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.

Brad is the author of several books from Addison Wesley, including Framework Design Guidelines.

Previously, Brad was the Product Unit Manager of the Application Framework team at Microsoft. He graduated from North Carolina State University in 1997 with a BS in Computer Science.

Presentations

Building Conversational Experiences with Actions on Google Session

By building ‘Actions on Google’, developers can reach Google Assistant users and deliver custom experiences, services and information. In this talk, we'll describe the key components of Actions on Google, show you how to build your first action using tools such as API.AI, and explore VUI best practices in order to design compelling conversational experiences.

Anima Anandkumar is a principal scientist at Amazon Web Services, and is currently on leave from U.C.Irvine, where she is an associate professor. Her research interests are in the areas of large-scale machine learning, non-convex optimization and high-dimensional
statistics. In particular, she has been spearheading the development and analysis of tensor algorithms. She is the recipient of several awards such as the Alfred. P. Sloan Fellowship, Microsoft Faculty Fellowship, Google research award, ARO and AFOSR Young
Investigator Awards, NSF CAREER Award, Early Career Excellence in Research Award at UCI, Best Thesis Award from the ACM SIGMETRICS society, IBM Fran Allen PhD fellowship, and several best paper awards. She has been featured in a number of forums such as the
Quora ML session, Huffington post, Forbes, O’Reilly media, and so on. She received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, an assistant
professor at U.C. Irvine between 2010 and 2016, and a visiting researcher at Microsoft Research New England in 2012 and 2014.

Presentations

Distributed Deep Learning on AWS using MXNet Tutorial

During this workshop, members of the Amazon AI team will provide a short background on Deep Learning focusing on relevant application domains and an introduction to using the powerful and scalable Deep Learning framework, MXNet. At the end of this tutorial you’ll gain hands on experience targeting a variety of applications and be able quickly spin up AWS GPU clusters to train at record speeds!

Amir Banifatemi brings 25 years of experience in development and growth of emerging and transformative technologies. He currently leads Artificial intelligence at XPRIZE foundation and is managing partner of K5 Ventures. 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 contributed to the formation of more than 10 startups with emphasis on Predictive Technologies, IoT, and Healthcare.
Mr. Banifatemi is a guest lecturer and an adjunct MBA professor at UC Berkeley, Chapman University, Claremont McKenna College, UC Irvine, and HEC Paris.
He holds Masters degrees in Electrical Engineering from the University of Technology of Compiègne, a Doctorate in System Design and Cognitive Sciences from the University Paris Descartes, as well as an MBA from HEC Paris.

Presentations

XPRIZE invites Workshop Session

We will be inviting one of the notable board of advisors from the AI XPRIZE challenge to speak for a workshop on a strong direction in applied AI and its impact on society

Ron Bodkin is the founder and CEO of Think Big Analytics, the first and leading provider of independent consulting and integration services specifically focused on big data solutions. Previously, Ron was 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. Prior to that, Ron founded New Aspects, which provided enterprise consulting for aspect-oriented programming. Ron was also cofounder and CTO of B2B applications provider C-Bridge, where he headed a team of 900 people and led the company to a successful IPO. Ron graduated with honors from McGill University with a BS in math and computer science and holds a master’s degree in computer science from MIT, leaving the PhD program after presenting the idea for C-Bridge and placing in the finals of the $50K Entrepreneurship Contest.

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 Jens Christian Ipsen 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.

Lashon B. Booker is a Senior Principal Scientist in MITRE’s Information Technology Technical Center. Dr. Booker received his Ph.D. in Computer and Communication Sciences from the University of Michigan in 1982. From 1982 to 1990, Dr. Booker 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. Dr. Booker joined MITRE in August 1990.
Dr. Booker 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 the journal Evolutionary Intelligence and the Journal of Machine Learning Research. Dr. Booker has previously served as an associate editor of the journal Adaptive Behavior, on the editorial boards of the Machine Learning journal and the Evolutionary Computation journal, and regularly serves on the program committees for conferences in these areas.

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. We will describe 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 after receiving his PhD in machine learning from Carnegie Mellon University.

Presentations

Integrating Deep Learning Libraries with Apache Spark Session

This talk will cover best practices for integrating popular Deep Learning libraries with Apache Spark. Topics will include cluster setup, data ingest, configuring clusters, and monitoring jobs. The talk will demonstrate these techniques using Google’s TensorFlow library.

Yishay Carmiel is the head of Spoken Labs, a big data analytics unit that implements bleeding-edge deep learning and machine-learning technologies for speech recognition, computer vision, NLP, and data analysis. Yishay and his team are working on state-of-the-art technologies in artificial intelligence, deep learning, and large-scale data analysis. He has 15 years’ experience as an algorithm scientist and technology leader working on building large-scale machine-learning algorithms and serving as a deep learning, speech recognition and natural language processing expert.

Presentations

Conversational AI at large scale Session

In 2017 there is no doubt conversational AI has gained a quantum leap in performance due to Deep Learning. From Speech Recognition to Machine Translation and Language Understanding Deep Learning made it's mark. However one of the big challenges remains how to scale and productize these breakthroughs, In this session we will give a review the challenges for the present and future.

Functional software engineer fascinated with data science.

With 10 years of rollercoaster experience that encompasses software security, IT management, microwave networks profiling, quality engineering, and currently deep learning and functional programming for the fashion industry.

I’ve lived and worked in cosmopolitan Barcelona, paradisiac Mallorca, magnificent Vienna and now in welcoming Dublin.

Presentations

Deep Learning in Fashion Industry Session

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. Our customers will be able to search products by multiple facets as well as finding visually similar products.

Roger Chen is working on a new venture and cochairs 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, having spent a past life as an engineer and scientist prior to working in venture capital. He developed novel nanotechnology as a PhD researcher at UC Berkeley and spent stints as an engineer with Oracle, EMC and Vicor. 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, focusing on natural language semantics and dialogue management. Prior to joining Elemental Cognition, Jennifer was a Research Staff Member and manager at the IBM T.J. Watson Research Center. Jennifer’s most notable accomplishment at IBM was 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!. Previously, she was a Member of Technical Staff at Lucent Technologies Bell Laboratories focusing on spoken dialogue management.

Through 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. She has served as general chair of NAACL-HLT 2012, program committee co-chair of NAACL-HLT 2006, as area chairs and program committees of many key conferences, and on the editorial boards of multiple journals. Jennifer received a Ph.D. in Computer Science from the University of Delaware.

Presentations

Why is Reading Comprehension Hard? Session

Why is Reading Comprehension Hard? What are the current approaches, where do they fall short and what are our ultimate expectations?

Anca 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. She 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

Keynote by Anca Dragan Keynote

Keynote by Anca Dragan

Nigel Duffy leads the research, development, and commercialization of Sentient’s Artificial Intelligence technologies. A recognized expert in machine learning, Nigel was previously the co-founder and CTO at Numerate Inc. where he led technology development and managed the application of Numerate’s platform in collaborations with academics, biotechs, large pharmaceuticals companies, and the U.S. Government. Nigel invented Numerate’s core technologies which rapidly and repeatedly designed novel drug candidates for diseases including cancer, HCV, HIV, and heart disease. Prior to Numerate, Nigel was VP of Engineering at Pharmix and worked as a research scientist at AiLive (developer of the Wii Motion Plus), where he played a key role in applying machine learning to computer games. Nigel also spent time at Amazon A9 working on tools for large scale analytics in product search.

The author of eleven granted or pending patents, Nigel’s technology is in regular use by more than 50 million users. He has published 15 academic papers including highly cited papers on machine learning, computational biology, and linguistics. Nigel holds a Masters in Mathematics from University College Dublin in Ireland and a PhD in Computer Science (Machine Learning Theory) from the University of California at Santa Cruz.

Presentations

AI Building AI: How Evolutionary Algorithms are Revolutionizing Deep Learning Session

Using massively distributed evolutionary algorithms to evolve the actual architectures of deep networks.

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, and contributes to customer-inspired innovation, systems thinking, lean analytics, and Autonomy/Mastery/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.

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 is often opening new opportunities, not yet, or easily, quantified. I will cover three years of learnings from taking ideas to results-delivering production solutions at various organizations, including global 500 enterprises, tech companies, and non profits.

Rana el Kaliouby, PhD, is co-founder and CEO of Affectiva, the pioneer in Emotion AI, the next frontier of Artificial Intelligence. She leads the company’s award winning, emotion recognition technology, that is 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. Prior to founding Affectiva, as a research scientist at MIT Media Lab, Rana 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, Entrepreneur Magazine named her as one of the “7 Most Powerful Women To Watch In 2014”. She got inducted into the “Women in Engineering” Hall of Fame and is a recipient of the 2012 Technology Review’s “Top 35 Innovators Under 35” Award, listed on Ad Age’s “40 under 40” and recipient of 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, University of Cambridge.

Presentations

The science and applications of the emerging field of Artificial Emotional Intelligence Session

Our interactions with technology is becoming conversational and perceptual. Emotion AI is a branch of artificial intelligence that brings emotional intelligence to these interfaces and AI systems. This talk will review the state of Emotion AI, the commercial applications as well as the underlying deep learning methods and research roadmap such as multi-modal emotion recognition and emotion chip.

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. How can you apply these benefits to your use case? Josh Patterson and Susan Eraly of Skymind cover the use of DeepLearning4J to build Recurrent Neural Networks for Time Series Data.

Our speaker, Tim Estes, founder and CEO of Digital Reasoning, first began applying AI to major human challenges in the shadow of 9/11. Technology developed by his firm was used to help United States defense in the fight against terrorism and in the field in Afghanistan. He is a leading influencer, advocate and speaker for the use of technology and analytics to achieve social good in industry, government, healthcare and academia. His guiding mission is to create technology that learns and gets smarter, with the goal of helping humans see the world more clearly so they can solve the world’s toughest problems.

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. In this session, we’ll talk about two Digital Reasoning customers, Nasdaq, who found a way to use AI to help safeguard the financial markets, and Thorn, who found a way to use AI to combat human trafficking and rescue children.

Dr. David Ferrucci is the award-winning Artificial Intelligence (“AI”) researcher who 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. Considered a landmark in AI, Watson delivered amazing results that out-performed all expectations. From 2011 through 2012, Dr. Ferrucci pioneered Watson’s applications in health which helped lay the technical foundation for a new Healthcare Division at IBM. In 2013, Dr. Ferrucci joined Bridgewater Associates, where he leads the Systematized Intelligence Lab. He joined to explore the application of AI in building explicable data-driven systems for optimal management and people analytics.

Dr. Ferrucci’s more than 25 years in AI and his passion to see computers fluently think, learn, and communicate inspired him to found Elemental Cognition LLC in 2015. Elemental Cognition is focused on creating AI systems that autonomously learn from human language and interaction to become powerful and fluent thought partners facilitating complex decision making in areas ranging from healthcare to economics. AI systems should not only predict possible solutions but should be able to explain why they make sense.

Dr. Ferrucci graduated from Rensselaer Polytechnic Institute with a Ph.D. in Computer Science. He 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 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 multi-modal analytics. Dr. Ferrucci has keynoted in highly distinguished venues around the world including many of the top computing conferences. He has been interviewed in numerous settings from the New York Times to Bloomberg West to the Computer History Museum. Dr. Ferrucci was awarded the title of IBM Fellow and has won many awards for his work including the Chicago Mercantile Exchange’s Innovation Award and the AAAI Feigenbaum Prize.

Presentations

Keynote by David Ferrucci Keynote

Keynote by David Ferrucci

Codruta Gamulea is a business developer and data strategist with a passion for using AI technology to improve the quality of journalism.

Codruta Gamulea leads Orbit, an AI technology venture by leading Norwegian digital studio & venture builder Bakken & Bæck. Orbit provides artificial intelligence technology as a service. Orbit NLP uses advanced machine learning algorithms to automatically categorise, enrich and tag large pieces of text-based content. In 2016, Orbit launched its NLG module, thus advancing into natural language processing combined with natural language generation to create a system that reads human-written content, understands it, and then writes a new take on the same information. Today Orbit is used by several media companies to create innovative AI-driven news services. Recently, Orbit teamed up with Norwegian tabloid Dagbladet to explore use of AI to elevate debate journalism in a large project funded by Google Digital News Initiative in 2017-2018.

Before joining Orbit, Codruta led data strategy at Amedia, Norway’s largest local news publisher, overseeing the company’s efforts to monetise data for its over 70 titles. Codruta has over 12 years consulting experience from Accenture, a master’s degree from BI Norwegian School of Management, and studied news reporting at Harvard/Nieman Journalism Lab.

Presentations

The AI-Powered Newsroom Session

The AI promise 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, we show examples of how AI can help solve the industry resource constraints and improve the quality of journalism.

Garrett Goh is a Scientist at the Pacific Northwest National Lab (PNNL), in the Advanced Computing, Mathematics & Data Division. He was previously awarded the Howard Hughes Medical Institute (HHMI) fellowship which supported his PhD in Computational Chemistry at the University of Michigan. At PNNL, he was awarded the Pauling Fellowship that supports his new research direction of combining deep learning and artificial intelligence with traditional computational chemistry applications. His current interests is in AI-assisted computational chemistry, which is the application of deep learning to predict chemical properties and the discovery of new chemical insights, while using minimal expert knowledge.

Presentations

AI-Assisted Computational Chemistry: Predicting chemical properties with minimal expert knowledge Session

Using deep learning and with virtually no expert knowledge, we construct computational chemistry models that perform favorably to existing state-of-the-art models developed by expert practitioners. Our work demonstrates the potential for AI assistance in accelerating the scientific discovery process, from a typical span of years to a matter of months.

Laura Graesser is studying for a Masters degree in computer science at New York University, focusing 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

This tutorial is a hands on introduction to neural networks using the popular Python library, Keras. We’ll focus on building intuition for the core components of a neural network and what it means for a network to “learn”. Then attendees will have the opportunity to build and train their own network.

Eric Greene is a senior architect for Wells Fargo Digital Innovation labs, where he brings technical innovation to fruition by working with business and technical leaders across the organization. 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. Eric has been with Wells Fargo for 15 years working on a variety of applications within the Architecture and Development groups.

Presentations

Deep learning and predictive payments in Wells Fargo AI Labs Session

Next-generation payment applications will soon be intelligent enough to automatically schedule payments for customers effortlessly. Eric Greene explores how Wells Fargo AI Labs developed accurate predictive models leveraging deep learning and terabytes of transaction history data that propose payment amounts, time, and recipient for recurring expenses such as rent, loans, utilities, and services.

Nadeem Gulzar is the Head of Advanced Analytics & Architecture within Danske Bank Group, a Nordic bank with strong roots in Denmark and focus on becoming the most trusted financial partner in the Nordics. For the last 2 years, Nadeem has taken the lead in establishing Advanced Analytics and Big Data Technologies within Danske. Previously, Nadeem worked with Credit and Marketrisk, where he headed the programme to build-up capabilities to calculate risk using Monte Carlo simulation methods. Nadeem has graduated from Copenhagen University with a BS in Computer Science, Mathematics and Psychology and holds a Master’s Degree in Computer Science 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 Jens Christian Ipsen 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.

Yufeng is a Developer Advocate for the Google Cloud Platform, where he bridges the gap between the developer community and engineering teams.
He is interested in finding new and interesting applications of machine learning. You can find him on Twitter @YufengG

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. This session will step 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

TensorFlow is an increasingly popular open source Machine Intelligence library that is especially well-suited for deep learning. Google Cloud Machine Learning (CloudML) lets you do distributed training and serving at scale. In this workshop, we'll give an introduction to TensorFlow concepts and walk through how to use CloudML to do distributed training and scalable serving of your models.

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. Kris 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. Kris 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.

What, How and Why: The Dynamic of Advanced NLG Session

An overview of the subfield of artificial intelligence, advanced natural language generation, and the assorted technical systems involved with this emerging technology along with the mechanisms that drive them.

Mark Hammond is co-founder and CEO at Bonsai. Mark has a deep passion for understanding how the mind works and has been thinking about AI throughout his career. Upon graduating from Caltech with a degree in computation and neural systems, Mark went on to positions at Microsoft and numerous startups and academia, including turns at Numenta and the Yale neuroscience department.

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. Learn how various approaches can be applied to build explainability into control and optimization tasks including robotics, manufacturing and logistics.

Dr. 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. Dr. Hazen has also developed natural language technology used within Microsoft’s Bing and Cortana products. Prior to joining Microsoft in 2013, Dr. Hazen spent six years as a member of the Human Language Technology Group at MIT Lincoln Laboratory and nine years as a Research Scientist at the MIT Computer Science and Artificial Intelligence Laboratory. During his career he has published over 70 article in top-tier peer-reviewed scientific research journals and conferences. Dr. Hazen holds S.B., S.M., and Ph.D. degrees 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 where deep neural networks (DNNs) trained on tens of millions of images can recognize thousands of different object types. These DNNs can also be easily customized to new use cases. This talk presents simple methods and tools developed that enable users to adapt Microsoft's start-of-the-art DNNs for use in their own computer vision solutions.

Founder and CEO of Isocline, a venture-backed AI hardware company. We make it easy to put powerful, local speech and vision AI into any product – from wearables to cars. Under the hood our tech uses new methods of computing inside of flash memory arrays. We can 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

Recent breakthroughs in deep learning and new analog-domain computation methods to deploy trained neural networks will deliver exciting new capabilities to consumer and industrial hardware products. 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.

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. We discuss 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. He contributes to oversight of technical activities across MITRE’s Intelligence programs, including participating in the development and integration of MITRE’s research program, direct technical support to projects, and review of technical aspects of Intelligence Community programs.

He was 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 has been a Principal Investigator on multiple MITRE research programs addressing various aspects of software assurance, safety cases, autonomy, and error handling. He was co-chair for the 2015 and 2016 Association for the Advancement of Artificial Intelligence workshops on “Cognitive Assistance in Government and Public Sector Applications”.

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. We will describe 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.

Anmol Jagetia is a Software Engineer at Media.net which is one of the biggest ad-tech company globally and was recently acquired by a Chinese Consortium for $900 Million USD in the 3rd Largest Ad Tech Deal Ever. It also powers the Yahoo-Bing contextual ads network. Anmol Jagetia is interested in Machine Learning, Web Technologies, Open Source Software (was part of HPCC as a Google Summer of Code Student in 2015), Data Science (interned on a scholarship at the prestigious Max Planck Institute for Software Systems, Germany in 2016 and another internship at Complutense University of Madrid, Spain), and introducing people to technology. He has also authored some popular Open Source projects (Flatabulous, which received over 2.2k stars on GitHub and has received close to 1 Million downloads). He graduated from the prestigious Indian Institute of Information Technology, Allahabad. He has also published some research papers in the field with IEEE and also has some upcoming papers of interesting applied aspects of Machine Learning.
In his free time, he enjoys traveling, reading and playing his guitar.

Presentations

Building game bots using OpenAI’s Gym and Universe Session

In this talk we will cover the use of OpenAi's, Gym and Universe which can interact with external computer programs like games etc and design bots that can learn to defeat the game or become extremely smart using Reinforcement Learning. This talk aims at introducing beginners to AI and Machine Learning in a fun and interesting way, by designing bots that can play their favourite retro games!

Oliver Jojic is a distinguished researcher in the Comcast Labs DC research group where he leads the deep learning and nlp research effort.

Presentations

How AI powers the Comcast X1 Voice Interface Session

AI plays an essential role in creating the Comcast X1 entertainment experience and how millions of our customers access their content on the TV. In this talk we will explain how AI enables us to understand what you are looking for when you talk to the X1 voice remote and how we scaled the voice interface to answer millions of voice queries every single night.

Arthur Juliani is a researcher working at the intersection of Cognitive Neuroscience and Deep Learning. He is currently obtaining his Phd from the University of Oregon.

Presentations

Deep Reinforcement Learning Tutorial Tutorial

In the past few years computers have been able to learn to play Atari games, Go, and recently First Person Shooters at a superhuman level. Underlying all these accomplishments has been Deep Reinforcement Learning (Deep RL). This tutorial will cover RL from the basics using lookup tables and gridworld all the way to solving complex 3D tasks such as First-Person shooters with deep neural networks.

Jason Laska leads the machine learning efforts at Clara Labs. He previously spearheaded the computer vision program at Dropcam (acquired by Google in 2014), developing massive scale online vision systems for the product. Jason received the Ph.D. in Electrical Engineering in 2011 from Rice University with contributions to 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

At Clara Labs, we've fused 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. This session will focus on the challenges of building a real-time-ish knowledge-workforce, how to integrate automation, and key strategies we've learned that enable scale.

Tianhui Michael Li is the founder and CEO of the Data Incubator. Michael has worked as a data scientist lead at Foursquare, a quant at D.E. Shaw and JPMorgan, and a rocket scientist at NASA. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup that lets him focus on what he really loves. He did his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall scholar.

Presentations

Deep Learning with Tensorflow 2-Day Training

Tensorflow is an increasingly popular tool for deep learning. We will introduce the TensorFlow graph, using its Python API, and demonstrate its use. Starting with simple machine-learning algorithms, we will move on to implementing neural networks using TFLearn. Several real-world deep-learning applications will be discussed, including machine vision, text processing, and generative networks.

Yonghua Lin is the founder and leader of SuperVessel Innvoation cloud from IBM. Meanwhile, she is the Senior Technical Staff Member and Senior Manager of Cognitive System and Cloud in IBM Research. She has worked on system architecture, cloud and cognitive platform research for more than 15 years. She was the initiator of mobile infrastructure on cloud from 2007 which has become the Network Function Virtualization today. She led IBM team built up the FIRST optimized cloud for 4G mobile infrastructure, and successfully demonstrated in ITU, Mobile World Congress, etc. She was the founder of SuperVessel cloud to support OpenPOWER research and development in industry. She herself has more than 40 patents granted worldwide and publications in top conferences and journals.

Presentations

VisionBrain – To enable deep learning based visual analysis in edge and cloud Session

We will dive deep into VisionBrain, a Deep Learning System from IBM for image and video analysis in both edge and cloud environment. The insights of system design, performance optimization and large scale capability for training and inference will be shared in this presentation.

Dr. Shaoshan Liu is the co-founder and president of PerceptIn, working on developing the next-generation robotics platform. Before founding PerceptIn, he was working on Autonomous Driving and Deep Learning Infrastructure at Baidu USA. Liu has a PhD in Computer Engineering from University of California, Irvine.

Presentations

The Road to Affordable AI-Capable Products Session

AI is a buzz word and a summary of high-profile technologies, it is imperative to make such high-profile technologies affordable in order to for these technologies to proliferate, and to benefit the general public. In this talk, we discuss 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 is an engineering and data science leader and advisor. Nikita currently leads machine learning teams building new monetization products at Facebook. Prior to that, Nikita led teams of machine learning engineers and data scientists at LinkedIn, making 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 obtained his Ph.D. in Computer Science at Rutgers University where his research focused on machine learning and its applications on textual and financial data. Nikita has co-authored 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.

Presentations

Recommending 1 Billion Products to 1.78 Billion People on Facebook Session

Using the context of personalized digital advertising, this talk will showcase an application of modern supervised machine learning methods such as Factorization Machines and Deep Neural Networks for recommending hundreds of millions of products to nearly two billion people on Facebook. This talk will be of great interest to business leaders and machine learning practitioners.

Vikash Mansinghka is a research scientist at MIT, where he leads the Probabilistic Computing Project, as well as 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.com, was an advisor to Google DeepMind, and held graduate fellowships from 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 collecting & analyzing structured data. New AI technologies are beginning to transform this process. This talk will focus on AI that (i) guides business analysts to ask statistically sensible questions and (ii) lets junior data scientists answer questions in minutes that previously took hours for trained statisticians.

Probabilistic Programming Tutorial

Probabilistic inference is a widely-used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain and/or incomplete. It has become central to multiple fields, from big data analytics to robotics and AI. This class will survey the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to non-experts.

Adam is a co-founder and CTO of B12. Previously, he 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 co-authoring a book, “Crowdsourced Data Management: Industry and Academic Perspectives.” Prior to that, he completed his Ph.D. in Computer Science at MIT. His dissertation is on database systems and human computation. He is a recipient of the NSF and NDSEG fellowships, and has previously worked at ITA, Google, IBM, and FactSet. In his free time, he builds course content to get people excited about data and programming.

B12 is building a better future of creative and analytical work, starting with design. Through Orchestra, our open source project management system for experts and machines, we automatically generate websites for clients (algorithmic design) and then recruit wonderful designers and art directors to fill in the details from the algorithmically generated starting points. This summer, B12 announced the close of our $12.4M Series A funding round.

Presentations

Human-assisted AI at B12: Ten 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. At B12, we’re building infrastructure that celebrates humans where they’re best while bringing machines in for the rest. In this talk, we’ll explain human-assisted AI, and show how even today it changes incredibly creative and fundamentally human fields like design.

Dana Mastropole studied physics as an undergraduate student at Georgetown University and received her master’s in physical oceanography from MIT. She also completed MIT’s Kaufman teaching certificate program and taught elementary school science prior to joining the Data Incubator team. She is currently a Data Scientist in Residence at the Incubator and contributes to curriculum development and instruction.

Presentations

Deep Learning with Tensorflow 2-Day Training

Tensorflow is an increasingly popular tool for deep learning. We will introduce the TensorFlow graph, using its Python API, and demonstrate its use. Starting with simple machine-learning algorithms, we will move on to implementing neural networks using TFLearn. Several real-world deep-learning applications will be discussed, including machine vision, text processing, and generative networks.

As co-founder and CTO of SwiftKey, Ben Medlock invented the intelligent keyboard for smartphones and tablets that has transformed typing on touchscreens. The company’s mission is to make it easy for everyone to create and communicate on mobile.

Ben is a prominent figure in mobile technology. He is ranked among the 2015 Wired 100, and is regularly asked to deliver keynotes at conferences including SXSW, WIRED2014, and 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

AI for good: Why this 'scary' technology is making us more human Session

In this session, Dr. Ben Medlock of SwiftKey (and now Microsoft) will talk about the future of AI and why the possibilities are endless - and not at all frightening. He will also give key insights into how the mobile industry and beyond can better understand and support this massive worldwide movement.

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

Transforming an investment firm with AI: A case study Session

Deploying AI across business functions brings benefits ranging from prosaic to game-changing, 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. His PhD work at Stanford was on randomized algorithms for large-scale linear regression problems.

Presentations

Integrating Deep Learning Libraries with Apache Spark Session

This talk will cover best practices for integrating popular Deep Learning libraries with Apache Spark. Topics will include cluster setup, data ingest, configuring clusters, and monitoring jobs. The talk will demonstrate these techniques using Google’s TensorFlow library.

David Moloney serves as the Director of Machine Vision Technology within the New Technologies Group at Intel. Prior to this role, David served as co-founder and CTO of Movidius which was acquired by Intel in November 2016. He has worked for over 20 years in the semiconductor industry since qualifying with a BEng from DCU in 1985.

He has a wealth of international experience having worked for Infineon (Siemens Semiconductor Division) in Munich for 5 years and SGS- Thomson Microelectronics (STM) in Milan for 4 years respectively.
In 1994 he returned from STM to lead the engineering team for the first product development at Parthus Technologies where he was a key member of the management team and where he spearheaded the development of the Parthus Bluetooth technology.

David left Parthus in 2003 to work towards his PhD in Trinity College Dublin and as an independent consultant for Frontier Silicon and Dublin City University. He received a PhD from Trinity College Dublin in 2010 for his research into high performance computer architectures.

David is inventor/co-inventor of 17 issued US patents, with additional patents pending and is the author of 10 conference and 8 journal papers.

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. The result of this is a “data wall” facing smaller companies. Thankfully, there are some innovative solutions around the data wall through synthetic datasets. This talk will outline the problem of the data wall, and how it can be addressed through synthetic datasets.

Philipp 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 RISE lab in Berkeley.

Presentations

Ray: A Distributed Execution Framework for Emerging AI Applications Session

AI applications are increasingly dynamic, interactive, and real-time. These properties impose new requirements on the distributed systems that support them. We will describe Ray, a new system designed to support these emerging applications.

Dr. Mugan specializes in artificial intelligence and machine learning. His current research focuses in the area of deep learning, where he seeks to allow computers to acquire abstract representations that enable them to capture subtleties of meaning. Dr. Mugan received his Ph.D. in Computer Science from the University of Texas at Austin. His thesis work was in the area of developmental robotics where he 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

The talk will survey the field of natural language processing (NLP), both from a symbolic and a sub-symbolic perspective. It will argue that the current limitations of NLP stem from computers having a lack of grounded understanding of our world, and it will point to ways that computers can achieve that understanding.

Mo Musbah is VP Product at Maluuba, a Canadian AI company that’s helping machines to think, reason and communicate seamlessly with humans. Maluuba’s technology is deployed in more than 50 million devices globally. In 2016 the company 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. Prior to Maluuba, Mo held product management positions at Microsoft and Facebook. He has a B.S. in Software Engineering from the University of Waterloo. Maluuba was acquired by Microsoft in January 2017.

Presentations

Bigger than bots: Machine reading and writing in enterprise Session

There has been a lot of hype around bots, but we’re just beginning to see the potential for language understanding. AI research in comprehension, communication and modelling human-like thinking skills are heralding the dawn of literate machines. In this session we outline key research areas and how these will power new products and services in language understanding powered by deep learning

Daryn Nakhuda is CTO & Co-Founder of Mighty AI, where he leads software development and data science. Prior to Mighty AI, Daryn was a software engineer and manager at Amazon.com, and spent many years in tech leadership roles at numerous Internet startups.

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? This session will highlight Mighty AI’s learnings from creating a training dataset for autonomous driving, including workflow tips and guidance for engineers building computer vision models.

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

Presentations

Challenges in Deep Learning Inference Session

Artificial Intelligence has had a tremendous impact on various applications at Baidu including speech recognition and autonomous driving. The performance requirements for all of these applications are very different. Sharan will outline 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 instructional design, 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’s the author of Just Enough Math, Probabilistic Data Structures in Python, and Enterprise Data Workflows with Cascading, and teaches Natural Language Processing in Python.

Presentations

AI within O'Reilly Media Session

How does Media employ AI? Obvious: chat bots, case studies about other firms. Less obvious: apply AI methods to show the structure of content in detail; enhance search and recommendations; guide editors for gap analysis, assessment, pathing, etc. Approaches explored include: vector embedding search, summarization, TDA for content gap analysis, speech-to-text to index video, etc.

Jan Neumann manages the research group at Comcast Labs DC where he and his team focus on using deep learning and large scale machine learning for content discovery, video analytics, customer care, and big data analysis with the goal to innovate the TV and home consumer experience. Before Comcast, he worked for Siemens Corporate Research on various computer vision related projects. He holds a Ph.D. in Computer Science from the University of Maryland, College Park.

Recent talks

  • How AI Powers the X1 Entertainment System. Jan Neumann, AI Summit NYC, Dec 2016, New York, NY
  • Data Science and Machine Learning to Improve the Customer Experience. Jan Neumann, Business Analytics Summit – Innovation Enterprise, Chicago, IL, May 2016
  • How Automatic Content Analytics enables the TV Experiences of the Future. Jan Neumann, INTX 2016, Boston, MA
    How Comcast Uses Data Science and ML to Improve the Customer Experience. Jan Neumann, Global Big Data Conference March 2016, Santa Clara, US
  • How Spark is working out at Comcast scale, Sridhar Alla and Jan Neumann, Strata Hadoop NYC 2015

More info at http://dclabs.comcast.com/research/

Presentations

How AI powers the Comcast X1 Voice Interface Session

AI plays an essential role in creating the Comcast X1 entertainment experience and how millions of our customers access their content on the TV. In this talk we will explain how AI enables us to understand what you are looking for when you talk to the X1 voice remote and how we scaled the voice interface to answer millions of voice queries every single night.

Since completing degrees in anthropology, law, and physics from Princeton, Yale, and Columbia respectively, Aileen Nielsen has worked in corporate law, physics research laboratories, and, most recently, NYC startups oriented towards improving daily life for under-served populations – particularly groups who have yet to fully enjoy the benefits of mobile technology. She has interests ranging from defensive software engineering to UX designs for reducing cognitive load to the interplay between law and technology. In addition to engineering One Drop’s diabetes-management products by day, Aileen 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.

Presentations

A.I.'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 A.I. will be treated by the American legal system. This talk will offer a historical overview of how the law has dealt with decision making technologies in the past and what that predicts 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, interactive, and real-time. These properties impose new requirements on the distributed systems that support them. We will describe 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

Keynote by Peter Norvig Keynote

Keynote by Peter Norvig

Michael Nova MD Ph.D

Dr. Nova is currently the Chief Innovation Officer of Pathway Genomics, and was a founding team member of the company. He is the inventor of the Pathway-IBM/Watson Machine Learning A.I. mobile application: Panorama/OME; and the entire Wellness (Pathway FIT/Healthy Weight, SkinFIT), Cardiac and Mental Health line of genetic testing products for Pathway. Dr Nova 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. His scientific career began 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 was previously the founder and CEO of Discovery Partners Inc. (Nasdaq: DPII), which completed a successful $150M IPO and marketed wireless drug discovery technology and radiofrequency combinatorial chemistry to large pharmaceutical companies. Intellectual property developed by Dr. Nova at DPII included 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, InQtel and large corporations such as Motorola and Qualcomm. Dr. Nova is also the 2005 World Economic Forum (WEF) Technology Pioneer Award Winner; and the physician of record on the first person ever to have their entire genome sequenced by Illumina (2009).

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.

Dr. Nova has over 30 issued, and 45 pending patents; and he has numerous publications in peer-reviewed journals. With degree’s in Biochemistry, Physics/Computer Science, and Medicine, Dr. Nova is a Board-certified Dermatologist and Dermatopathologist licensed in California. 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

In general, precision medicine is a big data and systems problem, especially with many different types of "siloed" healthcare information such as lab results, genetic tests, IoT/wearables, and insurance information. The use of Cognitive Computing and artificial intelligence (A.I.) that can dynamically learn using any healthcare data to will dramatically impact precision healthcare.

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 graduate of the University of Tennessee at Chattanooga with a master of computer science, where he did research in mesh networks and social insect swarm algorithms. 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.

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. How can you apply these benefits to your use case? Josh Patterson and Susan Eraly of Skymind cover the use of DeepLearning4J to build Recurrent Neural Networks for Time Series Data.

Dr. Christoph Peylo leads Bosch Center for Artificial Intelligence, which is part of Corporate Research (CR) at Robert Bosch GmbH. Prior to joining Bosch in 2017, he was VP at Deutsche Telekom Laboratories in Berlin and member of the management board of Deutsche Telekom Innovation Laboratories at Ben Gurion University of Negev. He worked in various positions in the area of Artificial Intelligence, (Cyber-)Security, Industrie 4.0 and M2M. Before joining Deutsche Telekom in 2006, he worked in various positions from software engineer to managing director of a software company.
Christoph has studied Computer Science, Computational Linguistics, and Artificial Intelligence (AI) and acquired his Ph.D. at University of Osnabrück in the field of AI.

Presentations

Beyond The Hype: Real AI Contributions in Industry & 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. In this talk we give some examples of meaningful commercial IoT deployment and illuminate obstacles that have to be taken.

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

In this tutorial, we will examine natural language processing using a set of machine learning techniques known as deep learning. You will learn different neural network architectures and different NLP tasks, and 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 undertakes a nonpartisan exploration 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.

Ruslan Salakhutdinov received his PhD in computer science from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an Assistant Professor in the Departments of Statistics and Computer Science. In 2016 he joined the Machine Learning Department at Carnegie Mellon University as an Associate Professor. Ruslan’s primary interests lie in deep learning, machine learning, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research and served on the senior programme 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 recipient of the Early Researcher Award, Google Faculty Award, Nvidia’s Pioneers of AI award, and is a Senior Fellow of the Canadian Institute for Advanced Research.

Presentations

Session by Russ Salakhutdinov Session

Session by Russ Salakhutdinov

Tuomas Sandholm is Professor at Carnegie Mellon University in the Computer Science Department, with affiliate professor appointments in the Machine Learning Department, Ph.D. Program in Algorithms, Combinatorics, and Optimization (ACO), and CMU/UPitt Joint Ph.D. Program in Computational Biology. He is the Founder and Director of the Electronic Marketplaces Laboratory. He has published over 450 papers. He has 27 years of experience building optimization-powered electronic marketplaces, and has fielded several of his systems. He is Founder and CEO of Optimized Markets, Inc., which is bringing a new paradigm to advertising campaign sales and scheduling – in TV (linear and digital), Internet display, mobile, game, radio, and cross-media advertising. He was Founder, Chairman, and CTO/Chief Scientist of CombineNet, Inc. from 1997 until its acquisition in 2010. During this period the company commercialized over 800 of the world’s largest-scale generalized combinatorial multi-attribute auctions, with over $60 billion in total spend and over $6 billion in generated savings. 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 Baidu, Yahoo!, Google, Chicago Board Options Exchange, swap.com, Granata Decision Systems, and others. He has developed the leading algorithms for several general classes of game, and with his PhD student Noam Brown developed Libratus, an AI that beat a team of top heads-up no-limit Texas Hold’em specialist pros in January 2017. He holds a Ph.D. and M.S. in computer science and a Dipl. Eng. (M.S. with B.S. included) with distinction in Industrial Engineering and Management Science. Among his many honors are 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 Fellow of the ACM, AAAI, and INFORMS. He holds an honorary doctorate from the University of Zurich.

Presentations

Keynote by Tuomas Sandholm Keynote

Keynote by Tuomas Sandholm

Robert Schroll is a data scientist in residence at the Data Incubator. Previously, he held postdocs in Amherst, Massachusetts, and Santiago, Chile, where he realized that his favorite parts of his job were teaching and analyzing data. He made the switch to data science and has been at the Data Incubator since. Robert holds a PhD in physics from the University of Chicago.

Presentations

Deep Learning with Tensorflow 2-Day Training

Tensorflow is an increasingly popular tool for deep learning. We will introduce the TensorFlow graph, using its Python API, and demonstrate its use. Starting with simple machine-learning algorithms, we will move on to implementing neural networks using TFLearn. Several real-world deep-learning applications will be discussed, including machine vision, text processing, and generative networks.

Gary Short is a Data Solution Architect for Microsoft. He specialises in machine learning and “big data” on the Azure Platform, but has an interest in data science, in all forms, especially computational linguistics and social network analysis.

Presentations

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

In this 3 hour tutorial we will teach you how to create a quantitative graph model from qualitative written information. We will then go on to show you how to put a conversational front-end onto this using the Microsoft Bot Framework. A step-by-step lab will be provided for you to work through this tutorial.

Richard Socher is Chief Scientist at Salesforce. He leads the company’s research efforts and works on bringing state of the art artificial intelligence solutions to Salesforce.

Prior to Salesforce, Socher was the CEO and founder of MetaMind, a startup 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.

Socher 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 in 2012 and a 2013 “Magic Grant” from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.

Socher obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng and 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. In this talk, Salesforce Chief Scientist will explore how Salesforce is doing the heavy lifting to deliver seamless and scalable AI to Salesforce 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.

Joseph is the Manager of Deep Learning Product Management at AWS. His detailed background can be found at https://www.linkedin.com/in/jspisak

Presentations

Distributed Deep Learning on AWS using MXNet Tutorial

During this workshop, members of the Amazon AI team will provide a short background on Deep Learning focusing on relevant application domains and an introduction to using the powerful and scalable Deep Learning framework, MXNet. At the end of this tutorial you’ll gain hands on experience targeting a variety of applications and be able quickly spin up AWS GPU clusters to train at record speeds!

Rupert Steffner is the founder of WUNDER.AI – an E-commerce AI start-up that is giving customers a joy ride to the products they love. Before that Rupert has been Chief Platform Architect of Otto Group’s new Business Intelligence Platform BRAIN and Head of BI at Groupon EMEA&APAC. He worked for several European and US companies as Business Intelligence Leader (e-commerce, retail, finance, telco). Rupert has over 25 years of experience in designing and implementing highly sophisticated technical and business solutions with a focus on customer centric marketing. He holds a Master of Business Administration of the WU Vienna and was Head of Marketing Department at the University of Applied Sciences, Salzburg.

Presentations

Deep Shopping bots – Building machines that think and sell like humans Session

According to market research 70% of consumers do NOT feel that online offers resonate with their personal interests and needs. With cognitive AI there’s a way to 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. That includes Machine Learning as well as Deep Learning technologies. Previously Barbara worked as SVP for SAP Labs LLC, as a founding member of SAP HANA and head of SAP’s EIM products and the SAP Healthcare Platform.

Presentations

Scalable Deep Learning with Microsoft Cognitive Toolkit Tutorial

We will be introducing the Cognitive Toolkit from Microsoft, which is native on both Windows and Linux, and offers flexible symbolic graph, friendly Python API, almost linear scalability across multi-GPU and multiple machines. We strongly believe the audiences will benefit learning and using our toolkit to speed up their experiments and find better deep learning algorithms.

Matt Taylor is the Open Source Community Flag-Bearer for the Numenta Platform for Intelligent Computing. He’s been working with and on open source projects for years, and he spends most of his time managing, encouraging and interacting with the NuPIC OS community. Originally from a farming community in rural Missouri, Matt has been living in California since 2010, and is finding it hard to leave.

Presentations

The Biological Path Towards 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. This talk will describe and visualize core HTM concepts like sparse distributed representations, spatial pooling and temporal memory.

Presentations

Building machines that learn and think like people Keynote

Building 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 founder and CTO at StreamBase, a CEP company that merged with TIBCO in 2013, as well as 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 collecting & analyzing structured data. New AI technologies are beginning to transform this process. This talk will focus on AI that (i) guides business analysts to ask statistically sensible questions and (ii) lets junior data scientists answer questions in minutes that previously took hours for trained statisticians.

Probabilistic Programming Tutorial

Probabilistic inference is a widely-used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain and/or incomplete. It has become central to multiple fields, from big data analytics to robotics and AI. This class will survey the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to non-experts.

Anusua Trivedi is a data scientist on Microsoft’s Advanced Data Science & Strategic Initiatives team, where she works on developing advanced predictive analytics and deep learning models. Prior to joining Microsoft, 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

Medical-Imaging-BOT in Azure Session

In this talk, we propose to apply AI in the form of deep vision and advanced cognitive analytics to the analysis of medical imaging scans. This work talks about application of deep learning models as AI Bots, thus improving the development, training and deployment time in a real-world scenario.

Scalable Deep Learning with Microsoft Cognitive Toolkit Tutorial

We will be introducing the Cognitive Toolkit from Microsoft, which is native on both Windows and Linux, and offers flexible symbolic graph, friendly Python API, almost linear scalability across multi-GPU and multiple machines. We strongly believe the audiences will benefit learning and using our toolkit to speed up their experiments and find better deep learning algorithms.

Ferhan Ture is a member of the Comcast Labs DC research group and 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.

Recent talks:

  • Ask Your TV: Real-Time Question Answering with Recurrent Neural Networks. Ferhan Ture and Oliver Jojic, SIGIR 2016, Pisa, Italy
  • Learning to Translate for Multilingual Question Answering. Ferhan Ture and Elizabeth Boschee, 2016 Conference on Empirical Methods in Natural Language Processing, Austin, TX
  • Structured TV Shows — “You have been Chopped”. Ferhan Ture, Jonghyun Choi, Hongcheng Wang and Vamsi K. Potluru, ICML workshop 2016.

Presentations

How AI powers the Comcast X1 Voice Interface Session

AI plays an essential role in creating the Comcast X1 entertainment experience and how millions of our customers access their content on the TV. In this talk we will explain how AI enables us to understand what you are looking for when you talk to the X1 voice remote and how we 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, with a focus 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

Scaling machine learning with TensorFlow Tutorial

TensorFlow is an increasingly popular open source Machine Intelligence library that is especially well-suited for deep learning. Google Cloud Machine Learning (CloudML) lets you do distributed training and serving at scale. In this workshop, we'll give an introduction to TensorFlow concepts and walk through how to use CloudML to do distributed training and scalable serving of your models.

Before founding Gamalon Machine Intelligence, Ben Vigoda was technical co-founder and CEO of Lyric Semiconductor, a startup that created the first integrated circuits and processor architectures for statistical machine learning and signal processing. 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. Lyric was successfully acquired by Analog Devices, and Lyric’s products and technology are being deployed in leading smart phones and consumer electronics, medical devices, wireless base stations, and automobiles.

Ben completed his PhD at MIT developing circuits for implementing machine learning algorithms natively in hardware. He has won entrepreneurship competitions at MIT and Harvard, 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. He has authored over 120 patents and academic publications. He recently served on the DARPA Information Science and Technology (ISAT) steering committee.

Ben also co-founded Design That Matters, a not-for-profit that for the past decade has helped solve engineering and design problems in under-served 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.

Presentations

Eliminating the Human Cost of Training AI – Bayesian Program Synthesis Session

This session will focus on how Bayesian Program Synthesis (BPS) will provide significant advantages over deep learning technologies and remove some of the biggest obstacles preventing AI from being adopted in the enterprise.

Yiheng Wang is a software development engineer on the Big Data Technology team at Intel who works in the area of big data analytics. He 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

This tutorial will introduce BigDL, a distributed deep learning library on Apache Spark. Based on BigDL, users can easily integrate most advanced deep learning algorithms(CNN, RNN, etc.) into popular big data platform in industry. We will show how to develop with BigDL, and introduce some use cases in practice.

Galiya is currently working at Microsoft as a Data Solution Architect, where she works closely with the enterprise customers on their journey of adoption of Microsoft Azure Data technologies spanning from Big Data workloads to Machine Learning and Chatbots.

Presentations

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

In this 3 hour tutorial we will teach you how to create a quantitative graph model from qualitative written information. We will then go on to show you how to put a conversational front-end onto this using the Microsoft Bot Framework. A step-by-step lab will be provided for you to work through this tutorial.

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. Thomas holds a PhD from Brown University. A recognized international speaker, he has given talks at various conferences and meetups across the US, Europe, and Asia.

Presentations

Deep Learning in Quantitative Finance Session

In this talk, I will give an introduction to deep learning, its recent developments, and how they relate to algorithmic trading.

Xiaofan Xu is a research Engineer specializing in artificial intelligence and robotics. Xiaofan joined Movidius in 2015 and has worked in the CTO office 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. The result of this is a “data wall” facing smaller companies. Thankfully, there are some innovative solutions around the data wall through synthetic datasets. This talk will outline the problem of the data wall, and how it can be addressed through synthetic datasets.

Reza Bosagh Zadeh is on the faculty at Stanford, where he teaches Distributed Algorithms and Optimization and Discrete Mathematics and Algorithms, and is the founder and CEO of Matroid. His work focuses on machine learning, distributed computing, and discrete applied mathematics. 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 and his work has been incorporated into industrial and academic cluster computing environments. Reza serves on the technical advisory board of Microsoft and Databricks. His awards include a KDD Best Paper award and the Gene Golub Outstanding Thesis award. Reza received his PhD in computational mathematics from Stanford University under the supervision of Gunnar Carlsson.

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. This talk describes our pipeline using TensorFlow, Kubernetes, and Amazon Web Services.

Matthew Zeiler is an artificial intelligence expert. His groundbreaking research in convolutional neural networks and visual recognition, alongside renowned machine learning pioneers Geoff Hinton and Yann LeCun, has propelled the image recognition industry from theory to real-world practice. As the founder of Clarifai, Matt 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. Matt holds a PhD in machine learning from NYU. You can reach him at @MattZeiler.

Presentations

Machine learning technical debt: Risks, hidden costs, and how to escape the black hole of ML-technical debt Session

AI-powered machine learning technologies bring a higher and more complex level of technical debt to applications especially if the AI and machine learning system has been built from the ground up. In this talk, Clarifai's CEO and renowned machine learning expert Matt Zeiler discusses best practices for companies hoping to build AI into their businesses.