Presented By O’Reilly and Intel Nervana
Put AI to work
September 17-18, 2017: Training
September 18-20, 2017: Tutorials & Conference
San Francisco, CA

Speakers

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

Filter

Search Speakers

Brandon Ballinger is a cofounder at Cardiogram. Previously, Brandon was a cofounder at Sift Science and an engineer at Google on speech recognition and ads quality. He was also one of the engineers called in by the White House to help fix Healthcare.gov. Brandon holds a BS in computer science from the University of Washington.

Presentations

Deep learning with limited labeled data session

Deep learning is fueled by large labeled datasets, but in domains like medicine, each label represents a human life at risk. Avesh Singh and Brandon Ballinger offer an overview of autoencoders, heuristic training, and few-shot learning, with an emphasis on practical tips to create high-performing models utilizing hundreds of thousands of unlabeled data points and only thousands of labeled points.

Lukas Biewald is the founder and chief data scientist of CrowdFlower, a data enrichment platform that taps into an on-demand to workforce to help companies collect training data and do human-in-the-loop machine learning. Previously, Lukas led the search relevance team for Yahoo Japan and was a senior data scientist at Powerset (acquired by Microsoft in 2008). He was featured on Inc. magazine’s 30 under 30 list. Lukas holds a BS in mathematics and an MS in computer science from Stanford. He is also an expert Go player.

Presentations

Active learning and transfer learning session

Making the best possible use of training data is essential for effective machine learning. Active learning can make your training data collection 10x–1,000x more efficient, while transfer learning opens up a world of new training data possibilities. Lukas Biewald explores the state of the art in training data, active learning, and transfer learning, especially as applied to deep learning.

Ron Bodkin is the vice president and general manager for artificial intelligence at Teradata, where he is responsible for leading a cross-functional team spanning product development, strategic offerings, and analytics consulting to identify and commercialize value that AI technologies. Previously, Ron was CTO of services and architecture at Teradata; the founding CEO of Think Big Analytics (acquired by Teradata in 2014), which provides end-to-end support for enterprise big data, including data science, data engineering, advisory and managed services, and frameworks such as Kylo for enterprise data lakes; vice president of engineering at Quantcast, where he led the data science and engineer teams that pioneered the use of Hadoop and NoSQL for batch and real-time decision making; founder of New Aspects, which provided enterprise consulting for aspect-oriented programming; and cofounder and CTO of B2B applications provider C-Bridge, where he managed a team of 900 people and led the company to a successful IPO. Ron holds a BS in math and computer science with honors from McGill University and a master’s degree in computer science from MIT, where he was also pursuing a PhD. He left the program after his idea for C-Bridge placed in the finals of MIT’s $50K Entrepreneurship Contest.

Presentations

Deep learning in the enterprise: Opportunities and challenges session

Tools, frameworks, access to high-value data, and practical approaches to deployment and integration with existing systems and applications are just some of the considerations facing companies adopting deep learning. Ron Bodkin explores tools, open source technology, frameworks, and strategies to cost-effectively achieve strategic results with deep learning in the enterprise.

Blake Borgeson is the cofounder and CTO of Recursion Pharmaceuticals, where he is leading the computational development of a drug discovery platform combining high-throughput experiments and machine learning that is capable of finding potential treatments for hundreds of diseases rapidly and in parallel. Previously, he researched and built real-time navigation software for surgical procedures at the M.E. Müller Institute in Bern, Switzerland and cofounded BuildASign.com, an ecommerce company that currently employs over 350 in Austin, Texas. Blake holds a PhD in bioinformatics from UT Austin’s Marcotte Lab, where his research used machine learning to exploit new experimental techniques in rapidly mapping protein complexes, and a BS in electrical engineering from Rice University.

Presentations

AI and cellular images for universal drug discovery session

Blake Borgeson and Nan Li offer a technical overview of how Recursion—a company that applies computer vision and machine learning to create a high-dimensional feature space in which to evaluate cellular health broadly across hundreds of disease states—leverages cellular phenotyping for drug discovery.

Gary Brown leads product marketing and strategic partnerships for Movidius, a division of Intel’s new technology group. Gary’s background in embedded DSP and his passion for computer vision and deep learning technology help him navigate Movidius into this new era of machine intelligence. He holds an MS in electrical engineering from Stanford University.

Presentations

Meet the Expert with Gary Brown (Intel) Meet the Experts

If you are interested in AI--powered IoT devices or new applications in machine vision and visual intelligence, stop by and chat with Gary.

The low-power silicon platforms fueling the new era of vision-based AI

Gary Brown shares the under-the-hood technologies that are enabling devices to function at lower power and with more sophisticated deep learning computation, discussing the new innovations at the silicon level fueling the realization of visually intelligent things.

Marcos Campos is head of artificial intelligence at Bonsai, where he leads the artificial intelligence team and is helping create a next-generation AI platform that will unlock intelligence across different types of products and applications. Marcos has been working at making machine learning and artificial intelligence accessible to a large community of users for over a decade. Previously, he set the vision and strategy roadmap for machine learning at Oracle and Uber, focusing on creating scalable and easy-to-use platforms. He has been awarded over 23 patents for this work.

Presentations

Introduction to reinforcement learning Tutorial

Marcos Campos offers an overview of reinforcement learning, walking you through the various classes of reinforcement learning algorithms, the types of problems that can be solved with this technique, and how to build and train AI models using reinforcement learning and reward functions.

Gunnar Carlsson is a professor of mathematics (emeritus) at Stanford University and is cofounder and president at Ayasdi, which is commercializing products based on machine intelligence and topological data analysis. Gunnar has spent his career devoted to the study of topology, the mathematical study of shape. Originally, his work focused on the pure aspects of the field, but in 2000 he began work on the applications of topology to the analysis of large and complex datasets, which led to a number of projects, notably a multi-university initiative funded by the Defense Advanced Research Projects Agency. He has taught at the University of Chicago, the University of California, San Diego, Princeton University, and, since 1991, Stanford University, where he has served as the chair of the Mathematics Department. He is also a founder of the ATMCS series of conferences focusing on the applications of topology, and is a founding editor of the Journal for Applied and Computational Topology. Gunnar is the author of over 100 academic papers and has given numerous addresses to scholarly meetings. He holds a BA in mathematics from Harvard and a PhD in mathematics from Stanford. He is married with three grown children.

Presentations

Topological data analysis as a framework for machine intelligence Tutorial

Topological data analysis (TDA) is a framework for machine learning that synthesizes and combines machine learning algorithms to identify the shape of data. The technique is responsible for several major breakthroughs in our understanding of science and business. Gunnar Carlsson offers an overview of TDA's mathematical underpinnings and its practical application through software.

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

Presentations

Applications of neural-based models for conversational speech session

Today almost every achievement in language understanding is based on neural networks. Yishay Carmiel explains why analyzing conversational speech is still a challenging proposition despite all the recent breakthroughs in natural language processing and offers some potential solutions.

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

Presentations

Closing remarks Keynote

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

The state of AI adoption Keynote

Program chairs Ben Lorica and Roger Chen kick off the O'Reilly AI Conference in San Francisco with an overview of the current trends they have observed in the industry.

Tuesday opening remarks Keynote

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

Wednesday opening remarks Keynote

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

Xiaofang Chen is a software engineer at Pinterest working on home feed ranking. Previously, Xiaofang was a software developer at Amazon. She holds a PhD in computer science from the University of Utah.

Presentations

Escaping the forest, falling into the net: The winding path of Pinterest’s migration from GBDT to neural nets session

Pinterest’s power is grounded in its personalization systems. Over the years, these recommender systems have evolved through different types of models. Xiaofang Chen and Derek Cheng explore Pinterest's recent transition from a GBDT system to one based in neural networks powered by TensorFlow, covering the challenges and solutions to providing recommendations to over 160M monthly active users.

Amy Chenault is a senior UX designer at Insulet, where she uses design, research, and passion to help improve the quality of everyday life. During her career, Amy has had the opportunity to collaborate with amazing teams to create an artificial pancreas for people with diabetes at Insulet; improve radiologist workflows and the experience of pharmacovigilance to ensure patient safety at IBM Watson Health; democratize financial information and empower individuals through the power of financial literacy at Society of Grownups; give local communities a voice while maintaining a newspaper’s journalistic integrity at Neighborhood Square; find new and innovative ways to get kids moving in the classroom at Pearson; and build an entirely new community platform for one of the most robust communities on the internet at the Huffington Post/AOL. Work she has contributed to has garnered the 2011 Italian national prize for innovation and a 2014 CODiE nomination for best professional learning solution for education.

Presentations

The potential ick factor: Ethical considerations for designing in healthcare session

With great cognitive computing comes great responsibility. As AI becomes ubiquitous in our society, it's critical to discuss the ethical concerns of AI and ask the tough questions. This multidisciplinary roundtable opens a dialogue on how bioethical principles might be applied to everyday design practice within healthcare.

Derek Zhiyuan Cheng is software engineer on the discovery team at Pinterest, where he builds large-scale machine learning models and features to improve Pinterest’s personalization recommendation systems. Previously, he worked at Google Research, where he helped improve personalized search and recommendation systems for Google Play, News, and Google Plus. Derek has authored over 20 peer-reviewed articles published in prestigious conferences and journals for applied machine learning, information retrieval, and data mining. He holds a PhD with a focus on geosocial data mining from Texas A&M University.

Presentations

Escaping the forest, falling into the net: The winding path of Pinterest’s migration from GBDT to neural nets session

Pinterest’s power is grounded in its personalization systems. Over the years, these recommender systems have evolved through different types of models. Xiaofang Chen and Derek Cheng explore Pinterest's recent transition from a GBDT system to one based in neural networks powered by TensorFlow, covering the challenges and solutions to providing recommendations to over 160M monthly active users.

Lili Cheng is a corporate vice president of Microsoft’s AI and Research Division, where she is responsible for the AI developer platform, which includes Cognitive Services, a collection of powerful cognitive AI APIs for vision, speech, language understanding, knowledge and search that enables developers to easily add AI to their apps and services, and the Bot Framework, which makes it easy for developers to build and connect intelligent conversational AI to their customer experiences and deploy these in their own custom UI and embed them in Skype, Microsoft Teams, Cortana, Bing, Facebook, Slack, etc. Prior to Microsoft, Lili worked in Apple’s advanced technology group on the user interface research team, where she focused on Quicktime conferencing and Quicktime VR. Lili is a registered architect; she worked in Tokyo and Los Angeles for Nihon Sekkei and Skidmore, Owings & Merrill on commercial urban design and large-scale building projects. She has taught in NYU’s Interactive Telecommunications program as well as at Harvard University.

Presentations

AI mimicking nature: Flying and talking (sponsored by Microsoft) Keynote

Lili Cheng shares two examples of AI inspired by nature. In the first, Microsoft researchers created a system that uses artificial intelligence that draws on the way birds fly to keep a sailplane aloft. The second explores what makes people unique, our language instinct, and our ability to model how people socialize and accomplish work.

Astrid Chow is a senior UX product designer and strategist at IBM Watson Health, where she leads design teams working on complex health applications, such as Watson for Genomics, that utilize IBM Watson’s cognitive computing technology and develops best practices around consumer health and wellness products to leverage behavior change and service design approaches. Astrid has a background in user experience design, graphic, interactive, and branding design, information architecture, usability testing and research, content strategy, user advocacy, and leading multidisciplinary teams. Previously, she worked as a UX and graphic designer in the agency world for such clients as Lego, Healthways, Adidas, (Red), and Cadillac and led internal product teams developing analytic components at Blackboard and native mobile applications for Specialty Pharmacy at CVS Health.

Presentations

The potential ick factor: Ethical considerations for designing in healthcare session

With great cognitive computing comes great responsibility. As AI becomes ubiquitous in our society, it's critical to discuss the ethical concerns of AI and ask the tough questions. This multidisciplinary roundtable opens a dialogue on how bioethical principles might be applied to everyday design practice within healthcare.

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

Learning the learner: Using machine learning to monitor. . .machine learning? session

The best practice in machine learning is to define a clear performance measurement for each model. However, when multiple models are deployed in parallel or feed into each other, it is infeasible to manually monitor them. Ira Cohen explains how Anodot devised a way to intelligently monitor the performance of its highly complex unsupervised machine learning models.

Bradford Cross is a founding partner at DCVC, a leading machine learning and big data venture capital fund. Previously, Bradford founded Prismatic, which used machine learning for personalized content ranking and natural language processing for topic classification, and FlightCaster, which used machine learning to predict the real-time state of the global air traffic network using FAA, carrier, and weather data. A hedge fund investor and a venture investor, Bradford started his career working on statistical value and momentum strategies at O’Higgins Asset Management and was a founding partner of Data Collective. He was also a systems engineer and worked on distributed systems at Google. Bradford studied computer engineering and finance at Virginia Tech and mathematics at Berkeley.

Presentations

Vertical AI: Solving full stack industry problems using subject-matter expertise, unique data, and AI to deliver a product's core value proposition session

Low-level task-based AI gets commoditized quickly, and more general AI is decades off. While most of the machine learning talent works in big tech companies, massive, timely problems lurk in every major industry outside tech. Bradford Cross explains how vertical AI startups leverage subject-matter expertise, AI, and unique data to deliver their product's core value proposition.

Jason Dai is a senior principal engineer and chief architect for big data technologies at Intel, where he leads the development of advanced big data analytics, including distributed machine learning and deep learning. Jason is an internationally recognized expert on big data, the cloud, and distributed machine learning; he is the cochair of the Strata Data Conference in Beijing, a committer and PMC member of the Apache Spark project, and the chief architect of BigDL, a distributed deep learning framework on Apache Spark.

Presentations

Very large-scale distributed deep learning with BigDL session

Jason Dai and Ding Ding offer an overview of BigDL, an open source distributed deep learning framework built for big data platforms. By leveraging the cluster distribution capabilities in Apache Spark, BigDL successfully unleashes the power of large-scale distributed training in deep learning, providing good performance, efficient scaling on large clusters, and good convergence results.

Kenny Daniel is founder and CTO of Algorithmia. Kenny’s goal with Algorithmia is to accelerate AI development by creating a marketplace where algorithm developers can share their creations and application developers can make their applications smarter by incorporating the latest machine learning algorithms—an idea he came up with while working on his PhD, when he encountered a plethora of algorithms that never see the light of day. Kenny has also worked with companies like wine enthusiast app Delectable to build out their deep learning-based image recognition systems. Kenny holds degrees from Carnegie Mellon University and the University of Southern California, where he studied artificial intelligence and mechanism design.

Presentations

The operating system for AI: How microservices and serverless computing enable the next generation of machine intelligence session

Kenny Daniel explains why AI and machine learning are a natural fit for serverless computing and shares a general architecture for scalable and serverless machine learning in production. Along the way, Kenny discusses the issues Algorithmia ran into when implementing its on-demand scaling over GPU clusters and outlines one possible vision for the future of cloud-based machine learning.

Greg Diamos leads computer systems research at Baidu’s Silicon Valley AI Lab (SVAIL), where he helped develop the Deep Speech and Deep Voice systems. Previously, Greg contributed to the design of compiler and microarchitecture technologies used in the Volta GPU at NVIDIA. Greg holds a PhD from the Georgia Institute of Technology, where he led the development of the GPU-Ocelot dynamic compiler, which targeted CPUs and GPUs from the same program representation.

Presentations

High-performance computing opportunities in deep learning session

Accuracy scales with data and compute, transforming some difficult AI problems into problems of computational scale. Greg Diamos covers challenges to further improving performance and outlines a plan of attack for tearing down the remaining obstacles standing in the way of strong scaling deep learning to the largest machines in the world.

Ding Ding is a software engineer on Intel’s big data technology team, where she works on developing and optimizing distributed machine learning and deep learning algorithms on Apache Spark, focusing particularly on large-scale analytical applications and infrastructure on Spark.

Presentations

Very large-scale distributed deep learning with BigDL session

Jason Dai and Ding Ding offer an overview of BigDL, an open source distributed deep learning framework built for big data platforms. By leveraging the cluster distribution capabilities in Apache Spark, BigDL successfully unleashes the power of large-scale distributed training in deep learning, providing good performance, efficient scaling on large clusters, and good convergence results.

Alexei (Alyosha) Efros is an associate professor of electrical engineering and computer science at UC Berkeley. Previously, Alyosha spent nine years on the faculty of Carnegie Mellon University and has also been affiliated with École Normale Supérieure/Inria and the University of Oxford. Alyosha’s research is in the areas of computer vision and computer graphics, especially at their intersection. He is particularly interested in using data-driven techniques to tackle problems that are very hard to model parametrically but where large quantities of data are readily available. His awards include a CVPR Best Paper Award (2006), a NSF CAREER award (2006), a Sloan fellowship (2008), a Guggenheim fellowship (2008), an Okawa grant (2008), the Finmeccanica Career Development Chair (2010), the SIGGRAPH Significant New Researcher Award (2010), an ECCV Best Paper honorable mention (2010), and the Helmholtz Test-of-Time Prize (2013). Alyosha holds a PhD from UC Berkeley.

Presentations

Self-supervised visual learning and synthesis session

Alyosha Efros shares several case studies exploring the paradigm of self-supervised learning and discusses several ways of defining objective functions in high-dimensional spaces. Alyosha also covers the applications of this technology for image synthesis, including automatic colorization, image-to-image translation, curiosity-based exploration, and, terrifyingly, #edges2cats.

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

Presentations

AI for business Tutorial

Now is the time for us to define roles and capabilities for AI in business. Jana Eggers demonstrates how to deliver on an AI project for business, walking you through defining your project, setting expectations, assembling your team, hunting for data, assessing capabilities, implementing it, and rinsing and repeating.

It's the organization, stupid. session

Having spent the last three years working with Global 200 customers to get AI systems into production, Jana Eggers can tell you that the technology is (finally) ready—but the organization is not. Jana discusses the top five reasons orgs struggle—data silos, the tech-business gap, driving innovation, resistance to change, and the hype-reality gap—and shares ideas on how to overcome them.

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

Presentations

The inevitable merger of IQ and EQ in technology Keynote

Rana el Kaliouby lays out a vision for an emotion-enabled world of technology, sharing the inner workings of a multimodal emotion sensing platform that identifies emotions through facial expressions and tone of voice. Along the way, Rana explores the broad applications and ethical implications of this technology.

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

Presentations

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

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

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

Presentations

We found a way. session

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

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

The conversational business: Use cases and best practices for chatbots in the enterprise session

Drawing on her report The Conversational Business: How Chatbots Will Reshape Digital Experiences, Susan Etlinger shares use cases, emerging best practices, and design and CX principles from organizations building consumer-facing chatbots.

Shahin Farshchi is a principal at Lux Capital, where he empowers entrepreneurs aiming to accelerate humanity toward a fantastic future through feats of engineering. Shahin’s recent investments include deep learning company Nervana, recently acquired by Intel; Planet Labs, which is launching the world’s largest fleet of Earth-imaging satellites; Plethora, which is rolling out a fleet of robotic machine shops; Flex Logix, making chips that can reprogram themselves; and Zoox, designing what comes after the automobile.

Presentations

We're in the final stretch for—and early innings of—autonomous vehicles: Fireside chat with Shahin Farshchi and Ashu Rege session

Join Shahin Farshchi in conversation with Ashu Rege, who is reinventing the automobile from scratch at Zoox to offer consumers an unforgettable, autonomous transportation experience. They'll discuss early challenges that turned out to be straightforward, easy problems that turned out to be very hard, and the obstacles that lie ahead.

Bruno Fernandez-Ruiz is cofounder and CTO at Nexar, where he and his team are using large-scale machine learning and machine vision to capture and analyze millions of sensor and camera readings in order to make our roads safer. Previously, Bruno was a senior fellow at Yahoo, where he oversaw the development and delivery of Yahoo’s personalization, ad targeting, and native advertising teams; his prior roles at Yahoo included chief architect for Yahoo’s cloud and platform and chief architect for international. Prior to joining Yahoo, Bruno founded OneSoup (acquired by Synchronica and now part of the Myriad Group) and YamiGo; was an enterprise architect for Fidelity Investments; served as manager in Accenture’s Center for Strategic Research Group, where he cofounded Meridea Financial Services and Accenture’s claim software solutions group. Bruno holds an MSc in operations research and transportation science from MIT, with a focus on intelligent transportation systems.

Presentations

End-to-end deep learning at the edge session

Current driving policy models are limited to models trained using homogenous data from a small number of vehicles running in controlled environments. Bruno Fernandez-Ruiz offers an overview of a network of connected devices that is building an end-to-end driving policy to leverage the 10 trillion miles driven every year.

Laura Froelich is a data scientist at Think Big Analytics, a Teradata Company, where she is dedicated to utilizing data to discover patterns and underlying structure to enable optimization of businesses and processes, particularly through deep learning methods. Previously, she was part of a research group investigating nonspecific effects of vaccines using survival analysis methods. Laura holds a PhD from the Technical University of Denmark. For her dissertation, Decomposition and Classification of Electroencephalography Data, Laura used unsupervised decomposition and supervised classification methods to research brain activity and developed rigorous, interpretable approaches to classifying tensor data.

Presentations

Training vision models with public transportation datasets Tutorial

Computer vision is a key component in the artificial intelligence revolution. Assisted by deep learning, object detection allows automotive applications to make key navigation, guidance, and driving decisions to avoid collisions and navigation errors. Laura Froelich and Mo Patel demonstrate how to train deep learning models for object detection using publicly available transportation datasets.

Siddha Ganju is a data scientist at Deep Vision, where she works on building deep learning models and software for embedded devices. Siddha is interested in problems that connect natural languages and computer vision using deep learning. Her work ranges from visual question answering to generative adversarial networks to gathering insights from CERN’s petabyte scale data and has been published at top tier conferences like CVPR. She is a frequent speaker at conferences and advises the Data Lab at NASA. Siddha holds a master’s degree in computational data science from Carnegie Mellon University, where she worked on multimodal deep learning-based question answering. When she’s not working, you might catch her hiking.

Presentations

Embedded deep learning: Deep learning for embedded systems session

Deep learning is necessary to bring intelligence and autonomy to the edge. Siddha Ganju offers an overview of Deep Vision's solution, which optimizes both the hardware and the software, and discusses the Deep Vision embedded processor, which is optimized for deep learning and computer vision and offers 50x higher performance per watt than existing embedded GPUs without sacrificing programmability.

Timnit Gebru is a postdoctoral researcher at Microsoft New York working in the fairness accountability, transparency, and ethics (FATE) group, where she is working on how to take dataset bias into account while designing machine learning algorithms and the ethical considerations underlying any data mining project. Previously, Timnit worked at Apple, where she designed circuits and signal-processing algorithms for various Apple products, including the first iPad. She also spent an obligatory year as an entrepreneur (as all Stanford undergrads seem to do). Timnit is a PhD candidate in the Stanford Artificial Intelligence Laboratory, where she is studying computer vision under Fei-Fei Li. Her research focuses on data mining large-scale, publicly available images to gain sociological insight and the computer vision problems that arise as a result and has been covered by the Economist and other publications.

Presentations

Using deep learning and Google Street View to estimate the demographic makeup of the US session

Targeted socioeconomic policies require an accurate understanding of a country’s demographics, and the US spends more than $1 billion a year gathering such data. Timnit Gebru shares a solution that leverages Google Street View images and a computer vision pipeline to predict income, carbon emission, crime rates, and other city attributes from a single source of publicly available data.

Aaron Goldstein is the practice director for incident response and forensics at Cylance, where he leverages his unique experience in complex, large-scale breaches to provide strategic solutions to secure environments of all sizes. Aaron has over nine years’ experience in incident response and digital forensics investigations, during which he has responded to several high-profile investigations and led over 150 security engagements, ranging from incident response to creating and customizing full-scale training exercises. He is highly skilled in translating difficult topics into easy to understand training sessions on the ever-growing challenge of securing critical systems. Aaron holds a bachelor’s degree from the University of Central Florida and several industry certifications, including GREM, GCFA, GCIH, and CISSP.

Presentations

Incident response evolved: How AI is revolutionizing how we combat cyberthreats session

The current threat landscape is in a state of evolution that poses a significant risk to organizations' assets, reputations, and identities. Aaron Goldstein explores new and existing threats (and why traditional defenses fail to address them) and explains how leveraging AI techniques can improve the speed and efficiency of incident response tactics, even when combating the toughest threat actors.

Bruno Gonçalves is a Moore-Sloan fellow at NYU’s Center for Data Science. With a background in physics and computer science, Bruno has spent his career exploring the use of datasets from sources as diverse as Apache web logs, Wikipedia edits, Twitter posts, epidemiological reports, and census data to analyze and model human behavior and mobility. More recently, he has been focusing on the application of machine learning and neural network techniques to analyze large geolocated datasets.

Presentations

word2vec and friends Tutorial

Bruno Gonçalves explores word2vec and its variations, discussing the main concepts and algorithms behind the neural network architecture used in word2vec and the word2vec reference implementation in TensorFlow. Bruno then presents a bird's-eye view of the emerging field of "anything"-2vec methods that use variations of the word2vec neural network architecture.

Otavio Good is a software engineer at Google Translate, where he works on machine learning and computer vision. Previously, Otavio founded Quest Visual (acquired by Google in 2014). Quest Visual made the smartphone app Word Lens, the first real-time augmented reality translation app. He is also the cofounder of video game company Secret Level (acquired by Sega in 2006), which became Sega Studios San Francisco. Otavio loves working on side projects related to computers, math, science, and technology and led All Your Shreds Are Belong to US, the team that won the 2011 DARPA Shredder Challenge.

Presentations

A visual and intuitive understanding of deep learning session

Otavio Good demonstrates how Word Lens (part of Google Translate) uses machine learning to detect and translate printed text and explores various other machine learning concepts and their significance.

Carlos Guestrin is the director of machine learning at Apple and the Amazon Professor of Machine Learning in Computer Science and Engineering at the University of Washington. Carlos joined Apple through its acquisition of Turi (formerly Dato and GraphLab), a machine learning company of which he was the cofounder and CEO. Previously, he was the Finmeccanica Associate Professor at Carnegie Mellon University and senior researcher at the Intel Research Lab in Berkeley. A world-recognized leader in the field of machine learning, Carlos was named one of the 2008 brilliant 10 by Popular Science. His work has been recognized by over a dozen best-paper awards at top conferences and journals. He is the recipient of the Alfred P. Sloan Fellowship, IBM Faculty Fellowship, IJCAI Computers and Thought Award for his contributions to artificial intelligence, and the Presidential Early Career Award for Scientists and Engineers (PECASE). He is a former member of the Information Sciences and Technology (ISAT) advisory group for DARPA. Carlos holds a PhD and master’s degree from Stanford University and a degree in mechatronics engineering from the University of Sao Paulo, Brazil.

Presentations

Intelligent applications are everywhere. session

Matt McIlwain interviews Carlos Guestrin. Drawing on his experience as an AI pioneer, Carlos discusses the intelligent applications powered by data and data science that are being built and deployed at a rapid pace, including on smartphones and edge devices, and shares consumer, commercial, and embedded examples.

Daniel Guillory is the head of global diversity and inclusion at Autodesk, the leader in the future of making things, where he works to integrate all dimensions of diversity and inclusion into many parts of the organization, including customer acquisition, recruitment, hiring, people development, advancement, investment, and acquisition. He is interested in the application of people analytics to different initiatives. Previously, Daniel was CEO of Innovations International, a consulting firm that assists companies globally on leadership, innovation, and diversity through assessment, strategic planing, learning and development, and internal communications.

Presentations

Building an unbiased AI: End-to-end diversity and inclusion in AI development session

Diversity has many dimensions relevant to AI development. If designers don't consider and integrate diversity from the very beginning, they risk creating systems that are irrelevant to excluded groups and worse, make excluded groups irrelevant. Daniel Guillory and Matthew Scherer discuss the importance of ensuring diversity and inclusion when developing AI and share tips on how to do so.

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

Presentations

Getting started with TensorFlow Tutorial

Yufeng Guo and Amy Unruh walk you through training and deploying a machine learning system using TensorFlow, a popular open source library. Yufeng and Amy take you from conceptual overviews all the way to building complex classifiers and explain how you can apply deep learning to complex problems in science and industry.

James Guszcza is chief data scientist at Deloitte and a pioneering member of Deloitte’s original data science practice, where he has applied statistical and machine learning methods to such diverse business problems as healthcare utilization, customer and employee retention, talent management, customer segmentation, insurance pricing and underwriting, credit scoring, child support enforcement, patient safety, claims management, and fraud detection. He also spearheaded Deloitte’s use of behavioral nudge tactics to more effectively act on model indications. A frequent author and conference speaker, Jim designs and teaches hands-on business analytics training seminars for both the Society of Actuaries and the Casualty Actuarial Society, of which he is a fellow and a member of its board of directors. Jim is a former professor at the University of Wisconsin-Madison business school. He holds a PhD in the philosophy of science from the University of Chicago.

Presentations

Why AI needs human-centered design

James Guszcza shares the principles of human-computer collaboration, organizes them into a framework, and offers several real-life examples in which human-computer cognitive collaboration has been crucial to the economic success of a project.

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

Presentations

Here and now: Bringing AI into the enterprise Tutorial

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. Kristian Hammond shares a practical framework for understanding the role of AI technologies in problem solving and decision making.

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

Presentations

Deep reinforcement learning in the enterprise: Bridging the gap from games to industry session

Mark Hammond explores how enterprises can move beyond games and leverage deep reinforcement learning and simulation-based training to build programmable, adaptive, and trusted AI models for their real-world applications.

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

Presentations

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

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

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

Presentations

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

Breakthroughs in deep learning and new analog-domain computation methods to deploy trained neural networks will deliver exciting new capabilities. Michael B. Henry explains why the combination of human-like levels of recognition and massive computation capabilities in a tiny package will enable products with true awareness and understanding of the user and environment.

Bruce Horn is an Intel fellow and chief technical officer for the Intel Saffron cognitive solutions group, where he is responsible for driving new applications and uses for Intel Saffron’s memory-based reasoning system, a fundamentally new approach in the development of intelligent devices and systems. Previously at Intel, Bruce built a team to develop advanced conversational interfaces; that team provided the spoken language technology and mobile application for the Oakley Radar Pace running and cycling coach. Prior to joining Intel, Bruce was a principal research software development engineer at Microsoft, where he worked on the creation and deployment of natural language systems for Bing; worked at Powerset, where he was responsible for the computational infrastructure of the Powerset Natural Language Search System; worked at Apple, where he created and developed the Macintosh Finder, the first widely used desktop graphical user interface, among other components of macOS; and served as a member of the learning research group at the Xerox Palo Alto Research Center, where he contributed to several implementations of the Smalltalk virtual machine. Bruce holds a BS in mathematical sciences from Stanford University and an MS and PhD in computer science from Carnegie Mellon University.

Presentations

Why complementary learning is the future of AI (sponsored by Intel Saffron) session

Deep learning needs cognitive memory and vice versa. In complementary learning, both forms work together to build a more complete AI system. Bruce Horn explores Intel's Saffron's cognitive approach, which provides one-shot learning using associative and episodic memories and is more appropriate for individual and dynamic patterns.

Jeremy Howard is a founding researcher at fast.ai (a research institute dedicated to making deep learning more accessible), a distinguished research scientist at the University of San Francisco, a faculty member at Singularity University, and a young global leader with the World Economic Forum. An entrepreneur, business strategist, developer, and educator, Jeremy has served as the president and chief scientist of the data science platform Kaggle, where he was the top-ranked participant in international machine learning competitions for two years running; the founding CEO of two successful Australian startups, FastMail and Optimal Decisions Group (acquired by Lexis-Nexis); and a management consultant at McKinsey & Company and AT Kearney. Jeremy has contributed to a number of open source projects and created, invested in, mentored, and advised many startups. His most recent startup, Enlitic, was the first company to apply deep learning to medicine and has twice been named one of the world’s top 50 smartest companies by MIT Tech Review. Jeremy is a frequent guest on television and other video; he appeared regularly on Australia’s highest-rated breakfast news program, gave a talk on TED.com (which has over two million views), and has led quite a few data science and web development tutorials and discussions.

Presentations

Using GPU acceleration with PyTorch to make your algorithms 2,000% faster session

Although most devs are aware of the benefits of GPU acceleration, many assume that the technique is only applicable to specialist areas like deep learning and that learning to program a GPU takes complex specialist knowledge. Jeremy Howard explains how to easily harness the power of a GPU using a standard clustering algorithm with PyTorch and demonstrates how to get a 20x speedup over NumPy.

Magnus Hyttsten is a senior staff developer advocate for TensorFlow at Google, where he helps develop the TensorFlow product, supports the developer community, and creates developer materials. Magnus is a developer fanatic and has spoken about machine learning and mobile development at major industry events such as Google I/O, AnDevCon, and MWC. His current focus is effective model inference for mobile as well as reinforcement learning models. Previously, Magnus was the founder and CTO of a successful telecommunications software company.

Presentations

Pushing the boundaries of ML using TensorFlow and Google Cloud (sponsored by Google Cloud) session

Magnus Hyttsten explains how Google is pushing the boundaries of machine learning with TensorFlow and Google Cloud, sharing some of the latest models Google teams have been working on and the technical challenges they've encountered, new APIs in TensorFlow, how the Tensor Processing Unit (TPU) works, and how Google Cloud can be used to train extremely large models.

Bill Jenkins is a senior product line specialist at Intel, where he is involved in marketing, planning, and strategy. Previously, he was an application engineer at Intel and held a variety of roles at government and defense research and development companies, specializing in signal and image processing using CPUs, GPUs, and FPGAs. Bill holds a master’s degree in electrical engineering and an MBA from the University of Massachusetts Lowell, where he focused on computer engineering and signal processing.

Presentations

Accelerating deep learning session

Field-programmable gate arrays (FPGAs) provide deterministic low latency and highly efficient implementations with various levels of precision due to their customizable architecture.​ Bill Jenkins shares Intel's deep learning accelerator library, which offers a variety of primitives and architectures highly optimized for FPGAs and allows seamless integration into the Intel ecosystem.

Meet the Expert with Bill Jenkins (Intel) Meet the Experts

If you are working with FPGAs (or plan to) a conversation with Bill could be invaluable.

Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. His research interests bridge the computational, statistical, cognitive, and biological sciences; in recent years, he has focused on Bayesian nonparametric analysis, probabilistic graphical models, spectral methods, kernel machines, and applications to problems in distributed computing systems, natural language processing, signal processing, and statistical genetics. Previously, he was a professor at MIT. Michael is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences and a fellow of the American Association for the Advancement of Science, the AAAI, ACM, ASA, CSS, IEEE, IMS, ISBA, and SIAM. He has been named a Neyman Lecturer and a Medallion Lecturer by the Institute of Mathematical Statistics. He received the David E. Rumelhart Prize in 2015 and the ACM/AAAI Allen Newell Award in 2009. Michael holds a master’s degree in mathematics from Arizona State University and a PhD in cognitive science from the University of California, San Diego.

Presentations

How to escape saddle points efficiently Keynote

Many new theoretical challenges have arisen in the area of gradient-based optimization for large-scale data analysis, driven by the needs of applications and the opportunities provided by new hardware and software platforms. Michael Jordan shares recent research on the avoidance of saddle points in high-dimensional nonconvex optimization.

Joshua Joseph is CSO of Alpha Features. Previously Josh co-founded 33X, a machine learning consulting company, which works on problems such as technical due diligence for a $2B asset allocator, an autonomous cell identification system for a life sciences company, and dynamic pricing of coworking office spaces. Prior to 33X, Josh worked at a proprietary trading company which focused on fully automated trading and machine learning based strategy discovery by merging a variety of structured and unstructured data sources. He received his Ph.D. from Massachusetts Institute of Technology where his work focused on methods for decision making where we must learn from limited data of complex systems. During his time in academia, Josh worked on a variety of real-world projects, such as modeling iRobot Roomba battery degradation, predicting taxi routes, and autonomous robot interaction with turbulent water flow.

Presentations

A practical guide to conducting an AI snake oil sniff test session

As artificial intelligence (and specifically machine learning) firmly takes hold in industry, there has been a significant increase in the amount of AI snake oil being developed, pitched, and sold. Joshua Joseph shares a practical guide for detecting AI products of questionable value or benefit, whether intentional or not.

Steve Jurvetson is a partner at DFJ. Steve has a reputation for investing in pioneering companies that create new sectors. Two early bets include the Elon Musk-led companies SpaceX and Tesla. (He owns both a Tesla sedan and an SUV and holds board seats at both companies.) Steve serves on the board of directors for Planet, a company that has launched the largest constellation of Earth-observation microsatellites; D-Wave, a quantum computing company which counts Google, NASA, and Lockheed-Martin as customers; and Mythic, a provider of a local AI hardware and software platform that turns devices into secure and trusted intelligent assistants. He previously held a board seat at deep learning company Nervana (acquired by Intel in 2016). Steve holds an undergraduate degree at Stanford in electrical engineering. He maintains a fascination for space and has turned DFJ’s office into a museum showcasing his space artifact collection.

Presentations

Accelerating AI Keynote

Keynote by Steve Jurvetson

Fireside chat with Naveen Rao and Steve Jurvetson Keynote

Join Naveen Rao and Steve Jurvetson for a fireside chat.

David Kale is a deep learning engineer at Skymind and a PhD candidate in computer science at the University of Southern California (advised by Greg Ver Steeg of the USC Information Sciences Institute). David’s research uses machine learning to extract insights from digital data in high-impact domains, such as healthcare. Recently, he has pioneered the application of recurrent neural nets to modern electronic health records data. At Skymind, he is developing the ScalNet Scala API for DL4J and working on model interoperability between DL4J and other major frameworks. David organizes the Machine Learning and Healthcare Conference (MLHC), is a cofounder of Podimetrics, and serves as a judge in the Qualcomm Tricorder XPRIZE competition. David is supported by the Alfred E. Mann Innovation in Engineering Fellowship.

Presentations

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

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

Jason Knight is senior technology officer at Intel, where he advances what is possible with machine learning using Intel Nervana. Jason holds a PhD in computational biology. His research included developing hierarchical Bayesian statistical models for classification of cancer tumor expression data and high-performance Markov chain Monte Carlo techniques to discover gene regulatory networks in this data using Bayesian networks. He then applied these techniques on the world’s largest database of human genomes at Human Longevity Inc.

Presentations

Intel Nervana Graph: A universal deep learning compiler session

With the chaotic and rapidly evolving landscape around deep learning, we need deep learning-specific compilers to enable maximum performance in a wide variety of use cases on a wide variety of hardware platforms. Jason Knight offers an overview of the Intel Nervana Graph project, which was designed to solve this problem.

Meet the Expert with Jason Knight (Intel) Meet the Experts

Jason will be on hand to discuss things like how hardware independent intermediate representations (IRs) can enable the deep learning software and hardware ecosystems to thrive and DL infrastructure in our containerized microservice futures.

Anirudh Koul is a senior data scientist at Microsoft AI and Research. An entrepreneur at heart, he has been running a mini-startup team 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. Anirudh brings a decade of production-oriented applied research experience on petabyte-scale social media datasets, including Facebook, Twitter, Yahoo Answers, Quora, Foursquare, and Bing. A regular at hackathons, he has won close to three dozen awards, including top-three finishes for three years consecutively in the world’s largest private hackathon, with 16,000 participants. Some of his recent work, which IEEE has called “life changing,” has been showcased at a White House AI event, Netflix, and National Geographic and to the Prime Ministers of Canada and Singapore.

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. Anirudh Koul explains how to bring the power of deep learning to memory- and power-constrained devices like smartphones.

Akhilesh Kumar is a principal engineer on the data center processor architecture team at Intel, where he is currently responsible for the Skylake-SP and Cascade Lake processor architectures. In his 21 years at Intel, Akhilesh has contributed to the architecture of various server processors, chipsets, and system fabrics. He holds a PhD in computer science from Texas A&M University.

Presentations

Intel Xeon scalable processor architecture and AI workload performance session

Banu Nagasundaram and Akhilesh Kumar offer an overview of the architectural features of the latest Intel Xeon scalable processor, outline the changes from previous generations, and discuss the architectural benefits that favor AI workloads. Along the way, Banu and Akhilesh explore AI workload performance for data center CPUs.

Meet the Expert with Banu Nagasundaram and Akhilesh Kumar (Intel) Meet the Experts

Meet Banu and Akhilesh to talk about AI in data centers.

Alex Kurakin is a research engineer for Google Brain, where his research focuses on various aspects of adversarial machine learning, including the security and robustness of neural networks. Alex holds a PhD in computer vision and image processing from the Moscow Institute of Physics and Technology.

Presentations

Adversarial machine learning session

Adversarial machine learning session by Alex Kurakin

Danny B. Lange is vice president of AI and machine learning at Unity Technologies. Previously, Danny was head of machine learning at Uber, where he led an effort to build the world’s most versatile machine learning platform to support Uber’s rapid growth; the general manager of Amazon Machine Learning, an AWS product that offers machine learning as a cloud service; principal development manager at Microsoft; where he led a product team focused on large-scale machine learning for big data; and a computer scientist at IBM Research. Danny holds a PhD in computer science from the Technical University of Denmark.

Presentations

Bringing gaming, VR, and AR to life with deep learning session

Game development is a difficult and time-consuming pursuit that requires highly skilled labor to succeed. Drawing on his experience at Unity, Danny Lange demonstrates how deep learning and deep reinforcement learning can help developers at various stages in the development process create awesome digital experiences in gaming, VR, and AR.

Jia Li is the head of R&D, cloud AI, and machine learning at Google. Previously, Jia was the head of research at Snap, where she led the company’s research innovation effort, and led the visual computing and learning group at Yahoo Labs. She currently serves as the program chair of the ACM Multimedia Conference, the associate editor of The Visual Computer: International Journal of Computer Graphics, and a member of the Computer Vision Foundation Industrial Advisory Board. She previously served as the industry relationship chair for CVPR 2016, the volunteers chair for CVPR 2010, and the area chair for WACV 2015. In 2014, she received the Super Star award from Yahoo, the highest award at the company. She was also awarded the Master Inventor award for her innovations in computer vision, machine learning, artificial intelligence, mobile vision, ecommerce, product recommendation, and image search. Jia led the OPTIMOL team, which won the first prize in the 2007 Semantic Robotics Vision Challenge sponsored by NSF and AAAI. Her work has been widely reported in the media, including The Next Web, Ars Technica, ZDNet, Gigaom, Venture Beat, Mirror, Business Insider, New Scientist, and MIT Technology Review. In 2016, Business Insider named her one of the secret power players who run Snapchat. Jia holds a PhD in computer science from Stanford University.

Presentations

Why democratizing AI matters: Computing, data, algorithms, and talent Keynote

Jia Li has contributed to some of the most influential datasets in the world and helped transform computer vision from an academic niche into a dominant technology. Jia explains why a democratized approach to AI ensures that the compute, data, algorithms, and talent behind these technologies reach the widest possible audience.

Li Erran Li is a senior research scientist in Uber’s Advanced Technologies Group and an adjunct professor in the Computer Science Department at Columbia University. His current research interests include AI, computer vision, and machine learning algorithms and systems. He is an IEEE fellow and an ACM distinguished scientist. Li holds a PhD in computer science from Cornell University, where he was advised by Joseph Halpern.

Presentations

Deep reinforcement learning: Recent advances and frontiers session

Deep reinforcement learning has enabled artificial agents to achieve human-level performance across many challenging domains (for example, playing Atari games and Go). Li Erran Li shares several important algorithms, discusses major challenges, and explores promising results.

Nan Li brings a mixed technology, investing and entrepreneurial background to Obvious Ventures. He is also an adjunct lecturer at Stanford on venture capital. Nan has been a venture investor and advisor for the past five years, working with companies applying technology toward solving big problems. Previously, he managed early-stage tech investments for Eric Schmidt’s Innovation Endeavors; led product, operations, and finance at Gigwalk, a mobile, crowdsourced data and analytics company funded by Greylock Partners and August Capital; was a VC at Bain Capital Ventures; was a management consultant at Bain & Company; and served as a PM at Microsoft. Nan holds a BSE in computer science engineering from the University of Michigan. He grew up in Detroit after emigrating from China. He enjoys music, photography, culture, puzzles, all Detroit sports, and general nerdom.

Presentations

AI and cellular images for universal drug discovery session

Blake Borgeson and Nan Li offer a technical overview of how Recursion—a company that applies computer vision and machine learning to create a high-dimensional feature space in which to evaluate cellular health broadly across hundreds of disease states—leverages cellular phenotyping for drug discovery.

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

Presentations

Enabling computer-vision-based autonomous driving with affordable and reliable sensors session

Autonomous cars, like humans, need good eyes and a good brain to drive safely. Shaoshan Liu explains how PerceptIn designed and implemented its high-definition, stereo 360-degree camera sensors targeted for computer-vision-based autonomous driving.

Yinyin Liu is the head of data science for AIPG at Intel, where she works with a team of data scientists on applying deep learning and Intel Nervana technologies to business applications across different industry domains and driving the development and design of the Intel Nervana platform. She and the Intel Nervana team have developed open source deep learning frameworks, such as neon and Intel Nervana Graph, bringing state-of-the-art models on image recognition, image localization, and natural language processing into the frameworks. Yinyin has research experience in computer vision, neuromorphic computing, and robotics.

Presentations

Data science and NLP in the era of deep learning session

Deep learning is providing new opportunities for and solutions to natural language processing problems, enabling new approaches for text, language, and conversation-based use cases. Yinyin Liu shares the latest NLP advances, practices, and resources for data and explores enterprise NLP use cases using the Intel Nervana platform.

Meet the Expert with Yinyin Liu (Intel) Meet the Experts

Yinyin will be available to discuss AI/DL models and NLP use cases for enterprises.

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

Presentations

Closing remarks Keynote

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

The state of AI adoption Keynote

Program chairs Ben Lorica and Roger Chen kick off the O'Reilly AI Conference in San Francisco with an overview of the current trends they have observed in the industry.

Tuesday opening remarks Keynote

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

Wednesday opening remarks Keynote

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

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

Presentations

Personalizing ecommerce for two billion people on Facebook session

Nikita Lytkin explains how Facebook uses machine learning technologies developed by its ads ranking, applied machine learning, and AI research teams to enable personalized ecommerce that recommends a vast diversity of products to nearly two billion people.

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

Presentations

Probabilistic programming Tutorial

Probabilistic inference, a widely used, mathematically rigorous approach for interpreting ambiguous information using models that are uncertain or incomplete, is central to everything from big data analytics to robotics and AI. Vikash Mansinghka surveys the emerging field of probabilistic programming, which aims to make modeling and inference broadly accessible to nonexperts.

Matt McIlwain is a managing director at Madrona Venture Group and one of the top VCs in the world when it comes to investing in AI and ML. A longtime venture capitalist focusing on software-driven companies, Matt is currently investing in companies in the cloud, machine learning, and intelligent apps spaces. He serves on the boards of a number of Madrona companies, including 2nd Watch, Apptio, Booster, ExtraHop, Igneous Systems, Placed.com, Qumulo, Smartsheet, and Skytap. Matt is also on the boards of the Fred Hutchinson Cancer Research Center and Washington Policy Center. Previously, Matt served on the boards of Isilon Systems (acquired by EMC), Turi (acquired by Apple), Farecast (acquired by Microsoft), World Wide Packets (acquired by Ciena), PayScale (acquired by Warburg Pincus), iConclude (acquired by Opsware), Performant (acquired by Mercury Interactive), and Tier3 (acquired by CenturyLink Savvis). He is a graduate of Dartmouth College and holds an MBA from Harvard Business School and a master’s degree in public policy from Harvard’s Kennedy School of Government.

Presentations

Intelligent applications are everywhere. session

Matt McIlwain interviews Carlos Guestrin. Drawing on his experience as an AI pioneer, Carlos discusses the intelligent applications powered by data and data science that are being built and deployed at a rapid pace, including on smartphones and edge devices, and shares consumer, commercial, and embedded examples.

Michael (Mike) Mendelson is a curriculum designer and certified instructor at NVIDIA’s Deep Learning Institute. Mike began experimenting with deep learning while working to enable active personalized (human) learning. Previously, he built and taught world-class project-based STEM curriculum at EL Education. Mike is inspired by the power of deep learning to solve some of the world’s most important challenges.

Presentations

NVIDIA Deep Learning Institute bootcamp 2-Day Training

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

Stephen Merity is a senior research scientist at Salesforce Research (formerly MetaMind), where he works on researching and implementing deep learning models for vision and text, with a focus on memory networks and neural attention mechanisms for computer vision and natural language processing tasks. Previously, Stephen worked on big data at Common Crawl, data analytics at Freelancer.com, and online education at Grok Learning. Stephen holds a master’s degree in computational science and engineering from Harvard University and a bachelor of information technology from the University of Sydney.

Presentations

Backing off toward simplicity: Understanding the limits of deep learning session

Deep learning is used broadly at the forefront of research, achieving state-of-the-art results across a variety of domains. However, that doesn't mean it's a fit for all tasks—especially when the constraints of production are considered. Stephen Merity investigates what tasks deep learning excels at, what tasks trigger a failure mode, and where current research is looking to remedy the situation.

Sherry Moore is a software engineer on the Google Brain team. Her other projects at Google include Google Fiber and Google Ads Extractor. Previously, she spent 14 years as a systems and kernel engineer at Sun Microsystems.

Presentations

TensorFlow, machine learning, and learning to learn session

TensorFlow is the world's most popular machine learning framework. Google Brain team member Sherry Moore discusses the latest developments in TensorFlow and offers a dive into her research on evolving deep learning models using genetic algorithms.

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.

Banu Nagasundaram is a product marketing engineer with the data center group at Intel, where she supports performance marketing for Xeon Phi, Intel FPGA, and Xeon for AI. Previously, Banu was a design engineer on the exascale supercomputing research team with Intel Federal. Prior to Intel, Banu worked at Qualcomm doing design verification of mobile processors. Banu holds an MS in electrical and computer engineering from the University of Florida and is working toward an MBA at UC Berkeley’s Haas School of Business.

Presentations

Intel Xeon scalable processor architecture and AI workload performance session

Banu Nagasundaram and Akhilesh Kumar offer an overview of the architectural features of the latest Intel Xeon scalable processor, outline the changes from previous generations, and discuss the architectural benefits that favor AI workloads. Along the way, Banu and Akhilesh explore AI workload performance for data center CPUs.

Meet the Expert with Banu Nagasundaram and Akhilesh Kumar (Intel) Meet the Experts

Meet Banu and Akhilesh to talk about AI in data centers.

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

Presentations

AI within O'Reilly Media session

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

Andrew Ng is a globally recognized leader in AI. He was until recently chief scientist at Baidu, where he led the company’s nearly 1,300 person AI group and was responsible for driving the company’s global AI strategy and infrastructure. Andrew was also the founding lead of the Google Brain team. He is cochairman and cofounder of Coursera, the world’s leading MOOC (massive open online course) platform and an adjunct professor in Stanford University’s Computer Science department. He has authored or coauthored over 100 research papers in machine learning, robotics, and related fields. Andrew holds degrees from Carnegie Mellon University, MIT, and the University of California, Berkeley.

Presentations

AI is the new electricity. Keynote

Much like the rise of electricity, which started about 100 years ago, AI will revolutionize every major industry. Andrew Ng explains how AI can transform your business, shares major technology trends and thoughts on where your biggest future opportunities may lie, and explores best practices for incorporating AI, machine learning, and deep learning into your organization.

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.

Lonny Northrup is the senior health infomaticist in the Office of the Chief Data Officer at Intermountain, where he helps explore, validate, and implement big data technologies and data innovations to improve healthcare outcomes and reduce healthcare costs.

Presentations

Applying AI to healthcare's biggest opportunity: Clinical variation session

Mercy and Intermountain, two of the largest and most innovative hospital systems in the United States, have recently applied AI to tackle clinical variation within their systems. Todd Steward and Lonny Northrup discuss the application of machine intelligence for optimizing care and provide valuable insights into practice variation for improving clinical pathways.

Peter Norvig is a director of research at Google. In his prior role, as director of search quality, he directed Google’s core search algorithms group, which means he was the manager of record responsible for answering more queries than anyone else in the history of the world. Previously, he was the head of the Computational Sciences Division at the NASA Ames Research Center (NASA’s senior computer scientist) and received the NASA Exceptional Achievement Award in 2001. He was also an assistant professor at the University of Southern California and a research faculty member in the Computer Science Department at the University of California, Berkeley. He has over 50 publications in computer science, concentrating on artificial intelligence, natural language processing, and software engineering. He is the author of Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX and coauthor of Artificial Intelligence: A Modern Approach, the leading textbook in the field. He is also the author of the Gettysburg Powerpoint Presentation and has written the world’s longest palindromic sentence. Peter is a fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. He holds a PhD from UC Berkeley, where he was recognized with a distinguished alumni award in 2006.

Presentations

Deep learning to fight cancer: Fireside chat Keynote

Abu Qader’s personal experience is a testament to the increasing impact and accessibility of AI technology. As a high school student, he taught himself machine learning using open online resources and launched an AI company for breast cancer diagnostics. Peter Norvig sits down with Abu to share anecdotes, discuss the state of artificial intelligence, and explore where things are heading.

Tim O’Reilly has a history of convening conversations that reshape the computer industry. In 1998, he organized the meeting where the term “open source software” was agreed on and helped the business world understand its importance. In 2004, with the Web 2.0 Summit, he defined how “Web 2.0” represented not only the resurgence of the web after the dot-com bust but a new model for the computer industry, based on big data, collective intelligence, and the internet as a platform. In 2009, with his Gov 2.0 Summit, Tim framed the conversation about the modernization of government technology that has shaped policy and spawned initiatives at the federal, state, and local levels and around the world. He has now turned his attention to implications of the on-demand economy, AI, robotics, and other technologies that are transforming the nature of work and the future shape of the economy. He shares his thoughts about these topics in his new book, WTF? What’s the Future and Why It’s Up to Us (Harper Business, October 2017). Tim is the founder and CEO of O’Reilly Media and a partner at O’Reilly AlphaTech Ventures (OATV). He sits on the boards of Maker Media (which was spun out from O’Reilly Media in 2012), Code for America, PeerJ, Civis Analytics, and POPVOX.

Presentations

Our Skynet moment Keynote

Tim O’Reilly draws on lessons from networked platforms to show how our economy and financial markets have also become increasingly managed by algorithms, making the case that income inequality, declining upward mobility, and job losses due to technology are not inevitable; they are the result of design choices we have made in the algorithms that manage our markets.

Vijay Pande is a general partner at Andreessen Horowitz, where he leads the firm’s investments in companies at the cross-section of biology and computer science, including areas such as the application of computation, machine learning, and artificial intelligence in biology and healthcare and the application of novel transformative scientific advances. Previously, Vijay was the Henry Dreyfus Professor of Chemistry and and a professor of structural biology and computer science at Stanford University, where he also led a team of researchers pioneering computational methods and their application to medicine and biology, resulting in over 200 publications, two patents, and two novel drug treatments. Concurrently, he was the director of the biophysics program at Stanford, where he led a team of more than 50 faculty members. Under his leadership, it become one of top programs in the country.

Vijay is the founder of the Folding@home distributed computing project for disease research, which pushes the boundaries of the development and application of computer science techniques such as distributed systems, machine learning, and exotic computer architectures into biology and medicine. He is a cofounder of Globavir Biosciences, which discovered cures for dengue fever and Ebola. In his teens, he was the first employee at video game startup Naughty Dog Software, maker of Crash Bandicoot. His awards include the DeLano Prize in Computation, a Guinness world record for Folding@home, ACS Thomas Kuhn Paradigm Shift Award, and an MIT TR100 recognition. Vijay serves on the boards of Apeel Sciences, Freenome, Omada Health, PatientPing, and Rigetti Computing—all Andreessen Horowitz portfolio companies. He also leads the firm’s investments in Benchling, Cardiogram, SolveBio, TL Biolabs, twoXAR, Q.bio, and uBiome. Vijay holds a BA in physics from Princeton University and a PhD in physics from MIT.

Presentations

How AI is ushering in a new era of healthcare session

Expanding on his keynote, Vijay Pande explains how machine learning techniques together with the recent explosion in data are leading to a new approach to prevention, allowing us to more effectively tackle some of the deadliest and costliest health challenges, including heart disease, cancer, and Type 2 diabetes.

How AI is ushering in a new era of healthcare Keynote

Vijay Pande explains how machine learning techniques are leading to a new approach to prevention, allowing us to more effectively tackle some of the deadliest and costliest health challenges, including heart disease, cancer, and Type 2 diabetes.

Mo Patel is a practice director for AI and deep learning at Teradata, where he mentors and advises Teradata clients and provides guidance on ongoing deep learning projects. 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, Mo was a management consultant and a software engineer. 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

Training vision models with public transportation datasets Tutorial

Computer vision is a key component in the artificial intelligence revolution. Assisted by deep learning, object detection allows automotive applications to make key navigation, guidance, and driving decisions to avoid collisions and navigation errors. Laura Froelich and Mo Patel demonstrate how to train deep learning models for object detection using publicly available transportation datasets.

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

Presentations

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

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

Art Popp is the senior hardware test engineer at ServiceNow. Previously, Art spent 25 years in the telecommunications industry, the last eight as the principal architect of a large telco carrier’s engineering data warehouse ecosystem, which grew to a mixed environment with 4 PB of IBM TwinFins (formerly Netezzas), 20+ racks of Hadoop gear, and dozens of racks of reporting and presentation systems. (Over time the focus of this environment shifted from reporting to predicting, which is where it got fun.)

Presentations

Choosing a high-performance computing development direction for original algorithms session

Art Popp walks you through a “from scratch" implementation of two algorithms to demonstrate the tools available for original algorithm development, using both SIMD and SIMT designs, the leading hardware architectures of which are Xeon Phi and NVIDIA Cuda. Along the way, Art explores the performance per watt, performance per dollar (initial cost), and performance per dollar (TCO) of each.

Philippe Poutonnet is the lead product marketer on the cloud AI team for Google Cloud Platform. Philippe has spent his career helping companies build great products on the leading edge of what’s next, starting with his early days working at Forrester Research.

Presentations

Build smart applications with your new super power: Cloud AI (sponsored by Google Cloud) Keynote

Google has invested deeply in machine learning for many years and is using it successfully across its highly successful consumer businesses. Philippe Poutonnet explains how to leverage the power of ML with Google Cloud, using the platform's powerful data management tools, support for collaborative experiments, and predictions at Google scale.

Derik Pridmore is cofounder and CEO of Osaro, a San Francisco-based machine learning company building products powered by deep reinforcement learning. A venture capitalist, hedge fund manager, and angel investor with over a decade of investment and operational experience, Derik was previously a principal at Founders Fund, a Silicon Valley venture fund led by PayPal founder Peter Thiel, where he drove investments in a variety of companies, including DeepMind. He also founded and managed Arda Capital Management, a quantitative equity hedge fund, and is an investor in Clarifai. Derik holds a BS in physics and computer science and an MEng in computer science and electrical engineering from MIT.

Presentations

Industrial robotics and deep reinforcement learning session

There continues to be a gap between the most advanced papers and the reality of deployed industrial robots. Derik Pridmore explores the most recent advances in deep and reinforcement learning for robotics, the current state of industrial robotics, and how Osaro is working to bridge the gap.

Bharadwaj Pudipeddi is the cofounder and CTO of NVXL, a company building a new clustered acceleration platform for deep learning, machine learning, and SQL workloads. A product entrepreneur and hardware architect, Bharadwaj previously worked at Intel and a number of startups in the areas of CPU design, high-performance fabrics, flash memory storage, and scalable computing.

Presentations

Highly dense modular acceleration clusters for deep learning session

Bharadwaj Pudipeddi proposes a highly dense modular acceleration cluster completely disaggregated from generic servers in the data center that is specifically targeted for deep learning- and AI-related workloads. This cluster is scalable and lightweight (and devoid of Xeons) with the ability to run very deep neural networks through data and model parallelism for extreme performance.

Ruchir Puri is an IBM fellow and the chief architect of IBM Watson. Previously, Ruchir led deep learning and machine learning platform initiatives at IBM Research as well as IBM’s efforts in software-hardware acceleration for cognitive and analytic workloads; he also drove strategy for differentiated cognitive computing infrastructure. Ruchir is a fellow of the IEEE, an ACM distinguished speaker, and an IEEE distinguished lecturer and was recognized as the 2014 Asian American Engineer of the Year. Ruchir has been a visiting faculty member in the Department of Computer Science at Stanford University and an adjunct professor in the Department of Electrical Engineering at Columbia University. He holds the John Von-Neumann Chair at the Institute of Discrete Mathematics at Bonn University, Germany. Ruchir is an inventor of over 50 United States patents and has authored over 120 publications and a book on analytics.

Presentations

Engineering the future of AI for businesses (sponsored by IBM Watson) session

Ruchir Puri expands on his keynote address, exploring the opportunities and challenges of AI for business and focusing on what is needed to truly scale out AI applications and systems across the breadth of enterprises.

Engineering the future of AI for businesses (sponsored by IBM Watson) Keynote

Ruchir Puri explores the opportunities and challenges of AI for business, focusing on what is needed to truly scale out AI applications and systems across the breadth of enterprises.

Abu Qader is the CTO of GliaLab, an artificial intelligence company utilizing artificial intelligence to build an image analytics platform that can detect and diagnose anomalies within a variant of medical images such as mammograms. Abu is an 18-year-old visionary and entrepreneur with a passion for innovating fields such as healthcare with cognitive science and artificial intelligence. He received the Fifty for the Future award, was featured at Google I/O, and spoke at TEDxTeen in London. Abu is a Posse scholar at Cornell University.

Presentations

Deep learning to fight cancer: Fireside chat Keynote

Abu Qader’s personal experience is a testament to the increasing impact and accessibility of AI technology. As a high school student, he taught himself machine learning using open online resources and launched an AI company for breast cancer diagnostics. Peter Norvig sits down with Abu to share anecdotes, discuss the state of artificial intelligence, and explore where things are heading.

Joy Qiao is a Senior Solution Architect in the AI & Research Group at Microsoft, where she is responsible for driving end-to-end AI and Machine Learning solutions on Azure among the partner eco-system. Joy has over 15 years of IT industry experience including 11 years at Microsoft working as technical lead/architect roles in various Microsoft Azure teams, as well as senior consultant/architect in the Microsoft services team. Joy has mainly been focusing on Microsoft Azure, Big Data and Machine Learning technologies, leading and delivering various Machine Learning, Big Data and Cloud-based solutions for both internal and external MS enterprise customers and partners.

Presentations

Using deep learning toolkits with Kubernetes clusters session

Joy Qiao and Wee Hyong Tok demonstrate how to combine Kubernetes clusters and deep learning toolkits to get the best of both worlds and jumpstart the development of innovative deep learning applications. Along the way, Joy and Wee Hyong explain how to train deep neural networks using GPU-enabled containers orchestrated by Kubernetes with common deep learning toolkits, such as CNTK and TensorFlow.

Ashwin Ram is senior manager of AI science for Alexa, the intelligent agent that powers Amazon’s Echo and other devices, where he leads R&D initiatives to create advanced technologies for conversational agents, including the university-facing Alexa Prize competition. Ashwin is a distinguished artificial intelligence researcher and entrepreneur. Previously, he managed the interactive intelligence research area at PARC, leading a team to invent new behavior change technologies to help people adopt healthier lifestyles; was a professor in the College of Computing at Georgia Tech and director of the Cognitive Computing Lab; and cofounded multiple startups, including online social learning network OpenStudy (acquired by Brainly) and Enkia (acquired by Sentiment360), which developed AI software for social media applications. He is the author of two books and over 100 scientific articles published in international forums. Ashwin holds a PhD from Yale University, an MS from the University of Illinois, and a BTech from IIT Delhi. He is a closet anthropologist and loves travel, people, and culture.

Presentations

Conversational AI in Amazon Alexa session

Conversational AI in Amazon Alexa

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

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

Presentations

Fireside chat with Naveen Rao and Steve Jurvetson Keynote

Join Naveen Rao and Steve Jurvetson for a fireside chat.

Ashu Rege is the vice president of software engineering at Zoox, where he is responsible for Zoox’s entire software platform, spanning AI and machine learning, motion planning, computer vision and 3D perception, localization, mapping, simulation, server-client, and base ops software. Previously, Ashu was vice president of computer vision and robotics at NVIDIA, where he was in charge of the company’s autonomous vehicle and drone technology projects; held a number of executive roles at NVIDIA, including vice president of the content and technology group developing core graphics, physics simulation, and GPU computing technologies and associated software; and cofounded or worked at various startups related to computer graphics, laser scanning, internet, and network technologies. Ashu holds a PhD in computer science from UC Berkeley, where he was a Regents’ fellow and Pardee scholar.

Presentations

We're in the final stretch for—and early innings of—autonomous vehicles: Fireside chat with Shahin Farshchi and Ashu Rege session

Join Shahin Farshchi in conversation with Ashu Rege, who is reinventing the automobile from scratch at Zoox to offer consumers an unforgettable, autonomous transportation experience. They'll discuss early challenges that turned out to be straightforward, easy problems that turned out to be very hard, and the obstacles that lie ahead.

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

Presentations

AI for manufacturing: Today and tomorrow session

Artificial intelligence in manufacturing has been around for a long time, but are you aware of how it can make your operations more efficient and profitable? David Rogers explains how existing technologies like the digital twin approach, advanced decision making, and downtime cause detection have primed manufacturing for a profitable and efficient future.

Matt Scherer is an attorney and legal scholar based in Portland, Oregon. He is an associate with Littler Mendelson, PC, and a member of the firm’s robotics, artificial intelligence, and automation practice group. Matt writes and speaks on the intersection of law and artificial intelligence and is the author of several articles, including “Regulating Artificial Intelligence Systems: Risks, Challenges, Competencies, and Strategies,” which was published in the Harvard Journal of Law and Technology, and “AI in HR: Civil Rights Implications of Employers’ Use of Artificial Intelligence and Big Data,” which was published in the SciTech Lawyer. Matt also writes at Law and AI, a blog devoted to studying the emerging legal and policy issues surrounding artificial intelligence and autonomous machines.

Presentations

Building an unbiased AI: End-to-end diversity and inclusion in AI development session

Diversity has many dimensions relevant to AI development. If designers don't consider and integrate diversity from the very beginning, they risk creating systems that are irrelevant to excluded groups and worse, make excluded groups irrelevant. Daniel Guillory and Matthew Scherer discuss the importance of ensuring diversity and inclusion when developing AI and share tips on how to do so.

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

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

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

Deep learning with limited labeled data session

Deep learning is fueled by large labeled datasets, but in domains like medicine, each label represents a human life at risk. Avesh Singh and Brandon Ballinger offer an overview of autoencoders, heuristic training, and few-shot learning, with an emphasis on practical tips to create high-performing models utilizing hundreds of thousands of unlabeled data points and only thousands of labeled points.

Nate Soares is the executive director of the Machine Intelligence Research Institute (MIRI), a Berkeley research nonprofit focused on the challenge of making superhumanly capable AI systems robust and reliable. Nate first joined MIRI as a research fellow, during which time he was the primary author of the organization’s technical agenda. He has contributed to ongoing work in decision theory, game theory, algorithmic information theory, computational reflection, online machine learning, mathematical logic, and a number of other areas. Previously, Nate worked as a software engineer at Google.

Presentations

Ensuring smarter-than-human intelligence has a positive outcome. session

The field of artificial intelligence has made major strides in recent years, but there is a growing movement to consider the implications of machines that can rival humans in general problem-solving abilities. Nate Soares outlines the underresearched fundamental technical obstacles to building AI that can reliably learn to be "aligned" with human values.

Lisa A. Spelman is vice president of Intel’s data center group and general manager of Intel Xeon products and data center marketing, where she manages a team that spans product marketing and management for Intel Xeon processors and platforms; outbound activities, including product launches, social media, and go-to-market activities with equipment manufacturers and software vendors; technologies and solutions; and performance marketing. Her team is also responsible for Intel’s data center channel business. Previously, Lisa held a number of positions in Intel’s IT organization, including director of client services, where she led the global team responsible for providing personal productivity tools to Intel employees; served as technical adviser to Intel’s chief information officer; held positions in finance, server product and brand marketing, and sales for a multinational customer account; and worked as a financial analyst supporting Intel’s server business. Lisa holds a bachelor’s degree in business administration with a concentration in finance and marketing from the University of Washington.

Presentations

Fast-forwarding AI in the data center

Lisa Spelman explains how businesses are already benefiting from the industry’s most flexible and most optimized solutions for AI and how Intel is fostering the continued growth of the AI ecosystem so that you too can fast-forward AI in the data center.

Jeremy Stanley is the vice president of data science at Instacart, where he works closely with data scientists who are integrated into product teams to drive growth and profitability through logistics, catalog, search, consumer, shopper, and partner applications. Previously, Jeremy was chief data scientist and EVP of engineering at Sailthru, a company building data-driven solutions for marketers to drive long-term customer engagement and optimize revenue opportunities, where he was responsible for the intelligence in the marketing personalization platform, led development, operations, database, and engineering support teams, and partnered with the CTO to drive innovation and stability while scaling; the CTO of Collective, where he led a team of product managers, engineers, and data scientists creating technology platforms that used machine learning and big data to address challenging multiscreen advertising problems; and the founder of Ernst & Young’s global markets analytics group, which analyzed the firm’s markets, financial, and personnel data to inform executive decision making. Jeremy’s background in data-driven technology products spans a decade consulting with numerous global financial services firms on predictive modeling applications as a leader in the customer analytics advisory practice at Ernst & Young.

Presentations

How Instacart is using AI to create the most efficient shoppers ever session

In the on-demand economy, if something doesn’t happen in real time, it’s too late. The secret ingredient that makes this possible? Data science. Jeremy Stanley explains how Instacart uses deep learning to enable its shoppers to become the most efficient shoppers ever, putting the company at the top of the food chart in the on-demand economy.

Kenneth O. Stanley is an associate professor in the Department of Computer Science at the University of Central Florida, where he is the director of the evolutionary complexity research group, and a senior research scientist at Uber AI Labs, which he joined through the acquisition of his company, Geometric Intelligence Inc. Kenneth is an inventor of the Neuroevolution of Augmenting Topologies (NEAT), HyperNEAT, and novelty search neuroevolution algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e., evolving neural networks), generative and developmental systems (GDS), nonobjective search, machine learning for video games, and interactive evolution. He is also a coauthor of the popular science book Why Greatness Cannot Be Planned: The Myth of the Objective (Springer) and has spoken widely on its subject. Kenneth has won best paper awards for his work on NEAT, NERO, NEAT Drummer, FSMC, HyperNEAT, ES-HyperNEAT, adaptive HyperNEAT, novelty search, Galactic Arms Race, and NA-IEC. He is an associate editor of IEEE Transactions on Computational Intelligence and AI in Games and the Evolutionary Robotics section of Frontiers in Robotics and AI.

Presentations

Evolving neural networks through neuroevolution session

Kenneth Stanley offers an overview of the field of neuroevolution, an emerging paradigm for training neural networks through evolutionary principles that has grown up alongside more conventional deep learning, highlighting major algorithms such as NEAT, HyperNEAT, and novelty search, the field's emerging synergies with deep learning, and promising application areas.

Meet the Expert with Kenneth Stanley (Uber AI Labs | University of Central Florida) Meet the Experts

Want a fascinating conversation about evolving artificial neural networks through evolutionary algorithms? Don't miss Kenneth's office hour.

Andy Steinbach is a senior director in the financial services industry vertical at NVIDIA. Andy has spent the last 12 years in the semiconductor industry in product management, strategy, and technology development. Previously, he built the team deploying the very first machine learning efforts in a $1B business unit of the German technology company Zeiss. Andy holds a PhD in physics from the University of Colorado at Boulder.

Presentations

The AI revolution’s impact on the financial services industry session

Andy Steinbach shares case studies and applications in artificial intelligence that are having an impact on financial markets.

Todd Stewart is vice president of clinical integrated solutions at Mercy, where he coordinates clinical needs with technology across the Mercy ministry. Todd began his affiliation with Mercy when he opened a practice at Mercy Hospital Fort Smith and has spent much of his career utilizing clinical analytics and advanced data systems. He holds a PhD in medical science from the University of Arkansas.

Presentations

Applying AI to healthcare's biggest opportunity: Clinical variation session

Mercy and Intermountain, two of the largest and most innovative hospital systems in the United States, have recently applied AI to tackle clinical variation within their systems. Todd Steward and Lonny Northrup discuss the application of machine intelligence for optimizing care and provide valuable insights into practice variation for improving clinical pathways.

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.

Ray: A distributed execution framework for reinforcement learning applications session

Ion Stoica offers an overview of 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.

Ameet Talwalkar is cofounder and chief scientist at Determined AI and an assistant professor in the School of Computer Science at Carnegie Mellon University. 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

Scalable deep learning session

Ameet Talwalkar offers an overview of Hyperband, a novel algorithm for hyperparameter optimization that is simple, flexible, theoretically sound, and an order of magnitude faster than leading competitors, and shares research aimed at understanding the underlying landscape of training deep learning models in parallel and distributed environments.

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

Presentations

Meet the Expert with Hanlin Tang (Intel) Meet the Experts

Are you looking for deep learning solutions for specific problems, or moving DL algorithms from prototype to deployment? Hamlin is a great source of advice.

The practitioner’s guide to AI session

Training deep learning networks is often seen as a dark art. Hanlin Tang demystifies the process, sharing lessons learned from building AI algorithms across multiple verticals and tips and tricks for designing models. Hanlin also offers an overview of the Intel Nervana deep learning stack, which accelerates the iteration cycle for data scientists.

Paul Tepper is the worldwide head of Nuance’s cognitive innovation group (CIG), focused on applying the latest advancements in machine learning and artificial intelligence to automate and improve the customer experience across channels. Paul is responsible for setting Nuance’s AI strategy and leading product development efforts in collaboration with Nuance’s definitional customers and Nuance’s AI Lab. Currently, he is focused on machine learning advancements in analytics, prediction, knowledge, and human-assisted learning. Paul has over a decade of experience in software development and AI research. He holds a PhD in computer science and communication studies from Northwestern University, an MSc in AI and NLP from the University of Edinburgh, and a BA in computer science, linguistics, and cognitive science from Rutgers University.

Presentations

Critical factors in building successful AI-powered conversational interfaces session

Many industries are now exploring chatbots powered by artificial intelligence as a source for improved insights and better understanding of customer preferences. Paul Tepper explores the unique challenges chatbots present, shares available solutions, and outlines a number of critical factors in building successful chatbots and virtual assistants.

Rachel Thomas is the cofounder of fast.ai and a researcher in residence at USF Data Institute, where she teaches numerical linear algebra. Rachel helped create the free Practical Deep Learning for Coders MOOC, which 50,000 students have started. Previously, she worked as a quant in energy trading, a data scientist and engineer at Uber, and a senior instructor at Hackbright. Rachel is a popular writer on data science and diversity in tech. Her writing has made the front page of Hacker News and Medium, has been included in newsletters by O’Reilly, Fortune, crunchbase, and Mattermark, and has been translated into Spanish, Portuguese, and Chinese. Rachel holds a PhD in mathematics from Duke.

Presentations

All the linear algebra you need for AI session

If the math used in AI seems intimidating, this tutorial is for you. Rachel Thomas walks you through working with arrays of different dimensions and how broadcasting handles data dimensions. You'll also gain hands-on experience with PyTorch, the Python framework for GPU computing developed by Facebook.

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 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 strongly believes in story-driven innovation.

Presentations

Using deep learning toolkits with Kubernetes clusters session

Joy Qiao and Wee Hyong Tok demonstrate how to combine Kubernetes clusters and deep learning toolkits to get the best of both worlds and jumpstart the development of innovative deep learning applications. Along the way, Joy and Wee Hyong explain how to train deep neural networks using GPU-enabled containers orchestrated by Kubernetes with common deep learning toolkits, such as CNTK and TensorFlow.

Nigel Toon is the cofounder and CEO of Graphcore. Previously, Nigel was CEO of two VC-backed silicon companies: Picochip, (acquired by Mindspeed in 2012) and XMOS; cofounder of Icera (acquired by NVIDIA in 2011), a 3G cellular modem chip company, where he led sales and marketing and served on the board of directors; and vice president and general manager at Altera, where he was responsible for establishing and building the European business unit that grew to over $400M in annual revenue. He is the author of three patents. Nigel currently serves as chairman of the board of directors at XMOS and as a nonexecutive director at Imagination Technologies.

Presentations

Why do we need new hardware for machine intelligence? session

Nigel Toon explains how new processing platforms will enable the next wave of machine intelligence beyond deep learning and how these machine learning innovations will impact businesses and improve competitiveness.

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

Presentations

Scalable deep learning with Microsoft Cognitive Toolkit (sponsored by Microsoft) session

Anusua Trivedi offers an overview of Microsoft’s Cognitive Toolkit, also known as CNTK. CNTK has unique advantages over other toolkits, especially in speed and scalability. Anusua compares five well-known toolkits to demonstrate how CNTK achieves almost linear scalability, which is far superior to all the other well-known toolkits.

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

Presentations

Getting started with TensorFlow Tutorial

Yufeng Guo and Amy Unruh walk you through training and deploying a machine learning system using TensorFlow, a popular open source library. Yufeng and Amy take you from conceptual overviews all the way to building complex classifiers and explain how you can apply deep learning to complex problems in science and industry.

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

When machines have ideas: A new approach to AI session

Ben Vigoda demonstrates new advances in AI technology that enable companies to accurately read millions of complex customer messages and take action.

Mary Wahl is a member of Microsoft’s Boston-based algorithms and data science team, which develops custom machine learning solutions for enterprise customers. Previously, Mary studied recent human migration, disease risk estimation, and forensic reidentification using crowdsourced genomic and genealogical data at the Whitehead Institute at Columbia University under Yaniv Erlich.

Presentations

Scalable operationalization of trained CNTK and TensorFlow DNNs session

Mary Wahl shares a cloud-based Hadoop ecosystem solution for deploying deep neural networks (DNNs) with scalable compute resources to accommodate changing workloads and demonstrates how to apply trained Microsoft CNTK and TensorFlow DNNs to a large image set in HDFS (Azure Data Lake Store) using the Python bindings for these deep learning frameworks and a Microsoft HDInsight Spark cluster.

Gang Wang is an Engineering Fellow at Intuit, responsible for the core tax engine that is used by 33 million US tax filers every year across mobile, web, and desktops. He has initiated and been leading the design and implementation of the next generation intelligent tax engine. His work focuses on large scale intelligent systems, knowledge engineering, enterprise architecture for mission critical financial applications. Gang is well published academically having written 26 papers, articles and book chapters. His publications cover speech recognition, natural language understanding, computer architecture, and electronic design automation, and financial software. He also holds 23 issued patents in US and Europe. Gang has a Ph.D. in Electrical and Computer Engineering from University of California at Santa Barbara.

Presentations

Let artificial intelligence handle one of the two certainties in this world session

Taxes are one of consumers' most complex financial transactions, thanks to a tax code that is 80,000 pages long. Gang Wang explains how Intuit built the industry’s only Tax Knowledge Engine, a constraint-based engine that encodes changing financial regulations and provides the foundation for a host of artificial intelligence technologies that save customers time and money.

Jisheng Wang is senior director of data science at Aruba, a Hewlett Packard Enterprise company, where he leads the overall effort to apply data science in different enterprise network areas. Jisheng has over 12 years of extensive research and professional experience in applying state-of-art big data and data science technologies to solve challenging security problems. Previously, he was the chief scientist at Niara, where he led the overall innovation and development effort in big data infrastructure and data science, and was a technical lead for various security products at Cisco. Jisheng has published articles in top-tier publications and holds a number of patents, including one for an industry-first modular and data-agonistic UEBA solution. He holds a PhD in electrical engineering from Penn State University and an MS and BS in electrical engineering from Shanghai Jiao Tong University.

Presentations

Deep learning in enterprise IoT: Use cases and challenges session

Recently, both deep learning and the IoT have attracted tremendous attention. Jisheng Wang shares firsthand experience in applying deep learning to solving some real-world enterprise IoT problems (e.g., IoT device identification and IoT security) and outlines some challenges for deep learning in enterprise applications, along with suggestions to overcome them.

Melanie Warrick is a senior developer advocate at Google with a passion for machine learning problems at scale. Melanie’s previous experience includes work as a founding engineer on Deeplearning4j and as a data scientist and engineer at Change.org.

Presentations

Reinforcement Learning Overview session

Reinforcement learning is a popular subfield in machine learning because of its success in beating humans at complex games like Go and Atari. The field’s value is in utilizing an award system to develop models and find more optimal ways to solve complex, real-world problems. This approach allows software to adapt to its environment without full knowledge of what the results should look like.

John Whalen leads Brilliant Experience, a consultancy that supports intra- and entrepreneurs to ensure the success of mission-critical innovation projects, using a unique blend of user-centered design, psychology, design thinking, and Lean Startup techniques. John draws on his over 15 years of user-centered design experience to help large enterprises integrate brain science into Agile, design thinking, and UCD projects. His specialty is providing businesses with competitive advantages using a mix of user research insights and expert knowledge of human vision, attention, and memory. John has experience (and great stories to tell from) working with Fortune 500 clients in the ecommerce, financial, healthcare, and government verticals. He holds a PhD in cognitive science.

Presentations

Next-generation intelligent applications require cognitive design. session

John Whalen explores the concept of cognitive design, describing how humans structure their commands to AI systems (syntax, word usage, prosody) and how to measure human reactions to AI responses using biometrics (facial emotion recognition, heart rate, GSR). Along the way, John shares insights into how to optimally architect the customer experience.

Joel Wu is a visiting scholar and clinical ethics fellow at Children’s Minnesota; a senior fellow and lecturer in the Division of Health Policy and Management at the University of Minnesota’s School of Public Health; and a member of the editorial board for the Journal of Pediatric Ethics. Previously, Joel conducted health policy research and development at the National Academies of Sciences, Engineering, and Medicine at the Institute of Medicine (now the National Academy of Medicine) and at the Brookings Institution’s Engelberg Center for Health Care Reform (now the Center for Health Policy) and completed a fellowship in bioethics at the Mayo Clinic. He holds a JD and an MA in bioethics from Case Western Reserve University and an MPH in epidemiology from the University of Minnesota.

Presentations

The potential ick factor: Ethical considerations for designing in healthcare session

With great cognitive computing comes great responsibility. As AI becomes ubiquitous in our society, it's critical to discuss the ethical concerns of AI and ask the tough questions. This multidisciplinary roundtable opens a dialogue on how bioethical principles might be applied to everyday design practice within healthcare.

Reza Bosagh Zadeh is on the faculty at Stanford, where he teaches Distributed Algorithms and Optimization and Discrete Mathematics and Algorithms, and is the founder and CEO of Matroid. His work focuses on machine learning, distributed computing, and discrete applied mathematics. As part of his research, Reza built the machine learning algorithms behind Twitter’s who-to-follow system, the first product to use machine learning at Twitter. Reza is the initial creator of the linear algebra package in Apache Spark and his work has been incorporated into industrial and academic cluster computing environments. Reza serves on the technical advisory board of Microsoft and Databricks. He has been recognized with a KDD Best Paper award and the Gene Golub Outstanding Thesis award. Reza holds his PhD in computational mathematics from Stanford University, completed under the supervision of Gunnar Carlsson.

Presentations

Scaling CNNs with Kubernetes and TensorFlow session

Reza Zadeh presents a Kubernetes deployment on Amazon AWS that provides customized computer vision to a large number of users.