AI Frontiers Conference

The Largest Conference of AI Builders

Learn and Get Inspired by the Best AI Minds

November 9-11, 2018

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San Jose Convention Center

Our Speakers

Ilya Sutskever

Ilya Sutskever

Co-Founder & DirectorOpenAICo-Inventor of AlphaGo and AlexNet
Ilya SutskeverIlya SutskeverCo-Founder & DirectorOpenAICo-Inventor of AlphaGo and AlexNet
Day 19:00 - 9:50amRecent Advances in Deep Learning and AI from OpenAII will present several advances in deep learning from OpenAI. First, I will present OpenAI Five, a neural network that learned to play on par with some of the strongest professional Dota 2 teams in the world in an 18-hero version of the game. Next, I will present Dactyl, a human-like robot hand trained entirely in simulation with reinforcement learning that has achieved unprecedented dexterity on a physical robot. I will also present our results on unsupervised learning in language, that show that pre-training and finetuning can achieve a significant improvement over state of the art. Finally, I will present an overview of the historical progress in the field.
Learn more about Ilya Sutskever in the post: The man who revolutionized computer vision, machine translation, games and robotics
Speaker BioIlya Sutskever received his Ph.D. in computer science from the University of Toronto, under the supervision of Geoffrey Hinton. He was a postdoctoral fellow with Andrew Ng at Stanford University for a brief period, after which he dropped out to co-found DNNResearch which Google acquired the following year. Sutskever joined the Google Brain team as a research scientist, where he developed the Sequence to Sequence model, contributed to the design of TensorFlow, and helped establish the Brain Residency Program. He is a co-founder of OpenAI, where he currently serves as research director.

Sutskever has made many contributions to the field of Deep Learning, including the convolutional neural network that convinced the computer vision community of the power of deep learning by winning the 2012 ImageNet competition. He was listed in MIT Technology Review’s 35 innovators under 35.
Percy Liang

Percy Liang

Assistant ProfessorStanford UniversityCreator of SQuAD competition
Percy LiangPercy LiangAssistant ProfessorStanford UniversityCreator of SQuAD competition
Day 11:00 - 1:30pmPushing the Limits of Machine LearningIn recent years, machine learning has undoubtedly been hugely successful in driving progress in AI applications. However, as we will explore in this talk, even state-of-the-art systems have "blind spots" which make them generalize poorly out of domain and render them vulnerable to adversarial examples. We then suggest that more unsupervised learning settings can encourage the development of more robust systems. We show positive results on two tasks: (i) text style and attribute transfer, the task of converting a sentence with one attribute (e.g., sentiment) to one with another; and (ii) solving SAT instances (classical problems requiring logical reasoning) using end-to-end neural networks.
Learn more about Percy Liang in the post: Percy Liang Is Teaching Machines to Read
Speaker BioPercy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning. His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).
Quoc Le

Quoc Le

Research ScientistGoogle BrainCreator of AutoML
Quoc LeQuoc LeResearch ScientistGoogle BrainCreator of AutoML
Day 11:30 - 2:30pmUsing Machine Learning to Automate Machine LearningTraditional machine learning systems are hand-designed and tuned by machine learning experts. To scale up the impact of machine learning to many real-world applications, we must figure out a way to automate the designing process of these pipelines. In this talk, I will discuss the use of machine learning to automate the process of designing neural architectures and data augmentation strategies (Neural Architecture Search and AutoAugment).
Learn more about Quoc Le in the post: An Unassuming Genius: The Man behind Google’s AutoML
Speaker BioQuoc is a researcher at Google Brain. He is an early member of the Google Brain team and known for his work on large scale deep learning, sequence to sequence learning (seq2seq), Google's Neural Machine Translation System (GNMT), and automated machine learning (automl). Prior to Google Brain, Quoc did his PhD at Stanford. He was recognized at one of top innovators in 2014 by MIT Technology Review.
Pieter Abbeel

Pieter Abbeel

ProfessorUC Berkeley
Pieter AbbeelPieter AbbeelProfessorUC Berkeley
Day 29:40 - 10:40amDeep Learning for RoboticsProgramming robots remains notoriously difficult. Equipping robots with the ability to learn would by-pass the need for what otherwise often ends up being time-consuming task specific programming. This talk will describe recent progress in deep reinforcement learning (robots learning through their own trial and error), in apprenticeship learning (robots learning from observing people), and in meta-learning for action (robots learning to learn). This work has led to new robotic capabilities in manipulation, locomotion, and flight.
Learn more about Pieter Abbeel in the post: How to Build a Household Robot: Pieter Abbeel’s Story
Speaker BioPieter Abbeel is Professor and Director of the Robot Learning Lab at UC Berkeley [2008- ], Co-Founder of covariant.ai [2017- ], Co-Founder of Gradescope [2014- ], Advisor to OpenAI, Founding Faculty Partner AI@TheHouse, Advisor to many AI/Robotics start-ups. He works in machine learning and robotics. In particular his research focuses on making robots learn from people (apprenticeship learning), how to make robots learn through their own trial and error (reinforcement learning), and how to speed up skill acquisition through learning-to-learn (meta-learning). His robots have learned advanced helicopter aerobatics, knot-tying, basic assembly, organizing laundry, locomotion, and vision-based robotic manipulation. He has won numerous awards, including best paper awards at ICML, NIPS and ICRA, early career awards from NSF, Darpa, ONR, AFOSR, Sloan, TR35, IEEE, and the Presidential Early Career Award for Scientists and Engineers (PECASE). Pieter's work is frequently featured in the popular press, including New York Times, BBC, Bloomberg, Wall Street Journal, Wired, Forbes, Tech Review, NPR.
Jay Yagnik

Jay Yagnik

VPGoogle AI
Jay YagnikJay YagnikVPGoogle AI
Day 17:00 - 9:00pmA History Lesson on AIWe have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
Speaker BioJay Yagnik is currently a Vice President and Engineering Fellow at Google, leading large parts of Google AI. While at Google he has led many foundational research efforts in machine learning and perception, computer vision, video understanding, privacy preserving machine learning, quantum AI, applied sciences, and more. He has also created multiple engineering and product successes for the company, in areas including Google Photos, YouTube, Search, Ads, Android, Maps, and Hardware. Jay’s research interests span the fields of deep learning, reinforcement learning, scalable matching, graph information propagation, image representation and recognition, temporal information mining, and sparse networks. He is an alumnus of the Indian Institute of Science and the Institute of Technology, Nirma University for graduate and undergraduate studies.
Kai-Fu Lee

Kai-Fu Lee

Chairman & CEOSinovation Ventures
Kai-Fu LeeKai-Fu LeeChairman & CEOSinovation Ventures
Day 15:15 - 5:45pmKeynoteThe talk will be given by Kai-Fu Lee, CEO of Sinovation Ventures.
Speaker BioHis latest book AI Superpowers (aisuperpowers.com) releasing fall 2018 discusses US-China co-leadership in the age of AI as well as the greater societal impacts brought upon by the AI technology revolution.

Dr. Kai-Fu Lee is the Chairman and CEO of Sinovation Ventures (www.sinovationventures.com) and President of Sinovation Venture’s Artificial Intelligence Institute. Sinovation Ventures, managing US$2 billion dual currency investment funds, is a leading venture capital firm focusing on developing the next generation of Chinese high-tech companies. Prior to founding Sinovation in 2009, Dr. Lee was the President of Google China. Previously, he held executive positions at Microsoft, SGI, and Apple. Dr. Lee received his Bachelor degree from Computer Science from Columbia University, Ph.D. from Carnegie Mellon University, as well as Honorary Doctorate Degrees from both Carnegie Mellon and the City University of Hong Kong. He is also a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), and followed by over 50 million audience on social media.

In the field of artificial intelligence, Dr. Lee built one of the first game playing programs to defeat a world champion (1988, Othello), as well as the world’s first large-vocabulary, speaker-independent continuous speech recognition system. Dr. Lee founded Microsoft Research China, which was named as the hottest research lab by MIT Technology Review. Later renamed Microsoft Research Asia, this institute trained the great majority of AI leaders in China, including CTOs or AI heads at Baidu, Tencent, Alibaba, Lenovo, Huawei, and Haier. While with Apple, Dr. Lee led AI projects in speech and natural language, which have been featured on Good Morning America on ABC Television and the front page of Wall Street Journal. He has authored 10 U.S. patents, and more than 100 journal and conference papers. Altogether, Dr. Lee has been in artificial intelligence research, development, and investment for more than 30 years.
Matt Feiszli

Matt Feiszli

Research ManagerFacebook
Matt FeiszliMatt FeiszliResearch ManagerFacebook
Day 110:00 - 11:30amVideo Understanding: Modalities, Time, and ScaleI will discuss the state of the art of video understanding, particularly its research and applications at Facebook. I will focus on two active areas: multimodality and time. Video is naturally multi-modal, offering great possibility for content understanding while also opening new doors like unsupervised and weakly-supervised learning at scale. Temporal representation remains a largely open problem; while we can describe a few seconds of video, there is no natural representation for a few minutes of video. I will discuss recent progress, the importance of these problems for applications, and what we hope to achieve.
Learn more about Matt Feiszli in the post: Video Understanding and Facebook’s AI Strategy
Speaker BioMatt Feiszli, Facebook Research Scientist Manager, leads computer vision efforts on video understanding at Facebook. He was previously leading video understanding as Senior Machine Learning Scientist at Sentient Technologies and also spent four years at Yale University as Gibbs Assistant Professor in mathematics. He received his Ph.D. of Applied Mathematics from Brown University with David Mumford and has undergraduate degrees in Computer Science and Psychology from Yale University.
Sumit Gulwani

Sumit Gulwani

Partner Research ManagerMicrosoft
Sumit GulwaniSumit GulwaniPartner Research ManagerMicrosoft
Day 11:30 - 2:30pmProgramming by ExamplesProgramming by examples (PBE) is a new frontier in AI that enables users to create scripts from input-output examples. A killer application is in the space of data wrangling to automate tasks like string/number/date transformations (e.g., converting “FirstName LastName” to “LastName, FirstName”), column splitting, table extraction from log-files, webpages, and PDFs, normalizing semi-structured spreadsheets into structured tables, transforming JSON from one format to another, etc. This presentation will educate the audience about this new PBE-based programming paradigm: its applications, form factors inside different products, and the science behind it.
Learn more about Sumit Gulwani in the post: Programming by Examples and Its Inventor
Speaker BioSumit Gulwani is a research manager at Microsoft, where he leads the PROSE research and engineering team that develops APIs for program synthesis (programming by examples and natural language) and incorporates them into real products. He is the inventor of the popular Flash Fill feature in Microsoft Excel, used by hundreds of millions of people. He has published 120+ peer-reviewed papers in top-tier conferences and journals across multiple computer science areas, delivered 40+ keynotes and invited talks at various forums, and authored 50+ patent applications (granted and pending). Sumit is a recipient of the prestigious ACM SIGPLAN Robin Milner Young Researcher Award, ACM SIGPLAN Outstanding Doctoral Dissertation Award, and the President’s Gold Medal from IIT Kanpur.
Divya Jain

Divya Jain

DirectorAdobe
Divya JainDivya JainDirectorAdobe
Day 110:00 - 11:30amVideo SummarizationAs video content is becoming mainstream, video summarization is becoming a hot research topic in academia and industry. Video thumbnail generation and summarization has been worked on for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in GANs are improving the quality, aesthetics and relevancy of the frames to represent the original videos. Come join this session to get an understanding of various challenges and emerging solutions around video summarization.
Learn more about Divya Jain in the post: An Inside Look Into Adobe’s AI Effort
Speaker BioDivya Jain is an industry recognized product and technology leader in machine learning and AI. She has 15+ years of industry experience at various startups and Fortune 500 companies. She is currently serving as an Engineering Director for Sensei ML platform at Adobe. Before this she was a Research Director at Tyco Innovation Garage and led various deep learning initiatives in video surveillance space. She also co-founded a startup, dLoop Inc., which was acquired by Box in 2013. At Box, Divya led the team that built the first machine learning capabilities into the Box platform. She is very passionate about open sharing of knowledge and information and always working towards abridging technology gap for product innovation.
Mario Munich

Mario Munich

SVP TechnologyiRobot
Mario MunichMario MunichSVP TechnologyiRobot
Learn more about Mario Munich in the post: Bringing Robots to Homes: Mario Munich’s Story
Junling Hu

Junling Hu

CEOQuestion.ai
Junling HuJunling HuCEOQuestion.ai
Day 18:50 - 9:00amOpening Remarks
by Junling Hu, Conference Chair
Learn more about Junling Hu in the post: Understanding AutoML and Neural Architecture Search
Speaker BioJunling Hu is the founder and CEO of question.ai. She is also the Chair of AI Frontiers Conference (https://aifrontiers.com). Before starting her company, she was Director of Data Mining at Samsung, where she led a team to build large-scale recommender systems. Prior to Samsung, Dr. Hu led data science teams at PayPal and eBay, providing machine learning solution to company-wide operation, ranging from product search ranking, sales prediction, user opinion mining to targeted marketing. Dr. Hu has more than 1,000 scholarly citations on her papers. She is a recipient of CAREER award from National Science Foundation, for her work on Multi-agent Reinforcement Learning. She holds a Ph.D. in AI from University of Michigan at Ann Arbor.
Li Deng

Li Deng

Chief AI OfficerCitadel
Li DengLi DengChief AI OfficerCitadel
Day 13:15 - 4:15pmFrom Modeling Speech and Language to Modeling Financial MarketsI will first survey how deep learning has disrupted speech and language processing industries since 2009. Then I will draw connections between the techniques for modeling speech and language and those for financial markets. Finally, I will address three unique technical challenges to financial investment.
Learn more about Li Deng in the post: A Journey with AI: From Speech to Finance
Speaker BioLi Deng joined Citadel as its Chief AI Officer in May 2017. Previously, he was chief scientist of AI and partner research manager at Microsoft; he has also been a professor at the University of Waterloo in Ontario and held teaching/research positions at MIT (Cambridge), ATR (Kyoto, Japan), and HKUST (Hong Kong). He is a fellow of the IEEE, the Acoustical Society of America, and the ISCA. He has also been an affiliate professor at University of Washington since 2000. In recognition of the pioneering work on disrupting speech recognition industry using large-scale deep learning, he received the 2015 IEEE SPS Technical Achievement Award for Outstanding Contributions to Automatic Speech Recognition and Deep Learning. He also received numerous best paper and patent awards for contributions to artificial intelligence, machine learning, information retrieval, multimedia signal processing, speech processing, and human language technology. Deng is an author or coauthor of six technical books on deep learning, speech processing, discriminative machine learning, and natural-language processing.
Ashok Srivastava

Ashok Srivastava

Chief Data OfficerIntuit
Ashok SrivastavaAshok SrivastavaChief Data OfficerIntuit
Day 13:15 - 4:15pmUsing AI to Solve Complex Economic ProblemsNearly half of all small businesses fail within their first 5 years. However, AI-driven solutions can help solve complex economic problems for consumers and small businesses like missed bill payments, insufficient capital, overinvestment in fixed assets, and more.
Ashok Srivastava discusses technology's role in solving economic problems and details how Intuit is using its unrivaled financial dataset to power prosperity around the world. Leveraging technology enablers like deep learning, natural language processing, and automated reasoning and combining with a delightful end-user experience and sophisticated UX, Intuit is using technology to help its users have more confidence in their financial management.
Speaker BioAshok N. Srivastava is the senior vice president and chief data officer at Intuit, where he is responsible for setting the vision and direction for large-scale machine learning and AI across the enterprise to help power prosperity across the world-and in the process is hiring hundreds of people in machine learning, AI, and related areas at all levels. Ashok has extensive experience in research, development, and implementation of machine learning and optimization techniques on massive datasets and serves as an advisor in the area of big data analytics and strategic investments to companies including Trident Capital and MyBuys. Previously, Ashok was vice president of big data and artificial intelligence systems and the chief data scientist at Verizon, where his global team focused on building new revenue-generating products and services powered by big data and artificial intelligence; senior director at Blue Martini Software; and senior consultant at IBM. He is an adjunct professor in the Electrical Engineering Department at Stanford and is the editor-in-chief of the AIAA Journal of Aerospace Information Systems. Ashok is a fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA). He has won numerous awards, including the Distinguished Engineering Alumni Award, the NASA Exceptional Achievement Medal, the IBM Golden Circle Award, the Department of Education Merit Fellowship, and several fellowships from the University of Colorado. Ashok holds a PhD in electrical engineering from the University of Colorado at Boulder.
Roland Memisevic

Roland Memisevic

Chief ScientistTwentyBN
Roland MemisevicRoland MemisevicChief ScientistTwentyBN
Day 110:00 - 11:30amTeaching machines common sense understanding of the world around themIn this talk, I will introduce an AI system that interacts with you while "looking" at you - to understand your behaviour, your surroundings and the full context of the engagement.  At the core of this technology is a crowd acting-platform, that allows humans to engage with and teach the system about everyday aspects of our lives and of our physical world.  Combining this with deep neural networks makes it possible to generate a high degree human-like "awareness" of everyday scenes and situations.  I will describe how this technology allows devices, ranging from information kiosks to cars, to engage with humans more naturally and instinctively, and how TwentyBN uses this ability to create commercial value for our customers.
Speaker BioRoland Memisevic received the PhD in Computer Science from the University of Toronto in 2008 doing research on neural networks. He subsequently held positions as a research scientist at PNYLab, Princeton, as a post-doc at the University of Toronto and at ETH Zurich, and as junior professor at the University of Frankfurt. In 2012 he joined the MILA deep learning group at the University of Montreal as an assistant professor. Since 2016 he has been Chief Scientist at the Canadian-German AI startup Twenty Billion Neurons, which he co-founded in 2015. Roland was named Fellow of the Canadian Institute for Advanced Research (CIFAR) in 2015. His research interests are in deep and recurrent neural networks, in particular, as applied to video understanding.
Roger Roberts

Roger Roberts

PartnerMcKinsey & Company
Roger RobertsRoger RobertsPartnerMcKinsey & Company
Sameer Sharma

Sameer Sharma

Global GM IoTIntel
Sameer SharmaSameer SharmaGlobal GM IoTIntel
Day 14:15 - 5:15pmGoing for the Edge - AI @ IOTIOT implementations have moved from “Connect the Unconnected” to Smart Connected devices and solutions. The next big inflection point will be around autonomous systems. AI + Data will play a pivotal role in enabling this autonomy. This will play out in Intelligent factories, cities and buildings. We will use Computer Vision as an example to walk through this imminent step-function transition.
Speaker BioSameer Sharma is the Global GM (New Markets/Smart Cities) for IOT Solutions at Intel and a thought leader in IOT/Mobile ecosystem, having driven multiple strategic initiatives over the past 19 years. Sameer leads a global team that incubates and scales new growth categories and business models for Intel in IOT and Smart Cities. His team also focuses on establishing leadership across the industry playing a pivotal role in deploying solutions for the development of smart cities around the world—an important effort in furthering the goal of sustainability. These solutions include Intelligent Transportation, AI+Video, Air Quality Monitoring and Smart Lighting in cities. With far-reaching impact, each of these solutions are providing local governments a plethora of data to enhance the daily quality of life for citizens while simultaneously promoting responsible practices to protect the environment.

Sameer has an MBA from The Wharton School at UPenn, and a Masters in Computer Engineering from Rutgers University. He holds 11 patents in the areas of IOT and Mobile.
Himagiri Mukkamala

Himagiri Mukkamala

GM & SVPARM
Himagiri MukkamalaHimagiri MukkamalaGM & SVPARM
Learn more about Himagiri Mukkamala in the post: An Inside Look at Arm’s Big Push Into IoT
Arnaud Thiercelin

Arnaud Thiercelin

Head of R&DDJI
Arnaud ThiercelinArnaud ThiercelinHead of R&DDJI
Speaker BioArnaud Thiercelin is the Head of R&D of North America for DJI, overseeing global developer technologies and enterprise R&D projects. Within this role, he is responsible for driving the vision for DJI's developer technologies and enterprise solutions, managing teams located in Palo Alto and Shenzhen, China. Arnaud has more than 15 years of experience in software development from embedded systems to cloud infrastructure. Arnaud has been in the United States for nearly a decade and is originally from France where he studied at Epitech.
Sumit Gupta

Sumit Gupta

VP of AIIBM
Sumit GuptaSumit GuptaVP of AIIBM
Day 21:30pm - 2:30pmAI for the EnterpriseThe use of AI for voice search and image recognition is talked about often. Enterprises, however, have different challenges and requirements. In this talk, we will focus on talking about use cases in the enterprise and challenges in building out AI solutions. We will talk about how an Auto-machine learning software for videos and images called PowerAI Vision enables quick AI model training & deployment for various enterprise use cases.
Speaker BioSumit Gupta is VP, AI, Machine Learning, and HPC in the IBM Cognitive Systems business. Sumit leads the business strategy & software and hardware products for machine learning, deep learning, & HPC. Prior to IBM, Sumit was the general manager of the AI & GPU accelerated data center business at NVIDIA and was central in building that business from the ground-up to what is now a multi-billion dollar business for NVIDIA. Sumit has a Ph.D. in CS from UC, Irvine, and a BS in EE from IIT Delhi.
Sarah Guo

Sarah Guo

General PartnerGreylock Partners
Sarah GuoSarah GuoGeneral PartnerGreylock Partners
Speaker BioSarah is interested in almost everything where technology can be used as a weapon to get us to the future, faster. She spends a lot of her time thinking about opportunities in B2B applications and infrastructure, cyber security, artificial intelligence, augmented reality and healthcare.

Sarah joined Greylock Partners as an investor in 2013. She led Greylock’s investment in Cleo and is on the board of Cleo and Obsidian and also works closely with Awake, Crew, Rhumbix and Skyhigh. Prior to joining Greylock, Sarah was at Goldman Sachs, where she invested in growth-stage technology startups such as Dropbox, and advised pre-IPO technology companies such as Workday (as well as public clients including Zynga, Netflix and Nvidia). Previously, Sarah worked with Casa Systems (NASDAQ:CASA), a publicly traded technology company that develops a software-centric networking platform for cable and mobile service providers.

She is an advocate for STEM education for women and the underserved. She has taught Marketing in the Wharton Undergraduate Program and served as a teaching fellow in lower-income high schools for the Philadelphia World Affairs Council. Sarah has four degrees from the Wharton School and the University of Pennsylvania. She is part of Linkedin’s Next Wave and the Forbes’ 30 Under 30.
Rohit Tripathi

Rohit Tripathi

GM & Head of ProductsSAP Digital Interconnect
Rohit TripathiRohit TripathiGM & Head of ProductsSAP Digital Interconnect
Speaker BioRohit Tripathi is Head of Products and Go-to-Market for SAP Digital Interconnect and brings with him over 20 years of experience in software and business operations.

In his current role, Rohit focuses on bringing to market value-added products and solutions that help SAP Digital Interconnect customers get more engaged, secure, and gather actionable insights in the Digital World. Rohit has been an author of CIO guides on using Big Data technologies.

Prior to joining SAP, Rohit was with The Boston Consulting Group where he advised senior executives of Fortune 500 companies on business strategy and operations.
Long Lin

Long Lin

Director of AIElectronic Arts
Long LinLong LinDirector of AIElectronic Arts
Day 22:30 - 3:10pmAI in GamingGames have been leveraging AI since the 1950s, when people built a rules-based AI engine that played tic-tac-toe. With technological advances over the years, AI has become increasingly popular and widely used in the gaming industry. The typical characteristics of games and game development makes them an ideal playground for practicing and implementing AI techniques, especially deep learning and reinforcement learning. Most games are well scoped; it is relatively easy to generate and use the data; and states/actions/rewards are relatively clear. In this talk, I will show a couple of use cases where ML/AI helps in-game development and enhances player experience. Examples include AI agents playing game and services that provide personalized experience to players.
Learn more about Long Lin in the post: Artificial Intelligence In Game Design
Speaker BioLong Lin, Director of Engineering @ Data & AI Team from Electronic Arts, where he leads the overall technical aspect of Player Profile Service, Player Relationship Management Platform, Recommendation Engine, Experimentation platform, and AI Agent & Simulation. Prior to EA, Long worked at WalmartLabs and eBay, where he has led R&D effort of Ad Platform, Customer Profile Service and multiple E-Commerce related services. Long is an expert in data engineering and applied machine learning, as well as live services.
Mark Moore

Mark Moore

Engineering DirectorUber
Mark MooreMark MooreEngineering DirectorUber
Yuandong Tian

Yuandong Tian

Research ManagerFacebook AI Research
Yuandong TianYuandong TianResearch ManagerFacebook AI Research
Day 22:30 - 3:10pmDeep Reinforcement Learning Framework for GamesDeep Reinforcement Learning (DRL) has made strong progress in many tasks, such as board games, robotics, navigation, neural architecture search, etc. I will present our recent open-sourced DRL frameworks to facilitate game research and development. Our framework is scalable so we can can reproduce AlphaGoZero and AlphaZero using 2000 GPUs, achieving super-human performance of Go AI that beats 4 top-30 professional players. We also show usability of our platform by training agents in real-time strategy games, and show interesting behaviors with a small amount of resource.
Speaker BioYuandong Tian is a Research Scientist and Manager in Facebook AI Research, working on deep reinforcement learning and its applications in games, and theoretical analysis of deep models. He is the lead scientist and engineer for ELF OpenGo and DarkForest Go project. Prior to that, he was a researcher and engineer in Google Self-driving Car team in 2013-2014. He received Ph.D in Robotics Institute, Carnegie Mellon University on 2013, Bachelor and Master degree of Computer Science in Shanghai Jiao Tong University. He is the recipient of 2013 ICCV Marr Prize Honorable Mentions.
Samir Kumar

Samir Kumar

Managing DirectorM12
Samir KumarSamir KumarManaging DirectorM12
Jason Costa

Jason Costa

Venture PartnerGGV Capital
Jason CostaJason CostaVenture PartnerGGV Capital
Lukasz Kaiser

Lukasz Kaiser

Staff Research ScientistGoogle Brain
Lukasz KaiserLukasz KaiserStaff Research ScientistGoogle Brain
Speaker BioLukasz joined Google in 2013 and is currently a senior Research Scientist in the Google Brain Team in Mountain View, where he works on fundamental aspects of deep learning and natural language processing. He has co-designed state-of-the-art neural models for machine translation, parsing and other algorithmic and generative tasks and co-authored the TensorFlow system and the Tensor2Tensor library. Before joining Google, Lukasz was a tenured researcher at University Paris Diderot and worked on logic and automata theory. He received his PhD from RWTH Aachen University in 2008 and his MSc from the University of Wroclaw, Poland.
Ni Lao

Ni Lao

Chief Scientist and Co-FounderMosaix.ai
Ni LaoNi LaoChief Scientist and Co-FounderMosaix.ai
Speaker BioNi Lao, Chief scientist and co-founder, Mosaix.ai (picture attached) Biograph: Dr. Ni Lao, Chief scientist and co-founder of Mosaix.ai, is an expert in Knowledge Graph (KG) and weakly supervized Natural Language Understanding (NLU). He is well known for his work on large scale inference for the CMU Never-Ending Language Learning (NELL) project, and Google Knowledge Vault project. He recently led research projects applying innovative reinforcement learning approaches to achieve new state-of-the-art in weakly supervized NLU tasks. His past research has contributed to Google’s KG and search-based question answering products.
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Moderators
T.M. Ravi

T.M. Ravi

Director and FounderThe Hive
T.M. RaviT.M. RaviDirector and FounderThe Hive
Speaker BioT. M. Ravi is Managing Director and Co-founder of The Hive (www.hivedata.com). The Hive based in Palo Alto, CA is a venture fund and co-creation studio for Artificial Intelligence (AI) powered startups. The Hive engages with entrepreneurs and corporations to create companies focused on data and AI driven applications in the enterprise and different industry segments. The Hive also has a presence in India and Brazil. Ravi is a frequent speaker at conferences on the topics of AI, enterprise transformation and innovation. Ravi has a successful track record as a serial entrepreneur and operating executive. He has helped start over 25 startups including three where he was founder & CEO: Mimosa Systems (acquired by Iron Mountain), Peakstone Corporation, and Media Blitz (acquired By Cheyenne Software). Ravi was also CMO for Iron Mountain, VP of Marketing at Computer Associates (CA) and VP at Cheyenne Software. Ravi earned a MS and PhD from UCLA and a Bachelors of Technology from IIT, Kanpur, India. He is on the board of Montalvo Art Center based in Saratoga, CA.
Gregory La Blanc

Gregory La Blanc

Faculty DirectorHaas School of Business
Gregory La BlancGregory La BlancFaculty DirectorHaas School of Business
Fang Yuan

Fang Yuan

Vice PresidentBaidu Ventures
Fang YuanFang YuanVice PresidentBaidu Ventures
Vijay Reddy

Vijay Reddy

InvestorIntel Capital
Vijay ReddyVijay ReddyInvestorIntel Capital
Mohan Reddy

Mohan Reddy

CTOThe Hive
Mohan ReddyMohan ReddyCTOThe Hive
Previous Speakers
Jeff Dean

Jeff Dean

Head of Google AIGoogle
Jeff DeanJeff DeanHead of Google AIGoogle
Andrew Ng

Andrew Ng

FounderDeeplearning.ai
Andrew NgAndrew NgFounderDeeplearning.ai
Speaker BioDr. Andrew Ng is a globally recognized leader in AI (Artificial Intelligence). He was until recently Chief Scientist at Baidu, where he led the company’s ~1300 person AI Group and was responsible for driving the company's global AI strategy and infrastructure. He was also the founding lead of the Google Brain team. Dr. Ng is also Co-Chairman and Co-founder of Coursera, the world’s leading MOOC (Massive Open Online Courses) platform, and an Adjunct Professor at Stanford University's Computer Science Department. Dr. Ng has authored or co-authored over 100 research papers in machine learning, robotics and related fields. He holds degrees from Carnegie Mellon University, MIT and the University of California, Berkeley.
Nikko Strom

Nikko Strom

Sr. Principal ScientistAmazon Alexa
Nikko StromNikko StromSr. Principal ScientistAmazon Alexa
Xuedong Huang

Xuedong Huang

Chief Scientist
of Speech & Language
Microsoft
Xuedong HuangXuedong HuangChief Scientist
of Speech & Language
Microsoft
Speaker BioDr. Xuedong Huang is a Microsoft Technical Fellow in Microsoft AI and Research. He leads Microsoft's Speech and Language Team. As Microsoft's Chief Speech Scientist, he pioneered to lead the team achieving a historical conversational speech recognition human parity milestone in 2016. In 1993, Huang joined Microsoft to found the company's speech technology group. As the general manager of Microsoft's spoken language efforts for over a decade, he helped to bring speech recognition to the mass market by introducing SAPI to Windows in 1995 and Speech Server to the enterprise call center in 2004. Prior to his current role, he spent five years in Bing as chief architect working to improve search and ads. Before Microsoft, he was on the faculty at Carnegie Mellon University and achieved the best performance of all categories in 1992’s DARPA speech recognition benchmarking. He received Alan Newell research excellence leadership medal in 1992 and IEEE Best Paper Award in 1993. He is an IEEE & ACM fellow. He was named as the Asian American Engineer of the Year (2011), and one of Wired Magazine's 25 Geniuses Who Are Creating the Future of Business (2016). He holds over 100 patents and published over 100 papers & 2 books.
Alex Acero

Alex Acero

Sr. DirectorApple Siri
Alex AceroAlex AceroSr. DirectorApple Siri
Speaker BioAlex Acero is Senior Director in the Siri team in charge of speech recognition, speech synthesis, and machine translation. Prior to joining Apple, he spent 20 years at Microsoft Research managing teams in speech, computer vision, NLP, machine translation, machine learning, and information retrieval. Dr. Acero is an IEEE Fellow and ISCA Fellow. He has served as President of the IEEE Signal Processing Society and is currently a member of the IEEE Board of Directors. He is a co-author of the textbook Spoken Language Processing. Dr. Acero has published over 250 technical papers and has over 150 US patents. He received his Ph.D. from Carnegie Mellon in 1990.
Yangqing Jia

Yangqing Jia

Research ScientistFacebook
Yangqing JiaYangqing JiaResearch ScientistFacebook

Our Topics

Video Understanding

Video Understanding

How to apply deep learning to understand videos? What are the advances in computer vision?

Natural Language Processing

Natural Language Processing

How has deep learning revolutionized natural language processing? What applications can we build today?

Robots

Robots

What is the status of robots? How do they become smarter?

Drones

Drones

A look at the future of autonomous drones and how they will transform our life.

Deep Learning Breakthrough

Deep Learning Breakthrough

The major breakthroughs in deep learning algorithm and their implications.

AI in Healthcare

AI in Healthcare

A look at the application of machine learning in Healthcare industry.

AI in Finance

AI in Finance

The growing presence of machine learning in financing.

Edge Computing

Edge Computing

How will edge computing impact the industry?

Games

Games

The growing presence of deep learning in game play and its impact for future games.

Internet Of Things

Internet Of Things

How do we apply deep learning to understand data from connected devices?

AI in Enterprises

AI in Enterprises

A look at future AI applications in enterprises.

Schedule

In this three-day AI conference at San Jose, we bring together leading scientists and practitioners who have deployed large-scale AI products. You will gain a front-row seat of the frontiers of AI and machine learning, and have opportunities to network with others who are enthusiastic about AI technologies and products.

Day 1November 9, 2018
8:50 - 9:00am
Opening Remarks
by Junling Hu, Conference Chair
Opening Remarks
by Junling Hu, Conference Chair
9:00 - 9:50am
Recent Advances in Deep Learning and AI from OpenAI
Ilya SutskeverCo-Founder & DirectorOpenAI
Recent Advances in Deep Learning and AI from OpenAI
I will present several advances in deep learning from OpenAI. First, I will present OpenAI Five, a neural network that learned to play on par with some of the strongest professional Dota 2 teams in the world in an 18-hero version of the game. Next, I will present Dactyl, a human-like robot hand trained entirely in simulation with reinforcement learning that has achieved unprecedented dexterity on a physical robot. I will also present our results on unsupervised learning in language, that show that pre-training and finetuning can achieve a significant improvement over state of the art. Finally, I will present an overview of the historical progress in the field.
10:00 - 11:30am
Video Understanding
Matt FeiszliResearch ManagerFacebook
Divya JainDirectorAdobe
Roland MemisevicChief ScientistTwentyBN
moderator:
Vijay ReddyInvestorIntel Capital
Video Understanding

Matt Feiszli : Video Understanding: Modalities, Time, and Scale

I will discuss the state of the art of video understanding, particularly its research and applications at Facebook. I will focus on two active areas: multimodality and time. Video is naturally multi-modal, offering great possibility for content understanding while also opening new doors like unsupervised and weakly-supervised learning at scale. Temporal representation remains a largely open problem; while we can describe a few seconds of video, there is no natural representation for a few minutes of video. I will discuss recent progress, the importance of these problems for applications, and what we hope to achieve.

Divya Jain : Video Summarization

As video content is becoming mainstream, video summarization is becoming a hot research topic in academia and industry. Video thumbnail generation and summarization has been worked on for years, but deep learning and reinforcement learning is changing the landscape and emerging as the winner for optimal frame selection. Recent advances in GANs are improving the quality, aesthetics and relevancy of the frames to represent the original videos. Come join this session to get an understanding of various challenges and emerging solutions around video summarization.

Roland Memisevic : Teaching machines common sense understanding of the world around them

In this talk, I will introduce an AI system that interacts with you while "looking" at you - to understand your behaviour, your surroundings and the full context of the engagement.  At the core of this technology is a crowd acting-platform, that allows humans to engage with and teach the system about everyday aspects of our lives and of our physical world.  Combining this with deep neural networks makes it possible to generate a high degree human-like "awareness" of everyday scenes and situations.  I will describe how this technology allows devices, ranging from information kiosks to cars, to engage with humans more naturally and instinctively, and how TwentyBN uses this ability to create commercial value for our customers.
11:30 - 1:00pm
Lunch
1:00 - 1:30pm
Pushing the Limits of Machine Learning
Percy LiangAssistant ProfessorStanford University
Pushing the Limits of Machine Learning
In recent years, machine learning has undoubtedly been hugely successful in driving progress in AI applications. However, as we will explore in this talk, even state-of-the-art systems have "blind spots" which make them generalize poorly out of domain and render them vulnerable to adversarial examples. We then suggest that more unsupervised learning settings can encourage the development of more robust systems. We show positive results on two tasks: (i) text style and attribute transfer, the task of converting a sentence with one attribute (e.g., sentiment) to one with another; and (ii) solving SAT instances (classical problems requiring logical reasoning) using end-to-end neural networks.
1:30 - 2:30pm
Deep Learning Breakthrough
Quoc LeResearch ScientistGoogle Brain
Sumit GulwaniPartner Research ManagerMicrosoft
Deep Learning Breakthrough
Leading scientists are presenting the current breakthrough in deep learning. Our speakers are Quoc Le, Research Scientist of Google Brain, Sumit Gulwani, Partner Research Manager of Microsoft, and others.

Quoc Le : Using Machine Learning to Automate Machine Learning

Traditional machine learning systems are hand-designed and tuned by machine learning experts. To scale up the impact of machine learning to many real-world applications, we must figure out a way to automate the designing process of these pipelines. In this talk, I will discuss the use of machine learning to automate the process of designing neural architectures and data augmentation strategies (Neural Architecture Search and AutoAugment).

Sumit Gulwani : Programming by Examples

Programming by examples (PBE) is a new frontier in AI that enables users to create scripts from input-output examples. A killer application is in the space of data wrangling to automate tasks like string/number/date transformations (e.g., converting “FirstName LastName” to “LastName, FirstName”), column splitting, table extraction from log-files, webpages, and PDFs, normalizing semi-structured spreadsheets into structured tables, transforming JSON from one format to another, etc. This presentation will educate the audience about this new PBE-based programming paradigm: its applications, form factors inside different products, and the science behind it.
2:30 - 2:45pm
Industry talk
Roger RobertsPartnerMcKinsey & Company
Industry talk
McKinsey report on the impact of AI.
2:45 - 3:15pm
Coffee Break
3:15 - 4:15pm
AI in Finance
Li DengChief AI OfficerCitadel
Ashok SrivastavaChief Data OfficerIntuit
moderator:
Fang YuanVice PresidentBaidu Ventures
AI in Finance
The growing presence of machine learning in financing. Our speakers are Li Deng, Chief AI Officer of Citadel, Ashok Srivastava, Chief Data Officer of Intuit, and others.

Li Deng : From Modeling Speech and Language to Modeling Financial Markets

I will first survey how deep learning has disrupted speech and language processing industries since 2009. Then I will draw connections between the techniques for modeling speech and language and those for financial markets. Finally, I will address three unique technical challenges to financial investment.

Ashok Srivastava : Using AI to Solve Complex Economic Problems

Nearly half of all small businesses fail within their first 5 years. However, AI-driven solutions can help solve complex economic problems for consumers and small businesses like missed bill payments, insufficient capital, overinvestment in fixed assets, and more.
Ashok Srivastava discusses technology's role in solving economic problems and details how Intuit is using its unrivaled financial dataset to power prosperity around the world. Leveraging technology enablers like deep learning, natural language processing, and automated reasoning and combining with a delightful end-user experience and sophisticated UX, Intuit is using technology to help its users have more confidence in their financial management.
4:15 - 5:15pm
Internet of Things
Sameer SharmaGlobal GM IoTIntel
Rohit TripathiGM & Head of ProductsSAP Digital Interconnect
Himagiri MukkamalaGM & SVPARM
moderator:
Gregory La BlancFaculty DirectorHaas School of Business
Internet of Things
Industry experts from leading companies are discussing how AI is integrating with IoT in the future. Our speakers are Sameer Sharma, Global GM IoT of Intel, Rohit Tripathi, GM & Head of Products of SAP, Himagiri Mukkamala, GM & SVP of ARM, and others.

Sameer Sharma : Going for the Edge - AI @ IOT

IOT implementations have moved from “Connect the Unconnected” to Smart Connected devices and solutions. The next big inflection point will be around autonomous systems. AI + Data will play a pivotal role in enabling this autonomy. This will play out in Intelligent factories, cities and buildings. We will use Computer Vision as an example to walk through this imminent step-function transition.
5:15 - 5:45pm
Keynote
Kai-Fu LeeChairman & CEOSinovation Ventures
Keynote
The talk will be given by Kai-Fu Lee, CEO of Sinovation Ventures.
7:00 - 9:00pm
Dinner Banquet
Jay YagnikVPGoogle AI
banquet
Dinner Banquet
Keynote speech

Jay Yagnik : A History Lesson on AI

We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.

Banquet and Networking
The evening consists of sponsor pitch, and presentation from Jay Yagnik, VP of Google AI. The banquet is a full course sit down dinner with wine, and a chance to interact with the speakers on an one to one basis. You will have the opportunity to meet and mingle with other guests, build connections, raise company profile, form potential business and research partnerships.
Day 2November 10, 2018
9:00 - 9:40am
Morning Keynote
Morning Keynote
Overview of the development of AI and its broad applications in Finance.
9:40 - 10:40am
Robots
Pieter AbbeelProfessorUC Berkeley
Mario MunichSVP TechnologyiRobot
moderator:
Mohan ReddyCTOThe Hive
Robots
Industry experts from companies that are actively developing robots come together to discuss their work. What is the status of robots? How do we build smart home robot? Our speakers are Pieter Abbeel, Professor of UC Berkeley, Mario Munich, SVP Technology of iRobot, and others.

Pieter Abbeel : Deep Learning for Robotics

Programming robots remains notoriously difficult. Equipping robots with the ability to learn would by-pass the need for what otherwise often ends up being time-consuming task specific programming. This talk will describe recent progress in deep reinforcement learning (robots learning through their own trial and error), in apprenticeship learning (robots learning from observing people), and in meta-learning for action (robots learning to learn). This work has led to new robotic capabilities in manipulation, locomotion, and flight.
10:40 - 11:00am
Coffee Break
11:00 - 12:00pm
Drones
Arnaud ThiercelinHead of R&DDJI
Mark MooreEngineering DirectorUber
Drones
Industry experts from companies that are actively developing drones come together to discuss their work. What is the status of drones? Our speakers are Arnaud Thiercelin, Head of R&D of DJI, Mark Moore, Engineering Director of Uber, and others.
12:00 - 1:00pm
Lunch
1:00pm - 1:30pm
AI in Security
Rajarshi GuptaHead of AIAvast
AI in Security
The talk will be given by Rajarshi Gupta, Head of AI of Avast.

Rajarshi Gupta : Security is AI’s biggest challenge, and AI is Security’s greatest opportunity

The progress of AI in the last decade has seemed almost magical. But we will discuss the unique challenges posed by Security and what makes this domain the biggest challenge for AI. Reporting from the frontlines, we will describe the deployment of large-scale production-grade AI systems to combat security breaches, using lessons learned at Avast from defending over 400 million consumers every single day. Topics will cover the recent AI advancements in file-based anti-malware solutions, behavior-based on-device solutions, and network-based IoT security solutions.
1:30pm - 2:30pm
AI in Enterprise
Sumit GuptaVP of AIIBM
moderator:
T.M. RaviDirector and FounderThe Hive
AI in Enterprise
A look at the application of AI in enterprise. The talk will be given by Sumit Gupta, VP of AI of IBM, AI leaders from Slack, and others.

Sumit Gupta : AI for the Enterprise

The use of AI for voice search and image recognition is talked about often. Enterprises, however, have different challenges and requirements. In this talk, we will focus on talking about use cases in the enterprise and challenges in building out AI solutions. We will talk about how an Auto-machine learning software for videos and images called PowerAI Vision enables quick AI model training & deployment for various enterprise use cases.
2:30 - 3:10pm
AI in Games
Long LinDirector of AIElectronic Arts
Yuandong TianResearch ManagerFacebook AI Research
AI in Games
A panel of industry experts from companies that are actively developing game simulator and AI players come together to discuss their works. The talk will be given by Long Lin, Director of AI of Electronic Arts, Yuandong Tian, Research Scientist of Facebook AI Research, and others.

Long Lin : AI in Gaming

Games have been leveraging AI since the 1950s, when people built a rules-based AI engine that played tic-tac-toe. With technological advances over the years, AI has become increasingly popular and widely used in the gaming industry. The typical characteristics of games and game development makes them an ideal playground for practicing and implementing AI techniques, especially deep learning and reinforcement learning. Most games are well scoped; it is relatively easy to generate and use the data; and states/actions/rewards are relatively clear. In this talk, I will show a couple of use cases where ML/AI helps in-game development and enhances player experience. Examples include AI agents playing game and services that provide personalized experience to players.

Yuandong Tian : Deep Reinforcement Learning Framework for Games

Deep Reinforcement Learning (DRL) has made strong progress in many tasks, such as board games, robotics, navigation, neural architecture search, etc. I will present our recent open-sourced DRL frameworks to facilitate game research and development. Our framework is scalable so we can can reproduce AlphaGoZero and AlphaZero using 2000 GPUs, achieving super-human performance of Go AI that beats 4 top-30 professional players. We also show usability of our platform by training agents in real-time strategy games, and show interesting behaviors with a small amount of resource.
3:10 - 3:30pm
Coffee Break
3:30 - 4:00pm
VC Speech
Sarah GuoGeneral PartnerGreylock Partners
VC Speech
A look at the future of AI applications and potentials for AI investment. Our speaker is Sarah Guo, General Partner of Greylock Partners.
4:00 - 5:40pm
Startup Demo Session
Samir KumarManaging DirectorM12
Jason CostaVenture PartnerGGV Capital
Startup Demo Session
Demos from 10 startups, each startup has 5 minutes for the demo and 5-minute comments from the VC panel. Our VC Panelists are Samir Kumar, Managing Director of Microsoft Ventures, Jason Costa, Venture Partner of GGV Capital, and others.
Day 3November 11, 2018
8:30am-5:30pm (Full Day)
Training - Image Understanding with TensorFlow on GCP
Training - Image Understanding with TensorFlow on GCP
You will get hands-on practice building and optimizing your own image classification models on a variety of public datasets.
Prerequisites: Basic SQL, familiarity with Python and TensorFlow
If you don't know TensorFlow, please take this Coursera course before attending this bootcamp: https://www.coursera.org/learn/serverless-machine-learning-gcp
Outline
1. Introductory Module
    The rapid growth in high-resolution image data available and Image applications.
2. Linear and DNN Models
    Image classification problem with a linear model in TensorFlow.
    Tackling the same problem using a Deep Neural Network.
3. Convolutional Neural Networks (CNNs)
    This module will introduce Convolutional Neural Networks.
4. Dealing with Data Scarcity
5. Going Deeper Faster
    How to train deeper, more accurate networks and do such training faster.
6. Pre-built ML Models for Image Classification
    Use Cloud Vision API and AutoML Vision.
8:30am-12:30pm (Half Day)
Tutorial - Sequence to Sequence Learning with Tensor2Tensor
Lukasz KaiserStaff Research ScientistGoogle Brain
Tutorial - Sequence to Sequence Learning with Tensor2Tensor
Instructor: Lukasz Kaiser
Sequence to sequence learning is a powerful way to train deep networks for machine translation, various NLP tasks, but also image generation and recently video and music generation. We will give a hands-on tutorial showing how to use the open-source Tensor2Tensor library to train state-of-the-art models for translation, image generation, and a task of your choice!
8:30am-12:30pm (Half Day)
Training - Natural Language Processing (for Beginners)
Training - Natural Language Processing (for Beginners)
Instructor: Mat Leonard
Outline
1. Text Processing
    Using Python + NLTK
    Cleaning
    Normalization
    Tokenization
    Part-of-speech Tagging
    Stemming and Lemmatization
2. Feature Extraction
    Bag of Words
    TF-IDF
    Word Embeddings
    Word2Vec
    GloVe
3. Topic Modeling
    Latent Variables
    Beta and Dirichlet Distributions
    Laten Dirichlet Allocation
4. NLP with Deep Learning
    Neural Networks
    Recurrent Neural Networks (RNNs)
    Word Embeddings
    Sentiment Analysis with RNNs
1:30pm-5:30pm (Half Day)
Tutorial - Weakly Supervized Natural Language Understanding
Ni LaoChief Scientist and Co-FounderMosaix.ai
Tutorial - Weakly Supervized Natural Language Understanding
Instructor: Ni Lao
In this tutorial I will introduce recent work in applying weak supervision and reinforcement learning to Questions Answering (QA) systems. Specifically we discuss the semantic parsing task for which natural language queries are converted to computation steps on knowledge graphs or data tables and produce the expected answers. State-of-the-art results can be achieved by novel memory structure for sequence models and improvements in reinforcement learning algorithms. Related code and experiment setup can be found at https://github.com/crazydonkey200/neural-symbolic-machines. Related paper: https://openreview.net/pdf?id=SyK00v5xx.
1:30pm-5:30pm (Half Day)
Training - Self-Driving Car
Training - Self-Driving Car
Instructors from LaiOffer will present a shorter version of the Self-Driving Car Program, focusing on Motion planning and Decision Making, which are the 2 essentials and key techniques of building a self-driving car.
For those who’re interested in knowing about the industry and starting a career as a self driving car engineer, it will be a great experience to gain a solid foundation.

Instructors:
1. Jonathan Butzke: M.S. and Ph.D. in Robotics from Carnegie Mellon University; focusing on search-based planning for large dynamic environments. Lead operator for University of Pennsylvania Multi Autonomous Ground-robotic International Challenge team. Lead Robotics Researcher, RobotWits, LLC.
2. William Yeoh: Assistant Professor at Washington University in St. Louis. M.S. and Ph.D. in Computer Science from University of Southern California; focusing on AI and multi-agent systems. LaiOffer technical lead for the Self-Driving Car Program, rising star in AI.

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venue

Partner Hotel
Marriott San Jose
301 S Market St, San Jose, CA 95113
San Jose Convention Center150 W San Carlos St
San Jose, CA 95113
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