Schedule

08:15

REGISTRATION & LIGHT BREAKFAST

09:00

WELCOME

Alex Brokaw

Alex Brokaw, Freelance

Compere

Alex Brokaw is a writer and consultant based in New York City. He spent many years as a finance and technology reporter, most recently at The Verge. He now specializes in writing and creative services for artificial intelligence and robotics companies. Alex is passionate about helping these companies explain their technologies to customers and larger audiences, whether that’s through technical writing, marketing copy, or overall brand and marketing strategy.

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MACHINE LEARNING METHODS

09:15

Nick Pentreath

Nick Pentreath, IBM

End-to-end Machine Intelligence

End-to-end Machine Intelligence

The common perception of machine learning is that it starts with data and ends with a model. In real-world production systems, the traditional data science and machine learning workflow of data preparation, feature engineering and model selection, while important, is only one aspect. True machine intelligence requires continuous, real-time learning by closing the feedback loop. Equally important for enterprise-grade and mission-critical systems are the typical elements of software systems, such as scalability, fault tolerance and security.

Nick is a Principal Engineer at IBM’s Spark Technology Center. He is a member of the Apache Spark PMC and author of Machine Learning with Spark. Previously, he co-founded Graphflow, a startup focused on recommendations and customer intelligence. He has worked at Goldman Sachs, Cognitive Match, and led the Data Science team at Mxit, Africa’s largest social network. He is passionate about combining commercial focus with machine learning and cutting-edge technology to build intelligent systems that learn from data to add business value.

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09:40

Suzanne Gildert

Suzanne Gildert, Kindred

On the Path to Non-Biological Sentience

On the Path to Non-Biological Sentience

At Kindred our mission is to create human-like intelligence in machines, and our core hypothesis is that intelligence requires a body. We build robotic bodies of many different form factors and imbue them with new types of AI minds inspired by biological creatures’ brains. We pre-train these algorithms by demonstration, using large amounts of data from human operators, and craft reward functions such that the entities can subsequently become fully autonomous agents. Breakthroughs in robotics, increases in computational power, and advancements in machine learning algorithms are finally enabling us to train AI minds to have a more “general” form of intelligence. This presentation will describe our technology and address some of the questions that might arise as we begin to share our world with increasingly smarter robots.

Suzanne is co-founder and CSO of Kindred. She oversees the design and engineering of the company's human-like robots and is responsible for the development of cognitive architectures that allow these robots to learn about themselves and their environments. Before founding Kindred, Suzanne worked as a physicist at D-Wave, designing and building superconducting quantum processors, and as a researcher in quantum artificial intelligence software applications. Suzanne likes science outreach, retro tech art, coffee, cats, electronic music and extreme lifelogging. She is a published author of a book of art and poetry. She is passionate about robots and their role as a new form of symbiotic life in our society. Suzanne received her Ph.D. in experimental physics from the University of Birmingham (UK) in 2008, specializing in quantum device physics, microfabrication techniques, and low-temperature measurements of novel superconducting circuits.

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10:00

Mihajlo Grbovic

Mihajlo Grbovic, Airbnb

Search Ranking And Personalization at Airbnb

Search Ranking And Personalization at Airbnb

Search ranking is a fundamental problem of crucial interest to major Internet companies, including web search engines, content publishing websites and marketplaces. However, despite sharing some common characteristics a one-size-fits-all solution does not exist in this space. Given a large difference in content that needs to be ranked and the parties affected by ranking, each search ranking problem is somewhat specific. Correspondingly, search ranking at Airbnb is quite unique, being a two-sided marketplace in which one needs to optimize for host and guest preferences, in a world where a user rarely consumes the same item twice and one listing can accept only one guest for a certain set of dates. In this talk, I will discuss challenges we have encountered and Machine Learning solutions we have developed for listing ranking at Airbnb. Specifically, the listing ranking problem boils down to prioritizing listings that are appealing to the guest but at the same time demoting listings tha t would likely reject the guest, which is not easily solvable using basic matrix completion or a straightforward linear model. I will shed the light on how we jointly optimize the two objectives by leveraging listing quality, location relevance, reviews, host response time as well as guest and host preferences and past booking history. Finally, we will talk about our recent work on using neural network models to train listing, query and user embeddings for purposes of enhancing search relevance and personalization, two core concepts in any modern search.

Mihajlo Grbovic, Ph.D. is a Senior Machine Learning Scientist on the Search Ranking Team at Airbnb. Prior to that, he was a Senior Research Manager at Yahoo Labs working on Advertising Sciences. He has more than 10 years of technical experience in applied Machine Learning, acting as a Science Lead in a portfolio of advertising technology projects on Monetization of Tumblr, Yahoo Email and Yahoo Search. Some of his biggest accomplishments include building a large scale Interest and Gender Targeting Pipeline for Tumblr, training Email Classifiers used in Yahoo Mail Smart Views that millions of people interact with every day, and introducing the next generation query-ad matching algorithm to Yahoo Sponsored Search. Dr. Grbovic published more than 40 peer-reviewed publications at top Machine Learning and Web Science Conferences and co-authored more than 10 pending patents. His work was featured in Wall Street Journal, Scientific American, MIT Technology Review, Popular Science and Market Watch.

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10:20

Prabhat

Prabhat, Lawrence Berkeley National Lab

Deep Learning for Climate Science

Deep Learning for Climate Science

Climate change is one of the most important problems facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to understand the evolution of the climate system subject to various CO2 emission scenarios. Unfortunately, large scale climate simulations produce 100TB-sized spatio-temporal, multi-variate datasets, making it difficult to conduct sophisticated analytics. In this talk, I will present our results in applying Deep Learning for semi-supervised learning of extreme weather patterns. I will conclude with a summary of current applications of Deep Learning for a broad set of scientific use cases, and open research challenges for the future.

Prabhat leads the Data and Analytics Services team at NERSC. His current research interests applied statistics, machine learning, and high performance computing. He has worked on topics in scientific data management, parallel I/O, scientific visualization, computer graphics and computer vision in the past. Prabhat received an ScM in Computer Science from Brown University (2001) and a B.Tech in Computer Science and Engineering from IIT-Delhi (1999). He is currently pursuing a PhD in the Earth and Planetary Sciences Department at U.C. Berkeley.

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10:40

COFFEE

TRAINING NEURAL NETWORKS

11:20

Hanie Sedghi

Hanie Sedghi, Allen Institute for Artificial Intelligence

Beating Perils of Non-convexity: Guaranteed Training of Neural Networks using Tensor Methods

Beating Perils of Non-convexity: Guaranteed Training of Neural Networks using Tensor Methods

Neural networks have revolutionized performance across multiple domains such as computer vision and speech recognition. However, training a neural network is a highly non-convex problem and the conventional stochastic gradient descent can get stuck in spurious local optima. We propose a computationally efficient method for training neural networks that also has guaranteed risk bounds. It is based on tensor decomposition which is guaranteed to converge to the globally optimal solution under mild conditions. We explain how this framework can be leveraged to train feedforward and recurrent neural networks.

Hanie Sedghi is a Research Scientist at Allen Institute for Artificial Intelligence (AI2). Her research interests include large-scale machine learning, high-dimensional statistics and probabilistic models. More recently, she has been working on inference and learning in latent variable models. She has received her Ph.D. from University of Southern California with a minor in Mathematics in 2015. She was also a visiting researcher at University of California, Irvine working with professor Anandkumar during her Ph.D. She received her B.Sc. and M.Sc. degree from Sharif University of Technology, Tehran, Iran.

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11:45

Melody Guan

Melody Guan, Google Brain

Who Said What: Modeling Individual Labelers Improves Classification

Who Said What: Modeling individual Labelers Improves Classification

Data are often labeled by many different experts, with each expert labeling a small fraction of the data and each sample receiving multiple labels. When experts disagree, the standard approaches are to treat the majority opinion as the truth or to model the truth as a distribution, but these do not make any use of potentially valuable information about which expert produced which label. We propose modeling the experts individually and then learning averaging weights for combining them, possibly in sample-specific ways. This allows us to give more weight to more reliable experts and take advantage of the unique strengths of individual experts at classifying certain types of data. We show that our approach performs better than three competing methods in computer-aided diagnosis of diabetic retinopathy.

Melody is deep learning resident at Google Brain. Previously she interned as a trader at D. E. Shaw and conducted stem cell research in Doug Melton's lab. She received an M.A. in Statistics and B.A. in Chemistry and Physics from Harvard University, where she graduated with highest ranking. In her youth she medalled at the International Physics, Chemistry, and Biology Olympiads and was invited to the Canadian International Math Olympiad training camp. Melody has published in The Huffington Post, Harvard Political Review, and The Harvard Crimson. She is constantly fawning over music, reading rationality and psychoanalysis blogs, and enjoying long random walks along undirected paths and other outdoor activities.

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12:10

Abi Komma

Abi Komma, Uber

An Application of Gradient Boosted Decision Trees (GBDT) for Query Runtime Prediction

An Application of Gradient Boosted Decision Trees (GBDT) for Query Runtime Prediction

In 2016, Uber's went through an exponential growth. To support such an unprecedented growth in business, Uber had to rapidly scale out its infrastructure. The rapid growth in the core business and the number of products offered by Uber coupled with an increase in the number of data scientists and analysts meant an increasing load on our data systems. Specifically, having a stable and an efficient data warehouse was a fundamental requirement to sustain the pace at which Uber was growing and operating in the marketplace. The starting point of every data-driven decision at Uber typically begins with fetching data using our internal web-based tool called Querybuilder. More than 150K SQL queries are issued every week on our data warehouse through Querybuilder. To manage this load efficiently, it is very important to be able to accurately predict the runtime of the query before it hits the warehouse. In this study, we share our approach on how we used a Gradient Boosted Decision Tree model for predicting the runtime class of a query (short query or long query). Further, the predicted label was used to route the incoming query into an appropriate queue for execution in real-time. This approach of routing the queries based on the predictions from the classifier increased the overall efficiency of our warehouse. We observed a significant decrease in the average waiting for a query execution and increased the throughput of our system under peak load.

Abi Komma is a Senior Data Scientist in the Applied Machine Learning group at Uber. He focuses on applying regression, classification, and survival analysis techniques to build predictive models for various business problems at Uber. He holds a Master's degree in Transportation Engineering from University of Florida and a Bachelor’s degree in Civil Engineering from Indian Institute of Technology (IIT) Madras.

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12:30

LUNCH

NATURAL LANGUAGE PROCESSING & RECOMMENDATIONS

13:40

Zornitsa Kozareva

Zornitsa Kozareva, Amazon

What Do Users Want? Using Semantics to Predict User Intents at Scale

What Do Users Want? Using Semantics to Predict User Intents at Scale

Over the years search paradigm has shifted from document retrieval to deeper user intent understanding. Today’s users are no longer satisfied with seeing a list of relevant documents, instead they want to complete tasks and take actions on them. The question I will address in this talk is how to build an automated user intent understanding system, where given a query like "Mia" the user sees relevant and personalized recommendations such as "buy latest album of the singer”, or "check-in flight to Miami". I will begin by introducing the task and the main challenges with semantic understanding. Then, I will describe categorization and structured prediction algorithms for entity detection and intent prediction. Finally, I will highlight results and findings for user intent prediction from shopping, movies, restaurant and sport domains.

Dr. Zornitsa Kozareva is a Manager at Amazon leading the Natural Language Processing group. Before that she was a Senior Manager at Yahoo! leading the Query Processing group that powers Mobile Search and Advertisement. From 2009 to 2014, Dr. Kozareva was a Research Assistant Professor at the University of Southern California and a Research Scientist at the Information Sciences Institute. Her interests lie in Web-based knowledge acquisition, semantics, ontology population, multilingual information extraction and sentiment analysis. Dr. Kozareva regularly serves as Area Chair and PC of top tier NLP conferences. She has organized four SemEval scientific challenges and has published over 60 research papers. Dr. Kozareva is a recipient of the John Atanasoff Award given by the President of Republic of Bulgaria in 2016 for her contributions and impact in science, education and industry; the Yahoo! Labs Excellence Award in 2014 and the RANLP Young Scientist Award in 2011.

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14:05

Hossein Taghavi

Hossein Taghavi, Netflix

Balancing Discovery and Continuation in Recommendations

Balancing Discovery & Continuation in Recommendations

When a member logs on to Netflix, she may be in one or a combination of different watching modes: discovering a new content, continuing to watch a partially-watched movie or a TV show, playing one of the contents she had put in her play list during an earlier session, etc. If, for example, we can reasonably predict when a member is more likely to be in the continuation mode, and which videos she is more likely to resume, it makes sense to place those videos in more prominent places of the home page. In this talk we focus on understanding the discovery vs. continuation behavior and explain how we have used machine learning to improve the member experience by learning a personalized balance between those two modes.

Hossein Taghavi received the B.Sc. degree from Sharif University of Technology, Tehran, Iran, in 2003, and the Ph.D. degree from the University of California at San Diego, La Jolla, in 2008, in electrical engineering. From 2008 to 2011, he was with Qualcomm, Inc. in San Diego, CA, conducting research and development on algorithms and systems for wireless networks. From 2011 to 2013, he was with Opera Solutions, LLC., in San Diego, CA, applying machine learning and data analytics to a variety of business applications. Since 2013, he has been with Netflix, Inc., contributing to algorithms and software driving the personalization and recommendation systems for Netflix members. His current interests include machine learning, recommendation systems, and distributed computing.

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COMPUTER VISION

14:25

Robinson Piramuthu

Robinson Piramuthu, NPD, eBay Inc.

Elements of Machine Intelligence for Visual eCommerce

Elements of Machine Intelligence for Visual eCommerce

Recent developments in deep learning has helped computer vision to harness the information in large image data collections and be viable for practical applications. Although computer vision has huge potential for commerce, only a small portion of it has been materialized. In this talk, we will present various elementary components that enable machine intelligence using computer vision. This includes image quality, visual search, segmentation, pose estimation, iris/face recognition, coarse and fine grained prediction.

Robinson Piramuthu is currently head of “AI - Computer Vision” at the New Product Development organization at eBay Inc. He has over 20 years of experience in computer vision. Robinson joined eBay Research Labs in 2011 and was later head of the computer vision team, which specialized in computer vision research for visual commerce. Robinson has technical publications at top conferences such as CVPR, ICCV, KDD, WSDM, WACV, ICIP. He co-organized the workshop on Large Scale Visual Commerce at ICCV ’13 Sydney and at CVPR ’15. He received his PhD from University of Michigan Ann Arbor in 2000.

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14:45

Junhyuk Oh

Junhyuk Oh, University of Michigan

Control of Memory, Active Perception, & Action in 3D World

Control of Memory, Active Perception, and Action in 3D world

In this work, we introduce a set of reinforcement learning tasks in Minecraft. We use these tasks to systematically compare existing deep reinforcement learning (DRL) architectures with our new memory-based architectures. These tasks are designed to emphasize issues that pose challenges including partial observability, delayed rewards, high-dimensional visual observations, and the need to use active perception so as to perform well in the tasks. We evaluate the generalization performance of the architectures on environments not used during training. The experimental results show that our new architectures generalize to unseen environments better than existing DRL architectures.

Junhyuk is a 2rd-year PhD student in Computer Science and Engineering at University of Michigan. He is working on the intersection between deep learning and reinforcement learning under the supervision of Professor Honglak Lee and Professor Satinder Singh.

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15:05

COFFEE

THE COMPETITIVE ADVANTAGE OF MACHINE INTELLIGENCE

15:45

PANEL: Does AI Provide A Competitive Advantage For Businesses?

Nick Gaylord

Nick Gaylord, CrowdFlower

Panelist

Nick is a Senior Data Scientist at CrowdFlower, where he works primarily to help build their new machine learning offering, CrowdFlower AI. Prior to CrowdFlower, he worked as a data scientist at SF text analytics startup Idibon. He has a Ph.D. from the University of Texas at Austin, where his research focused on human language comprehension and the construction of datasets for NLP applications. In his spare time he fixes bikes and collaborates on work applying cognitive science principles to the public health domain.

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Kevin Hightower

Kevin Hightower, AirMap

Panelist

Kevin is responsible for the global capabilities of the AirMap product portfolio. His responsibilities include defining strategic and tactical capabilities roadmaps, managing the product team, and coordinating with the various development teams. Kevin joined AirMap after 15 years with Lockheed Martin working on Air Traffic Control systems as a Systems Architect and working as the Aviation Chief Technologist for all global aviation projects.

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Chris Slowe

Chris Slowe, Reddit

Panelist

As the founding engineer of Reddit, Chris Slowe helped build it, sell it, and has recently come back with the other co-founders to fix it. Though a software engineer by vocation, his first attempt at a career started with his finishing a PhD in experimental physics where he learned about the importance of modeling, critical thinking, statistics, and (honestly) welding. He uses the first three skills regularly. Chris currently acts as Director of Foundation Engineering at Reddit, Inc.

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Nick Pentreath

Nick Pentreath, IBM

Moderator

This discussion will center around best practices, key challenges and “war stories” for turning data science and machine learning projects into production systems. Some key questions that we'll discuss include:

How to go from exploratory data science and “proof of concept” projects to production systems with all their requirements of scalability, fault tolerance, performance monitoring & feedback?

How to bridge the gaps between the teams involved in each aspect of the Data Product Continuum, and the differing requirements of each phase?

What are some of the best practices in productionizing data science and machine learning workflows?

What role do standards play (e.g. data interchange formats, model evaluation standards such as PMML and PFA)?

What is the state of systems catering to this need, whether open-source or proprietary?

What are the important tools and software systems?

What are the important processes and non-software systems?

What are practitioners currently using in their production systems? How have they solved these problems and what are the “pro tips” and “lessons learnt” that can be shared?

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16:20

Erik Schmidt

Erik Schmidt, Pandora

Music Discovery at Pandora

Music Discovery at Pandora

Finding the music of the moment can often be a challenging problem, even for humans with well-versed musical tastes. These challenges further explode into a myriad of complexities when attempting to construct algorithmic approaches for automatic playlist generation. A variety of factors play a role in influencing a listener’s perception of what music is appropriate on a given seed (e.g., musicological, social, geographical, generational), and these factors vary across different contexts and listeners. The talk will present an overview of recommendation at Pandora, followed by a deep dive into the challenges of recommending content in the long tail.

Erik Schmidt is a Senior Scientist at Pandora. Most recently, he led the development of Thumbprint Radio, a hyper-personalized product that has attracted over 20 million listeners to date. He has contributed numerous recommendation strategies to the Pandora ecosystem spanning playlists, station discovery, and concerts. Before joining Pandora, Schmidt was a postdoctoral researcher in the Music and Entertainment Technology Laboratory (MET-lab) at Drexel University in Philadelphia. His general research interests lie in the areas of machine learning, recommendation systems, and digital signal processing.

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16:40

Amy Gershkoff

Amy Gershkoff, Ancestry

Is Your Organization Ready for ML?

Is Your Organization Ready for ML?

As data science has become increasingly popular, many organizations rush to hire ML experts without laying the proper foundation to ensure their success, including creating proper database architecture, building out essential data science technology, establishing data governance, and instilling data-driven decision-making throughout the organization. Absent these elements, many ML experts join companies excited to deploy their data science expertise only to end up marred in data cleaning or lobbying for tech resources. In this presentation, I discuss how organizations can prepare their organization for success, as well as how candidates can diagnose whether the organization is truly ready for ML.

Dr. Gershkoff is Chief Data Officer for Ancestry, which specializes in genealogy and consumer genomics. Previously, she was Chief Data Officer at Zynga. During her career, she has led the Customer Analytics & Insights team at eBay, served as Chief Data Scientist at WPP, and was Head of Media Planning at Obama for America, where she designed the campaign’s advertising and analytics strategy. Gershkoff was named one of America’s “40 Under 40” leading entrepreneurs, one of the Top 50 Women to Watch in Tech, and one of San Francisco's Most Influential Women in Business. She holds a Ph.D. from Princeton University.

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17:00

CONVERSATION & DRINKS

Download the PDF Agenda Below

08:15

REGISTRATION & LIGHT BREAKFAST

09:00

WELCOME

Alex Brokaw

Alex Brokaw, Freelance

Compere

Alex Brokaw is a writer and consultant based in New York City. He spent many years as a finance and technology reporter, most recently at The Verge. He now specializes in writing and creative services for artificial intelligence and robotics companies. Alex is passionate about helping these companies explain their technologies to customers and larger audiences, whether that’s through technical writing, marketing copy, or overall brand and marketing strategy.

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STARTUP SESSION

09:15

Jeremy Stanley

Jeremy Stanley, Instacart

How Instacart is Using Deep Learning to Create the Most Efficient Shoppers Ever

How Instacart is Using Deep Learning to Create the Most Efficient Shoppers Ever

Instacart has revolutionized grocery shopping by bringing groceries to your door in a little as an hour. The crux of the company is their shoppers, who shop in brick and mortar stores and bring the food to customers thousands of times per hour. Making these shoppers as efficient as possible is critical to the business. Hear how Instacart is applying deep learning to the shopping list to improve shopper efficiency, predicting the sequence that shoppers pick items in specific store locations - in some cases saving significant time in-store. Jeremy will discuss the data collection, mobile technology and machine learning approaches Instacart is applying to enable on-demand grocery delivery.

Jeremy is currently the VP of data science at Instacart, conquering the world one carrot at a time. Jeremy leads a team of data scientists who are integrated into product teams to drive growth and profitability through logistics, catalog, search, consumer, shopper, and partner applications. Previously, Jeremy was chief data scientist and EVP of engineering at Sailthru, CTO of Collective, and founded and led the Global Markets Analytics Group at Ernst & Young (EY), which analyzed the firm’s markets, financial and personnel data to inform executive decision making. Jeremy holds an MBA from Columbia.

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09:35

Stuart Feffer

Stuart Feffer, Reality AI

Using AI with Sensor & Signal Data

Reality AI makes advanced AI-enabled tools for the practicing engineer working with sensors and signals. Solutions available for predictive maintenance / machine health, wearables, surface inspections (including by UAV), automotive and robotics. Work with accelerometry, vibration, sound, current/voltage, imagery, LiDAR, multi-spectral and hyperspectral remote sensing.

Stuart loves to make products and connect customers. His last company was a technology and operations startup that was acquired by Wells Fargo, and he previously served as an advisor to SR2 Group. He has degrees from the University of Chicago and the University of California, Berkeley.

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09:55

Cory Kidd

Cory Kidd, Catalia Health

Automatically Generating Patient Adherence Conversations

Automatically Generating Patient Adherence Conversations

Catalia Health has created an interactive robot for helping patients stay on therapy longer. Our challenges lie at the intersection of the regulatory and technical and this talk will discuss how we generate conversations and what the implications of using this product in the real world are.

Dr. Cory Kidd is the founder and CEO of Catalia Health, a patient care management company. The company develops a hardware and software platform that uses a combination of psychology and artificial intelligence to engage patients through interactive conversations. These conversations happen through mobile, web, and interactive robotic interfaces; together these interfaces create a relationship that can reach patients at any time they need support. The data reported back through the system gives Catalia Health’s customers valuable information to understand the daily activities and needs of their patients. Dr. Kidd is a serial entrepreneur who has been working in healthcare technology for nearly two decades. He received his M.S. and Ph.D. at the MIT Media Lab in human-robot interaction and his B.S. in Computer Science at the Georgia Institute of Technology.

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10:15

Modar Alaoui

Modar Alaoui, Eyeris

Vision AI for People Places & Things (PPT), & The Promise of Ambient Intelligence

Driver Monitoring For Connected Semi-autonomous Vehicles & The Future Of Automotive HMI

This session will cover an artificially intelligent driver attention, cognitive awareness and emotion distraction monitoring system. We reveal how the technology reads facial micro expressions in real time to authenticate drivers and distinctly detect their seven universal emotions, gender, age group, eye tracking, 3D head pose and gaze estimation. During the second half of this session, we will cover a number of driver derivative metrics that trigger the activation of various reactive support systems, necessary to saving lives and improving driving behavior through better Human Machine Interfaces. This session will end with a highly rated 1-minute live demo on stage!

Modar is a serial entrepreneur and expert in AI-based vision software development. He is currently founder and CEO at Eyeris, developer of a Deep Learning-based emotion recognition software, EmoVu, that reads facial micro-expressions. Eyeris uses Convolutional Neural Networks (CNN's) as a Deep Learning architecture to train and deploy its algorithm in to a number of today’s commercial applications. Modar combines a decade of experience between Human Machine Interaction (HMI) and Audience Behavioral Measurement. He is a frequent keynoter on “Ambient Intelligence”, a winner of several technology and innovation awards and has been featured in many major publications for his work.

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10:35

COFFEE

INVESTING IN MACHINE INTELLIGENCE

11:15

PANEL: Challenges & Opportunities of Investing in AI

Rudina Seseri

Rudina Seseri , Glasswing Ventures

Panelist

Rudina Seseri is founder and managing partner at Glasswing Ventures, an Entrepreneur-In-Residence at Harvard Business School and an Executive-In-Residence for Harvard University’s Innovation-Lab. With over 14 years of investing and transactional experience, Rudina has led technology investments and acquisitions in startup companies in the fields of robotics, Internet of Things (IoT), SaaS marketing technologies and digital media. Most recently, Rudina co-founded and launched Glasswing Ventures, an early-stage firm focused on companies with enabling Artificial Intelligence technologies that specifically address the connected world and the security of this ecosystem.

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Shelley Zhuang

Shelley Zhuang, Eleven Two Capital

Panelist

Shelley has over ten years of experience in technology as a software engineer, research scientist, business executive, and venture capitalist. Shelley was formerly EVP of Business Development at Ecoplast Technologies, where she oversaw business development & sales efforts in North America. Previously, Shelley was a Principal at DFJ, where she was actively involved in a number of investments including Ecoplast Technologies, FeedBurner (acquired by Google for $100M), Flurry (acquired by Yahoo for $240M), PPLive (acquired by Suning for $420M), TicketsNow (acquired by Ticketmaster for $265M), Xfire (acquired by Viacom for $102M), YeePay. Shelley is a techie at heart and holds a BS in Computer Science and Computer Engineering from the University of Missouri, and a PhD in Computer Science from the University of California, Berkeley. She is currently an advisor at Skydeck and G7 Fellow at Creative Destruction Lab, and serves on Enigma 2016's program committee.

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Veronica Osinski

Veronica Osinski, Trifecta Capital

Panelist

Veronica Osinski is managing partner of Trifecta Capital a Silicon Valley-based, seed stage investment fund. Veronica previously apprenticed in venture at S-Cubed Capital under Mark Steven's, former Managing Partner at Sequoia Capital. While at S-Cubed, Veronica sourced deals in Secret, Second Spectrum and Embark (acq. by APPL). She served as a board observer for Elemental Technologies (acq. AMZN), Zapproved, Deal Decor and Second Spectrum. Previous to venture, she launched a dairy farm while living in rural South Africa. She graduated Summa Cum Laude with a degree in finance from the University of Southern California at the age of 20. While in college she conducted research on the financial crash and executive compensation.

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Saman Farid

Saman Farid, Comet Labs

Moderator

Saman is a passionate supporter of early- stage startups. He has built three companies in the areas of e-commerce, IP television, and logistics management, and faced the challenges of building, focusing, and scaling a company. His latest venture, Comet Labs, is both a venture capital firm and an experimental lab to support early-stage intelligent machines companies. Comet Labs invests in and connects AI and robotics startups with mentors and corporate partners to share resources and drive innovation forward. Saman has spent more than 15 years in China and has held positions at Honeywell, Verizon, Deloitte Consulting, and Microsoft in roles ranging from R&D to operations optimization. He received his Bachelor's degree in Control Systems Engineering from the Cooper Union and his MBA from Tsinghua and MIT.

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REINFORCEMENT LEARNING & NLP

11:50

Ryan Lowe

Ryan Lowe, OpenAI

Learning to Communicate

Recent advances in machine learning applied to large text corpora have enabled strong results in natural language processing by capturing statistical patterns between words. While such approaches are useful, they are arguably insufficient for building general-purpose agents that can interact with humans, as the words lack grounding in an external environment. We present new research from OpenAI that investigates the emergence of a simple grounded language. Using methods from deep reinforcement learning, we show that compositional language can emerge when agents cooperate to solve various tasks in an environment, such as moving to or pushing objects. We also detail ongoing research focused on teaching these agents to speak simple forms of English.

Ryan is a Ph.D. student in the Reasoning & Learning Lab at McGill University, supervised by Joelle Pineau. He is currently interning at OpenAI, where he is working on the emergence of language in multi-agent systems. He previously investigated deep learning methods for dialogue systems, deriving the popular Ubuntu Dialogue Corpus and establishing the poor performance of automatic dialogue evaluation methods. When in Montreal, he co-organizes the Montreal AI Ethics Group, and is an editor of the AI series for Graphite Publications. Before McGill, he spent time at the Institute for Quantum Computing, the Max Planck Institute, and the National Research Council.

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12:15

LUNCH

13:20

Minjoon Seo

Minjoon Seo, University of Washington

Automated Question Answering With Deep Learning & Neural Attention

Automated Question Answering With Deep Learning & Neural Attention

Automated question answering (QA) has been one of the most important tasks in artificial intelligence community for several decades. While creating traditional QA systems required significant human efforts, the advancement of deep learning enabled us to train QA systems with minimal human intervention at a large scale. In this talk, I will present our recent work that achieved the state-of-the-art results on several QA tasks, showing that a machine can learn to read a long text, reason over multiple facts, and engage in a conversation with a human. Along with the presentation of other recent works, my talk will also overview the trend in and the future direction of QA research.

Minjoon is a 4th year PhD student in computer science at the University of Washington. His research focuses on natural language understanding and question answering. He is currently advised by Hannaneh Hajishirzi and Ali Farhadi, and he was also formerly advised by Oren Etzioni who is now the CEO of the Allen Institute for Artificial Intelligence. His work on designing a system for answering SAT geometry questions was featured in New York Times, Washington Post, and Geekwire. His recent work on deep neural networks for question answering on Wikipedia articles (SQuAD) achieved the first place in the dataset’s leaderboard.

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13:40

Stacey Svetlichnaya

Stacey Svetlichnaya, Flickr

Quantifying Visual Aesthetics on Flickr

Quantifying Visual Aesthetics on Flickr

What makes an image beautiful? More pragmatically, can an algorithm distinguish high-quality photographs from casual snapshots to improve search results, recommendations, and user engagement at scale? We leverage social interaction with Flickr photos to generate a massive dataset for computational aesthetics and train machine learning models to predict the likelihood images being of high, medium, or low quality. I will present our approach and findings, address some of the challenges of quantifying subjective preferences, and discuss applications of the aesthetics model to finding, sharing, and creating visually compelling content in an online community.

Stacey Svetlichnaya is a software engineer on the Yahoo Vision & Machine Learning team. Her recent deep learning research includes object recognition, image aesthetic quality and style classification, photo caption generation, and modeling emoji usage. She has worked extensively on Flickr image search and data pipelines, as well as automating content discovery and recommendation. Prior to Flickr, she helped develop a visual similarity search engine with LookFlow, which Yahoo acquired in 2013. Stacey holds a BS and MS in Symbolic Systems from Stanford University.

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REAL WORLD APPLICATIONS OF MACHINE INTELLIGENCE

14:00

Katherine Livins

Katherine Livins, Stitch Fix

Better Together: Building Human/Computer Hybrid Systems For Better Client Outcomes

Better Together: Building Human/Computer Hybrid Systems For Better Client Outcomes

Human Computation is an interdisciplinary field that combines human and machine computing. It aims to leverage the natural strengths of both systems in order to produce a better overall algorithm. This approach is at the core of Stitch Fix’s business model, and this talk will discuss our approach to it and our strategies for optimizing it.

Katherine is a Data Scientist at Stitch Fix, working on Human Computation under the Algorithms team. Stitch Fix makes clothing recommendations by employing a hybrid-system that marries computer algorithms with human “stylists”. Katherine works on understanding and optimizing the human side of this system, by studying stylist behavior through data and experimentation. Before Stitch Fix she completed a PhD in Cognitive and Information Science where she researched how to shape human behavior through the manipulation of attention.

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14:20

Conal Sathi

Conal Sathi, Slice Technologies

A Human Touch in Machine Learning

Machine learning engines are often designed to require little human input. However, while machines are fast, scalable, and reliable, selectively leveraging human input can be an excellent resource to provide continuous feedback and improvement. This is especially important given the inevitable changes in input data and the long tail of edge cases, which are traditionally difficult for machine learning engines. This talk discusses how we built a human-in-the-loop product categorization engine at Slice that involves different types of human workers - engineers, analysts, outsourced and crowdsourced workers - to allow for continuous improvement of the system with limited resources.

Conal Sathi is the Data Alchemist at Slice Technologies, where he is responsible for using machine learning and data mining algorithms to bring structure and deeper insights to consumer purchase data. At Slice, he was hired as the first machine learning engineer, where he built end-to-end systems from R&D to production, involving natural language processing, graph mining, and crowdsourcing. Now he leads a team of stellar machine learning engineers to build the world's largest purchase graph. Prior to Slice, Conal focused on research in the intersection of social network analysis and natural language processing. He examined how sentiment flows through hyperlink networks and explored how to create a content prediction system for Twitter users. Conal has a M.S. in Computer Science with a focus in Artificial Intelligence and a B.S. in Symbolic Systems with a focus in Neuroscience from Stanford University.

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14:40

Gilles Backhus

Gilles Backhus, Lilium Aviation

Designing Intelligence for Realtime Autonomous Systems

Designing Intelligence for Realtime Autonomous Systems

AI research is advancing at tremendous speeds. It seems as if every day there is a breakthrough in a certain field. While these breakthroughs can come from fundamental changes in model structures, essentially it often boils down to an increase in computational cost. A CNN with millions of parameters being compared to a linear regression for the same use case is something that for realtime autonomous systems borders on irrelevance since constraints such as power consumption, hardware cost and latency are often completely ignored. With the help of examples, insights will be given into why many AI use cases are still unlocked today although it does not necessarily have to stay this way.

Gilles is a passionate engineer at heart with an entrepreneurial mindset. He has spent his last couple of years working on various industrial problems in the domain of realtime machine learning and signal processing systems. With a Master in Electrical Engineer and a degree in Technology Management his focus revolves around machine learning designs required to consider special constraints that such systems are facing. Holistic designs targeted at the ideal symbiosis of hardware and algorithms such as stripped down neural networks on microcontrollers is what drives Gilles throughout his projects in the automotive, wireless and sensor industry as a developer and engineering lead.

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15:00

END OF SUMMIT

Day 1
13:40

Nick Pentreath

Nick Pentreath, IBM

ROUNDTABLE DISCUSSION: Productionizing Data Science

This discussion will center around best practices, key challenges and “war stories” for turning data science and machine learning projects into production systems. Some key questions that we'll discuss include:

How to go from exploratory data science and “proof of concept” projects to production systems with all their requirements of scalability, fault tolerance, performance monitoring & feedback?

How to bridge the gaps between the teams involved in each aspect of the Data Product Continuum, and the differing requirements of each phase?

What are some of the best practices in productionizing data science and machine learning workflows?

What role do standards play (e.g. data interchange formats, model evaluation standards such as PMML and PFA)?

What is the state of systems catering to this need, whether open-source or proprietary?

What are the important tools and software systems?

What are the important processes and non-software systems?

What are practitioners currently using in their production systems? How have they solved these problems and what are the “pro tips” and “lessons learnt” that can be shared?

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