including natural language processing, to in-depth business strategy, operating model transformation engagements, and big data analytics initiatives across a wide range of industries.

Presentations

The future of AI is now (sponsored by IBM) Session

Damion Heredia and Bjorn Austraat explore how augmented intelligence is helping companies disrupt industries and enabling them to make better decisions.

Amir Banifatemi leads artificial intelligence at XPRIZE Foundation and is managing partner of K5 Ventures. Amir has 25 years of experience in development and growth of emerging and transformative technologies. He began his career at the European Space Agency and then held executive positions at Airbus, AP-HP, and the European Commission Division for Information Society and Healthcare. He has contributed to the formation of more than 10 startups with emphasis on predictive technologies, the IoT, and healthcare. Amir is a guest lecturer and an adjunct MBA professor at UC Berkeley, Chapman University, Claremont McKenna College, UC Irvine, and HEC Paris. He holds a master’s degree in electrical engineering from the University of Technology of Compiègne, a PhD in system design and cognitive sciences from the University Paris Descartes, and an MBA from HEC Paris.

Presentations

XPRIZE Workshop: Using AI for Impact Session

Amir Banifatemi and Balazs Kegl discuss the XPRIZE Foundation, which has launched the largest global AI competition to use AI for impact, outlining the foundation's goals, what the prize entails, and what the 146 teams from 22 countries are working on. One of the teams will share its project and explore various methods and practical ways to interact with AI.

Ron Bodkin is technical director for applied artificial intelligence at Google, where he helps Global Fortune 500 enterprises unlock strategic value with AI, acts as executive sponsor for Google product and engineering teams to deliver value from AI solutions, and leads strategic initiatives working with customers and partners. Previously, Ron was vice president and general manager of artificial intelligence at Teradata; the founding CEO of Think Big Analytics (acquired by Teradata in 2014), which provides end-to-end support for enterprise big data, including data science, data engineering, advisory and managed services, and frameworks such as Kylo for enterprise data lakes; vice president of engineering at Quantcast, where he led the data science and engineer teams that pioneered the use of Hadoop and NoSQL for batch and real-time decision making; founder of enterprise consulting firm New Aspects; and cofounder and CTO of B2B applications provider C-Bridge. Ron holds a BS in math and computer science with honors from McGill University and a master’s degree in computer science from MIT.

Presentations

Fighting financial fraud at Danske Bank with artificial intelligence Session

Fraud in banking is an arms race with criminals using machine learning to improve their attack effectiveness. Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection, covering model effectiveness, TensorFlow versus boosted decision trees, operational considerations in training and deploying models, and lessons learned along the way.

Meet the Experts with Ron Bodkin (Teradata) and Nadeem Gulzar (Danske Bank Group) Event

Rona and Nadeem will discuss deep learning, fraud detection, and how Danske Bank fought fraud with artificial intelligence.

Lashon B. Booker is a senior principal scientist in MITRE’s Information Technology Technical Center. Previously, he worked at the Naval Research Laboratory, where he was eventually promoted to section head of the Intelligent Decision Aids section in the Navy Center for Applied Research in Artificial Intelligence. Lashon has published numerous technical papers in the areas of machine learning, probabilistic methods for uncertain inference, and distributed interactive simulation. He serves on the editorial boards of Evolutionary Intelligence and the Journal of Machine Learning Research and previously served as an associate editor of Adaptive Behavior and on the editorial boards of Machine Learning and Evolutionary Computation. He also regularly serves on the program committees for conferences in these areas. Lashon holds a PhD in computer and communication sciences from the University of Michigan.

Presentations

"Fairness cases" as an accelerant and enabler for AI adoption Session

Lack of confidence in the fairness of an AI-based system will limit support for its use and likely preclude adoption, even if that adoption could provide significant benefits. Chuck Howell and Lashon Booker explore tools, techniques, and best practices from the safety-critical software community that can be adapted to provide a “fairness case” framework to address fairness concerns effectively.

Joseph Bradley is a software engineer working on machine learning at Databricks. Joseph is an Apache Spark committer and PMC member. Previously, he was a postdoc at UC Berkeley. Joseph holds a PhD in machine learning from Carnegie Mellon University, where he focused on scalable learning for probabilistic graphical models, examining trade-offs between computation, statistical efficiency, and parallelization.

Presentations

Integrating deep learning libraries with Apache Spark Session

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.

Cormac Brick is director of machine intelligence in the Movidius group at Intel Corporation, where he builds new foundational algorithms for computer vision and machine intelligence to enhance the Myriad VPU product family. Cormac contributes to internal architecture and helps customers build products using the very latest techniques in deep learning and embedded vision through a set of advanced applications and libraries. He has worked with Movidius since its early days and has contributed heavily to the design of the ISA and the hardware systems as well as computer vision software development and tools. Cormac holds a BEng in electronic engineering from University College Cork.

Presentations

Anaerobic AI: Developing in a data-starved environment Session

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.

Patrick Buehler is a senior data scientist at Microsoft Boston and has been in the field for over 10 years. His main interests are machine learning and computer vision. He holds a PhD from the VGG group at Oxford.

Presentations

Scalable deep learning with the Microsoft Cognitive Toolkit Tutorial

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.

Rene Buest is the director of market research and technology evangelism at Arago. Previously, Rene was a senior analyst and cloud practice lead at Crisp Research, principal analyst at New Age Disruption, and a member of the worldwide Gigaom Research Analyst Network. Considered one of the top cloud computing analysts in Germany and beyond and one of the world’s top cloud computing influencers, Rene is one of the top 100 cloud computing experts on Twitter and Google+. Since the mid-’90s, he has focused on the strategic use of information technology in businesses and the IT impact on our society as well as disruptive technologies. Rene is the author of numerous professional technology articles and regularly writes for well-known IT publications like Computerwoche, CIO magazine, LANline, and Silicon.de and has been widely cited in German and international media, such as the New York Times, Forbes, Handelsblatt, Frankfurter Allgemeine Zeitung, Wirtschaftswoche, Manager magazine, and Harvard Business Manager. He is also a speaker at conferences and forums worldwide. Rene is founder of CloudUser.de, where he writes about cloud computing, IT infrastructure, technology, management, and strategy. He holds a diploma in computer engineering from the Hochschule Bremen (Dipl.-Informatiker (FH)) and an MSc in IT management and information systems from the FHDW Paderborn.

Presentations

Live and let die: The need for an AI-enabled enterprise (sponsored by Arago) Session

The internet giants are fully embracing AI. The services they offer are all aimed at using data to draw a map of the world, and they are using AI to build disruptive approaches that can't be replicated by established enterprises, which are threatened by these disruptions. However, as Rene Buest explains, most leaders still underestimate the effect this will have on their businesses.

Yishay Carmiel is the founder of IntelligentWire, a company that develops and implements industry-leading deep learning and AI technologies for automatic speech recognition (ASR), natural language processing (NLP) and advanced voice data extraction. Yishay and his team are currently working on bleeding-edge innovations that make the real-time customer experience a reality – at scale. Yishay has nearly 20 years’ experience as an algorithm scientist and technology leader building large-scale machine learning algorithms and serving as a deep learning expert.

Presentations

Conversational AI at large scale Session

There has been a quantum leap in the performance of conversational AI. From speech recognition to machine translation and language understanding, deep learning made its mark. However, scaling and productizing these breakthroughs remains a big challenge. Yishay Carmiel shares techniques and tips on how to take advantage of large datasets, accelerate training, and create an end-to-end product.

Pau Carré is a deep learning software engineer at Gilt. Pau has 10 years of experience encompassing software security, IT management, microwave networks profiling, quality engineering, and deep learning and functional programming for the fashion industry. Over his career, he has lived and worked in cosmopolitan Barcelona, paradisiac Mallorca, and magnificent Vienna and is now based in welcoming Dublin.

Presentations

Deep learning in the fashion industry Session

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.

Rakesh Chada is a data scientist at x.ai building machine learning systems to understand human intents from emails. He holds a master’s degree in computer science from Stony Brook University with focus on machine learning and natural language processing, where he worked on question-answering systems, Wikipedia graph mining, topic modeling, and the like under Steven Skiena.

Presentations

How Amy, an artificial intelligence capable of scheduling meetings, understands human intents Session

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.

Roger Chen is the program cochair for the O’Reilly Artificial Intelligence Conference. Previously, he was a principal at O’Reilly AlphaTech Ventures (OATV), where he invested in and worked with early-stage startups primarily in the realm of data, machine learning, and robotics. Roger has a deep and hands-on history with technology; before he worked in venture capital, he was an engineer at Oracle, EMC, and Vicor and developed novel nanotechnology as a PhD researcher at UC Berkeley. Roger holds a BS from Boston University and a PhD from UC Berkeley, both in electrical engineering.

Presentations

Closing remarks Keynote

Program chairs Ben Lorica and Roger Chen close the first day of keynotes.

Closing remarks Keynote

Program chairs Ben Lorica and Roger Chen close the second day of keynotes.

Thursday opening remarks Keynote

Program chairs Ben Lorica and Roger Chen open the second day of keynotes.

Wednesday opening remarks Keynote

Program chairs Ben Lorica and Roger Chen open the first day of keynotes.

Soumith Chintala is a research engineer at Facebook AI Research, where he works on generative models and high-performance computing.

Presentations

Dynamic deep learning: A paradigm shift in AI research and tools Session

Soumith Chintala discusses paradigm shifts in cutting-edge AI research and applications such as self-driving cars, robots, and game playing.

Jennifer Chu-Carroll is a research scientist at Elemental Cognition, where she focuses on natural language semantics and dialogue management. Previously, Jennifer was a research staff member and manager at the IBM T.J. Watson Research Center, where her most notable accomplishment was serving as a key technical lead on the Watson project, in which a high-performing question-answering system defeated the two best human players at the game of Jeopardy!, and a member of the technical staff at Lucent Technologies Bell Laboratories focusing on spoken dialogue management. Throughout her career, Jennifer has maintained a strong focus on research and development in natural language processing and related areas. She has published extensively in top conferences and journals and is very engaged in her research community. Jennifer served as general chair of NAACL-HLT 2012, program committee cochair of NAACL-HLT 2006, as area chairs and program committees of many key conferences, and on the editorial boards of multiple journals. She holds a PhD in computer science from the University of Delaware.

Presentations

Beyond the state of the art in reading comprehension Session

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.

Anca Dragan is an assistant professor in the EECS Department at UC Berkeley. Her goal is to enable robots to work with, around, and in support of people. Anca runs the InterACT Lab, where she focuses on algorithms for human-robot interaction—algorithms that move beyond the robot’s function in isolation and generate robot behavior that also accounts for interaction and coordination with end users. The lab works across different applications, from assistive robots to manufacturing to autonomous cars, and draws from optimal control, planning, estimation, learning, and cognitive science. Anca also helped found and serves on the steering committee for the Berkeley AI Research (BAIR) Lab and is a co-PI of the Center for Human-Compatible AI.

Presentations

Cars that coordinate with people Keynote

Autonomous cars tend to treat people like obstacles whose motion needs to be anticipated so that the car can best stay out of their way, resulting in ultradefensive cars that can't coordinate with people. Anca Dragan demonstrates how learning and optimal control can be leveraged to generate car behavior that results in natural coordination strategies.

Inverse reward design Session

As AI agents become more capable of optimizing their objective functions, it's becoming increasingly important to make sure that we give them the right objectives in the first place. Anca Dragan explains why agents should have uncertainty about their objectives and use human input as valuable observations to improve their estimates.

Chetan Dube is the president and CEO of IPsoft, where he has led the company to create a radical shift in the way IT is managed. Previously, he served as an assistant professor at New York University, where his research focused on deterministic finite-state computing engines. Chetan is a widely recognized speaker on autonomics, cognitive computing, and the future impact of a digital workforce and serves on the board of numerous IT-related institutions.

Presentations

AI in financial services: Opportunities and obstacles Session

AI is changing every area of the financial industry, but the promise of improved performance is accompanied by looming challenges. Greg Phalin leads a panel discussion with Chetan Dube, Doug Kim, and Aida Mehonic on the future of the AI industry, the applicability of AI to use cases in financial services, and the headwinds that could slow adoption of AI at scale.

Pradeep Dubey is an Intel fellow and director of Parallel Computing Lab (PCL), part of Intel Labs. His research focus is computer architectures to efficiently handle new data- and compute-intensive application paradigms for the future computing environment. Pradeep holds over 36 patents, has published over 100 technical papers, won the Intel Achievement Award in 2012 for breakthrough parallel computing research, and was honored with Outstanding Electrical and Computer Engineer Award from Purdue University in 2014. He holds a PhD in electrical engineering from Purdue University and is a fellow of IEEE.

Presentations

AI: What makes it hard (and fun) (sponsored by Intel) Session

We are witnessing a renewed industry interest in machine learning and artificial intelligence and an unprecedented convergence of massive compute with massive data. This confluence has the potential to significantly impact how we do computing and what computing can do for us. Pradeep Dubey shares some of the research Intel is pursuing to enable this compute industry transformation.

Douglas Eck is a research scientist at Google working in the areas of music and machine learning. Currently, Doug leads Magenta, a Google Brain project to generate music, video, images, and text using deep learning and reinforcement learning. A main goal of Magenta is to better understanding how AI can enable artists and musicians to express themselves in innovative new ways. Previously, Doug led the Google Play Music search and recommendation team and was an associate professor in computer science at the University of Montreal’s MILA lab, where he worked on expressive music performance and automatic tagging of music audio.

Presentations

Magenta: Machine learning and creativity Keynote

Doug Eck offers an overview of Magenta, a Google Brain project to develop new generative machine learning models for art and sound creation, allowing us to better understand how machine learning can be used by artists and musicians to make something new. Doug provides demos and explains where this work fits in with other AI research being done at Google and elsewhere.

Meet the Expert with Doug Eck (Google Brain) Meet the Experts

Stop by to discuss Magenta, a Google Brain project working on generative models for music, art, and text. Or just ask Doug about working on the Google Brain team and how that experience relates to new directions in AI and machine learning.

Jana Eggers is a tech executive focused on products and the messages surrounding them. Jana has started and grown SMBs and led large organizations within enterprises. She supports, subscribes to, and contributes to customer-inspired innovation, systems thinking, Lean analytics, and autonomy, mastery, and purpose-style leadership. Jana’s software and technology experience includes technology and executive positions at Intuit, Blackbaud, Basis Technology (internationalization technology), Lycos, American Airlines, Los Alamos National Laboratory (computational chemistry and supercomputing), Spreadshirt (customized apparel ecommerce), and acquired startups that you’ve never heard of. Jana is a frequent speaker, writer, and CxO educator on innovation, change, and technology. She holds a bachelor’s degree in mathematics and computer science from Hendrix College in Arkansas and pursued graduate studies in computer science at Rensselaer Polytechnic.

Presentations

From ∞ to 8: Translating abstract AI into real numbers for business Session

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.

Rana el Kaliouby is cofounder and CEO of Affectiva—a pioneer in emotion AI, the next frontier of artificial intelligence—where she leads the company’s award-winning emotion recognition technology, built on a science platform that uses deep learning and the world’s largest emotion data repository of nearly 4.9 million faces analyzed from 75 countries, amounting to more than 50 billion emotion data points. Previously, Rana was a research scientist at MIT Media Lab, where she spearheaded the applications of emotion technology in a variety of fields, including mental health and autism research. Her work has appeared in numerous publications including the New Yorker, Wired, Forbes, Fast Company, the Wall Street Journal, the New York Times, CNN, CBS, Time magazine, Fortune, and Reddit. A TED speaker, she was recognized by TechCrunch as a women founder who crushed it in 2016, by Entrepreneur magazine as one of the seven most powerful women to watch in 2014, and on Ad Age’s 40 under 40 list. Rana has also been inducted into the Women in Engineering Hall of Fame and is a recipient of Technology Review’s 2012 Top 35 Innovators Under 35 award and Smithsonian magazine’s 2015 American Ingenuity Award for Technology. Rana holds a BSc and MSc in computer science from the American University in Cairo and a PhD from the Computer Laboratory at the University of Cambridge.

Presentations

The science and applications of the emerging field of artificial emotional intelligence Session

Emotion AI is a branch of artificial intelligence that brings emotional intelligence to AI systems. Rana el Kaliouby reviews the state of emotion AI, its commercial applications, its underlying deep learning methods, and the research roadmap, which includes multimodal emotion recognition and the idea of an emotion chip.

Madeleine Clare Elish is a researcher at Data & Society in New York. A cultural anthropologist focusing on the social impact of artificial intelligence and automation, her research investigates how new technologies reshape understandings of values, efficacy, and ethical norms and how this may advantage or disadvantage different populations. Madeleine has published ethnographic and historical research aimed at grounding and reframing policy debates around the rise of machine intelligence, including An AI Pattern Language, which presents a taxonomy of current social challenges and responses drawn from interviews with AI industry practitioners. She will receive her PhD in anthropology from Columbia University in Fall 2017 and holds an SM in comparative media studies from MIT. She can be found occasionally on Twitter as @mcette.

Presentations

Planning for the social impact of AI Session

When we worry about the Terminator or superintelligence, we miss the social implications of AI that are already beginning to take shape. Madeleine Elish outlines the core challenges to the responsible design and deployment of AI systems and reviews current trends in the ways in which designers and engineers are addressing these challenges across sectors.

Susan Eraly is a software engineer at Skymind, where she contributes to Deeplearning4j. Previously, Susan worked as a senior ASIC engineer at NVIDIA and as a data scientist in residence at Galvanize.

Presentations

Neural networks for time series analysis using Deeplearning4j 2-Day Training

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Tom Hanlon, and Susan Eraly demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.

Neural networks for time series analysis using Deeplearning4j (Day 2) Training Day 2

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Tom Hanlon, and Susan Eraly demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.

Guy Ernest is a senior manager on the Solutions Architecture team at Amazon Web Services, where he helps customers take their first steps in the Amazon cloud and works with them on advanced use cases. Guy specializes in mobile development, big data, analytics, and machine learning. Previously, he founded startups in mobile search, personalization, and big data analytics.

Presentations

Scalable deep learning on AWS using Apache MXNet Session

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.

Tim Estes is the president and founder of Digital Reasoning, a leader in trusted cognitive computing. Driven by the belief that all software can learn and that all people should have access to it, Tim and his team work closely with leaders in government and industry to solve extraordinarily valuable and morally compelling problems in national security, finance, healthcare, and other markets by automating the understanding of human communication.

Presentations

We found a way Session

As AI moves from concept to reality, debates about ethics are evolving into excitement and the desire to learn more about AI and its promise of a better world. Tim Estes discusses two customer use cases: Nasdaq, which found a way to use AI to help safeguard financial markets, and Thorn, which found a way to use AI to combat human trafficking and rescue children.

David Ferrucci is the founder of Elemental Cognition, a company focused on creating AI systems that autonomously learn from human language and interaction to facilitate complex decision making in areas ranging from healthcare to economics, and leads the Systematized Intelligence Lab at Bridgewater Associates, where he explores the application of AI in building explicable data-driven systems for optimal management and people analytics. An award-winning artificial intelligence researcher, David started and led the IBM Watson team from its inception in 2006 to its celebrated success in 2011, when Watson defeated the greatest Jeopardy players of all time—a landmark in AI. Previously, he pioneered Watson’s applications in health, which helped lay the technical foundation for a new Healthcare division at IBM, and led numerous projects prior to Watson, including AI systems for manufacturing, automated configuration, document assembly, and open source software and standards for large-scale text and multimodal analytics.

David has over 50 patents and published papers in the areas of AI, automated reasoning, NLP, intelligent systems architectures, automatic text generation, and automatic question answering. He has given keynotes at highly distinguished events around the world, including many of the top computing conferences. David has been interviewed by media and organizations ranging from the New York Times to Bloomberg West to the Computer History Museum. He is an IBM fellow and has won many awards for his work, including the Chicago Mercantile Exchange’s Innovation Award and the AAAI Feigenbaum Prize. David holds a PhD in computer science from Rensselaer Polytechnic Institute.

Presentations

Machines as thought partners Keynote

AI systems should not only propose solutions or answers but also explain why they make sense. Statistical machine learning is a powerful tool for discovering patterns in data, but, David Ferrucci asks, can it produce understanding or enable humans to justify and take reasoned responsibility for individual outcomes?

Yarin Gal is a research fellow in computer science at St Catharine’s College at the University of Cambridge and a part-time fellow at the Alan Turing Institute, the UK’s national institute for data science. Yarin is working toward a PhD within the Cambridge Machine Learning group under Zoubin Ghahramani, funded by the Google Europe doctoral fellowship. Previously, he was a software engineer at IDesia Biometrics, where he developed code and UI for mobile platforms. Yarin holds an undergraduate degree in mathematics and computer science from the Open University in Israel and a master’s degree in computer science from Oxford under Phil Blunsom.

Presentations

Bayesian deep learning Session

Yarin Gal shares a new theory linking Bayesian modeling and deep learning and demonstrates the practical impact of the framework with a range of real-world applications. Yarin also explores open problems for future research—problems that stand at the forefront of this new and exciting field.

Codruta Gamulea is a business developer and data strategist with a passion for using AI technology to improve the quality of journalism. Codruta is a commercial lead and product manager at Bakken & Bæck, where she leads Orbit, which uses advanced machine learning algorithms to automatically categorize, enrich, and tag large pieces of text-based content. Previously, she led data strategy at Amedia, Norway’s largest local news publisher, where she oversaw the company’s efforts to monetize data for its over 70 titles. Codruta has over 12 years’ consulting experience at Accenture. She holds a master’s degree from BI Norwegian School of Management and studied news reporting at Harvard’s Nieman Journalism Lab.

Presentations

The AI-powered newsroom Session

The promise of AI in the newsroom is contradictory: NLG revolutionizes news writing, but robot journalists threaten jobs; NLP improves fact-checking but requires investments that slimmed-down newsrooms cannot afford. Drawing on Norwegian AI startup Orbit’s experience, Codruta Gamulea explains how AI can help solve the industry resource constraints and improve the quality of journalism.

Katy George is the managing partner of McKinsey’s Mid-Atlantic office and a leader in McKinsey’s Manufacturing practice. In her 20 years of client service, Katy has focused on manufacturing strategy and performance, end-to-end supply chain optimization, and quality system effectiveness. She has worked with companies in setting operations strategy, making technology platform decisions, understanding the impact of digital technologies on the end-to-end supply chain, defining and launching new operations organization structures and operating models, and optimizing supply and manufacturing performance. Katy has led McKinsey’s quality roundtables in the pharmaceutical and medical device industries, helping to convene industry and regulators around common objectives, and has led multiclient benchmarking around total cost of quality, compliance effectiveness, and risk management. In recent years, Katy has focused on advanced technology development and adoption in the manufacturing sector. She coleads McKinsey’s partnership with the Digital Manufacturing and Design Innovation Institute (DMDII), a 250-member consortium of leading government, academic, and corporate organizations. Katy’s publications include Manufacturing the Future: The Next Era of Global Growth and Innovation, Next-shoring: A CEO’s Guide, Industry 4.0: How to Navigate the Digitization of the Manufacturing Sector, The Impact of Automation on the Future of Enterprise and Nature of Work, and Harnessing Automation for a Future that Works. Previously, Katy worked as an associate analyst at National Economic Research Associates. She holds a high-honors degree in economics and government from Oberlin College and a PhD in business economics from Harvard University, where her research focused on production system design and supply chain improvements in assembly industries.

Presentations

Will we automate jobs faster than we create them? Session

The speed with which automation technologies are emerging today and the extent to which they could disrupt the world of work are largely without precedent. How big could the impact be on the world of work, and how rapidly will it be felt? Katy George explores these questions, drawing on a major new report from the McKinsey Global Institute.

Garrett Goh is a scientist in the Advanced Computing, Mathematics, and Data division at the Pacific Northwest National Lab (PNNL), where he holds the Pauling fellowship, which supports his research combining deep learning and artificial intelligence with traditional computational chemistry applications. His current interest is in AI-assisted computational chemistry—the application of deep learning to predict chemical properties and the discovery of new chemical insights using minimal expert knowledge. Previously, Garrett held a Howard Hughes Medical Institute (HHMI) fellowship, which supported his PhD in computational chemistry at the University of Michigan.

Presentations

AI-assisted computational chemistry: Predicting chemical properties with minimal expert knowledge Session

Garrett Goh demonstrates how to use deep learning to construct computational chemistry models that compare favorably to existing state-of-the-art models developed by expert practitioners—with virtually no expert knowledge—proving the potential of AI assistance to accelerate the scientific discovery process from a typical span of years to a matter of months.

Laura Graesser is a graduate student at New York University, where she is working toward a master’s degree in computer science with a focus on machine learning. In her spare time, Laura enjoys experimenting with and writing about machine learning techniques. Laura is particularly interested in neural networks and their application to computer vision problems, cross-fertilization between computer vision and NLP, and the representations perspective.

Presentations

Introduction to neural networks with Keras Tutorial

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.

Eric Greene is a principal architect for Think Big Analytics, where he brings technical innovation to fruition by working with business and technical leaders across industries. Eric has recently focused on cognitive computing applied to different domains within financial services and has designed and developed systems for real-time fraud detection, collections customer segmentation and automated prescription, customer account balance forecasting, and internal operations anomaly detectors.

Presentations

Deep learning applied to consumer transactions with Think Big Analytics Session

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.

Nadeem Gulzar is the head of advanced analytics and architecture at Danske Bank Group, a Nordic bank with strong roots in Denmark and a focus on becoming the most trusted financial partner in the Nordics. Nadeem has taken the lead in establishing advanced analytics and big data technologies within Danske. Previously, he worked with Credit and Marketrisk, where he headed a program to build-up capabilities to calculate risk using Monte Carlo simulation methods. Nadeem holds a BS in computer science, mathematics, and psychology and a master’s degree in computer science, both from Copenhagen University.

Presentations

Fighting financial fraud at Danske Bank with artificial intelligence Session

Fraud in banking is an arms race with criminals using machine learning to improve their attack effectiveness. Ron Bodkin and Nadeem Gulzar explore how Danske Bank uses deep learning for better fraud detection, covering model effectiveness, TensorFlow versus boosted decision trees, operational considerations in training and deploying models, and lessons learned along the way.

Meet the Experts with Ron Bodkin (Teradata) and Nadeem Gulzar (Danske Bank Group) Event

Rona and Nadeem will discuss deep learning, fraud detection, and how Danske Bank fought fraud with artificial intelligence.

Yufeng Guo is a developer advocate for the Google Cloud Platform, where he is trying to make machine learning more understandable and usable for all. He enjoys hearing about new and interesting applications of machine learning, so be sure to share your use case with him on Twitter.

Presentations

Running TensorFlow at scale in the cloud Session

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.

Scaling machine learning with TensorFlow Tutorial

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.

Patrick Hall is a senior director for data science products at H2o.ai where he focuses mainly on model interpretability. Patrick is also currently an adjunct professor in the Department of Decision Sciences at George Washington University, where he teaches graduate classes in data mining and machine learning.

Previously, Patrick held global customer-facing and R&D research roles at SAS Institute. He holds multiple patents in automated market segmentation using clustering and deep neural networks. Patrick is the eleventh person worldwide to become a Cloudera Certified Data Scientist. He studied computational chemistry at the University of Illinois before graduating from the Institute for Advanced Analytics at North Carolina State University.

Presentations

Interpretable AI: Not just for regulators Session

Interpreting deep learning and machine learning models is not just another regulatory burden to be overcome. People who use these technologies have the right to trust and understand AI. Patrick Hall and Sri Satish share techniques for interpreting deep learning and machine learning models and telling stories from their results.

Kristian Hammond is Narrative Science’s chief scientist and a professor of computer science and journalism at Northwestern University. Previously, Kris founded the University of Chicago’s Artificial Intelligence Laboratory. His research has been primarily focused on artificial intelligence, machine-generated content, and context-driven information systems. He currently sits on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris was also named 2014 innovator of the year by the Best in Biz Awards. He holds a PhD from Yale.

Presentations

Here and now: Bringing AI into the enterprise Tutorial

Kristian Hammond shares a practical framework for understanding the role of AI technologies in problem solving and decision making, focusing on how they can be used, the requirements for doing so, and the expectations for their effectiveness.

Meet the Expert with Kristian Hammond (Narrative Science) Meet the Experts

Join Kristian in an open discussion of the AI ecosystem and how to determine which technologies are right for the problems that you face and the solutions you need. In particular, he'll talk about opportunities and how to recognize them. He’s also available to answer questions about advanced natural language generation.

What, how, and why: The dynamic of advanced NLG Session

Kristian Hammond offers an overview of advanced natural language generation (NLG), a subfield of artificial intelligence, and the assorted technical systems involved with this emerging technology, along with the mechanisms that drive them.

Some are cognitive scientists; others are computer scientists and engineers. Mark Hammond is a cognitive entrepreneur bringing together both fields along with business acumen. He has a deep passion for understanding how the mind works, combined with an understanding of own human nature, and turns that knowledge into beneficial applied technology. As the founder and CEO of Bonsai, Mark is enabling AI for everyone. Mark has been programming since the first grade and started working at Microsoft as an intern and contractor while still in high school. He has held positions at Microsoft and numerous startups and in academia, including turns at Numenta and in the Yale Neuroscience Department. He holds a degree in computation and neural systems from Caltech.

Presentations

Programming your way to explainable AI Session

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.

Tom Hanlon is an instructor at Cloudera, where he delivers courses on the wonders of the Hadoop ecosystem. Before beginning his relationship with Hadoop and large distributed data, he had a happy and lengthy relationship with MySQL with a focus on web operations. He has been a trainer for MySQL, Sun, Percona.

Presentations

Neural networks for time series analysis using Deeplearning4j 2-Day Training

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Tom Hanlon, and Susan Eraly demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.

Neural networks for time series analysis using Deeplearning4j (Day 2) Training Day 2

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Tom Hanlon, and Susan Eraly demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.

Timothy J. Hazen is a principal data science manager in Microsoft’s Cloud and Enterprise Data group, where he leads a data science team in the development of customer-facing machine learning capabilities for the Microsoft Azure platform, primarily in the areas of image processing and natural language processing. Timothy has also developed natural language technology used within Microsoft’s Bing and Cortana products. Previously, he spent six years as a member of the Human Language Technology group at MIT’s Lincoln Laboratory and nine years as a research scientist at the MIT Computer Science and Artificial Intelligence Laboratory. Timothy holds an SB, SM, and PhD in electrical engineering and computer science from the Massachusetts Institute of Technology.

Presentations

Customizing state-of-the-art deep learning models for new computer vision solutions Session

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.

Patrick Hebron is a scientist in residence and adjunct graduate professor in NYU’s Interactive Telecommunications program. Patrick’s research relates to the development of machine learning-enhanced digital design tools. He is the creator of Foil, a next-generation design and programming environment that aims to extend the creative reach of its users through the assistive capacities of machine learning. Patrick has worked as a software developer and design consultant for numerous corporate and cultural institution clients, including Google, Oracle, Guggenheim/BMW Labs, and the Edward M. Kennedy Institute.

Presentations

Rethinking design tools in the age of machine learning Session

Is it possible to simplify design tools without limiting their expressivity? Patrick Hebron investigates how recent advances in machine learning and artificial intelligence will enable a new generation of tools that help novice and expert designers alike develop deeply nuanced and original ideas without committing to a steep learning curve or ceding creative control to the machine.

Abraham Heifets is the cofounder and CEO of Atomwise, which uses machine learning to help discover new medicines. Previously, Abe researched high-performance data processing at IBM’s T.J. Watson Research Center and helped develop the strategy and control AI system of the world-champion robotic soccer team at Cornell University. He created SCRIPDB, one of the largest public databases of patented chemical structures at the time, and LigAlign, a protein analysis tool used by researchers in 70 countries. He is an author on 19 papers, patents, and patent applications and was named Time magazine’s person of the year in 2006. Abe was a Massey fellow at the University of Toronto and a fellow of the Ontario Brain Institute. His doctoral work applied machine learning and classical artificial intelligence techniques to organic synthesis planning, a long-standing challenge in chemistry.

Presentations

AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery Session

Abe Heifets offers an overview of AtomNet, a structure-based deep convolutional neural network designed to predict the bioactivity of small molecules for drug discovery applications. Abe discusses training AtomNet on millions of training examples derived from ChEMBL and the PDB and explains how autonomously discovered filters can outperform previous docking approaches and existing DNN techniques.

Michael B. Henry is the founder and CEO of Isocline, a venture-backed AI hardware company that makes it easy to put powerful local speech and vision AI into any product, from wearables to cars. Under the hood, Isocline’s tech uses new methods of computing inside of flash memory arrays to deliver the processing capabilities of 10 mobile GPUs in a small, low-cost form factor.

Presentations

Software and hardware breakthroughs for deep neural networks at the edge Session

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.

Damion Heredia is the vice president of the “With Watson” business across IBM, where he is responsible for strategic partnerships with ISVs (Slack, Twitter, Salesforce, etc.) using Watson technology in their products and services to differentiate in the market. Previously, Damion was head of products and design for IBM Bluemix, the world’s largest Cloud Foundry-based platform as a service, launched IBM’s MobileFirst software strategy, and led the acquisition of Fiberlink MaaS360, the leading mobile device management platform. Prior to his time at IBM, Damion was vice president of product at Lombardi Software and Trilogy Software in Austin, Texas. Damion holds a BS in electrical engineering from Purdue University.

Presentations

The future of AI is now (sponsored by IBM) Keynote

Damion Heredia explores how augmented intelligence is helping companies disrupt industries and enabling them to make better decisions.

The future of AI is now (sponsored by IBM) Session

Damion Heredia and Bjorn Austraat explore how augmented intelligence is helping companies disrupt industries and enabling them to make better decisions.

Qirong Ho is vice president of technology at Petuum, Inc., an adjunct assistant professor at the Singapore Management University School of Information Systems, and a former principal investigator at A*STAR’s Institute for Infocomm Research. Qirong’s research focuses on distributed cluster software systems for machine learning at big data and big model scales, with a view toward theoretical correctness and performance guarantees, as well as practical needs like robustness, programmability, and usability. Qirong also works on statistical models for large-scale network analysis and social media, including latent space models for visualization, community detection, user personalization, and interest prediction. He is a recipient of the Singapore A*STAR National Science Search Undergraduate and PhD fellowships and the KDD 2015 Doctoral Dissertation Award (runner up).

Presentations

Software architectures for building enterprise AI Session

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.

Chuck Howell is the chief engineer for intelligence programs and integration at the MITRE Corporation, where he serves as the senior technical focal point for facilitating how MITRE addresses its intelligence customers’ key technical challenges. He contributes to oversight of technical activities across MITRE’s Intelligence programs, including participation in the development and integration of MITRE’s research program, direct technical support to projects, and review of technical aspects of intelligence community programs. Chuck has served as the chair of a DARPA panel refining a research agenda for building trustworthy systems, chair of a three-FFRDC study for DUSD (S&T) to develop a roadmap for S&T in software engineering, the MITRE lead for a team (MITRE, Aerospace, Johns Hopkins APL) that developed a recommended set of mission-assurance program guidelines for the Missile Defense Agency, and a principal investigator on multiple MITRE research programs addressing various aspects of software assurance, safety cases, autonomy, and error handling. He was a member of the Institute of Electrical and Electronics Engineers (IEEE) Software Engineering Body of Knowledge industrial advisory board.

Presentations

"Fairness cases" as an accelerant and enabler for AI adoption Session

Lack of confidence in the fairness of an AI-based system will limit support for its use and likely preclude adoption, even if that adoption could provide significant benefits. Chuck Howell and Lashon Booker explore tools, techniques, and best practices from the safety-critical software community that can be adapted to provide a “fairness case” framework to address fairness concerns effectively.

Kathryn Hume is Vice President Product & Strategy for integrate.ai, a SaaS startup applying AI to drive growth and customer satisfaction for large enterprises, and a Venture Partner at ffVC, a seed- and early-stage technology venture capital firm, where she advises early-stage artificial intelligence companies and sources deal flow. As the former Director of Sales and Marketing at Fast Forward Labs (Cloudera), Kathryn helped Fortune 500 companies accelerate their machine learning and data science capabilities. Prior to that, she was a Principal Consultant in Intapp’s Risk Practice, focused on data privacy, security, and compliance. A widely respected speaker and writer on AI, Kathryn excels at communicating how AI and machine learning technologies work in plain language. She has given lectures and taught courses on the intersections of technology, ethics, law, and society at Harvard Business School, Stanford, the MIT Media Lab, and the University of Calgary Faculty of Law. She speaks seven languages, and holds a PhD in comparative literature from Stanford University and a BA in mathematics from the University of Chicago.

Presentations

Demystifying AI hype Session

Kathryn Hume explores the potential advantages and disadvantages of the AI hype bubble and offers practical tips on how to navigate between real innovation and total nonsense.

Composer and multimedia artist Cole D. Ingraham is the music systems architect at Amper Music. Previously, Cole taught music composition, theory, technology, and flute at FaceArt Institute of Music in Shanghai, China. Originally from the San Francisco Bay Area, he is an active performer, improviser, creative programmer, both as a soloist and a collaborator. His aesthetic involves experimentalism, noise, drone, programming as performance, and all things abstract. Cole holds a BM in music composition from the University of the Pacific, an MFA in electronic music and recording media from Mills College, and a DMA in music composition from the University of Colorado at Boulder.

Presentations

Top down versus bottom up: Computational creativity Session

Drew Silverstein and Cole Ingraham discuss computational creativity.

Anmol Jagetia is a software engineer at Media.net interested in web technologies, open source software, data science, shipping cool products, and introducing people to technology. He has authored popular open source projects, including Flatabulous, which received over 2.2K stars on GitHub and has received close to 1 million downloads. Anmol holds a degree from the prestigious Indian Institute of Information Technology, Allahabad. He was part of HPCC as a Google Summer of Code Student in 2015 and interned on a scholarship at the prestigious Max Planck Institute for Software Systems, Germany, in 2016. In his free time, he enjoys traveling, reading, and playing his guitar.

Presentations

Building game bots using OpenAI’s Gym and Universe Session

Anmol Jagetia explains how to use OpenAI's Gym and Universe to design bots that can become extremely smart using reinforcement learning. You'll create a bot that uses reinforcement learning to beat games and learn how to reuse code to beat a set of games that includes Atari classics (Pac-Man or Pong), a Candy Crush clone, and a racing game.

Arthur Juliani is a machine learning engineer at Unity Technologies. A researcher working at the intersection of cognitive neuroscience and deep learning, Arthur is currently working toward a PhD at the University of Oregon.

Presentations

Deep reinforcement learning tutorial Tutorial

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.

Balázs Kégl is a senior research scientist at CNRS and head of the Center for Data Science of the Université Paris-Saclay. He has been in machine learning research for 20 years and is the cocreator of RAMP.

Presentations

XPRIZE Workshop: Using AI for Impact Session

Amir Banifatemi and Balazs Kegl discuss the XPRIZE Foundation, which has launched the largest global AI competition to use AI for impact, outlining the foundation's goals, what the prize entails, and what the 146 teams from 22 countries are working on. One of the teams will share its project and explore various methods and practical ways to interact with AI.

Charles Killam is a certified instructor and curriculum designer at NVIDIA’s Deep Learning Institute. Though Charlie works across all verticals, his efforts focus primarily on the application of deep neural networks (DNNs) in the healthcare space. Over his career, Charlie has delivered a data analytics bootcamp for Northeastern University, a geospatial Tableau project for Stanford University, and, working with MADlib, an open source machine learning algorithm library at Pivotal.

Presentations

NVIDIA Deep Learning Institute bootcamp 2-Day Training

NVIDIA Deep Learning Institute-certified instructor Charlie Killam walks you through solving the most challenging problems with deep learning. You'll start with deep learning basic concepts and quickly move to taking on real-word problems using deep learning.

NVIDIA Deep Learning Institute bootcamp (Day 2) Training Day 2

NVIDIA Deep Learning Institute-certified instructor Charlie Killam walks you through solving the most challenging problems with deep learning. You'll start with deep learning basic concepts and quickly move to taking on real-word problems using deep learning.

Doug Kim is chief revenue and customer success officer at Cogito—a pioneer working to improve NPS and operational efficiency in the contact centers of some of the world’s largest financial services—where he is responsible for the go-to-market, sales, consulting, delivery, and customer success functions. Previously, Doug worked to commercialize the BPM and CRM business for Pegasystems, helping the enterprise business application vendor grow to over $750MM in revenues. His many roles at Pegasystems included founding and running the cloud and SaaS business and the strategic alliances function and running global product marketing and sales success. Prior to Pegasystems, he was the founder of cloud-based rich-media email marketing technology firm Talksender Inc. and built and ran the Latin America and European regions at Novasoft. Doug studied biomedical and manufacturing engineering at Boston University’s School of Engineering.

Presentations

AI in financial services: Opportunities and obstacles Session

AI is changing every area of the financial industry, but the promise of improved performance is accompanied by looming challenges. Greg Phalin leads a panel discussion with Chetan Dube, Doug Kim, and Aida Mehonic on the future of the AI industry, the applicability of AI to use cases in financial services, and the headwinds that could slow adoption of AI at scale.

Kavya Kopparapu is the founder and CEO of GirlsComputingLeague. She is dedicated to sharing her passion for computer science with others, especially young girls, as the field has given her a world of opportunities. Kavya has been recognized by such organizations as the White House and the National Center for Women in Information Technology (NCWIT). Recently, she spoke on computer science for all at the March for Science in Washington, DC, and presented at a TEDx Conference. Kavya is a junior at Thomas Jefferson High School for Science and Technology.

Presentations

Harnessing the power of artificial intelligence to diagnose diseases Session

Artificial intelligence is revolutionizing medicine through computer-aided diagnostic systems. High school student Kavya Kopparapu presents the Eyeagnosis system, which utilizes artificial intelligence techniques and a smartphone camera to automatically screen for diabetic retinopathy, the leading cause of preventable blindness worldwide.

Coco Krumme heads the data team at Haven and is an adjunct faculty member in the UC Berkeley master’s in data science program.

Presentations

No fluff: Building real, disruptive AI companies Session

Join Matt Ocko in conversation with entrepreneurs Hilary Mason, Gloria Lau, and Coco Krumme for a "not your typical" VC panel. They'll discuss how to build disruptive companies that solve real problems with hard AI technologies, digging into the practicalities of getting started, raising money, landing that first big customer, and everything in between.

Karthik Lalithraj is a principal solutions architect at Kinetica. With over 18 years of software experience in a variety of roles and responsibilities, Karthik takes a holistic view at software architecture with special emphasis on helping enterprise IT organizations improve their service availability, application performance, and scale. Karthik has successfully helped recruit and build enterprise teams and architect, design, and implement business and technical solutions with numerous customers in various business verticals. Previously, Karthik led the presales big data organization at Terracotta/Software AG. Karthik holds a bachelor’s degree in electronics and communication from the National Institute of Engineering in India.

Presentations

Bring AI to BI: The benefits of using a GPU database for machine learning and deep learning (sponsored by Kinetica) Session

Karthik Lalithraj explains how a GPU-accelerated database helps you deploy an easy-to-use, scalable, cost-effective, and future-proof AI solution that enables data science teams to develop, test, and train simulations and algorithms while making them directly available on the same systems used by end users.

Jason Laska leads the machine learning efforts at Clara Labs. Previously, Jason spearheaded the computer vision program at Dropcam (acquired by Google in 2014), developing massive-scale online vision systems for the product. Jason holds a PhD in electrical engineering from Rice University, where he focused on inverse problems, dimensionality reduction, and optimization. He briefly dabbled in publishing as a cofounder and editor of Rejecta Mathematica, a publication for previously rejected mathematical articles.

Presentations

Strategies for integrating people and machine learning in online systems Session

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.

Gloria Lau is the cofounder of Unity Medical, which brings AI to healthcare for improved clinical workflow. Previously, she headed up data products at Linkedin, and was vice president of data at Timeful which was acquired by Google. She is also a consulting faculty at Stanford.

Presentations

No fluff: Building real, disruptive AI companies Session

Join Matt Ocko in conversation with entrepreneurs Hilary Mason, Gloria Lau, and Coco Krumme for a "not your typical" VC panel. They'll discuss how to build disruptive companies that solve real problems with hard AI technologies, digging into the practicalities of getting started, raising money, landing that first big customer, and everything in between.

Yonghua Lin is the founder and leader of IBM’s SuperVessel innovation cloud, a senior member of the technical staff, and senior manager of cognitive systems and cloud in IBM Research. Yonghua has worked on system architecture, the cloud, and cognitive platform research for more than 15 years. She was the initiator of mobile infrastructure in the cloud (now network function virtualization) and led the IBM team that built up the first optimized cloud for 4G mobile infrastructure. Yonghua has spoken widely at industry events, including ITU and Mobile World Congress, holds more than 40 patents granted worldwide, and has authored papers for top conferences and journals.

Presentations

AI Vision: Enable deep learning-based visual analysis in edge and cloud environments Session

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.

Shaoshan Liu is the cofounder and president of PerceptIn, a company working on developing a next-generation robotics platform. Previously, he worked on autonomous driving and deep learning infrastructure at Baidu USA. Shaoshan holds a PhD in computer engineering from the University of California, Irvine.

刘少山,PerceptIn联合创始人,董事长。加州大学欧文分校计算机博士,研究方向包括人工智能,无人驾驶,机器人,系统软件与异构计算。 PerceptIn专注于开发智能机器人系统,包括家用机器人,工业机器人,以及无人驾驶。 在创立PerceptIn之前,刘少山博士在人工智能以及系统方向有超过十年的研发经验,其经历包括英特尔研究院(INTEL RESEARCH),法国国家信息与自动化研究所(INRIA),微软研究院(MICROSOFT RESEARCH),微(MICROSOFT), 领英(LinkedIn),以及百度美国研究院 (Baidu USA)。

Presentations

The road to affordable AI-capable products Session

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.

Ben Lorica is the chief data scientist at O’Reilly Media. Ben has applied business intelligence, data mining, machine learning, and statistical analysis in a variety of settings, including direct marketing, consumer and market research, targeted advertising, text mining, and financial engineering. His background includes stints with an investment management company, internet startups, and financial services.

Presentations

Closing remarks Keynote

Program chairs Ben Lorica and Roger Chen close the first day of keynotes.

Closing remarks Keynote

Program chairs Ben Lorica and Roger Chen close the second day of keynotes.

Thursday opening remarks Keynote

Program chairs Ben Lorica and Roger Chen open the second day of keynotes.

Wednesday opening remarks Keynote

Program chairs Ben Lorica and Roger Chen open the first day of keynotes.

Nikita Lytkin leads machine learning teams building new monetization products at Facebook. An engineering and data science leader and advisor, previously, Nikita led teams of machine learning engineers and data scientists at LinkedIn working on making the LinkedIn News Feed highly personalized and engaging for over 400 million members and building novel data products empowering educational decision making by prospective college students. Before LinkedIn, Nikita led a team of machine learning experts in computational advertising at Quantcast. The team drove double-digit increases in performance of Quantcast’s online advertising products and company revenue by developing data-driven solutions for ad delivery, fraud detection, and campaign management. Nikita has coauthored over 20 US patent applications and continues to publish in top-tier machine learning and data mining venues. He also advises companies on building data teams and products powered by machine learning and analytics. Nikita holds a PhD in computer science from Rutgers University, where his research focused on machine learning and its applications on textual and financial data.

Presentations

Recommending products for 1.91 billion people on Facebook Session

Nikita Lytkin offers an overview of personalized digital advertising and explains how Facebook uses modern supervised machine learning methods, such as factorization machines and deep neural networks, to recommend ecommerce products to nearly two billion people.

Sunil Mallya is a solutions architect focused on deep learning at AWS, where he works with customers in various industry verticals. Sunil has an acute passion for serverless computing. Previously, he cofounded the neuroscience- and machine learning-based image analysis and video thumbnail recommendation company Neon labs and worked on building large-scale low-latency systems at Zynga. He hold a master’s degree in computer science from Brown University.

Presentations

Distributed deep learning on AWS using Apache MXNet Tutorial

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.

Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project, and a cofounder of Empirical Systems, a new venture-backed AI startup aimed at improving the credibility and transparency of statistical inference. Previously, Vikash cofounded a venture-backed startup based on his research that was acquired by Salesforce, was an advisor to Google DeepMind, and held graduate fellowships at the National Science Foundation and MIT’s Lincoln Laboratory. He served on DARPA’s Information Science and Technology advisory board from 2010 to 2012 and currently serves on the editorial boards for the Journal of Machine Learning Research and Statistics and Computation. Vikash holds a PhD in computation, an MEng in computer science, and BS degrees in mathematics and computer science, all from MIT. His PhD dissertation on natively probabilistic computation won the MIT George M. Sprowls dissertation award in computer science, and his research on the Picture probabilistic programming language won an award at CVPR.

Presentations

AI for structured business data Tutorial

Businesses have spent decades trying to make better decisions by analyzing structured data. New AI technologies are just beginning to transform this process. Vikash Mansinghka and Richard Tibbetts explore AI that guides business analysts to ask statistically sensible questions and lets junior data scientists answer in minutes questions that previously took hours for trained statisticians.

Probabilistic programming Tutorial

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.

Erik Marcade is the vice president of advanced analytics, products, and innovation at SAP, where he heads the Advanced Analytics development team within the Analytics and Insights end-to-end unit. His team manages several predictive and machine learning programs, such as SAP BusinessObjects Predictive Analytics and HANA Cloud Platform Predictive Services, and provides predictive and machine learning foundations leveraged by several SAP applications. Erik has over 30 years of experience in the machine learning industry. Previously, he was founder and CTO at KXEN; developed real-time software expertise at Cadence Design Systems; spearheaded a project to restructure the marketing database of the largest French automobile manufacturer for Atos, a leading European information technology services company; cofounded Mimetics, a French company that processes and sells development environment and optical character recognition (OCR) products and services using neural network technology; was a software engineer and project manager in Thomson-CSF’s Weapon System division working on the application of artificial intelligence for projects in weapons allocation, target detection and tracking, geostrategic assessment, and software quality control; contributed to the creation of Thomson Research Laboratories’s Pacific Rim Operation in Palo Alto, CA, as senior software engineer, where he collaborated with Stanford University on the automatic landing and flare system for Boeing and Kestrel Institute, a non-profit computer science research organization; and headed Esprit projects on neural networks development. Erik holds an engineering degree from École nationale supérieure de l’aéronautique et de l’espace, where he specialized in process control, signal processing, computer science, and artificial intelligence.

Presentations

Embedding machine learning into the fabric of enterprise apps (sponsored by SAP) Session

Erik Marcade explains why machine learning and artificial intelligence aren't just revolutionizing industry and knowledge-worker jobs. They're also transforming the way enterprise software is designed and delivered to customers.

Adam Marcus is a cofounder and CTO of B12, a company building a better future of creative and analytical work, starting with design. With Orchestra, its open source project management system for experts and machines, B12 automatically generates websites for clients (algorithmic design) and then recruits wonderful designers and art directors to fill in the details from the algorithmically generated starting points. (This summer, B12 announced the close of a $12.4M Series A funding round.) Previously, Adam was director of data at Locu, a startup that was acquired by GoDaddy. He has written widely on crowdsourcing and data management and processing, including coauthoring a book, Crowdsourced Data Management: Industry and Academic Perspectives. He is a recipient of the NSF and NDSEG fellowships and has worked at ITA, Google, IBM, and FactSet. Adam holds a PhD in computer science from MIT, where he researched database systems and human computation. In his free time, he builds course content to get people excited about data and programming.

Presentations

Human-assisted AI at B12: 10 lessons in giving humans superpowers Session

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.

Dana Mastropole is a data scientist in residence at the Data Incubator and contributes to curriculum development and instruction. Previously, Dana taught elementary school science after completing MIT’s Kaufman teaching certificate program. She studied physics as an undergraduate student at Georgetown University and holds a master’s in physical oceanography from MIT.

Presentations

Deep learning with TensorFlow 2-Day Training

Dana Mastropole demonstrates TensorFlow's deep learning capabilities through its Python interface as she walks you through building machine learning algorithms piece by piece and implementing neural networks using TFLearn. Along the way, you'll explore several real-world deep learning applications, including machine vision, text processing, and generative networks.

Deep learning with TensorFlow (Day 2) Training Day 2

Robert Schroll, Michael Li, and Dana Mastropole demonstrate TensorFlow's deep learning capabilities through its Python interface as they walk you through building machine-learning algorithms piece by piece and implementing neural networks using TFLearn. Along the way, you'll explore several real-world deep learning applications, including machine vision, text processing, and generative networks.

Jim McHugh is vice president and general manager at NVIDIA. He currently leads DGX-1, the world’s first AI supercomputer in a box. Jim focuses on building a vision of organizational success and executing strategies to deliver computing solutions that benefit from GPUs in the data center. With over 25 years of experience as a marketing and business executive with startup, mid-sized, and high-profile companies, Jim has a deep knowledge and understanding of business drivers, market/customer dynamics, technology-centered products, and accelerated solutions. Previously, Jim held leadership positions with Cisco Systems, Sun Microsystems, and Apple, among others.

Presentations

AI Now. For Every Industry. (sponsored by NVIDIA) Keynote

AI has the power to transform critical business processes, but new methods will be essential to analyze and visualize data—not as a one-time event but as a continuous process. As a result, a new computing paradigm and deep learning software stack will also be required to power, predict, and act on data to accelerate this transition and produce AI enterprise applications.

NVIDIA keynote (sponsored by NVIDIA) Keynote

Keynote by Jim McHugh

Ben Medlock is the head of product at SwiftKey, which was recently acquired by Microsoft. As cofounder and CTO of SwiftKey, Ben invented the intelligent keyboard for smartphones and tablets that has transformed typing on touchscreens, making it easy for everyone to create and communicate on mobile. Ben is a prominent figure in mobile technology. He was ranked among the 2015 Wired 100 and is regularly asked to deliver keynotes at conferences, including SXSW, WIRED2014, and the Global Webit Conference. Ben has a first-class degree in computer science from Durham University and a PhD in natural language and information processing from the University of Cambridge.

Presentations

Is the body the missing link for true AI? Session

Ben Medlock explores the future of AI, explaining why the potential it holds is not at all frightening. Ben argues that the key to achieving elusive human-like AI lies in a central piece of the puzzle: embodiment.

Aida Mehonic is an engagement manager at ASI Data Science with a focus on financial services. Previously, she worked in investment banking for four years, most recently as a front office strategist at JPMorgan Investment Bank developing quantitative models and publishing investment research. Aida is a bronze medallist at the International Physics Olympiad. She holds a BA and MMath in mathematics from Cambridge University and a PhD in theoretical physics from UCL. Her research has been published in Nature.

Presentations

AI in financial services: Opportunities and obstacles Session

AI is changing every area of the financial industry, but the promise of improved performance is accompanied by looming challenges. Greg Phalin leads a panel discussion with Chetan Dube, Doug Kim, and Aida Mehonic on the future of the AI industry, the applicability of AI to use cases in financial services, and the headwinds that could slow adoption of AI at scale.

Transforming an investment firm with AI: A case study Session

Deploying AI across business functions brings benefits that range from the prosaic to game changers, which in turn also depend on the overall digital and data maturity of the organization. Aida Mehonic shares a case study of an investment firm undergoing an AI transformation across several business units, including trading, reporting, and marketing.

Xiangrui Meng is an Apache Spark PMC member and a software engineer at Databricks. His main interests center around developing and implementing scalable algorithms for scientific applications. Xiangrui has been actively involved in the development and maintenance of Spark MLlib since he joined Databricks. Previously, he worked as an applied research engineer at LinkedIn, where he was the main developer of an offline machine learning framework in Hadoop MapReduce. He holds a PhD from Stanford, where he worked on randomized algorithms for large-scale linear regression problems.

Presentations

Integrating deep learning libraries with Apache Spark Session

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.

Risto Miikkulainen is vice president of research at Sentient Technologies and a professor of computer science at the University of Texas at Austin. His recent research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing and vision. Risto has published over 370 articles in these research areas and has 16 patents pending. He is an IEEE Fellow and a recipient of the 2017 Gabor Award of the International Neural Network Society. Risto holds an MS in engineering from the Helsinki University of Technology, Finland, and a PhD in computer science from UCLA.

Presentations

AI building AI: How evolutionary algorithms are revolutionizing deep learning Session

Risto Miikkulainen explains how to use massively distributed evolutionary algorithms to evolve the actual architectures of deep networks.

Meet the Expert with Risto Miikkulainen (Sentient.ai) Meet the Experts

Join Risto to discuss the evolutionary optimization of deep learning neural networks and how those concepts can be applied to other complex structures, such as web interfaces, trading strategies, and agricultural environments.

Philipp Moritz is a PhD candidate in EECS at UC Berkeley, with broad interests in artificial intelligence, machine learning, and distributed systems. He is a member of the Statistical AI Lab and the RISELab.

Presentations

Ray: A distributed execution framework for emerging AI applications Session

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.

Jonathan Mugan is CEO of DeepGrammar. Jonathan specializes in artificial intelligence and machine learning, and his current research focuses on deep learning, where he seeks to allow computers to acquire abstract representations that enable them to capture subtleties of meaning. Jonathan holds a PhD in computer science from the University of Texas at Austin. His thesis work concerned developmental robotics and focused on the problem of how to build robots that can learn about the world in the same way that children do.

Presentations

Adding meaning to natural language processing Session

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.

Mohamed Musbah is vice president of product at Maluuba, a Canadian AI company that’s helping machines to think, reason, and communicate seamlessly with humans (acquired by Microsoft in January 2017). Maluuba’s technology is deployed in more than 50 million devices globally, and the company recently opened an R&D lab in Montréal, a global hub for AI research. Mo leads the development of products at Maluuba alongside the company’s language understanding and deep learning teams. Previously, Mo worked in product management at Microsoft and Facebook. He holds a BS in software engineering from the University of Waterloo.

Presentations

Bigger than bots: Machine reading and writing in enterprise Session

AI research in comprehension, communication, and modeling human-like thinking skills is heralding the dawn of literate machines. Although there has been a lot of recent hype around bots, we’re only just beginning to see the potential for language understanding. Mohamed Musbah explores key research areas and explains how they will power new products and services in language understanding.

Sharan Narang is a researcher on the Systems team at Baidu’s Silicon Valley AI Lab (SVAIL), where he plays an important role in improving the performance and programmability of the deep learning framework used by researchers at SVAIL. Sharan’s research focuses on reducing the memory requirement of deep learning models, and he has explored techniques like pruning neural network weights and quantization to achieve this goal. He also proposed a DSD training flow that improved the accuracy of deep learning applications by ~5%. Previously, Sharan worked on next-generation mobile processors at NVIDIA.

Presentations

Benchmarking deep learning inference Session

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.

Paco Nathan leads the Learning Group at O’Reilly Media. Known as a “player/coach” data scientist, Paco led innovative data teams building ML apps at scale for several years and more recently was evangelist for Apache Spark, Apache Mesos, and Cascading. Paco has expertise in machine learning, distributed systems, functional programming, and cloud computing with 30+ years of tech-industry experience, ranging from Bell Labs to early-stage startups. Paco is an advisor for Amplify Partners and was cited in 2015 as one of the top 30 people in big data and analytics by Innovation Enterprise. He is the author of Just Enough Math, Intro to Apache Spark, and Enterprise Data Workflows with Cascading.

Presentations

AI within O'Reilly Media Session

Paco Nathan explains how O'Reilly employs AI, from the obvious (chatbots, case studies about other firms) to the less so (using AI to show the structure of content in detail, enhance search and recommendations, and guide editors for gap analysis, assessment, pathing, etc.). Approaches include vector embedding search, summarization, TDA for content gap analysis, and speech-to-text to index video.

Jan Neumann leads the Comcast Applied Artificial Intelligence Research group with team members in Washington, DC, Philadelphia, Chicago, Denver and Silicon Valley. His team combines large-scale machine learning, deep learning, NLP and computer vision to develop novel algorithms and product concepts that improve the experience of Comcast’s customers such as the voice interfaces, virtual assistants and video and IoT analytics.  
Before Comcast, he worked for Siemens Corporate Research on various computer vision related projects such as driver assistance systems and video surveillance. He has published over 20 papers in scientific conferences and journals, and is a frequent speaker on machine learning and data science. He holds a Ph.D. in Computer Science from the University of Maryland, College Park.

Presentations

How AI powers the Comcast X1 voice interface Session

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.

Aileen Nielsen is a software engineer at One Drop, a company working on diabetes-management products. Aileen has worked in corporate law, physics research laboratories, and, most recently, NYC startups oriented toward improving daily life for underserved populations—particularly groups who have yet to fully enjoy the benefits of mobile technology. Her interests range from defensive software engineering to UX designs for reducing cognitive load to the interplay between law and technology. She currently serves as a member of the New York City Bar Association’s Science and Law committee, where she chairs a subcommittee devoted to exploring and advocating for scientifically driven regulation (and deregulation) of new and existing technologies. Aileen holds degrees in anthropology, law, and physics from Princeton, Yale, and Columbia respectively.

Presentations

AI's legal history and some notions of the future Session

While the commercial use of AI in everything from hiring to medical diagnosis to work scheduling is exploding, legislation and case law alike have yet to make major statements about how AI will be treated by the American legal system. Aileen Nielsen offers a historical overview of how the law has dealt with decision-making technologies in the past and what this suggests about AI's legal future.

Robert Nishihara is a fourth-year PhD student working in the UC Berkeley RISELab with Michael Jordan. He works on machine learning, optimization, and artificial intelligence.

Presentations

Ray: A distributed execution framework for emerging AI applications Session

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.

Peter Norvig is a director of research at Google. Previously, he directed Google’s core search algorithms group. Peter is coauthor of Artificial Intelligence: A Modern Approach, the leading textbook in the field, and coteacher of an artificial intelligence course that signed up 160,000 students, helping to kick off the current round of massive open online classes. He is a fellow of the AAAI, ACM, California Academy of Science, and American Academy of Arts & Sciences.

Presentations

Artificial intelligence in the software engineering workflow Keynote

Artificial intelligence is playing an increasingly important role in new software products, but the workflow of an AI researcher is quite different from the workflow of the software developer. Peter Norvig explains how the two can come together.

Michael Nova is the chief innovation officer at Pathway Genomics and was a founding team member of the company. Michael is the inventor of the Pathway-IBM Watson machine learning AI mobile application Panorama/OME as well as Pathway’s entire wellness (Pathway FIT, Healthy Weight, SkinFIT), cardiac, and mental health lines of genetic testing products. Michael has executed major corporate alliances with companies such as Medco/ESI, PepsiCo, DASA (Brazil), Biogenetika (Brazil), Achibadem (Turkey), IHH/Parkway (Singapore), IBM, Florida Hospital Group, Equinox Health, and many others. Previously, Michael was the founder and CEO of Discovery Partners Inc., which completed a successful $150M IPO and marketed wireless drug discovery technology and radiofrequency combinatorial chemistry to large pharmaceutical companies, where he developed the original “barcode on bead” patents used by many diagnostic and DNA companies. He then founded the wireless sensory network company Graviton, where he was responsible for raising $60 million in capital from venture groups such as Kleiner Perkins and InQtel and large corporations, such as Motorola and Qualcomm. He began his scientific career as a research associate at the Salk Institute in the laboratory of Nobel Laureate Roger Guillemin, where he studied the genetics and proteomics of human growth factors and cancers.

Michael won the 2005 World Economic Forum (WEF) Technology Pioneer Award and was the physician of record on the first person ever to have their entire genome sequenced by Illumina. He is a member of the IBM Watson Advisory Board, the Metagenics Scientific Advisory Board, the Salk Institute NeuroAI group, and the Longevity Org Advisory Board. Michael has over 30 issued and 45 pending patents; and he has numerous publications in peer-reviewed journals. He is a board-certified dermatologist and dermatopathologist, licensed in California, with degrees in biochemistry, physics, computer science, and medicine. In his minimal spare time, Michael likes to surf big waves in Indonesia and Mexico, and he also helped build a WHO-sanctioned basic care clinic in the Fijian town of Nabila.

Presentations

Cognitive mobile healthcare for the patient and physician Session

Precision medicine is largely a big data and systems problem, especially with many different types of "siloed" healthcare information, such as lab results, genetic tests, IoT and wearables data, and insurance information. Michael Nova explains why cognitive computing and artificial intelligence that can dynamically learn using any healthcare data will dramatically impact precision healthcare.

Matt Ocko has three decades of experience as a technology entrepreneur and VC. Over his career, he has invested in Cotendo, Zynga, Facebook, XenSource, UltraDNS, FlashSoft, Fortinet, Aggregate Knowledge, Virtuata, DataMirror, Couchbase, Ayasdi, Kenshoo, D-Wave Systems, MetaMarkets, Uber, AngelList, and many others, including multiple acquisitions by Google, Facebook, Netapp, and other Fortune 1000 tech companies. Matt has been active in helping develop China’s venture capital and technology regulatory framework for two decades. He is the founder of Da Vinci Systems, a pioneering email software vendor with over 1 million users worldwide prior to its acquisition, and holds over 40 granted or in-process patents in areas as diverse as enterprise hardware and social games. Matt has a degree in physics from Yale University.

Presentations

No fluff: Building real, disruptive AI companies Session

Join Matt Ocko in conversation with entrepreneurs Hilary Mason, Gloria Lau, and Coco Krumme for a "not your typical" VC panel. They'll discuss how to build disruptive companies that solve real problems with hard AI technologies, digging into the practicalities of getting started, raising money, landing that first big customer, and everything in between.

Ryan Olson is a solutions architect in the Worldwide Field Organization at NVIDIA, where his primary responsibilities involve supporting deep learning and high-performance computing developers. Ryan is particularly interested in scalable software design that leverages the unique capabilities of the underlying hardware. Previously, he spent eight years working at Cray, where he helped architect novel solutions that enabled applications to run at scale on some of the world’s largest supercomputers, including Oak Ridge National Lab’s Jaguar and Titan machines and the National Science Foundation’s Blue Waters machine at NCSA. Ryan holds a PhD in physical chemistry from Iowa State University.

Presentations

Accelerating deep learning (sponsored by NVIDIA) Session

Ryan Olson explores the role of accelerated GPU computing in modern deep neural networks and explains how it will enable the technologies of the future.

Josh Patterson is the director of field engineering for Skymind. Previously, Josh ran a big data consultancy, worked as a principal solutions architect at Cloudera, and was an engineer at the Tennessee Valley Authority, where he was responsible for bringing Hadoop into the smart grid during his involvement in the openPDC project. Josh is a cofounder of the DL4J open source deep learning project and is a coauthor on the upcoming O’Reilly title Deep Learning: A Practitioner’s Approach. Josh has over 15 years’ experience in software development and continues to contribute to projects such as DL4J, Canova, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif. Josh holds a master’s degree in computer science from the University of Tennessee at Chattanooga, where he did research in mesh networks and social insect swarm algorithms.

Presentations

Neural networks for time series analysis using Deeplearning4j 2-Day Training

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Tom Hanlon, and Susan Eraly demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.

Neural networks for time series analysis using Deeplearning4j (Day 2) Training Day 2

Recurrent neural networks have proven to be very effective at analyzing time series or sequential data, so how can you apply these benefits to your use case? Josh Patterson, Tom Hanlon, and Susan Eraly demonstrate how to use Deeplearning4j to build recurrent neural networks for time series data.

Christoph Peylo leads the Bosch Center for Artificial Intelligence, part of Corporate Research (CR) at Robert Bosch GmbH. Previously, he was VP at Deutsche Telekom Laboratories in Berlin and a member of the management board of Deutsche Telekom Innovation Laboratories at Ben Gurion University of the Negev. Christoph has also worked in the areas of artificial intelligence, (cyber)security, Industrie 4.0, and M2M in positions ranging from software engineer to managing director of a software company. Christoph studied computer science, computational linguistics, and artificial intelligence and holds a PhD in AI from the University of Osnabrück.

Presentations

Beyond the hype: Real AI contributions in industry and engineering Session

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.

Greg Phalin is a senior partner in McKinsey & Company’s New York office, where he leads the retail banking operations and technology practice for North America and is a key leader of McKinsey’s service operations practice. Greg has over 25 years of experience in operations and technology, with a particular focus on credit cards, payments, retail banking, and property and casualty insurance, as well as retail, travel, and entertainment, high tech, and telecommunications. He is also a deep expert in technology-related issues and has assisted clients with IT strategy, IT architecture, big data management, application development and maintenance, infrastructure, and transformation program management and led dozens of large, complex technology programs for clients. Previously, Greg spent 19 years at Accenture, where he specialized in financial services operations and technology issues, particularly customer experience, operations, (customer service and back-office), technology, and outsourcing

Presentations

AI in financial services: Opportunities and obstacles Session

AI is changing every area of the financial industry, but the promise of improved performance is accompanied by looming challenges. Greg Phalin leads a panel discussion with Chetan Dube, Doug Kim, and Aida Mehonic on the future of the AI industry, the applicability of AI to use cases in financial services, and the headwinds that could slow adoption of AI at scale.

Delip Rao is the founder of Joostware, a San Francisco-based company specializing in consulting and building IP in natural language processing and deep learning. Delip is a well-cited researcher in natural language processing and machine learning and has worked at Google Research, Twitter, and Amazon (Echo) on various NLP problems. He is interested in building cost-effective, state-of-the-art AI solutions that scale well. Delip has an upcoming book on NLP and deep learning that will be published by O’Reilly Media.

Presentations

Natural language processing with deep learning 2-Day Training

Delip Rao explores natural language processing using a set of machine learning techniques known as deep learning. Delip walks you through neural network architectures and NLP tasks and teaches you how to apply these architectures for those tasks.

Natural language processing with deep learning (Day 2) Training Day 2

Delip Rao explores natural language processing using a set of machine-learning techniques known as deep learning. Delip walks you through neural network architectures and NLP tasks and teaches you how to apply these architectures for those tasks.

Tackling the fake news problem with AI Session

Not a single day goes by without a mention of "fake news" or the problems it causes. Delip Rao offers a nonpartisan overview of fake news, briefly exploring the technology landscape surrounding the content verification and validation problem and diving deeper into the Fake News Challenge and the stance detection problem.

Naveen Rao is the vice president and general manager of Intel’s artificial intelligence products group. Naveen’s fascination with computation in synthetic and neural systems began around age nine when he began learning about circuits that store information and encountered the AI themes prevalent in sci-fi at the time. He went on to study electrical engineering and computer science at Duke, but continued to stay in touch with biology by modeling neuromorphic circuits as a senior project. After studying computer architecture at Stanford, Naveen spent the next 10 years designing novel processors at Sun Microsystems and Teragen, specialized chips for wireless DSP at Caly Networks, video content delivery at Kealia, Inc., and video compression at W&W Comms. After a stint in finance doing algorithmic trading optimization at ITG, Naveen was part of the Qualcomm’s neuromorphic research group leading the effort on motor control and doing business development. Naveen was the founder and CEO of Nervana (acquired by Intel), which brings together engineering disciplines and neural computational paradigms to evolve the state of the art and make machines smarter. Naveen holds a PhD in neuroscience from Brown, where he studied neural computation and how it relates to neural prosthetics in the lab of John Donoghue.

Presentations

Evolve AI (sponsored by Intel Nervana) Keynote

Naveen Rao explains how Intel Nervana is evolving the AI stack from silicon all the way to the cloud so that true AI transformation can happen across every experience and every vertical.

Vijay Reddy is a machine learning specialist on the Google Cloud Customer Engineering team, where his mission is to democratize machine learning and help companies realize the power of machine learning via Google Cloud. Previously, he worked at a startup that used machine learning to detect bank fraud. Vijay studied computer science at Carnegie Mellon.

Presentations

Machine learning with TensorFlow and Google Cloud (sponsored by Google) Session

Vijay Reddy offers a brief overview of TensorFlow, explaining why it's so popular and how to leverage it to build machine learning applications. Vijay walks you through an end-to-end example using TensorFlow for data ingestion, training, and prediction and the Google Cloud Platform to supercharge training and prediction and remove pain from the development and operational workflows.

Alberto Rizzoli is an Italian entrepreneur and the cofounder of Aipoly, a startup bringing artificial intelligence to embedded devices to help the visually impaired identify objects and give machines a sense of sight. Born in Rome, Italy, Alberto discovered a passion for technology at a young age. Alberto started building companies at age 20, with 3D printing ed-tech, and was featured on Maker Faire’s 20 under 20 list. He coauthored The Future of Business, a series of insights on the future of 60 industries, in collaboration with fellow futurists from London. In March 2016, Alberto was recognized by Italian President Sergio Mattarella for his efforts in advancing artificial intelligence for a good cause, and in October 2016, he received the National Gentile Award for innovation. He occasionally holds talks and lectures on advanced technologies or futurology topics, such as job automation and AI. He attended Singularity University’s Global Solutions program under a scholarship from Google.

Presentations

AI for smartphones: Running neural networks locally on phones for real-time use of the camera as a sensor Session

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.

David Rogers is a data scientist at Sight Machine, where he solves complex manufacturing problems for Global 500 companies with digital twin and AI technologies. His background includes full stack software development and applying system thinking for Boeing and nonprofit organizations. David holds a BS in computer engineering from Michigan State University and an MS in systems engineering from the University of Virginia.

Presentations

AI for manufacturing: Today and tomorrow Session

Join David Rogers to learn how AI can make your operations more efficient and profitable. David explains how existing technologies like the digital twin approach, advanced decision making, and downtime cause detection have primed manufacturing for a profitable and efficient future.

Suman Deb Roy is the lead data scientist at Betaworks, a technology company that operates as a studio, building new products, growing companies, and seed investing. Previously, he worked with Microsoft Research and was a fellow at the Missouri School of Journalism. He is responsible for building several data products in Digg, Instapaper, and Poncho. Suman is also the author of Social Multimedia Signals: A Signal Processing Approach to Social Network Phenomena, a book that describes theories and algorithms to intelligently learn from online media. His work in transfer (machine) learning won the IEEE Communications Society’s MMTC Best Journal Paper Award in 2015, and his research has been covered in the New York Times, Bloomberg News, the Atlantic, the Economist, CJR, and Huffington Post. Suman was invited to speak about his work at various conferences and institutions, including the Bill and Melinda Gates Foundation, the Knight Foundation, and Amazon.

Presentations

Rules of machine learning verification: From data-driven bugs to explainable AI Session

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.

Ruslan Salakhutdinov is an associate professor in the Machine Learning Department at Carnegie Mellon University. Previously, he was an assistant professor in the Departments of Statistics and Computer Science at the University of Toronto and and spent two years as a postdoc at the Massachusetts Institute of Technology’s Artificial Intelligence Lab. Ruslan’s primary interests are deep learning, machine learning, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research and has served on the senior program committee of several learning conferences, including NIPS and ICML. He is an Alfred P. Sloan research fellow, Microsoft Research faculty fellow, Canada research chair in statistical machine learning, a senior fellow of the Canadian Institute for Advanced Research, and a recipient of the Early Researcher Award, Google Faculty Award, and the NVIDIA Pioneers of AI award. Ruslan holds a PhD in computer science from the University of Toronto.

Presentations

Teaching machines to reason and comprehend Session

Russ Salakhutdinov discusses some of the key challenges to making machines more intelligent, focusing on the Gated-Attention (GA) Reader model, which integrates a multihop architecture with a novel attention mechanism, along with extensions that make use of external linguistic knowledge.

Tuomas Sandholm is professor in the Computer Science Department at Carnegie Mellon University, with affiliate professor appointments in the Machine Learning Department, the PhD program in algorithms, combinatorics, and optimization (ACO), and the CMU-Pitt joint PhD program in computational biology. He is the founder and director of the Electronic Marketplaces Laboratory; founder and CEO of Optimized Markets, Inc., which is bringing a new expressive optimization-powered paradigm to advertising campaign sales and scheduling in TV (linear and digital), streaming, internet display, mobile, game, radio, and cross-media advertising; and the founder and CEO of Strategic Machine, Inc., which provides solutions for strategic reasoning under imperfect information. Previously, Tuomas was founder, chairman, and CTO and chief scientist of CombineNet, Inc. His algorithms also run the UNOS kidney exchange, which includes 66% of the transplant centers in the US. He has served as market design consultant or board member for a number of companies, including Baidu, Yahoo, Google, Chicago Board Options Exchange, Swap.com, and Granata Decision Systems. Tuomas has published over 450 papers. His many honors include the NSF Career Award, inaugural ACM Autonomous Agents Research Award, Sloan Fellowship, Carnegie Science Center Award for Excellence, Edelman Laureateship, and Computers and Thought Award. He is a fellow of the ACM, AAAI, and INFORMS and holds an honorary doctorate from the University of Zurich. He holds a PhD and MS in computer science and a Dipl. Eng. with distinction in industrial engineering and management science.

Presentations

Superhuman AI for strategic reasoning: Beating top pros in heads-up no-limit Texas hold’em Keynote

Tuomas Sandholm offers an overview of Libratus—an AI that beat a team of four top specialist pros in heads-up no-limit Texas hold’em, which has 10^161 decision points—and explains how Strategic Machine is applying the domain-independent algorithms behind Libratus to a variety of imperfect-information games.

Suchi Saria is an assistant professor of computer science, health policy, and statistics at Johns Hopkins University. Her research interests are statistical machine learning and “precision” healthcare—specifically designing novel data-driven computing tools for optimizing care delivery. Her work is being used to drive electronic surveillance for reducing adverse events in the inpatient setting and to individualize disease management in complex, chronic diseases. Suchi’s work has been recognized in Science Translational Medicine, by paper awards by the Association for Uncertainty in Artificial Intelligence and the American Medical Informatics Association, an annual scientific award by the Society of Critical Care Medicine, and competitive awards from the Gordon and Betty Moore Foundation and Google Research, a Rambus fellowship, and an NSF Computing Innovation fellowship. She has been selected by IEEE Intelligent Systems as one of 2015’s AI’s 10 to watch, a 2016 DARPA young faculty awardee, and one of Popular Science’s 2016 brilliant 10. Suchi holds a PhD from Stanford University, where she worked under Daphne Koller.

Presentations

Sri Satish is cofounder and CEO of H2O.ai, the builders of H2O. H2O democratizes big data science and makes Hadoop do math for better predictions. Previously, Sri spent time scaling R over big data with researchers at Purdue and Stanford; cofounded Platfora; was the director of engineering at DataStax; served as a partner and performance engineer at the Java multicore startup Azul Systems, where he tinkered with the entire ecosystem of enterprise apps at scale; and worked on a NoSQL trie-based index for semistructured data at in-memory index startup RightOrder. Sri is known for his knack for envisioning killer apps in quickly evolving spaces and assembling stellar teams to productize that vision. He is a regular speaker on the big data, NoSQL, and Java circuit and leaves a trail at @srisatish.

Presentations

Interpretable AI: Not just for regulators Session

Interpreting deep learning and machine learning models is not just another regulatory burden to be overcome. People who use these technologies have the right to trust and understand AI. Patrick Hall and Sri Satish share techniques for interpreting deep learning and machine learning models and telling stories from their results.

Dr. Friederike Schuur is the head of Data Science and Machine Learning at Fast Forward Labs, a premier emerging technologies consultancy. There, she advises startup and large enterprise clients on integrating machine learning capabilities into the core of their organizations. Additionally, she works on developing internal Data Science and Machine Learning products and technical advising. Friederike holds a PhD in Cognitive Neuroscience from University College London, MSc in Cognitive Science and MA in Philosophy/Logic and Radboud Universiteit Nijmegen.

Presentations

No fluff: Building real, disruptive AI companies Session

Join Matt Ocko in conversation with entrepreneurs Hilary Mason, Gloria Lau, and Coco Krumme for a "not your typical" VC panel. They'll discuss how to build disruptive companies that solve real problems with hard AI technologies, digging into the practicalities of getting started, raising money, landing that first big customer, and everything in between.

Shahin Sefati is a senior researcher in the Applied AI research group at Comcast in Washington, DC, where he leads the personalization research for search, browse, recommendation, and content discovery. Shahin’s research interests include machine learning and big data analytics.

Presentations

How AI powers the Comcast X1 voice interface Session

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.

Matt Shobe is the cofounder and chief product officer at Mighty AI, the world’s leading training-data-as-a-service platform. Mighty AI provides the highly accurate, domain-specific, structured human insights that companies need to apply their artificial intelligence and machine learning models (including autonomous driving solutions) and operates Spare5, the microtask platform that enables people to spend their spare time productively. Matt’s technology startup goes back nearly 20 years. Matt worked with the same three other cofounders on three Chicago startups—FeedBurner (acquired by Google in 2007), Spyonit, and DKA—and learned the ground rules in user experience roles with Accenture and Microsoft. He holds an MS in human-centered design and engineering from the University of Washington. Matt is an avid distance runner, private pilot, and skier, although no such triathlon exists (yet).

Presentations

Building training data for autonomous driving Session

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.

Gary Short is a data solution architect for Microsoft, where he specializes in machine learning and big data on the Azure Platform. Gary is interested in data science in all forms, especially computational linguistics and social network analysis.

Presentations

How to gain business insights from unstructured data by leveraging NERs, graphs, and conversational interfaces Tutorial

Galiya Warrier and Gary Short walk you through creating a quantitative graph model from qualitative written information and demonstrate how to add a conversational frontend using the Microsoft Bot Framework.

Drew Silverstein is the cofounder and CEO of Amper Music, a company that combines the highest levels of artistry with groundbreaking technology to empower anyone to create unique, professional music instantly. Previously, Drew was an award-winning composer, producer, and songwriter for film, television, and video games at Sonic Fuel Studios in Los Angeles. Drew studied music composition and Italian at Vanderbilt University’s Blair School of Music and holds an MBA from Columbia Business School.

Presentations

Top down versus bottom up: Computational creativity Session

Drew Silverstein and Cole Ingraham discuss computational creativity.

Richard Socher is chief scientist at Salesforce, where he leads the company’s research efforts and works on bringing state-of-the-art artificial intelligence solutions to Salesforce. Previously, Richard was the CEO and founder of MetaMind (acquired by Salesforce in April 2016). MetaMind’s deep learning AI platform analyzes, labels, and makes predictions on image and text data so businesses can make smarter, faster, and more accurate decisions than ever before. He was awarded the Distinguished Application Paper Award at the International Conference on Machine Learning (ICML) 2011, the 2011 Yahoo Key Scientific Challenges Award, a Microsoft Research PhD fellowship, a “Magic Grant” from the Brown Institute for Media Innovation, and the 2014 GigaOM Structure Award. Richard holds a PhD in deep learning from Stanford, where he worked with Chris Manning and Andrew Ng. His research won the Best Stanford CS PhD Thesis award.

Presentations

Tackling the limits of deep learning Keynote

AI presents a huge opportunity for businesses to personalize and improve customer experiences and improve efficiency, but the technical complexity of AI puts it out of reach for most companies. Richard Socher explains how Salesforce is doing the heavy lifting to deliver seamless and scalable AI to its customers.

Tackling the limits of deep learning Session

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.

Joseph Spisak manages deep learning product management at AWS. Joseph has experience driving strategies and technical and business engagements around machine learning-based cloud workloads, such as computer vision, natural language processing, video summarization and analysis, and speech recognition. Joseph has more than 15 years’ experience delivering products and services in digital video, cloud-based media transcoding, image processing, and machine and deep learning in the consumer mobile, broadcast, and cloud segments. Joseph holds a bachelor’s degree in electrical engineering from Michigan State University and an MBA and MS in finance from the University of Denver. He is a proud graduate of the Entrepreneurial and Innovation certificate program at Stanford University’s Graduate School of Business.

Presentations

Distributed deep learning on AWS using Apache MXNet Tutorial

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.

Rupert Steffner is the founder of WUNDER.ai, an ecommerce AI startup that gives customers a joy ride to the products they love. Rupert has over 25 years of experience in designing and implementing highly sophisticated technical and business solutions, with a focus on customer-centric marketing. Previously, Rupert was chief platform architect of Otto Group’s new business intelligence platform BRAIN and head of BI at Groupon EMEA and APAC. He also served as as business intelligence leader for several European and US companies in the ecommerce, retail, finance, and telco industries. He holds an MBA from WU Vienna and was head of the Marketing Department at the University of Applied Sciences in Salzburg.

Presentations

Deep shopping bots: Building machines that think and sell like humans Session

70% of consumers do NOT feel that online offers resonate with their personal interests and needs. Rupert Steffner explains how cognitive AI can help create deep shopping bots based on true personal relevance. This shift in the shopping paradigm is built upon deep symbolic reinforcement learning, the psychometry of shopping, a new breed of playful UI, and cognified product metadata.

Barbara Stortz is a principal software manager at Microsoft working on data science customer projects running on Microsoft Azure and Cortana Intelligence, including machine learning and deep learning technologies. Previously, Barbara was a senior vice president for SAP Labs LLC, a founding member of SAP HANA, and head of SAP’s EIM products and the SAP Healthcare platform.

Presentations

Scalable deep learning with the Microsoft Cognitive Toolkit Tutorial

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.

Hanlin Tang is an algorithms engineer in Intel’s AI products group, where he builds deep learning models in computer vision and applies these models to various domains, ranging from satellite imagery to computational neuroscience. He also leads the group’s AI projects with defense and intelligence agencies. Hanlin joined Intel through its acquisition of deep learning startup Nervana Systems. Hanlin holds a PhD in biophysics from Harvard, where his work investigated recurrent neural networks in human brain. His research has appeared in scientific journals such as Neuron, Scientific Reports, and eLife.

Presentations

The practitioner’s guide to AI with Intel Nervana (sponsored by Intel Nervana) Session

Hanlin Tang offers an overview of the Intel Nervana deep learning stack and shares lessons learned from building deep learning solutions for multiple industries.

Matt Taylor is the open source community manager for the Numenta Platform for Intelligent Computing, where he spends most of his time managing, encouraging, and interacting with the NuPIC OS community. Matt has been working with and on open source projects for years. Originally from a farming community in rural Missouri, Matt now lives in California and increasingly finds it hard to leave.

Presentations

The biological path toward strong AI Session

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.

Presentations

Building machines that learn and think like people Keynote

Josh Tenenbaum explains how to build machines that learn and think like people.

Richard Tibbetts is CEO of Empirical Systems, an MIT spinout building an AI-based data platform for organizations that use structured data to provide decision support. Previously, he was was founder and CTO at StreamBase, a CEP company that merged with TIBCO in 2013, as well as a visiting scientist at the Probabilistic Computing Project at MIT.

Presentations

AI for structured business data Tutorial

Businesses have spent decades trying to make better decisions by analyzing structured data. New AI technologies are just beginning to transform this process. Vikash Mansinghka and Richard Tibbetts explore AI that guides business analysts to ask statistically sensible questions and lets junior data scientists answer in minutes questions that previously took hours for trained statisticians.

Meet the Expert with Richard Tibbetts (Empirical Systems) Meet the Experts

If you're working with data too expensive, sparse, or complex for most data science to handle—in investment banking, clinical trials, or business operations for example—stop by and talk to Richard. He’s working with AI to analyze messy tabular data and create generative models that can answer questions probabilistically.

Anusua Trivedi is a data scientist on Microsoft’s advanced data science and strategic initiatives team, where she works on developing advanced predictive analytics and deep learning models. Previously, Anusua was a data scientist at the Texas Advanced Computing Center (TACC), a supercomputer center, where she developed algorithms and methods for the supercomputer to explore, analyze, and visualize clinical and biological big data. Anusua is a frequent speaker at machine learning and big data conferences across the United States, including Supercomputing 2015 (SC15), PyData Seattle 2015, and MLconf Atlanta 2015. Anusua has also held positions with UT Austin and University of Utah.

Presentations

Scalable deep learning with the Microsoft Cognitive Toolkit Tutorial

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.

Ferhan Ture is a member of the Comcast Labs DC research group, where he focuses on combining deep learning and natural language processing to build data-driven solutions for various Comcast products involving language, including the algorithms behind the voice-enabled remote controller for the X1 entertainment system. He is a frequent speaker at conferences worldwide.

Presentations

How AI powers the Comcast X1 voice interface Session

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.

Amy Unruh is a developer programs engineer for the Google Cloud Platform, where she focuses on machine learning and data analytics as well as other Cloud Platform technologies. Amy has an academic background in CS/AI and has also worked at several startups, done industrial R&D, and published a book on App Engine.

Presentations

Machine learning on Google Cloud Platform (sponsored by Google) Keynote

Amy Unruh offers a quick overview of machine learning on Google Cloud Platform and demonstrates a couple of the Google Cloud ML APIs. She then briefly highlights a few OSS TensorFlow models and explains how to use transfer learning to fine-tune them with your own data.

Scaling machine learning with TensorFlow Tutorial

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.

Benjamin Vigoda is the CEO of Gamalon Machine Intelligence. Previously, Ben was technical cofounder and CEO of Lyric Semiconductor (acquired by Analog Devices), a startup that created the first integrated circuits and processor architectures for statistical machine learning and signal processing whose products and technology are being deployed in leading smartphones and consumer electronics, medical devices, wireless base stations, and automobiles. The company was named one of the 50 most innovative companies by Technology Review and was featured in the Wall Street Journal, New York Times, EE Times, Scientific American, Wired, and other media. Ben also cofounded Design That Matters, a not-for-profit that for the past decade has helped solve engineering and design problems in underserved communities and has saved thousands of infant lives by developing low-cost, easy-to-use medical technology such as infant incubators, UV therapy, pulse oximeters, and IV drip systems that have been fielded in 20 countries. He has won entrepreneurship competitions at MIT and Harvard and fellowships from Intel and the Kavli Foundation/National Academy of Sciences and has held research appointments at MIT, HP, Mitsubishi, and the Santa Fe Institute. Ben has authored over 120 patents and academic publications. Ben holds a PhD from MIT, where he developed circuits for implementing machine learning algorithms natively in hardware.

Presentations

Idea learning: Structuring unstructured data in the enterprise with very little human effort Session

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.

Jiao (Jennie) Wang is a software engineer on the Big Data Technology team at Intel working in the area of big data analytics. She is engaged in developing and optimizing distributed deep learning framework on Apache Spark.

Presentations

BigDL: Distributed deep learning on Apache Spark Tutorial

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.

Yiheng Wang is a software development engineer on the Big Data Technology team at Intel working in the area of big data analytics. Yiheng and his colleagues are developing and optimizing distributed machine learning algorithms (e.g., neural network and logistic regression) on Apache Spark. He also helps Intel customers build and optimize their big data analytics applications.

Presentations

BigDL: Distributed deep learning on Apache Spark Tutorial

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.

Galiya Warrier is a data solution architect at Microsoft, where she helps enterprise customers adopt Microsoft Azure Data technologies, from big data workloads to machine learning and chatbots.

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How to gain business insights from unstructured data by leveraging NERs, graphs, and conversational interfaces Tutorial

Galiya Warrier and Gary Short walk you through creating a quantitative graph model from qualitative written information and demonstrate how to add a conversational frontend using the Microsoft Bot Framework.

Francisco Webber is the CEO and cofounder of Cortical.io, a company that develops natural language processing solutions for big text data. Francisco’s medical background in genetics combined with his more than two decades of experience in information technology inspired him to create semantic folding, a groundbreaking technology based on the latest findings on the way the human neocortex processes information. Prior to Cortical.io, Francisco founded Matrixware Information Services, a company that developed the first standardized database of patents. Francisco also initiated the Information Retrieval Facility, a nonprofit research institute, with the goal to bridge the gap between science and industry in the information retrieval domain.

Presentations

AI-powered natural language understanding applications in the financial industry Session

Financial industries are under increased pressure due to regulations that demand extended information management capabilities. Information largely consists of text data, which forces companies to increase headcount to keep up with the growing workload. Francisco Webber demonstrates how Cortical.io’s semantic folding, a neuroscience-based approach to NLU, helps automate these uses cases.

Thomas Wiecki is the lead data science researcher at Quantopian, where he uses probabilistic programming and machine learning to help build the world’s first crowdsourced hedge fund. Among other open source projects, he is involved in the development of PyMC—a probabilistic programming framework written in Python. A recognized international speaker, Thomas has given talks at various conferences and meetups across the US, Europe, and Asia. He holds a PhD from Brown University.

Presentations

Bayesian deep learning in PyMC3 Session

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.

Xiaofan Xu is a research engineer at Intel specializing in artificial intelligence and robotics. Previously, Xiaofan worked in the CTO office at Movidius on various research projects, including 3D volumetric object recognition using convolutional neural networks and training neural networks using synthetic data.

Presentations

Anaerobic AI: Developing in a data-starved environment Session

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.

Reza Bosagh Zadeh is Founder CEO at Matroid and an Adjunct Professor at Stanford University. His work focuses on Machine Learning, Distributed Computing, and Discrete Applied Mathematics. Reza received his PhD in Computational Mathematics from Stanford under the supervision of Gunnar Carlsson. His awards include a KDD Best Paper Award and the Gene Golub Outstanding Thesis Award. He has served on the Technical Advisory Boards of Microsoft and Databricks.

As part of his research, Reza built the Machine Learning Algorithms behind Twitter’s who-to-follow system, the first product to use Machine Learning at Twitter. Reza is the initial creator of the Linear Algebra Package in Apache Spark. Through Apache Spark, Reza’s work has been incorporated into industrial and academic cluster computing environments. In addition to research, Reza designed and teaches two PhD-level classes at Stanford: Distributed Algorithms and Optimization (CME 323), and Discrete Mathematics and Algorithms (CME 305).

Presentations

Scaling computer vision in the cloud Session

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.

Matthew Zeiler is the founder and CEO of Clarifai, where he is applying his award-winning research to create the best visual recognition solutions for businesses and developers and power the next generation of intelligent apps. An artificial intelligence expert, Matt led groundbreaking research in computer vision, alongside renowned machine learning pioneers Geoff Hinton and Yann LeCun, that has propelled the image recognition industry from theory to real-world practice. He holds a PhD in machine learning from NYU.

Presentations

Meet the Expert with Matt Zeiler (Clarifai) Meet the Experts

Matt is available to answer your questions about recent advances in computer vision and how you might be able to use them in your work.

Risks, hidden costs, and how to escape the black hole of machine learning technical debt Session

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.

Jun-Yan Zhu is a PhD student at the Berkeley AI Research (BAIR) Lab, where he’s working on computer vision, graphics, and machine learning with Alexei A. Efros. His research goal is to build machines capable of recreating the visual world. Jun-Yan holds a BE from Tsinghua University and was previously a PhD student at CMU. He is currently supported by the Facebook Graduate Fellowship.

Presentations

Learning to recreate our visual world Session

Jun-Yan Zhu explains how to learn natural image statistics directly from large-scale data and explores a class of image-generation and editing operations that constrain their output to look realistic according to the learned image statistics.

Lindsey Anderson-Zuloaga is a data scientist for HireVue. She holds a PhD in experimental physics.

Presentations

Algorithms for hire Session

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.