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Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

Speakers

Hear from innovative programmers, talented managers, and senior developers who are doing amazing things with artificial intelligence. More speakers will be announced; please check back for updates.

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Gaurav currently leads Business Development for Automotive at NVIDIA. Before this role, he was responsible for product management for NVIDIA Autonomous Driving SDK “DriveWorks”. Previously he has held business development, marketing and engineering leadership roles at Texas Instruments. Gaurav did his B..S. and M.S. in Electrical engineering and has several publications in the area of image processing and computer vision and holds two US patents.

Presentations

Artificial Intelligence in Autonomous Vehicles Session

Building a self-driving technology which can understand the nuances of the world and drive in all the scenarios is a hard problem. In this talk, the latest trends and challenges in Autonomous driving will be presented. Then the important role of Artificial intelligence/deep learning to enable this technology will be discussed.

Alasdair Allan is a scientist and researcher who has authored more than 80 peer-reviewed papers and eight books and has been involved with several standards bodies. Originally an astrophysicist, Alasdair now works as a consultant and journalist, focusing on open hardware, machine learning, big data, and emerging technologies, with expertise in electronics, especially wireless devices and distributed sensor networks, mobile computing, and the internet of things. He runs a small consulting company and has written for Make: magazine, Motherboard/VICE, Hackaday, Hackster.io, and the O’Reilly Radar. In the past, he has mesh-networked the Moscone Center, caused a US Senate hearing, and contributed to the detection of what was at the time the most distant object yet discovered.

Presentations

Do-it-yourself Artificial Intelligence Session

The AIY Projects kits bring Google's machine learning algorithms to developers with limited experience in the field, allowing them to prototype machine learning applications and smart hardware more easily. We walkthrough how to setup and build the kits, and how to use the kits Python SDK to use machine learning both in the cloud, and locally on the Raspberry Pi.

Varun Arora is a Senior AI Product Engineer and Manager at Baidu USA, where he works with AI researchers on productization of deep learning technologies. He is also involved in the development and adoption of Baidu’s deep learning platform, PaddlePaddle.

Varun was formerly the CEO of a YC-backed education technology company OpenCurriculum, where he worked with thousands of teachers and hundreds of education administrators in improving curriculum in K-12 classrooms. He has also worked for Inkling, the UN, and a One Laptop Per Child deployment. He has a Bachelors and Masters from CMU.

Presentations

AI for improving teaching and learning Session

We haven't figured out how to make the perfect robot tutors. But we have figured out how make them much more effective in improving student learning outcomes with modern AI techniques. This talk will cover some of those important techniques, with real-world examples.

TBD

Presentations

Building Deep Learning Applications with Amazon SageMaker Tutorial

Outline: Provide a quick product overview of the Amazon SageMaker machine learning platform; Setup and Amazon SageMaker Notebook (hosted Jupyter Notebook server); Hands on training using a built-in SageMaker deep learning algorithm; Dive into building your own neural network architecture using SageMaker's pre-built TensorFlow containers.

Mayukh Bhaowal is a Director of Product Management at Salesforce Einstein, working on automated machine learning. Mayukh received his Masters in Computer Science from Stanford University. Prior to Salesforce, Mayukh worked at startups in the domain of machine learning and analytics. He served as Head of Product of a ML platform startup, Scaled Inference, backed by Khosla Ventures, and led product at an ecommerce startup, Narvar, backed by Accel. He was also a Principal Product Manager at Yahoo and Oracle.

Presentations

Trustworthiness of Machine Learning Applications Session

Machine learning is eating software. As decisions are automated, model interpretability becomes an integral part of the ML pipeline, rather than an afterthought. In the real world, the demand for being able to explain a model is rapidly gaining on model accuracy. This talk will discuss the steps taken at Salesforce Einstein towards making machine learning transparent and less of a black box.

Sarah Bird is a Technical Program Manager in Facebook AI Research and the Applied Machine Learning lab. She leads strategic projects at the intersection of research and product.

Presentations

Artificial Intelligence Open Source Libraries Session

Earlier this year, Amazon, Facebook and Microsoft partnered together to help advance AI together, by creating ONNX. ONNX stands for Open Neural Network Exchange (ONNX). It is an open format to represent deep learning models. This session will explain in detail how the ONNX framework can help you take AI from research to reality as quickly as possible.

Robin is the Chief Executive Officer at Figure Eight, the essential Human-in-the-Loop AI platform for data science and machine learning teams who want to make AI work in the real world.

Figure Eight’s technology and expertise supports a wide range of use cases including autonomous vehicles, intelligent personal assistants, medical image labeling, facial recognition, aerial imagery, consumer product identification, content categorization, customer support ticket classification, social data insight, CRM data enrichment, product categorization, and search relevance.

Robin has spent the past two decades helping high growth technology companies launch and scale platforms and products into rapidly transforming markets. Prior to Figure Eight, Robin held leadership roles at Marketo, Jive Software, Worksimple, Yahoo, Excite@Home, Micromuse, and McKinsey & Company.

Robin holds a Master’s degree in Engineering from Cambridge University where he was a Rolls Royce scholar and a Master’s degree in Business Administration from Stanford University where he was an Arjay Miller scholar.

Presentations

Deploying AI in The Real World Session

AI in the real world is a reality. AI is beginning to do things that are uniquely human, seeing things and labeling, but without contextual details or any human values. Humans and machines need to work together in AI. This talk will highlight the importance of training AI so that its application in the real world goes right.

Dominique Bouchon is the Director of Search and AI in the New Product Division at eBay. He leads the product definition for all Search and AI platform and capabilities supporting the eBay ShopBot, eBay Digital Assistants and other new search experience initiatives. He focuses on the defining the next set of AI functionalities that will let eBay stay a step ahead of the competition in many new markets and devices in terms of intent understanding, dialog skills, product and result retrieval and recommendations, via multiple modalities (text, voice, images).
Prior to his present role, Dominique leads multiple product teams in eCommerce at eBay, Apple, Microsoft and register.com.

Presentations

eCommerce – taking eBay from Web and App to ChatBots on Messenger and Google Assistant Session

Discover how eBay developed an AI-powered chatbot for the smart speakers, from developing a deep understanding of the shopper’s psychology to building dedicated AI capabilities to support the experience. In this session, we'll review this project from inception to launch: the do’s and don’t’s, the best practices, and the technology behind the scenes.

Jay Budzik is Chief Technology Officer at ZestFinance where he runs product development and engineering. Jay has spent 20 years bringing game changing technology to the Enterprise. He holds a PhD in AI from Northwestern, where he was a president’s fellow.

Presentations

Explaining Machine Learning Models Session

What does it mean to explain a machine learning model, and why is it important? Jay Budzik of ZestFinance will address those questions while discussing several modern explainability methods, including traditional feature contributions, LIME, and DeepLift. Each of these techniques offers a different perspective, and their clever application can reveal new insights and solve business requirements.

Chris Butler is Philosophie’s Director of AI. He leads the firm in human-centered AI engagements. Chris has over 18 years of product and business development experience at companies like Microsoft, KAYAK, and Waze. He first got introduced to AI through graph theory and genetic algorithms during his Computer Systems Engineering degree at Boston University. He has worked on AI-related projects at his startup Complete Seating (data science and constraint programming), Horizon Ventures (advising portfolio companies like Affectiva), and Philosophie (AI consulting and coaching). He has created techniques like Empathy Mapping for the Machine and Confusion Mapping to create cross-team alignment while building AI products.

Presentations

Design Thinking for AI Tutorial

Purpose, a well-defined problem, and trust from people are important factors to any system, especially those that AI. Chris Butler leads you through exercises that borrow from the principals of design thinking to help you create more impactful solutions and better team alignment.

Dr Paris Buttfield-Addison is co-founder of Secret Lab, a game development studio based in beautiful Hobart, Australia. Secret Lab builds games and game development tools, including the multi-award winning ABC Play School iPad games, “Night in the Woods” (http://www.nightinthewoods.com), the Qantas airlines “Joey Playbox” games, and the YarnSpinner narrative game framework. Paris formerly worked as mobile product manager for Meebo (acquired by Google), has a degree in medieval history, a PhD in Computing, and writes technical books on mobile and game development (more than 20 so far) for O’Reilly Media. Paris particularly enjoys game design, statistics, blockchain, machine learning, and human-centred technology research. He can be found on Twitter at @parisba (http://twitter.com/parisba) and online at http://paris.id.au

Presentations

Learning from video games Session

Video games have been using sophisticated AI techniques for decades. Long before many other fields looked to solve their problems using intelligent agents, planning algorithms, and complex computer opponents, video games used AI to drive everything from area design, to navigation, to enemies, to conversation and planning. This session explores AI in games, and what other fields can learn from it.

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

Presentations

Data Privacy and its implication to Deep Learning Session

In recent years, there has been a quantum leap in the performance of AI. From speech recognition to machine translation and computer vision, deep learning made its mark. However, the more artificial intelligence is gaining popularity, the issues of data privacy are getting more traction. This session will review these issues and how they impact the future of deep learning development.

Amanda Casari is a Senior Product Manager + Data Scientist for Concur Labs at SAP Concur, where she leads prototypes, interfaces and future tech for travel and expense. Before joining SAP Concur, Amanda’s experience ranged from operations research, underwater robotics, complex networks, data science, and serving as a Lieutenant in the United States Navy. Most recently, she co-authored Feature Engineering for Machine Learning from O’Reilly Media with Alice Zheng. She is a frequent speaker, working to foster an inclusive data community with groups like PyLadies and NASA Datanauts.

Presentations

Executive Briefing: When Privacy Scales - Intelligent Product Design under Global Data Privacy Regulation Session

Data-driven companies making intelligent products must design for security and privacy to be competitive globally. This talk will outline the high-level changes that EU General Data Protection Regulation (GDPR) compliant businesses face and how this translates to teams designing products driven by machine learning and artificial intelligence.

Rudy Cazabon has a Bachelors degree in Space Science (minor in Mechanical Engineering) from the Florida Institute of Technology; with graduate studies in Aerospace and Astronautics from Georgia Tech and Management Science from Stanford. Rudy has served as engineering manager and architect on projects such as Autodesk 3DS Max, Havok StudioTools, and the Project Offset game-engine slated for the then Intel Larrabee graphics architecture.

Presentations

Create, Optimize, and Deploy a Deep Learning Solution using Tensorflow and Caffe 2-Day Training

This hands-on training will leave attendees knowing how to build, implement and deploy a deep learning solution. Every registered attendee will receive free hardware to work with during the training. The hardware is yours to keep at the end of the event.

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

Presentations

Closing remarks Keynote

Closing remarks by Program Chairs Ben Lorica and Roger Chen

Friday Opening remarks Keynote

Opening remarks by Program Chairs Ben Lorica and Roger Chen

Thursday Opening Remarks Keynote

Opening remarks by Program Chairs Ben Lorica and Roger Chen

Ira Cohen is a cofounder of Anodot and its chief data scientist, where he is responsible for developing and inventing its real-time multivariate anomaly detection algorithms that work with millions of time series signals. He holds a PhD in machine learning from the University of Illinois at Urbana-Champaign and has over 12 years of industry experience.

Presentations

Deep learning for time series data Session

Ira Cohen will present a novel approach for building more reliable prediction models by integrating anomalies in them. And then, Arun Kejariwal will walk the audience through how to marry correlation analysis with anomaly detection, discuss how the topics are intertwined and the challenges one may come across based on production data.

Simon Crosby is the CTO of SWIM Inc. Simon brings an established record of technology industry success, most recently as co-founder and CTO of Bromium, a security technology company. At Bromium, Simon built a highly secure virtualized system to protect applications. Prior to Bromium, he was the co-founder and CTO of XenSource before its acquisition by Citrix, and later served as the CTO of the Virtualization and Management Division at Citrix. Previously, he was a principal engineer at Intel. Simon was also the founder of CPlane, a network-optimization software vendor. Simon Crosby has been a tenured faculty member at the University of Cambridge and was named one of InfoWorld’s Top 25 CTOs in 2007.

Presentations

Edge Intelligence: Machine Learning at the Enterprise Edge Session

This talk presents an architecture for learning on time-series data using edge devices, based on the distributed actor model. This approach flies in the face of the traditional wisdom of cloud-based, big-data solutions to ML problems. Attendees will learn that there is more than enough resource at “the edge” to cost-effectively analyze, learn and predict from streaming data on-the-fly.

Adam Cutler
Distinguished Designer, Artificial Intelligence Design
IBM Corporation

Adam Cutler is a founding member of IBM Design and one of the first three Distinguished Designers at IBM. He was responsible for the design and build out of the flagship IBM Design Studio in Austin, TX. He was also responsible for the competency, culture and practices of design and designers at IBM. This includes IBM Design Thinking, the IBM Design Language and IBM Design Research.

For his Distinguished Designer mission, Adam is driving development of IBM’s point of view on the practice of AI Design. He recently gave a TED talk on creating meaningful human/machine relationships. https://www.ted.com/talks/adam_cutler_can_we_be_friends_with_our_ai

Previously while at IBM Interactive Experience, he was the Director of User Experience Design and helped to guide clients and internal initiatives in the creation of valuable, dynamic and effective user experiences. He has worked with many clients including OpenPediatrics for Boston Children’s Hospital, Nordea, The JFK Museum & Library, Liberty Mutual, Bank of America, Nationwide, Wachovia, L.L.Bean, State Street, American Express, IBM, Segway, Chubb Insurance and Tiffany & Co. among others.

Prior to joining IBM, Adam worked with Michael Jordan while at an advertising agency in Chicago, and helped to pioneer the first e-commerce transaction from outer space.

Presentations

Forming meaningful relationships between human and machine Session

We humans will form relationships with just about anything, our cars, our phones, our pets. Now that AI provides machines the ability to understand, reason, learn, and interact, the building blocks for forming meaningful relationships with machines is now possible. As we move past text fields and submit buttons, what does it mean to design for relationships instead of UIs?

Brian Dalessandro is the head of data science at Zocdoc, an online doctor marketplace and booking tool, and an adjunct professor for the NYU Center for Data Science graduate program. Previously, Brian was vice president of data science at online advertising firm Dstillery. A veteran data scientist and leader with over 15 years of experience developing machine learning-driven practices and products, Brian holds several patents and has published dozens of peer-reviewed articles on the subjects of causal inference, large-scale machine learning, and data science ethics. Brian is also the drummer for the critically acclaimed indie rock band Coastgaard.

Presentations

Reverse Engineering your AI Prototype and the Road to Reproducibility Session

With the help of better software, cloud infrastructure and pre-trained networks, AI models have become easier to build. But once your solution veers from a common path, hidden challenges in reproducibility and implementation arise. We cover our experience and learnings building a computer vision and OCR app for reading and classifying insurance cards.

Danielle Dean is a principal data scientist lead at Microsoft in the Algorithms and Data Science Group within the Artificial Intelligence and Research Division, where she leads a team of data scientists and engineers building predictive analytics and machine learning solutions with external companies utilizing Microsoft’s Cloud AI Platform. Previously, she was a data scientist at Nokia, where she produced business value and insights from big data through data mining and statistical modeling on data-driven projects that impacted a range of businesses, products, and initiatives. Danielle holds a PhD in quantitative psychology from the University of North Carolina at Chapel Hill, where she studied the application of multilevel event history models to understand the timing and processes leading to events between dyads within social networks.

Presentations

How to use Transfer Learning to Bootstrap Image Classification and Question Answering (QA) Session

Transfer learning enables you to use pre-trained deep neural networks and adapt them for various deep learning tasks (e.g. image classification, question answering, and more). Join Wee Hyong Tok and Danielle Dean, as they share the secrets of transfer learning, and how you can custom these pre-trained models for your use cases, and jumpstart.

Allison conducts research and coordinates the technical programs. Her research focus is on the reduction of existential risks, especially from Artificial General Intelligence. At Existentialhope.com she keeps an index of readings, podcasts, organizations, and people that inspire an optimistic long-term vision for humanity. The index is collaborative and for everyone who wants to improve the world but doesn’t know where to start. Allison speaks and moderates panels on existential risks & existential hope, AI safety, longevity, blockchains, ethics in technology, and more. Prior to Foresight she hosted and planned TEDx, panels, workshops, debates, and conferences for governments, companies, think tanks, NGOs and the public in Germany, France, Colombia, the UK, and the US. Allison holds a Summa Cum Laude MS in Philosophy & Public Policy from the London School of Economics, where she developed a moral framework for Artificial General Intelligence that relies on natural language processing.

Presentations

Executive Briefing: AI Safety - Problems, State of the art, Alternatives Session

The talk starts with an overview of AI philosophy and how traditional approaches need updating because they pave the way for a singleton AI that will hardly be benevolent. I will discuss potential alternative AI safety strategies and their shortcomings and close with a brief survey of interesting problems in AI safety and what we can hope for if we get it right.

Brian Eberman is the CEO of Jibo, Inc., after previously serving as the company’s CTO beginning in May 2017. Prior to joining Jibo, Inc., Brian has served in a number of executive roles in the areas of enterprise, online education and digital marketing, including President and COO of EnglishCentral, CEO and COO of CourseAdvisor/Avenue100 and VP of Product Management and Director of Product Management at Nuance Communications and SpeechWorks, respectively.
Brian holds a Ph.D. in Robotics and AI and a MS in Mechanical Engineering from the Massachusetts Institute of Technology.

Presentations

From Ideation to Realization – Launching the World’s First Social Robot Session

Rudina Seseri, Managing Partner, Glasswing Ventures, an early-stage venture capital firm dedicated to investing in the next generation of AI-powered technology companies and Brian Eberman, CEO of Jibo, Inc., makers of the eponymous robot, share their experience launching Jibo, Time Magazine's # 1 Best Invention of the Year and the world's first social robot for the home.

Jana’s a math and computer nerd who took the business path for a career. Today, she’s CEO of Nara Logics, a neuroscience-inspired artificial intelligence company, providing a platform for recommendations and decision support. Her career has taken her from 3-person business beginnings to 50,000-person enterprises. She opened the European logistics software offices as part of American Airlines, dove into the internet in ’96 at Lycos, founded Intuit’s corporate Innovation Lab, helped define mass customization at Spreadshirt, and researched conducting polymers at Los Alamos National Laboratory. Her passions are working with teams to define and deliver products customers love, algorithms and their intelligence, and inspiring teams to do more than they thought possible.

Presentations

The wiring diagram of arXiv's AI papers Session

In neuroscience, the wiring diagram of the brain is a connectome. We built a connectome of the AI/ML "brain" via arXiv papers. We'll share the results of how papers, topics, keywords, authors, institutions, publication dates, citations & more are linked & with what strength for interesting insights on how the AI research world is connected, plus give the audience a chance to query the connectome.

Susan Etlinger is an industry analyst at Altimeter. Her research focuses on the impact of artificial intelligence, data and advanced technologies on business and culture and is used in university curricula around the world. Susan’s TED talk, “What Do We Do With All This Big Data?,” has been translated into 25 languages and has been viewed more than 1.2 million times. She is a sought-after keynote speaker and has been quoted in such media outlets as the Wall Street Journal, the BBC, and the New York Times.

Presentations

Executive Briefing: Ethical AI - How To Build Products that Customers Will Love and Trust Session

In this talk, industry analyst Susan Etlinger explores how AI fundamentally changes the relationship between people and businesses, lays out its risks and opportunities and demonstrates emerging best practices for designing customer-centric and ethical products and services.

Zachary Glassman is a data scientist in residence at the Data Incubator. Zachary has a passion for building data tools and teaching others to use Python. He studied physics and mathematics as an undergraduate at Pomona College and holds a master’s degree in atomic physics from the University of Maryland.

Presentations

AI and Data Science for Managers 2-Day Training

In this course, we will be offering a non-technical overview of AI and data science. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

Dan is the Director of Machine Learning at Arterys, a startup focused on streamlining the practice of medical image interpretation and post-processing. After receiving a PhD in Electrical Engineering from Stanford, he stayed for a postdoc, focusing on using machine learning to predict outcomes and disease characteristics in cancer patients. From there, he joined CellScope, where he founded a machine learning team that used the then-nascent field of Deep Learning to diagnose ear disease and streamline the process of recording ear exams at home. He moved to Arterys to found their machine learning team in 2015.

Presentations

Lung cancer detection and segmentation using deep learning Session

Modern radiological lung cancer screening is an entirely manual process, leading to high costs and inter-reader variability. We have developed a deep learning-based system that automatically detects and segments lung nodules in lung CT exams. The system is FDA-cleared and segments nodules as accurately as a clinician. We explain the details of our system and how we proved its safety and efficacy.

Sean Gourley is founder and CEO of Primer. Previously, he was CTO of Quid, an augmented-intelligence company he co-founded in 2009. Prior to Quid, Gourley worked on self-repairing nano-circuits at NASA Ames. He holds a PhD in physics from Oxford, where his research as a Rhodes Scholar focused on complex systems and the mathematical patterns underlying modern war. This research was published on the cover of Nature. He has served as a political advisor, briefed USCENTCOM at the Pentagon and addressed the United Nations in Vienna. He is a two-time New Zealand track and field champion. Gourley sits on the Knight Commission, serves on the Board of Directors at Anadarko (NYSE:APC), and is a TED Fellow.

Presentations

Building machines that can read and write Session

Technology has opened up access to more information than ever before, but it’s still on humans to turn that data into knowledge. To solve this problem organizations are turning to AI and natural language processing to augment human analysts. Join Primer founder, Sean Gourley, as he discusses how the world’s largest organizations use AI to summarize thousands of documents and scale human analysts.

Goodman Xiaoyuan Gu is head of marketing data engineering at Atlassian, where he leads product strategy and engineering for marketing and growth data pipelines as well as customer acquisition and retention machine learning capabilities. Previously, he was vice president of technology at CPXi, director of engineering at Dell, and general manager at Amazon, where he built marketing and analytics applications. He has served on technical program committees of two IEEE flagship conferences and is the author of over a dozen academic publications in high-profile IEEE and ACM journals and conferences. Goodman holds a degree in engineering and management from MIT.

Presentations

Leaving No One Behind: Make Equal Access to Teamwork Possible With Deep Learning Enabled Sign Language and Gesture Recognition Session

Over 400MM people worldwide have some sort of speech/hearing disorders that prevent them from participating in the job market. It is our strong believe that disability should not mean disadvantage. Stride4All is an initiative of using AI to open work up for disabled people and empower them for teamwork. We showcase a prototype built with the latest deep learning and computer vision technologies.

Sharad Gupta is an executive leader responsible for the enterprise technology vision, strategy, and roadmap at one of the leading health plans in California. Sharad is also part of the adjunct faculty at the University of California, Davis, and teaches the Data Design & Representation course in the Master of Science in Business Analytics (MSBA) program.

https://www.linkedin.com/in/sharadgupta1

https://gsm.ucdavis.edu/faculty/sharad-gupta

Presentations

Executive Briefing: Multi-Channel ChatBots Strategy Session

AI-powered ChatBots are increasingly becoming viable solutions for customer service use cases. Technology leaders need to consider adopting a multi-channel ChatBots strategy to avoid siloed ChatBot solutions. The objective of this executive briefing session is provide the technology executives and the decision-makers with a framework to ensure long-term strategic investment in ChatBots.

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

Presentations

Bringing AI into the enterprise Tutorial

Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making.

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

Reinforcement Learning Unchained - How to leverage Machine Teaching to build AI into complex, real-world systems Session

Building complex, real-world reinforcement learning systems requires leveraging many techniques including curriculum learning, hierarchical RL, and reward shaping. In this session, Mark Hammond will examine many of these techniques and illustrate how they can be effectively combined into a comprehensive machine teaching program.

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

Presentations

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

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

As the CTO of a game-changing, well-funded consumer robotics company, I’m expanding the limits of our field on a daily basis. This has been the hallmark of my career: opening up new capabilities of robotics technology, with particular attention on shared autonomous teleoperation, imitation learning, tactile grasp adjustment, human-aware navigation and, most recently, robot mapping and localization.

I’m a tech-hungry robotics champion as well as a market-focused strategist committed to building relevant, helpful products people want to use. Design credits include agricultural robots, service robots, assistive robots for people with disabilities and, most recently, companion robots for the home.

Across my career at MIT (CSAIL and Media Lab), Bosch, and Willow Garage, I have assembled, accelerated, and led robotics teams producing marketable breakthroughs that have sparked startup businesses in the US and Europe. I’ve mentored the leaders of our industry and forged lasting alliances across top universities, research institutions, and robotics companies.

A few more accolades: Robohub honored me as one of the “25 Women in Robotics You Need to Know about,” and I’ve also been honored as one of Silicon Valley Business Journal’s “Women of Influence.” I authored a chapter in the Springer Handbook of Robotics, and I’ve advised scientists, roboticists, and entrepreneurs around the world for years, in the areas of robot perception, manipulation, grasping, navigation, and localization.

The field of robotics is evolving into the mainstream market, and I’m proud to lead a company that’s defining the path forward.

Presentations

Adorable Intelligence Session

Many people have dreamed of having their own personal “Rosie the Robot” in their home. And while we’re moving to a future where robots are becoming a part of our everyday lives, consumers are still skeptical. Join Kaijen Hsiao, CTO of Mayfield Robotics, to learn how she created Kuri, a first-of-its-kind home robot that houses complex technology to create an adorable robot.

Forrest Iandola completed a PhD in Electrical Engineering and Computer Science at UC Berkeley, where his research focused on deep neural networks. His advances in scalable training and efficient implementation of deep neural networks led to the founding of DeepScale, where he is CEO. DeepScale is focused entirely on building perception systems for automated vehicles, and DeepScale has a number of engagements with automakers and automotive suppliers.

Presentations

Executive Briefing: How to Develop a Full-Stack Deep Learning Team Session

Now more than ever, success in AI requires expertise in multiple disciplines, including big data, efficient software, and novel models/algorithms. In this talk, we present an approach for developing "full-stack" AI teams that have these diverse skills and can execute on industrial-scale AI problems.

George Iordanescu is a Data Scientist with Algorithms and Data Science team, Microsoft Cortana Intelligence Suite. He received his Ph.D. in EE from “Politehnica” University in Bucharest, Romania, before training as a post-doctoral visiting fellow in computer assisted detection at NIH (www.nih.gov). His research interests include semi-supervised learning and anomaly detection. Before joining Microsoft, he was a research scientist in academia and a consultant in healthcare and insurance industry.

Presentations

Building Intelligent Mobile Applications in Health Care Tutorial

We will give a tutorial on how to build a deep learning model and build intelligent applications on edge devices including iOS, Android, and Windows. Our working example in this tutorial is from radiology. Chest X-rays are currently playing a crucial role in lung disease detection. How do we power clinicians to identify possible lung diseases in areas with less access to radiologists?

Ankit currently works as a Data Scientist at Uber where his primary focus is on forecasting using Deep Learning methods and self driving car’s business problems.Prior to that, he has worked in a variety of data science roles at Runnr, Facebook, BofA and Clearslide. Ankit holds a Masters from UC Berkeley and BS from IIT Bombay (India).

Presentations

Achieving Personalization with LSTMs Session

Personalization is a common theme in social networks and e-commerce businesses. However, personalization at Uber will involve understanding of how each driver/rider is expected to behave on the platform. In this talk, we will focus on how Deep Learning (LSTM's) and Uber's huge database can be used to understand/predict future behavior of each and every user on the platform.

Lucas Joppa is Microsoft’s Chief Environmental Scientist, where he serves as the company’s focal point at the intersection of science, technology, and environmental sustainability. A key component of his role at Microsoft includes leading the AI for Earth program, a significant investment in deploying Microsoft’s AI technologies for environmental solutions in the areas of climate, water, agriculture, and biodiversity conservation. Dr. Joppa is an internationally recognized environmental scientist, and serves as a strategic advisor to many non-profit, government, and corporate institutions nationally and internationally. A former Peace Corps volunteer, Lucas holds degrees from the University of Wisconsin-Madison and Duke University, an honorary fellowship at the Zoological Society of London, and is a recipient of the Society for Conservation Biology’s Early Career Award.

Presentations

AI for Earth: How Microsoft is Saving the World with Technology Session

The AI for Earth team at Microsoft helps NGOs apply AI to challenges in conservation biology and environmental science. This session will introduce our main initiatives and share progress on projects that apply AI to agriculture, poacher detection, monitoring the spread of pathogens/hosts, species abundance modeling, and animal identification in camera trap and citizen scientist photography.

Arun Kejariwal is a statistical learning principal at Machine Zone (MZ), where he leads a team of top-tier researchers and works on research and development of novel techniques for install and click fraud detection and assessing the efficacy of TV campaigns and optimization of marketing campaigns. In addition, his team is building novel methods for bot detection, intrusion detection, and real-time anomaly detection. Previously, Arun worked at Twitter, where he developed and open-sourced techniques for anomaly detection and breakout detection. His research includes the development of practical and statistically rigorous techniques and methodologies to deliver high-performance, availability, and scalability in large-scale distributed clusters. Some of the techniques he helped develop have been presented at international conferences and published in peer-reviewed journals.

Presentations

Deep learning for time series data Session

Ira Cohen will present a novel approach for building more reliable prediction models by integrating anomalies in them. And then, Arun Kejariwal will walk the audience through how to marry correlation analysis with anomaly detection, discuss how the topics are intertwined and the challenges one may come across based on production data.

Mayank Kejriwal is a researcher at the USC Information Sciences Institute and currently conducts research on the IARPA HFC, and DARPA LORELEI, CauseEx, MEMEX (covered in the press by 60 minutes, Forbes, Scientific American, WSJ, BBC, Wired,…) and D3M projects. Prior to joining ISI in 2016, he obtained his Ph.D. from the University of Texas at Austin. His dissertation, titled "Populating a Linked Data Entity Name System”, was awarded the Best Dissertation Award by the Semantic Web Science Association in 2017. He is currently co-authoring a textbook on knowledge graphs (MIT Press, 2018).

Presentations

Fighting Human Trafficking with AI Session

Human trafficking is a form of modern day slavery. Online sex advertisement activity on portals like backpage provide important clues that, if harnessed and analyzed at scale, can help resource-strapped law enforcement crack down on trafficking activity. I will describe our efforts in building an AI architecture called DIG that law enforcement have used (and are using) to combat sex trafficking.

Elsie Kenyon is a senior product manager at AI platform company Nara Logics, where she works with enterprise customers to define product needs and with engineers to build implementations that address them, with a focus on data processing and machine learning. Previously, Elsie was a researcher and casewriter at Harvard Business School. She holds a BA from Yale University.

Presentations

The wiring diagram of arXiv's AI papers Session

In neuroscience, the wiring diagram of the brain is a connectome. We built a connectome of the AI/ML "brain" via arXiv papers. We'll share the results of how papers, topics, keywords, authors, institutions, publication dates, citations & more are linked & with what strength for interesting insights on how the AI research world is connected, plus give the audience a chance to query the connectome.

Chief Data Scientist with Zighra with more than 15 years experience in Machine Learning. Previously worked with Samsung Electronics Research and Development division and Amazon. Holds a PhD in Theoretical Statistical Physics.

Presentations

Preventing Cyber Attacks using Deep Reinforcement Learning Session

Cyber attacks are becoming increasingly more sophisticated with the use of AI powered tools. To counter these one should use autonomous systems which can adapt to new attack methods and detect them very early. In this presentation I would be talking about how Sequential Anomaly Detection methods implemented using Deep Reinforcement Learning can be used for this purpose.

Anirudh Koul is a Senior Data Scientist at Microsoft Research, and founder of Seeing AI – Talking Camera App for the Blind community. Anirudh brings over a decade of production-oriented Applied Research experience on Peta Byte scale datasets, with features shipped to about a billion people. An entrepreneur at heart, he has been running a mini-startup teams within Microsoft, prototyping ideas using computer vision and deep learning techniques for augmented reality, productivity, and accessibility – building tools for communities with visual, hearing, and mobility impairments. A regular at hackathons, he has won close to three dozen awards, including top-three finishes for four years consecutively in the world’s largest private hackathon, with 18,000 participants. Some of his recent work, which IEEE has called “life changing” has been showcased at White House event, Netflix, National Geographic and received awards from American Foundation for the Blind and Mobile World Congress. You can reach him at @anirudhkoul

Presentations

Deep learning on Mobile - The How-To Guide Session

Over the last few years, convolutional neural networks (CNN) have risen in popularity, especially in computer vision. In this session, we will talk about bringing the power of deep learning, to memory and power constrained devices like smartphones.

Besir Kurtulmus is an Algorithm Engineer at Algorithmia that helps making complicated things simpler. He develops Machine Learning algorithms that are designed to scale. He also helps maintain the development environment for running Machine Learning models in the Algorithmia marketplace. He believes that Machine Intelligence will have a huge impact on our lives in the days to come, and hopes to have a defining role in shaping this new future.

Presentations

Trustless Machine Learning Contracts: Evaluating and Exchanging Machine Learning Models on the Ethereum Blockchain Session

Machine Learning algorithms are being developed and improved at an incredible rate, but are not necessarily accessible to the broader community. That’s about to change, thanks to DanKu, a new blockchain-based protocol for evaluating and purchasing ML models on a public blockchain such as Ethereum. DanKu enables anyone to get access to high quality, objectively measured machine learning models.

Danny Lange is vice president of AI and machine learning at Unity Technologies, where he leads multiple initiatives around applied artificial intelligence. Previously, Danny was head of machine learning at Uber, where he led the efforts to build a highly scalable machine learning platform to support all parts of Uber’s business, from the Uber app to self-driving cars; general manager of Amazon Machine Learning, where he provided internal teams with access to machine intelligence and launched an AWS product that offers machine learning as a cloud service to the public; principal development manager at Microsoft, where he led a product team focused on large-scale machine learning for big data; CTO of General Magic, Inc.; and founder of his own company, Vocomo Software, where he worked on General Motor’s OnStar Virtual Advisor, one of the largest deployments of an intelligent personal assistant until Siri. Danny started his career as a computer scientist at IBM Research. He is a member of ACM and IEEE Computer Society and has numerous patents to his credit. Danny holds an MS and PhD in computer science from the Technical University of Denmark.

Presentations

On the Road to Artificial General Intelligence Session

Join this session to discuss the role of intelligence in biological evolution and learning. The speaker will demonstrate why a game engine is the perfect virtual biodome for AI’s evolution. Attendees will recognize how the scale and speed of simulations is changing the game of AI while learning about new developments in reinforcement learning.

Jason Laska is the head of engineering 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 his 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 co-founder and editor of Rejecta Mathematica, a publication for previously rejected mathematical articles.

Presentations

Speed vs. Specificity: Designing text annotation tasks for the people and algorithms that drive human-in-the-loop (HIL) products Session

Clara’s human-in-the-loop scheduling service combines the precision of machine intelligence and the judgement of an expert team. This session will focus on the tradeoffs between text annotations defined for fast data entry vs. those meant solely for training machine learning models. We’ll use the application of datetime text as it pertains to meeting-attendee availability to guide the discussion.

Wilson Lee is an experienced Senior Software Engineer with a demonstrated history of working in the computer software industry. Skilled in Software Design, Scrum, Java, SQL, and C++. Strong engineering professional with a Bachelor of Computer Science focused in Computer Science from University of Waterloo.

Presentations

Building Intelligent Mobile Applications in Health Care Tutorial

We will give a tutorial on how to build a deep learning model and build intelligent applications on edge devices including iOS, Android, and Windows. Our working example in this tutorial is from radiology. Chest X-rays are currently playing a crucial role in lung disease detection. How do we power clinicians to identify possible lung diseases in areas with less access to radiologists?

Sergey Levine received a BS and MS in Computer Science from Stanford University in 2009, and a Ph.D. in Computer Science from Stanford University in 2014. He joined the faculty of the Department of Electrical Engineering and Computer Sciences at UC Berkeley in fall 2016. His work focuses on machine learning for decision making and control, with an emphasis on deep learning and reinforcement learning algorithms. Applications of his work include autonomous robots and vehicles, as well as computer vision and graphics. His research includes developing algorithms for end-to-end training of deep neural network policies that combine perception and control, scalable algorithms for inverse reinforcement learning, deep reinforcement learning algorithms, and more. His work has been featured in many popular press outlets, including the New York Times, the BBC, MIT Technology Review, and Bloomberg Business.

Presentations

Session by Sergey Levine Session

Session by Sergey Levine

Lisha is a principal at Amplify Partners. She invests in technical founders solving ambitious problems. From compute substrate to the creative process, medicine to manufacturing, she is excited to be investing at a time when machine intelligence and data-driven methods have such incredible potential for impact. Investments she has been involved with include Embodied Intelligence and Primer. Lisha completed her PhD at UC Berkeley focusing on deep learning and probability. While at Berkeley she also did statistical consulting, advising on methods and analysis for experimentation and interpretation, and interned as a data scientist at Pinterest and Stitch Fix. She was the lecturer of discrete mathematics, as well as the graduate instructor for probability and computer science theory. Other things she has dabbled in include acting (unionized in Canada) and dance (was an artist with a modern dance company). A fun turn of events one summer visiting Paris as a research mathematician, Lisha found herself the subject of the short films “A Portrait of a Mathematician lady” and “Sizes of Infinity” by filmmaker Olivier Peyon that ties some of these eclectic interests.

Presentations

Differentiable Programming: A Framework for Applied AI Session

The talk will present an organizing framework to understand the myriad developments in deep learning and machine intelligence. Goal here was to also use the framework to enable one to anticipate future developments (rather then understand through bundle of deep learning techniques).

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

AI and Data Science for Managers 2-Day Training

In this course, we will be offering a non-technical overview of AI and data science. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize their input and analysis for your business’s strategic priorities and decision making.

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

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

Presentations

Enabling Affordable but Reliable Autonomous Driving Session

we describe the technology details of building a reliable autonomous vehicle with < $10,000 total cost.

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

Closing remarks by Program Chairs Ben Lorica and Roger Chen

Friday Opening remarks Keynote

Opening remarks by Program Chairs Ben Lorica and Roger Chen

Thursday Opening Remarks Keynote

Opening remarks by Program Chairs Ben Lorica and Roger Chen

Dr Jon Manning is the co-founder of Secret Lab, an independent game development studio. He’s written a whole bunch of books for O’Reilly Media about iOS development and game development, and has a doctorate about jerks on the internet. He’s currently working on Button Squid, a top-down puzzler, and on the critically acclaimed award winning adventure game Night in the Woods, which includes his interactive dialogue system Yarn Spinner. Jon can be found as @desplesda on Twitter.

Presentations

Learning from video games Session

Video games have been using sophisticated AI techniques for decades. Long before many other fields looked to solve their problems using intelligent agents, planning algorithms, and complex computer opponents, video games used AI to drive everything from area design, to navigation, to enemies, to conversation and planning. This session explores AI in games, and what other fields can learn from it.

Jennifer Marsman is the principal software engineer for Microsoft’s AI for Earth Group, where she uses data science, machine learning, and artificial intelligence to aid with clean water, agriculture, biodiversity, and climate change. She has been featured in Bloomberg for her work using EEG and machine learning to perform lie detection. Previously, Jennifer was a software developer in Microsoft’s Natural Interactive Services Division, where she authored two patents related to search and data mining algorithms. She has also held positions with Ford Motor Company, National Instruments, and Soar Technology. Since 2016, Jennifer has been recognized as one of the top 100 most influential individuals in artificial intelligence and machine learning by Onalytica, reaching the #2 slot in 2018, and in 2009 was chosen as the “techie whose innovation will have the biggest impact” by X-OLOGY for her work with GiveCamps, a weekend-long event where developers code for charity. She has also received many honors from Microsoft, including the Best in Role award for technical evangelism, Central Region Top Contributor Award, Heartland District Top Contributor Award, DPE Community Evangelist Award, CPE Champion Award, MSUS Diversity and Inclusion Award, Gold Club, and Platinum Club. Jennifer is a frequent speaker at software development conferences around the world. She holds a bachelor’s degree in computer engineering and a master’s degree in computer science and engineering from the University of Michigan in Ann Arbor, where she specialized in artificial intelligence and computational theory. To learn more, check out her blog: http://blogs.msdn.microsoft.com/jennifer.

Presentations

AI for Earth: How Microsoft is Saving the World with Technology Session

The AI for Earth team at Microsoft helps NGOs apply AI to challenges in conservation biology and environmental science. This session will introduce our main initiatives and share progress on projects that apply AI to agriculture, poacher detection, monitoring the spread of pathogens/hosts, species abundance modeling, and animal identification in camera trap and citizen scientist photography.

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

Presentations

Evolution: The Next “Deep Learning" Session

Deep learning (DL) has transformed much of AI, and demonstrated how machine learning can make a difference in the real world. In DL, massive expansion of available training data and compute gave Neural Networks a new instantiation that significantly increased its power. Evolutionary computation (EC) is on the verge of a similar breakthrough. This presentation will explain why EC is the next DL.

Aleksandra (Saška) Mojsilovic is a scientist, Head of AI Foundations at IBM Research, Co-Director of IBM Science for Social Good and IBM Fellow. She is a Fellow of the IEEE and a member of IBM Academy of Technology. Saška received the Ph.D. degree in electrical engineering from the University of Belgrade, Belgrade, Serbia in 1997. She was a Member of Technical Staff at the Bell Laboratories, Murray Hill, New Jersey (1998-2000), and then joined IBM Research. Saška is the author of over 100 publications and holds 16 patents. Her work has been recognized with several awards including IEEE Signal Processing Society Young Author Best Paper Award, INFORMS Wagner Prize, IBM Extraordinary Accomplishment Award, and IBM Gerstner Prize. Saška also serves on the board of directors for Neighborhood Trust Financial Partners, which provides financial literacy and economic empowerment training to low-income individuals.

Presentations

AI for Good Session

AI possesses an incredible potential to help address the challenges of our planet. Drawing on her experience as the Head of AI Foundations and Co-Director of Science for Social Good at IBM Research, Aleksandra Mojsilovic shares innovative examples of applying AI to humanitarian problems and discusses gaps that challenge us from making larger impact with our work.

Received a PhD in Computer Science from the University of Bari, Italy. He was co-founder and CTO of QuestionCube, a startup building next-gen question answering systems. He worked for Yahoo Labs in Barcelona on learning to rank, at IBM Watson in New York on Question Answering and at Geometric Intelligence on Grounded Language Understanding. He is now research scientist at Uber AI Labs working on Natural language processing and Dialogue Systems.

Presentations

Improving customer support with natural language processing and deep learning Session

At Uber we implemented a machine learning and natural language processing system that suggests to our customer support representatives the most likely solutions to a ticket. This makes them faster and more accurate while providing a better user experience. We describe how we built two versions of the system, with traditional and deep learning models, and discuss the lessons learned along the way.

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

Presentations

Building reinforcement learning applications with Ray Tutorial

Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the-art RL algorithms.

Based in Santa Clara, California, David Mueller manages product innovation in AI for Teradata’s Technology Innovation Office as Director, Product Management. His focus is on technology enabling enterprises to benefit from machine and deep learning at scale. David relocated to the US from Singapore in 2017 where he was heading the regional Data Science Practice for Think Big Analytics, Teradata’s business analytics consultancy. In his previous role he was leading an international team of Data Scientists supporting customer projects across South East Asia, India, Pakistan and South Korea as experts in the application of advanced statistical and analytical methods to the solution of business problems across industries. David’s background is in digital customer and marketing analytics. Prior to joining Teradata he was heading the Data Science team of a German ad tech company.

Presentations

PyTorch: A flexible approach for computer vision models Tutorial

From photo filter of social networks to self driving cars to detecting skin cancer, Computer Vision has brought applied deep learning to the masses. Built by the pioneers of Computer Vision software, PyTorch is allow developers to rapidly build computer vision models. Learn the fundamental concepts of computer vision and apply them in PyTorch for building computer vision applications.

Derek Murray is a Software Engineer on the Google Brain team, working on TensorFlow.

Presentations

tf.data: Fast, flexible, and easy-to-use data pipelines for TensorFlow Session

Training data is the lifeblood of a machine learning system, and modern accelerators like GPUs and TPUs are very thirsty for it. The tf.data library provides efficient access to your data in a variety of formats, and a wide range of transformations for augmenting your data. In this talk, we will introduce tf.data and show you how to use it to achieve peak performance in your training pipeline.

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

Building reinforcement learning applications with Ray Tutorial

Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the-art RL algorithms.

Dr Tim Nugent pretends to be a mobile app developer, game designer, tools builder, researcher, and tech author. When he isn’t busy avoiding being found out as a fraud, he spends most of his time designing and creating little apps and games he won’t let anyone see. Tim spent a disproportionately long time writing this tiny little bio, most of which was spent trying to stick a witty sci-fi reference in, before he simply gave up. Tim can be found at http://twitter.com/the_mcjones on Twitter.

Presentations

Learning from video games Session

Video games have been using sophisticated AI techniques for decades. Long before many other fields looked to solve their problems using intelligent agents, planning algorithms, and complex computer opponents, video games used AI to drive everything from area design, to navigation, to enemies, to conversation and planning. This session explores AI in games, and what other fields can learn from it.

Ben Odom leads Intel’s Technical Developer Evangelist team, focused on highlighting, training and showcasing Intel products and tools to developers worldwide. Currently, a portion of Ben’s team have been heavily focused on Artificial Intelligence, developing coursework for Intel’s developer ecosystem and then delivering trainings for both industry and academic developers interested in using Intel’s optimized frameworks and libraries. Ben has been in the tech industry for over 20 years, and has a Master’s Degree in Computer Science and Engineering from Oregon Health Sciences University.

Presentations

Create, Optimize, and Deploy a Deep Learning Solution using Tensorflow and Caffe 2-Day Training

This hands-on training will leave attendees knowing how to build, implement and deploy a deep learning solution. Every registered attendee will receive free hardware to work with during the training. The hardware is yours to keep at the end of the event.

Carl is a program manager focused on helping Google’s customers and business partners get trained and certified to run machine learning and data analytics workloads on Google Cloud. With over 16 years of experience in the IT industry, Carl worked with the world’s leading technology companies across United States and Europe, including in leadership roles on programs and projects in the areas of big data, cloud computing, service-oriented architecture, machine learning, and computational natural language processing. Carl is an author of over 20 articles in professional, trade, and academic journals, an inventor with 6 patents at USPTO, and holds 3 corporate awards from IBM for his innovative work. You can find out more about Carl on his blog http://www.cloudswithcarl.com

Presentations

Image Classification Models in TensorFlow Tutorial

This hands-on workshop walks through creating increasingly sophisticated image classification models using TensorFlow.

Richard Ott is a data scientist in residence at the Data Incubator, where he gets to combine his interest in data with his love of teaching. Previously, he was a data scientist and software engineer at Verizon. Rich holds a PhD in particle physics from the Massachusetts Institute of Technology, which he followed with postdoctoral research at the University of California, Davis.

Presentations

Deep learning with Apache Spark and BigDL, with Keras and TensorFlow support 2-Day Training

BigDL is a powerful tool for leveraging Hadoop and Spark clusters for deep learning. Rich Ott offers an overview of BigDL’s capabilities through its Python interface, exploring BigDL's components and explaining how to use it to implement machine learning algorithms. You'll use your newfound knowledge to build algorithms that make predictions using real-world datasets.

Beth Partridge, Founder/CEO/Chief Data Scientist at milk+honey

Beth brings nearly 30 years of executive-level experience in manufacturing, product engineering, quality control, technical support and operations. Her formal training includes a BS in Electrical Engineering, and a Master of Information and Data Science from UC Berkeley. Beth is that rare executive with a powerhouse combination of natural leadership, deep technical experience and impeccable execution skills. milk+honey is born from her hard-won understanding that success in business now requires a deep commitment to data-driven decision making, and development of a culture of experimentation and innovation at all levels of an organization. She is filled with excitement about the data revolution and the profound transformation that’s upon us.

Presentations

Taming Dragons: A Breakthrough Approach to AI for Business Leaders Tutorial

Utilizing AI technologies to advance business goals remains one of the most daunting challenges for many business leaders. In this interactive session, Beth Partridge offers a breakthrough approach that bridges the gap between data science and business. You will walk away with a clear understanding of what AI can do for your business, and how to go about implementing it.

As CTO and Chief Scientist, Labhesh is responsible for driving Jumio’s innovation in the identity verification space with deep learning, computer vision, and augmented intelligence – an alternative conceptualization of artificial intelligence that focuses on AI’s assisted role to enhance human intelligence. Mr. Patel is an accomplished leader with over 15 years of experience in corporate and entrepreneurial settings. Labhesh has proven experience leading engineering teams, launching new online services (from concept creation to customer delivery), and developing ground-breaking technologies. Mr. Patel has filed 175 patents filed with another 134 patents issued under his name.

Labhesh has served in similar capacities at Cisco, Abzooba, xpresso.ai, Spotsetter, and CellKnight. Mr. Patel holds a Masters of Science in Electrical Engineering (MSEE) from Stanford University and Bachelor of Technology from the Indian Institute of Technology in Kanpur.

Presentations

Productionalizing Deep Learning for Computer Vision Session

Labhesh Patel, Jumio’s Chief Scientist, will explore how deep learning is informing our computer vision through smarter data extraction, fraud detection, and risk scoring. Mr. Patel will discuss how Jumio is leveraging massive data sets and human review to dramatically improve the accuracy of our ML algorithms to detect bogus IDs and streamline the verification process of legitimate documents.

Mo Patel is an independent deep learning consultant advising individuals, startups, and enterprise clients on strategic and technical AI topics. Mo has successfully managed and executed data science projects with clients across several industries, including cable, auto manufacturing, medical device manufacturing, technology, and car insurance. Previously, he was practice director for AI and deep learning at Think Big Analytics, a Teradata Company, where he mentored and advised Think Big clients and provided guidance on ongoing deep learning projects, as well as a management consultant and a software engineer earlier in his career. A continuous learner, Mo conducts research on applications of deep learning, reinforcement learning, and graph analytics toward solving existing and novel business problems and brings a diversity of educational and hands-on expertise connecting business and technology. He holds an MBA, a master’s degree in computer science, and a bachelor’s degree in mathematics.

Presentations

PyTorch: A flexible approach for computer vision models Tutorial

From photo filter of social networks to self driving cars to detecting skin cancer, Computer Vision has brought applied deep learning to the masses. Built by the pioneers of Computer Vision software, PyTorch is allow developers to rapidly build computer vision models. Learn the fundamental concepts of computer vision and apply them in PyTorch for building computer vision applications.

David Patterson is a professor emeritus at UC Berkeley, a distinguished engineer in Google Brain, and vice chair of the board of the RISC-V Foundation. His most successful research projects are reduced instruction set computers (RISC), redundant arrays of inexpensive disks (RAID), and network of workstations, which together led to multibillion-dollar industries, seven books, and about 40 honors, including election to the National Academy of Engineering, the National Academy of Sciences, and the Silicon Valley Engineering Hall of Fame. He also shared the ACM Turing award, the IEEE von Neumann Medal, and NEC C&C prize with John Hennessy, past president of Stanford University and coauthor of two of his books. David holds an AB, MS, and PhD, all from UCLA.

Presentations

Keynote by David Patterson Keynote

Keynote by David Patterson

Jake Porway is the founder and executive director of DataKind, a nonprofit that harnesses the power of data science in the service of humanity. He is an alum of the New York Times R&D Lab and has worked at Google and Bell Labs. A recognized leader in the Data for Good Movement, he has spoken at IBM, Microsoft, Google, and the White House. Jake is also a PopTech Social Innovation fellow and a National Geographic Emerging Explorer. He holds a BS in computer science from Columbia University and an MS and PhD in statistics from UCLA.

Presentations

AI: A force for good Session

DataKind Founder and Executive Director Jake Porway will shed light on AI’s true potential to impact the world in a positive way. As the head of an organization applying AI for social good, Jake will share best practices, discuss the importance of using human-centered design principles, and address ethical concerns and challenges one may face in using AI to tackle complex humanitarian issues.

Delip Rao is the founder of R7 Speech Science, a San Francisco-based company focused on building innovative products on spoken conversations. Previously, Delip was the founder of Joostware, which specialized 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 from O’Reilly.

Presentations

Natural language processing with deep learning 2-Day Training

Delip Rao explores natural language processing with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks.

Meghana Rao is a Developer Evangelist with the Software and Services group at Intel. With her current focus on Artificial Intelligence, she works with universities and developers at large at evangelizing Intel’s AI portfolio and solutions helping them understand Machine Learning and Deep Learning concepts, building models using Intel optimized frameworks and libraries like Caffe*, Tensorflow* and Intel® Distribution of Python*. She has a Bachelor’s degree in Computer Science and Engineering and a Master’s degree in Engineering and Technology Management with past experience in embedded software development, Windows* app development and UX design methodologies.

Presentations

Create, Optimize, and Deploy a Deep Learning Solution using Tensorflow and Caffe 2-Day Training

This hands-on training will leave attendees knowing how to build, implement and deploy a deep learning solution. Every registered attendee will receive free hardware to work with during the training. The hardware is yours to keep at the end of the event.

Rachael Rekart leads the Machine Assistance team at Autodesk, responsible for developing and delivering the Autodesk Virtual Agent (AVA). AVA currently has over 100,000 conversations with customers per month and has reduced call resolution times for our most repetitive support contacts by 99%, enabling our agents to focus on more complex customer issues.

Rachael believes that AI and machine learning will continue to redefine the way Autodesk engages with customers for their service and support needs. She is an active evangelist of AI both inside and outside the company, speaking about AVA’s evolution and capabilities at conferences, internal engagements, and as a consultant for companies getting started with AI.

Prior to her work with Machine Assistance, Rachael led Autodesk teams focused on implementing process optimizations and system improvements to ensure sustainable business growth. Before joining Autodesk in early 2013, Rachael worked in demand planning and supply chain management.

Presentations

How Autodesk Is Humanizing Customer Support with AI: Meet AVA Session

Autodesk Virtual Agent (AVA) has revolutionized the way Autodesk approaches customer service. Built on IBM's cognitive technology, AVA responds to the most common customer questions. Customers chat with AVA as they would a human, in natural language, with AVA returning accurate answers, processing a transaction quickly, or gathering information to pass to a human counterpart to resolve the query.

Ofer Ronen leads the Chatbase bot analytics team within Area 120, an incubator for early-stage products operated by Google. Previously, he served as CEO of Pulse.io, an app performance monitoring service (acquired by Google), and CEO of ad network Sendori (acquired by IAC). Ofer is a startup mentor at Stanford and an angel investor in Lyft, Palantir, and Klout. He holds an MS in artificial intelligence from Michigan and an MBA from Cornell.

Presentations

Things nobody told you about building bots and conversational UI Session

Chatbots are expected to make machine communication feel human. But high-quality bot experiences are very hard to build.

Joe Rothermich is a Sr. Director of Quant Research and Data Science for Thomson Reuters Labs. He heads a research team in San Francisco which develop new quantitative models and analytics for systematic and fundamental investors. The lab is using traditional quantitative finance techniques and factor-based modeling as well as performing new research in machine learning, data science, and text mining / NLP. The lab is also investigating new data sources for investment research including big data and alternative data.

Prior to Thomson Reuters, Joe was a quantitative portfolio manager at a Lincoln Vale LLC, a Fintech startup founder, and a consultant. Joe received an M.Sc. in Natural Computation from the University of Birmingham, UK, and a B.S. in Systems Analysis from Miami University. He holds the Chartered Financial Analyst (CFA) designation and is a member of the CFA Institute.

Presentations

Applications of AI for Quantitative Finance at Thomson Reuters Session

The finance industry, after a slow start, is quickly catching up with others in its adoption of AI. This session will show examples of how Thomson Reuters Labs is using AI to perform research in building quantitative investment models. We’ll discuss our research in deep learning for credit risk, machine learning/NLP for unlocking alternative data sets, and financial graph-based analytics.

Brennan Saeta is a software engineer on the Google Brain team, working on TensorFlow and Cloud TPUs

Presentations

tf.data: Fast, flexible, and easy-to-use data pipelines for TensorFlow Session

Training data is the lifeblood of a machine learning system, and modern accelerators like GPUs and TPUs are very thirsty for it. The tf.data library provides efficient access to your data in a variety of formats, and a wide range of transformations for augmenting your data. In this talk, we will introduce tf.data and show you how to use it to achieve peak performance in your training pipeline.

Jake co-leads Emergence’s practice focused on machine learning-enabled enterprise applications. He’s passionate about the role ML can play in helping to augment workers, an approach Emergence has dubbed “Coaching Networks”.

Jake currently serves as on the boards of DroneDeploy, Guru, Comfy, and Textio.

Prior to joining Emergence, Jake worked in Kleiner Perkins’ Green Growth Fund, where he sourced and led diligence on companies in the geospatial, agricultural tech, and enterprise SaaS sectors.

Jake earned his B.A., magna cum laude, from Yale University and his MBA from Stanford Graduate School of Business, where he was an Arjay Miller Scholar. Jake also earned an MS in Environment and Resources from Stanford.

Jake loves antique swords and musical parodies.

Presentations

AI is my Co-Pilot Session

Much attention in enterprise AI today is focused on automation. We think the more interesting applications will focus on worker augmentation and call this phenomena Coaching Networks. Learn what Coaching Networks-based companies are, how to build them, and why we think they'll create a company that will dwarf Salesforce in scale.

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. Dylan Bargteil offers an overview of the TensorFlow graph using its Python API. You'll start with simple machine learning algorithms and move on to implementing neural networks. Along the way, Dana covers several real-world deep learning applications, including machine vision, text processing, and generative networks.

Noah Schwartz is co-founder and CEO of Quorum AI, a San Francisco-based AI SaaS company that specializes in lightweight, distributed AI architectures. Prior to founding Quorum, Noah spent 12 years in academic research, most recently at Northwestern University as the Assistant Chair of Neurobiology. Noah also served as Senior Data Scientist at Lumos Labs, creators of the popular Lumosity brain training app. During his time in academia, Noah’s work focused on information processing in the brain and he has translated his research into products in augmented reality, sensor fusion, brain-computer interfaces, computer vision, and embedded robotics control systems.

Presentations

The Future of AI is Distributed: Peer-to-Peer Learning and Multi-Agent AI at the Edge Session

Noah Schwartz explores the most recent advances in cooperative learning systems, including distributed and federated learning systems for real-world, edge-based AI. He also considers the pros and cons of multi-agent systems, and demonstrates how Quorum AI is working to bridge the gap with the Quorum AI Framework.

Rudina Seseri is a Founder and Managing Partner at Glasswing Ventures, an early-stage venture capital firm dedicated to investing in the next generation of AI-powered technology companies that connect consumers and enterprises and secure the ecosystem. With over 15 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. Rudina’s portfolio investments include Talla, Celtra, CrowdTwist, Jibo and SocialFlow. Rudina has been appointed by the Dean of the Harvard Business School (HBS) for a fourth consecutive year to serve as Entrepreneur-In-Residence for the Business School, as Executive-In-Residence for Harvard University’s innovation-Lab and has most recently been named to the 2018 HBS inaugural group of Rock Venture Capital Partners. She is also a Member of the Business Leadership Council of Wellesley College.
Rudina also serves as Advisor for L’Oreal USA Women in Digital, as Director on the Board of the Massachusetts Innovation and Technology Exchange (MITX) and on the Board of Overseers for Boston Children’s Hospital. She has been named a 2017 Boston Business Journal Power 50: Newsmaker, a 2014 Women to Watch honoree by Mass High Tech and a 2011 Boston Business Journal 40-under-40 honoree for her professional accomplishments and community involvement. She graduated magna cum laude from Wellesley College with a BA in Economics and International Relations and with an MBA from the Harvard Business School (HBS). She is a member of Phi Beta Kappa and Omicron Delta Epsilon honor societies.

Presentations

From Ideation to Realization – Launching the World’s First Social Robot Session

Rudina Seseri, Managing Partner, Glasswing Ventures, an early-stage venture capital firm dedicated to investing in the next generation of AI-powered technology companies and Brian Eberman, CEO of Jibo, Inc., makers of the eponymous robot, share their experience launching Jibo, Time Magazine's # 1 Best Invention of the Year and the world's first social robot for the home.

Avesh Singh is an engineer at Cardiogram, a startup that applies deep learning to wearable data. Previously, Avesh worked at Nest Labs and Google. He holds a a BS and MS in computer science from Carnegie Mellon University.

Presentations

Debuggable Deep Learning Session

Deep Learning is often called a black box, so how can we diagnose and fix problems in a Deep Neural Network (DNN)? Engineers at Cardiogram explain how they systematically debugged DeepHeart, a DNN that detects cardiovascular disease from heart rate data. You'll leave this talk with an arsenal of tools for debugging DNNs, including Jacobian analysis, TensorBoard, and "DNN Unit Tests".

Presentations

The human centered and ethical approach to designing intelligent systems with multidisciplinary teams Session

The public dialogue around artificial intelligence takes place at extremes, where some choose to focus on how robots will take everyone’s job and others speak of a utopia where AI will do everything from cure cancer to solve world hunger. I believe there’s an alternative to these extreme views of AI. Instead of artificial intelligence, we instead must focus on augmenting people’s intelligence.

Chris Smith is a senior principal software engineer at Zocdoc, an online doctor marketplace and booking startup. His current focus is on developing deep learning models to help Zocdoc’s patients understand the complex world of medical insurance. Over his career at Zocdoc he’s built numerous patient focused products, rebuilt their CI/CD systems, helped migrate Zocdoc from the datacenter to AWS and setup the first version of their microservice infrastructure. Through machine learning Chris is helping to bring data driven products to an industry traditionally resistant to change.

Presentations

Reverse Engineering your AI Prototype and the Road to Reproducibility Session

With the help of better software, cloud infrastructure and pre-trained networks, AI models have become easier to build. But once your solution veers from a common path, hidden challenges in reproducibility and implementation arise. We cover our experience and learnings building a computer vision and OCR app for reading and classifying insurance cards.

Evan Sparks is co-founder and CEO of Determined AI, a software company that makes machine learning engineers and data scientists fantastically more productive. Previously, Evan worked in quantitative finance and web intelligence. He holds a PhD in computer science from UC Berkeley, where, as a member of the AMPLab, he contributed to the design and implementation of much of the large-scale machine learning ecosystem around Apache Spark, including MLlib and KeystoneML. He also holds an AB in computer science from Dartmouth College.

Presentations

Software Development in the Age of Deep Learning Session

In spite of the enormous excitement about the potential of deep learning, several key challenges--from prohibitive hardware requirements to immature software offerings--are impeding its widespread enterprise adoption. We will discuss fundamental challenges facing organizations looking to adopt Deep Learning, and present novel solutions to overcome several of these challenges.

Ramesh Sridharan is a machine learning engineering manager at Captricity. Ramesh is passionate about using technology for social good, and his research has helped enable a cross-collaboration between researchers and doctors to understand large, complex medical image collections, particularly in predicting the effects of diseases such as Alzheimer’s on brain anatomy. He holds a PhD in electrical engineering and computer science from MIT’s Computer Science and Artificial Intelligence lab (CSAIL), where his thesis focused on developing machine learning and computer vision technologies to enhance medical image analysis.

Presentations

How Captricity Built a Human-Level Handwriting Recognition Engine Using Data-Driven AI Session

At Captricity, we've deployed a machine learning pipeline that can read handwriting at human-level accuracy. This talk highlights the big ideas we learned building and deploying this system: using data to identify specific problems to solve using AI, and to evaluate and validate the algorithm itself as well as the overall system once deployed.

Amit Srivastava is the Director of research engineering in the New Product Division at eBay. He currently leads the eBay ShopBot and eBay Digital Assistants initiatives where he is exploring the application of cutting-edge Artificial Intelligence (AI) techniques to power multimodal and conversational experiences for eBay’s customers. His team focuses on the development and integration of algorithms in the areas of natural language understanding, information extraction, machine translation, knowledge-base population and statistical inferencing, conversational dialog management, and natural language generation to develop e-commerce chatbots and digital assistants that will allow customers on multiple platforms to search and procure their version of perfect from eBay’s extensive product inventory. In addition to researching solutions to difficult AI problems, Amit is also passionate about cloud-based tools and software engineering methodologies for the development of distributed, highly-scalable, and reliable micro-services that encompass these novel algorithms and can be deployed and integrated in the Google Cloud Platform. Before joining eBay, Amit led the Media Exploitation Solutions group in the Speech, Language, and Multimedia business unit at Raytheon BBN Technologies. In this capacity, he managed the integration of cutting-edge speech and natural language processing research into robust products and services that were transitioned to several operational installations, both within the United States and abroad. Amit guided the expansion of BBN’s media monitoring solutions from just one to six products and services that formed the backbone of BBN’s speech solutions business.

Presentations

eCommerce – taking eBay from Web and App to ChatBots on Messenger and Google Assistant Session

Discover how eBay developed an AI-powered chatbot for the smart speakers, from developing a deep understanding of the shopper’s psychology to building dedicated AI capabilities to support the experience. In this session, we'll review this project from inception to launch: the do’s and don’t’s, the best practices, and the technology behind the scenes.

Ashok N. Srivastava is the senior vice president and chief data officer at Intuit, where he is responsible for setting the vision and direction for large-scale machine learning and AI across the enterprise to help power prosperity across the world and in the process is hiring hundreds of people in machine learning, AI, and related areas at all levels. Ashok has extensive experience in research, development, and implementation of machine learning and optimization techniques on massive datasets and serves as an advisor in the area of big data analytics and strategic investments to companies including Trident Capital and MyBuys. Previously, Ashok was vice president of big data and artificial intelligence systems and the chief data scientist at Verizon, where his global team focused on building new revenue-generating products and services powered by big data and artificial intelligence; senior director at Blue Martini Software; and senior consultant at IBM. He is an adjunct professor in the electrical engineering department at Stanford and is the editor-in-chief of the AIAA Journal of Aerospace Information Systems. Ashok is a fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA). He has won numerous awards, including the Distinguished Engineering Alumni Award, the NASA Exceptional Achievement Medal, the IBM Golden Circle Award, the Department of Education Merit Fellowship, and several fellowships from the University of Colorado. Ashok holds a PhD in electrical engineering from the University of Colorado at Boulder.

Presentations

Executive Briefing: Moving AI Off Your Product Roadmap and Into Your Products Session

In this session, Intuit Chief Data Officer Ashok Srivastava explores how to make your organization AI ready, how to determine which are the right AI applications for your business and products, and how to accelerate your AI efforts with speed and scale.

Ion Stoica is a professor in the EECS Department at the University of California, Berkeley, where he does research on cloud computing and networked computer systems. Ion’s previous work includes dynamic packet state, chord DHT, internet indirection infrastructure (i3), declarative networks, and large-scale systems, including Apache Spark, Apache Mesos, and Alluxio. He is the cofounder of Databricks—a startup to commercialize Apache Spark—and Conviva—a startup to commercialize technologies for large-scale video distribution. Ion is an ACM fellow and has received numerous awards, including inclusion in the SIGOPS Hall of Fame (2015), the SIGCOMM Test of Time Award (2011), and the ACM doctoral dissertation award (2001).

Presentations

Building reinforcement learning applications with Ray Tutorial

Ion Stoica, Robert Nishihara, and Philipp Moritz lead a deep dive into Ray, a new distributed execution framework for reinforcement learning applications, walking you through Ray's API and system architecture and sharing application examples, including several state-of-the-art RL algorithms.

David Talby is a chief technology officer at Pacific AI, helping fast-growing companies apply big data and data science techniques to solve real-world problems in healthcare, life science, and related fields. David has extensive experience in building and operating web-scale data science and business platforms, as well as building world-class, Agile, distributed teams. Previously, he was with Microsoft’s Bing Group, where he led business operations for Bing Shopping in the US and Europe, and worked at Amazon both in Seattle and the UK, where he built and ran distributed teams that helped scale Amazon’s financial systems. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

Presentations

Executive Briefing: What you must know to build AI systems that understand natural language Session

New AI solutions in question answering, chat bots, structured data extraction, text generation and inference all require deep understanding of the nuances of human language. This talk details best practices, challenges & risks in building NLU based systems. The examples and case studies come from real products and services, built by Fortune 500 companies and startups over the past six years.

Ameet Talwalkar is co-founder and chief scientist at Determined AI and an assistant professor in the School of Computer Science at Carnegie Mellon University. His research addresses scalability and ease-of-use issues in the field of statistical machine learning, with applications in computational genomics. Ameet led the initial development of the MLlib project in Apache Spark. He is the coauthor of the graduate-level textbook Foundations of Machine Learning (MIT Press) and teaches an award-winning MOOC on edX, Distributed Machine Learning with Apache Spark.

Presentations

Software Development in the Age of Deep Learning Session

In spite of the enormous excitement about the potential of deep learning, several key challenges--from prohibitive hardware requirements to immature software offerings--are impeding its widespread enterprise adoption. We will discuss fundamental challenges facing organizations looking to adopt Deep Learning, and present novel solutions to overcome several of these challenges.

Neil Tan is an ARM developer evangelist with a keen interest on IoT and machine learning. He is a speaker at FOSDEM and the main author of uTensor. He works closely at open source developer communities in Asia and has taken parts as design-contest judge and event guest speaker.

Presentations

uTensor - How small can AI get? Session

What if I told you AI inferencing be done on chips that cost less than a dollar? uTensor lets you do just that. It is a custom Tensorflow runtime for microcontrollers (MCUs), the first framework to streamline model deployments on MCUs. This allow you to push AI to the edge rather than sending everything to cloud. Now, with CMSIS-NN integration, uTensor is faster and more energy efficient.

Technical Leader, Software Architect, Consultant

12 years of software industry experience, ranging from hands-on coding to business development and software architecture.

Having Master’s degree in applied mathematics, Ivan has vast experience with mission-critical medical software for devices, apps and web based platforms. Some of the accomplishments include projects with implanted pacemakers, external defibrillator, cutting edge medical image visualization and distributed health assessment framework. His areas of interest are embedded software, cloud based analytics, high performance distributed systems and user interface engineering.

Ivan as certified Scrum Product Owner and excels at execution, having a proven track record of building high performing agile software development teams and driving them to build great products.

Specialties: embedded software, cloud software, devops, software architecture, team leadership, startups

Presentations

Building Intelligent Mobile Applications in Health Care Tutorial

We will give a tutorial on how to build a deep learning model and build intelligent applications on edge devices including iOS, Android, and Windows. Our working example in this tutorial is from radiology. Chest X-rays are currently playing a crucial role in lung disease detection. How do we power clinicians to identify possible lung diseases in areas with less access to radiologists?

Abhishek is a Senior Software Engineer with Cortex, the Machine Learning Platform Team at Twitter where he leads the ‘Entity Embeddings’ team. He started his journey with Twitter as part of the Ads prediction team for its direct response ad products. Previously, Abhishek worked with Tellapart, an Ad tech startup which was acquired by Twitter and the Prime Video Recommendations team at Amazon where he lead the development of the first generation ML based recommendation system for videos. He graduated with a Masters degree from University of Souther California in LA.

Presentations

Making Machine Learning Easy with Embeddings Session

This talk will provide a glimpse of how Cortex (ML platform team at Twitter) is developing models, related tooling & infrastructure with the objective of making Entity Embeddings a "First Class Citizen" within Twitters ML platform. Will share success stories on how developing such an ecosystem increases efficiency, productivity and leads to better outcomes across product ML teams.

Wee Hyong Tok is a principal data science manager at Microsoft, where he works with teams to cocreate new value and turn each of the challenges facing organizations into compelling data stories that can be concretely realized using proven enterprise architecture. Wee Hyong has worn many hats in his career, including developer, program and product manager, data scientist, researcher, and strategist, and his range of experience has given him unique superpowers to nurture and grow high-performing innovation teams that enable organizations to embark on their data-driven digital transformations using artificial intelligence. He strongly believes in story-driven innovation and has a passion for leading artificial intelligence-driven innovations and working with teams to envision how these innovations can create new competitive advantage and value for their business. He coauthored one of the first books on Azure Machine Learning, Predictive Analytics Using Azure Machine Learning, and authored another demonstrating how database professionals can do AI with databases, Doing Data Science with SQL Server.

Presentations

How to use Transfer Learning to Bootstrap Image Classification and Question Answering (QA) Session

Transfer learning enables you to use pre-trained deep neural networks and adapt them for various deep learning tasks (e.g. image classification, question answering, and more). Join Wee Hyong Tok and Danielle Dean, as they share the secrets of transfer learning, and how you can custom these pre-trained models for your use cases, and jumpstart.

KC Tung is the principal data scientist at AT&T Advertising & Analytics Company, which is the premier business partner to AT&T AdWorks, the leader in addressable TV advertising. KC is the lead data scientist and key architect to many industry-first, novel, scalable, open-source model based solutions to advertising media’s inventory planning, audience targeting or matching, cross-channel execution, testing and optimization. KC’s innovations leverage latest machine learning and Artificial Intelligence (AI) algorithms to solve many problems in audience measurement, attribution models, programmatic advertising, web browsing, bioinformatics, and social networks.

KC’s technical specialties include algorithms and methods that are important for programmatic advertising ecosystem, such as Deep Learning, LSTM, Artificial Intelligence (AI), Neural Networks, Gradient Boosting, Random Forest, Parametric and non-parametric Bayesian methods, stochastic process, Dirichlet process mixture multinomial model, and Markov Chain Monte Carlo (MCMC).

KC holds a PhD in Molecular Biophysics from The University of Texas Southwestern Medical Center in Dallas, TX.

Presentations

A Novel Adoption of LSTM in Customer Touchpoint Prediction Problems Session

The adoption of LSTM in touchpoint prediction stems from the need to model customer journey or the conversion funnel as a series of touchpoints. For an advertiser or marketer, taking into account the sequence of events that leads to a conversion will add tremendous value to the understanding of conversion funnel, impact of types of touchpoints, and even identify high potential leads

Mike Tung is the CEO of Diffbot, an adviser at the Stanford StartX accelerator and the leader of Stanford’s entry in the DARPA Robotics Challenge. In a previous life, he was a patent lawyer, a grad student in the Stanford AI lab, and a software engineer at eBay, Yahoo!, and Microsoft. Mike studied Electrical Engineering and Computer Science at UC Berkeley and Artificial Intelligence at Stanford.

Presentations

Executive Briefing: Knowledge Graphs for AI Session

A new web of information is being built--this time for machines, by machines--and this will lead us to a future of smarter systems that improve our lives and businesses.

Ayin Vala is the Founder of DeepMD and the Co-founder and Chief Data Scientist of the nonprofit organization Foundation for Precision Medicine. He and his research and development team work on statistical analysis and machine learning, pharmacogenetics, molecular medicine, and sciences relevant to the advancement of medicine and healthcare delivery. Ayin has won several awards and patents in the healthcare, aerospace, energy, and education sectors. Ayin holds master’s degrees from Harvard University and Georgia Tech, and resides in Silicon Valley.

Presentations

Predicting Alzheimer’s: Generating Neural Networks to Detect the Neurodegenerative Disease Session

Complex diseases like Alzheimer’s Disease cannot be cured by pharmaceutical or genetic sciences alone and current treatments lead to mixed successes. By introducing more data-driven investigation we can take into account individual variability in medicine intake, lifestyle, genetic factors, and medical images for each person and use the power of big data and AI to treat challenging diseases.

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

Presentations

Executive Briefing: Is there a Moore’s Law for artificial intelligence? Session

Is there a “Moore’s Law” for AI: for AI’s to serve every individual customers, we need much more complex natural language understanding, ideas, and behaviors. Will compositional deep learning put us on a new curve?

I am a Data Scientist in the Applied Machine Learning team at Uber.

My research interest and skills combine language processing technologies, machine learning methodologies, and social science theories to analyze large-scale data and understand social behavior in online environments.

I received my Ph.D. from the Language Technologies Institute under School of Computer Science at Carnegie Mellon University. My thesis developed a new machine learning model to measure self-disclosure in social networking site communication at scale and used it to better understand the contexts in which users disclose more or less about themselves. In prior research, I have also worked on question answering and information retrieval.

Keywords: Data science, natural language processing, machine learning, information extraction, social computing, social media analysis, and big data analysis.

Presentations

Improving customer support with natural language processing and deep learning Session

At Uber we implemented a machine learning and natural language processing system that suggests to our customer support representatives the most likely solutions to a ticket. This makes them faster and more accurate while providing a better user experience. We describe how we built two versions of the system, with traditional and deep learning models, and discuss the lessons learned along the way.

Emily Watkins is a Solutions Engineer at Pure Storage where she helps teams achieve faster time to insight with highly-parallelized data pipelines. Prior to Pure Storage, she helped create monitoring tools that bring “real-time analytics” closer to real-time. The more data the better.

Presentations

High-Performance Input Pipelines for Scalable Deep Learning Session

Learn how to keep your GPUs fed with the entirety of your data lake as you train the next-generation of deep learning architectures.

Daniel Whitenack is a PhD trained data scientist/engineer with industry experience developing data science applications for large and small companies, including predictive models, dashboards, recommendation engines, and more. Daniel has spoken at conferences around the world (Applied ML Days, Spark Summit, PyCon, ODSC, GopherCon, and more), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects. @dwhitena on Twitter.

Presentations

AI on Kubernetes Tutorial

In this tutorial, we will learn how to easily deploy and scale AI/ML workflows on any infrastructure using Kubernetes, the container orchestration engine used by all of the top technology companies. Kubernetes was built, from the ground up, to run and manage highly distributed workloads on huge clusters, and, thus, it provides a solid foundation for model development.

Candace Worley is Vice President and Chief Technical Strategist for McAfee. She manages a worldwide team of Technical Strategists responsible for driving thought leadership and advancing technical innovation in McAfee security solutions.

Prior to this role, Candace served as Vice President for Enterprise Solutions for the Intel Security Group at Intel Corporation. She had worldwide responsibility for all facets of product and vertical marketing for the complete corporate products solutions set.

Worley joined McAfee in 2000 and has held a number of technology leadership positions in her McAfee career including, five and a half years as the SVP and General Manager of the Enterprise Endpoint Security business. Prior to joining McAfee in 2000, she spent seven years with Mentor Graphics, where she led a team of product managers responsible for electronic design automation and electronic component software.

Worley holds a bachelor’s degree in management from Oregon State University and an MBA degree from Marylhurst University.

Presentations

Human Machine Teaming: Why the Human Element Will Always be Indispensable in Cybersecurity Session

Hear why the future isn’t about AI or machine learning; it’s about human-machine teaming. As long as there are human adversaries behind cybercrime and cyber warfare, there will always be a critical need for the human beings teamed with machines in cybersecurity.

Jian Wu is a data analytics developer at Samsung SDSRA AI and Machine Learning Lab where he works on AI and Machine Learning projects using Kubernetes, Python, TensorFlow, Scala, Spark, and JavaScript with AngularJS and D3.js.

He’s been a software developer in San Francisco Bay Area for 20 years, developing Android video streaming app at Huawei USA, Device Gateway and RESTful WS Server at Netflix, Payment Gateway at eBay/PayPal, and Java middleware server and applications at Oracle.

Presentations

Evaluate Deep Q-Learning for Sequential Targeted Marketing with 10-fold Cross Validation Session

This talk presents an end-to-end engineering work to train and evaluate deep q-learning model(s) for targeting sequential marketing campaigns using 10-Fold cross validation method, we also evaluate trained DQN model(s) with neural network based baseline models and show that trained deep q-learning model does produce better optimized long-term rewards at the majority of 10 testing datasets.

Kevin Wu is an engineer at Cardiogram, a startup that applies deep learning to wearable data. Previously, Kevin worked at Two Sigma and Credit Suisse. He holds a BS and MS in computer science from Stanford University.

Presentations

Debuggable Deep Learning Session

Deep Learning is often called a black box, so how can we diagnose and fix problems in a Deep Neural Network (DNN)? Engineers at Cardiogram explain how they systematically debugged DeepHeart, a DNN that detects cardiovascular disease from heart rate data. You'll leave this talk with an arsenal of tools for debugging DNNs, including Jacobian analysis, TensorBoard, and "DNN Unit Tests".

Mariya Yao is Chief Technology & Product Officer at Metamaven. Metamaven intelligently automates revenue growth for global companies like Paypal, LinkedIn, L’Oreal, LVMH, and WPP.

In parallel, she’s Editor-In-Chief of TOPBOTS, the largest publication and community for business leaders applying AI to their enterprises.

Mariya is also a Forbes writer covering the interplay of human and machine intelligence, and co-author of “Applied AI: A Handbook For Business Leaders”, which she launched onstage at CES 2018.

Presentations

Executive Briefing: Organizational Design For Effective AI Session

Executives in every business and function are asked to "innovate with AI", but barriers to successful adoption for most enterprises are organizational, not technical. Effective AI requires not only technical talent, but extended interdisciplinary coordination between teams, investments in retraining your workforces at all levels, and cultivation of an experimental, data-driven culture.

Ting-Fang Yen is Director of Research at DataVisor, the leading fraud, crime and abuse detection solution utilizing unsupervised machine learning to detect fraudulent and malicious activity such as fake account registrations, fraudulent transactions, spam, account takeovers and more. She has over 10 years of experience in applying big data analytics and machine learning to tackle problems in cybersecurity. Ting-Fang holds a PhD in electrical and computer engineering from Carnegie Mellon University.

Presentations

Deep Learning for Large-Scale Online Fraud Detection Session

Online fraud is oftentimes orchestrated by organized crime rings, where malicious user accounts actively target various modern online services for financial gain. In this talk, we present a real-time, scalable fraud detection solution backed by deep learning and built on Spark and Tensorflow. We demonstrate how our system outperforms traditional solutions such as blacklists and machine learning.

Wojciech Zaremba is a co-founder of OpenAI (2016-now), where he leads the robotics team, which is working on developing general purpose robots via new approaches to transfer learning and teaching robots unprecedentedly complex behaviors. He received his MS from Warsaw University / Ecole Polytechnique (2013), and his PhD from New York University (2015) under Prof. Rob Fergus and Prof. Yann LeCun. During his PhD, Wojciech spent one year at Facebook AI Research and one year at Google Brain. Wojciech’s PhD and subsequent research contributions have involved using neural network techniques to get computers to learn sophisticated algorithms from raw data: his contributions include the applying translation models to computer programs and building first neural Turing machines with discrete actions. Moreover, he worked on discovery of adversarial examples, improved training of GANs, and development of OpenAI gym. His current work is dedicated towards the development of the General Purpose Robots.

Presentations

Deep Reinforcement Learning for Robotics Session

Deep Reinforcement Learning for Robotics

Huaixiu Zheng is currently a Data Scientist at Uber focusing on NLP and deep learning related applications. He is a major contributor to several ongoing efforts at Uber in using deep-learning based NLP/ML/AI technologies to empower the intelligent business operations. He received his PhD in Quantum Physics and Quantum Computation in 2013 from Duke University. He made significant contributions in the fields of quantum waveguide-QED, quantum phase transition in dissipative environments, and photonic quantum computation. He conducted a Postdoctoral Research at Yale University working on quantum error corrections and topological quantum computation. He has published more than 25 journal and conference papers in prestigious journals such as Nature, Nature Physics and Physical Review Letters with more than 1000 citations. He received prestigious academic and industrial awards including Chinese Government Award for Outstanding Self-Financed Students Abroad, John T. Chambers Scholars, 2nd Place Award of SPIE-AAPM-NCI Prostate MR Classification Challenge, 2nd Place Award SPIE-AAPM-NCI Prostate MR Gleason Grade Group Challenge, Second Prize (as part of team Future Lifecare) of The 8th Intelligent System Summit & TEEC Cup Startup Contest etc.

Presentations

Improving customer support with natural language processing and deep learning Session

At Uber we implemented a machine learning and natural language processing system that suggests to our customer support representatives the most likely solutions to a ticket. This makes them faster and more accurate while providing a better user experience. We describe how we built two versions of the system, with traditional and deep learning models, and discuss the lessons learned along the way.

Xiaoyong Zhu is a program manager at Microsoft, where he focuses on distributed machine learning and its applications.

Presentations

Building Intelligent Mobile Applications in Health Care Tutorial

We will give a tutorial on how to build a deep learning model and build intelligent applications on edge devices including iOS, Android, and Windows. Our working example in this tutorial is from radiology. Chest X-rays are currently playing a crucial role in lung disease detection. How do we power clinicians to identify possible lung diseases in areas with less access to radiologists?

Shelley has over fifteen years of experience in technology as a software engineer, research scientist, business executive, and venture capitalist. Prior to founding 11.2 Capital, 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, ML7 Associate at Creative Destruction Lab, and served on Enigma 2016’s program committee.

Presentations

Data-Driven Healthcare Session

Given the revolution in data and healthcare, we are reflecting on how we see certain sectors unfold and opportunities for innovation. We will discuss innovations that advance precision medicine by bringing together interdisciplinary fields across biology, engineering, data science, and clinical care.