PLANNED TALKS

Learn how to automate your systems, how to build chat bots and the future of deep learning. Explore the applications of machine learning, NLP, and computer vision transferring Neural Network know-how from academia to architects

WORLD'S BIGGEST AI ONLINE
CONFERENCE FOR DEVELOPERS

AI WITH THE BEST

100 SPEAKERS - 2 DAYS - 4 TRACKS
29-30 April 2017
Location: Online
Access platform

STEP INTO THE AI WORLD with the best Experts in your living room

Join leading machine learning researchers and industry pros in exclusive live tech talks and benefit from 1-to-1 networking with speakers
100 speakers - 2 days

4 TRACKS

Machine Learning AI Online With The Best Conference

1

MACHINE LEARNING RESEARCH
Machine and Deep Learning Fundamentals, implementation and new modelling strategies for your frameworks straight from the labs.
Chatbot online developer AI conference

2

COMPUTER VISION
NLP / CHATBOTS
Detection, tracking & integration tips for robots, drones & autonomous vehicles. Leverage conversational architecture with bot-builders and a slick UX.
Computer Vision online developer AI conference

3

APPLIED AI: STARTUPS, INDUSTRY & SOCIETY
Discover the applications of AI from startups and industry, what it takes to manage an AI company and the impacts on society with this technology.
Platform and Programme Demos online developer AI conference

4

DEMOS & TUTORIALS
Be the first to discover the algorithms, APIs, platforms and tools enabling AI tech with these hands-on sessions, demos and workshops.

FEATURED SPEAKERS

Sam Altman
President of Y Combinator
Co-chair at OpenAI
Sam Altman is the president of Y Combinator and the co-chair of OpenAI. He was co-founder and CEO of Loopt, which was funded by Y Combinator in 2005 and acquired by Green Dot in 2012. At Green Dot, he was the CTO and is now on the Board of Directors. Sam also founded Hydrazine Capital. He studied computer science at Stanford, and while there worked in the AI lab.
Ian Goodfellow
Research Scientist at Google Brain
Ian Goodfellow is a staff research scientist at Google Brain. He is the lead author of the MIT Press textbook Deep Learning (www.deeplearningbook.org) and the inventor of generative adversarial networks. He is generally interested in all things deep learning, and usually focuses on generative models, machine learning security, and differential privacy.
Yoshua Bengio
Director of MILA
CIFAR Program co-director
Yoshua Bengio is the author of three books and over 200 publications, and heads up the Montreal Institute for Learning Algorithms (MILA), currently the largest academic research group on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. His current interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning and representation learning, the geometry of generalization in high-dimensional spaces, generative models, biologically inspired learning algorithms, natural language understanding and other challenging applications of machine learning.

SPEAKERS

Sam Altman
Sam Altman
President, Ycombinator
Sam Altman is the president of Y Combinator and the co-chair of OpenAI. He was co-founder and CEO of Loopt, which was funded by Y Combinator in 2005 and acquired by Green Dot in 2012. At Green Dot, he was the CTO and is now on the Board of Directors. Sam also founded Hydrazine Capital. He studied computer science at Stanford, and while there worked in the AI lab.
Yoshua Bengio
Yoshua Bengio
Director, MILA
Yoshua Bengio is the author of three books and over 200 publications, and heads up the Montreal Institute for Learning Algorithms (MILA), currently the largest academic research group on deep learning. He is on the board of the NIPS foundation and has been program chair and general chair for NIPS. His current interests are centered around a quest for AI through machine learning, and include fundamental questions on deep learning and representation learning, the geometry of generalization in high-dimensional spaces, generative models, biologically inspired learning algorithms, natural language understanding and other challenging applications of machine learning.
Ian Goodfellow
Ian Goodfellow
Research Scientist, Google Brain
Ian Goodfellow is a staff research scientist at Google Brain. He is the lead author of the MIT Press textbook Deep Learning (www.deeplearningbook.org) and the inventor of generative adversarial networks. He is generally interested in all things deep learning, and usually focuses on generative models, machine learning security, and differential privacy.
Aerin Kim
Aerin Kim
Founder, BYOR Lab
Aerin is the CEO and Founder of BYOR, a company whose products combine techniques from Natural Language Processing and Deep Learning - a focus on meaning and context - to bring intuitive and insightful suggestions/predictions to the given text data. Aerin is a data scientist and engineer, aka Unapologetic Abuser of Machines for the merciless amount of data that she processes on the machine. Before BYOR, she has priced structured products for financial institutions and built ad optimization engines for marketers.
Dennis Mortensen
Dennis Mortensen
CEO & Founder, x.ai
Dennis is the CEO and Founder of x.ai. He’s a pioneer and expert in leveraging Data and a serial entrepreneur who has successfully delivered a number of company exits on that theme. Dennis’s long term vision of killing the inbox triggered the formation of x.ai and the creation of an artificial intelligence personal assistant to schedule meetings. He’s an accredited Associate Analytics Instructor at the University of British Columbia, the Author of Data Driven Insights from Wiley and a frequent speaker on the subject of AI, intelligent agents, and the future of work. A native of Denmark, Mortensen currently calls New York City his home.
Feiyu Xu
Feiyu Xu
Head Of AI Labs, Lenovo
Feiyu Xu is now Vice President and Head of AI lab at Lenovo since March 2017. Her mission is to contribute AI technologies to the Lenovo device+cloud strategy. Before Dr. Xu joined Lenovo, she was Principal Researcher and Head of Research Group Text Analytics in the Language Technology Lab of German Research Center of Artificial Intelligence (DFKI), the largest non-profit AI research center in the world. Feiyu Xu studied technical translation at Tongji University in Shanghai from 1987 to 1990. She then studied computational linguistics at Saarland University in Germany from 1992 to 1998 and graduated by receiving a Diplom (MSc) with distinction. Her PhD-Thesis is about "bootstrapping relation extraction from semantic seed" in "information extraction”. In 2014, Feiyu Xu has completed a habilitation in big text data analytics. In 2013, Feiyu Xu has won a Google Focused Research Award for Natural Language Understanding as co-PI with Hans Uszkoreit and Roberto Navigli. In 2014, Feiyu Xu was honored as DFKI Research Fellow. Her research is documented in more than 90 publications including conference papers for ACL, COLING, EMNLP, CONLL, NAACL, LREC etc. She is area chair of EACL 2017 for text mining, information extraction and question answering. Dr. Xu is also co-founder and managing director of Yocoy Technologies GmbH, a 2007 spin-off from DFKI. Yocoy is developing next generation mobile language and travel guides. Since 2004, Dr. Xu is vice-director of the Joint Research Laboratory for Language Technology of Shanghai Jiao Tong University and Saarland University. She has extensive experience in multilingual information systems, information extraction, text mining, big data analytics, business intelligence, question answering and mobile applications of NLP technologies. She has successfully led more than 30 German national, EU and international research and development projects. She has broad and in-depth experience of the total cycle of innovation in her expert areas, from basic research, to application and development and finally to products and their commercialization.
Kirk Borne
Kirk Borne
Principal Data Scientist, Booz Allen Hamilton
Dr. Kirk Borne is the Principal Data Scientist at Booz Allen Hamilton (since 2015). He previously spent 12 years as Professor of Astrophysics and Computational Science at George Mason University where he taught and advised students in the Data Science degree programs. Before that, he worked 18 years supporting NASA projects in various roles, including Data Archive Project Scientist for the Hubble Space Telescope. He is an active contributor on social media, where he has been named consistently the #1 worldwide influencer in big data and data science since 2013, and he was named #2 influencer in AI and machine learning in 2016. In 2016 he was named Fellow of the International Astrostatistics Association.
Yi Wang
Yi Wang
Research Scientist, Baidu USA
Yi Wang is the tech lead of AI Platform at Baidu. The team is a primary contributor of PaddlePaddle, the open source deep learning platform originally developed in Baidu. Before Baidu, he was a founding member of ScaledInference, a Palo Alto-based AI startup company. Before that, he was a senior staff at LinkedIn, engineering director of advertising system at Tencent, and researcher at Google.
Christian Guttmann
Christian Guttmann
Director, HealthiHabits
Christian Guttmann (Phd/MSc) is an Adj. Assoc. Professor at the University of New South Wales, a Research Fellow at the Karolinska Institute and the CEO of HealthiHabits. His expertise and activities are firmly grounded in the intersection of AI and Health. He is an entrepreneurially driven technology leader with over 20 years of experience in industry and research. At BT, IBM, HP, successful startups and scientific projects, he led innovation teams using AI, data science and analytics, social/mobile/IoT technology with health authorities, hospitals, pharma/medical device industry, patient associations and primary health care. He is in international steering and program committees of leading conferences in AI and Health Care innovation. He co-authored and co-edited over 50 international peer reviewed publications and has 8 patent disclosures in his field. He completed his PhD in Distributed AI at Monash University. He received two CS Master degrees and a Psychology degree from Swedish and German Universities. HealthiHabits.com is a social network company using AI to connect patients with a chronic condition aiming to sustain "healthy habits" over time. To contact, follow his twitter or email him at guttmann.public@gmail.com.
Danko Nikolic
Danko Nikolic
Professor, CSC
Danko Nikolić received a degree in Psychology and a degree in Civil Engineering from the University of Zagreb. He received a Master’s degree and a Ph.D. in Cognitive Psychology from at the OU. In 2010 he received a Private Docent title from the University of Zagreb, as well as an Associate Professor title in 2014. He is now associated with FIAS and works at CSC in the field of AI and data science. He has a keen interest in addressing the explanatory gap between the brain and the mind. His interest is in how the physical world of neuronal activity produces the mental world of perception and cognition. For many years he headed an electrophysiological lab at MPI Brain. He approached the problem of explanatory gap from both sides, bottom-up and top-down led him to develop a theory: The work on behavior and experiences led to the discovery of the phenomenon of ideasthesia (meaning "sensing concepts"). The work on physiology resulted in the theory of practopoiesis (meaning "creation of actions").
Gerald Friedland
Gerald Friedland
Principal Data Scientist, LLNL
Dr. Gerald Friedland is Principal Data Scientist at Lawrence Livermore National Lab and is also teaching as an adjunct professor at the Electrical Engineering and Computer Science department of UC Berkeley. He mostly focusses on large scale machine learning problems, such as searching in the 100M images or 1M videos of the YFCC100M dataset that he co-initiated. Dr. Friedland has published more than 200 peer-reviewed articles in conferences, journals, and books. He also authored a new textbook on multimedia computing together with Dr. Ramesh Jain. He is associate editor for ACM Transactions on Multimedia and IEEE Multimedia Magazine and regularly reviews for IEEE Transactions on Acoustics, Speech, and Language Processing; IEEE Transaction on Multimedia; Springer's Machine Vision and Application; and other journals. He is the recipient of several research and industry recognitions, among them the European Academic Software Award and the Multimedia Entrepreneur Award by the German Federal Department of Economics. Dr. Friedland received his doctorate (summa cum laude) and master's degree in computer science from Freie Universitaet Berlin, Germany, in 2002 and 2006, respectively.
Patrick McDaniel
Patrick McDaniel
Director, INSR Pennsylvania State University
Patrick McDaniel is a Distinguished Professor in the School of Electrical Engineering and Computer Science and Director of the Institute for Networking and Security Research at the Pennsylvania State University. Professor McDaniel is a Fellow of the IEEE and ACM and program manager and lead scientist for the Army Research Laboratory's Cyber-Security Collaborative Research Alliance. Patrick’s research centrally focuses on a wide range of topics in security and technical public policy. Prior to joining Penn State in 2004, he was a senior research staff member at AT&T Labs-Research.
Nicolas Papernot
Nicolas Papernot
Research Assistant, Penn State
Nicolas Papernot is a PhD student in Computer Science and Engineering advised by Dr. Patrick McDaniel at the Pennsylvania State University. His research interests lie at the intersection of computer security and deep learning. He is supported by a Google PhD Fellowship in Security. In 2016, he received his MS in Computer Science and Engineering from the Pennsylvania State University and his MS in Engineering Sciences from the École Centrale de Lyon.
Carlos Azevedo
Carlos Azevedo
Machine Intelligence, Ericsson Lab
I’m a Machine Intelligence researcher architecting, analyzing, and implementing intelligent industrial systems based on simulation-driven multi-objective optimization technologies at Ericsson Research, since 2015. Before joining Ericsson, I served as a Postdoc at the University of Campinas, Brazil. My PhD thesis on anticipatory multi-objective ML was awarded in 2014 the Thesis National Prize in the area of Electrical, Computing, and Automation Engineering, by the Brazilian Ministry of Education. My problem-solving experience includes developing Bayesian, neural, and evolutionary-inspired computational tools for a range of applications such as financial decision support, predictive analytics, supply chain and logistics optimization, data clustering, pattern classification, and data compression. I also took part in the 2010 Global Solutions Program of Singularity University at NASA Ames Research Park, where I was engaged in the subgroup which investigated how to augment robotic autonomy for space exploration through AI.
Francisco Martin
Francisco Martin
Co-founder, BigML Inc
Francisco J. Martin, Ph.D. CEO, BigML, Inc Francisco is the CEO at BigML, Inc where he helps conceptualize, design, architect, and implement BigML's distributed Machine Learning platform. Formerly, Francisco founded and led Strands, Inc, a company that pioneered Behavior-based Recommender Systems. Previously, he founded and led Intelligent Software Components, SA (iSOCO), the first spin-off of the Spanish National Research Council (CSIC). He holds a 5-year degree in Computer Science, a Ph.D. in Artificial Intelligence, and a post-doc in Machine Learning. He is the holder of 20+ patents in the areas of Recommender Systems and Distributed Machine Learning.
Vlad Lata
Vlad Lata
CTO & Co-Founder, Konux
As Chief Technology Officer and co-founder of KONUX, Vlad Lata is in charge of product development with focus on AI and Big Data. Lata and the engineering department at KONUX create customized sensor and analytics solutions to increase asset availability and network capacity in the rail sector.
Natalie Stanley
Natalie Stanley
Ph.D. Student, UNC
Natalie is a 4th year PhD student in the bioinformatics and computational biology (BCB) program at UNC Chapel Hill. Her research is focused on the development of probabilistic models for community structure in networks as well as methods for network compression to enable more tractable community detection. She enjoys collaborating on clustering and classification problems with biologists.
Mohammed Abdoolcarim
Mohammed Abdoolcarim
Co-Founder, GoButler
Mohammed was the lead product manager at Siri. He is a former product manager at Google, Apple and Misfit (acquired by Fossil) and previously worked at GoButler building an on-demand messaging service. He has now founded a company building an AI-driven simulator in WhatsApp to train workers in acquiring soft-skills.
Moataz Rashad
Moataz Rashad
CEO/CTO, DeepVu
Moataz Rashad is currently founder & CEO/CTO of DeepVu (pka Vufind Inc) a deep-learning SaaS startup focused on cognitive supply chains and maximizing margins for manufacturers. Moataz had a career of 20+ years in the IT and consumer electronics sectors. He built products and led teams at several industry leaders including SonyEricsson, Conexant/Mindspeed/Rockwell, Samsung, Exponential, MIPS, and Silicon Graphics. Moataz led and was key principal engineer/architect on products ranging from cloud services/APIs, Xperia mobile phones and tablets, wireless cameras, mobile apps and games, and networking and wireless silicon components. Moataz Led Conexant’s investment in Tensilica (acquired by Cadence in 2012). He is an inventor on 20 issued US and International patents in the fields of computer vision, deep-learning, microprocessors, GPU/DSP architectures, and simulation. Moataz holds an MSEE from Stanford, and an MSCS from the University of Oregon, and a B.Sc. with Distinction & Honors in Computer Engineering and Control Systems from Alexandria University. He was a PhD student in Information Systems Lab of the EE department at Stanford before he dropped out in 1996 to pursue his industry career. Moataz has been obsessed with AI since his undergraduate years and studied it in graduate school at both Oregon and Stanford yet changed direction since it wasn’t ready for commercial real-world applications at the time. Moataz has over 8 technical academic publications and gave invited talks at industry conferences and major research universities.
Konstantina Christakopoulou
Konstantina Christakopoulou
Ph.D. Student, UMN
Konstantina Christakopoulou (Diploma University of Patras, Greece 2013; MS University of Minnesota 2015) is a fourth-year PhD student in the Computer Science & Engineering Department at the University of Minnesota. The broader area of her research is machine learning, with a particular focus on recommendation systems. She has interned with Google and Microsoft Research and has published her research in top-tier conferences.
Andrew Crozier
Andrew Crozier
Data Engineer, ASI Data Science
Andrew is a Data Engineer at ASI, a London artificial intelligence startup providing bespoke consultancy services, where he is a member of the data engineering team building a distributed data science platform around Jupyter. Andrew holds a PhD in Biomedical Engineering from King's College London and has extensive experience leveraging Python for data analysis and for tooling around high performance computing applications. Prior to joining the company, Andrew completed the ASI post-doctoral fellowship, during which he built an interactive web app for demographic analysis of a customer base using open data.
Anna Quach
Anna Quach
Ph.D. Student, Utah State University
Anna is a PhD candidate at Utah State University (USU). Her research is in improving Random Forests.
Behnaz Abdolahi
Behnaz Abdolahi
ML Engineer, Gen Nine
Behnaz got her PhD in Computer Engineering in 2015. She is currently working as a machine learning engineer in a startup company Gen-9 Inc, working on developing machine learning algorithms on wearable devices. During her PhD studies she worked as an intern in Genscape and she developed a new algorithm on refinery images to predict and detect anomalies using computer vision and machine learning. Behnaz has focus on developing machine learning algorithms in different domain such as healthcare, wearable devices and images.
Bruno Gonçalves
Bruno Gonçalves
Moore-Sloan Fellow, NYU
Bruno Gonçalves is currently a Moore-Sloan Fellow at NYU's Center for Data Science. With a background in Physics and Computer Science his career has revolved around 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 focused on the application of machine learning and neural network techniques to analyze large geolocated datasets. He is the editor of "Social Phenomena: From Data Analysis to Models" (Springer, 2015) and a co-author of the forthcoming "Twitterology: The Social Science of Twitter" (Springer, 2018).
Deborah Hanus
Deborah Hanus
NSF Fellow, Harvard University
Deborah is using machine learning to improve healthcare in Harvard's Computer Science Department as a PhD student. She graduated from MIT with an M.Eng. in Electrical Engineering & Computer Science and a dual-B.S. in Computer Science and Brain & Cognitive Sciences. Her work has been honored with an NSF Fellowship, NDSEG Fellowship, and Fulbright Fellowship.
Eyal Amir
Eyal Amir
CEO, Parknav
Eyal Amir is co-founder and CEO of Ai Incube (Parknav) and Adjunct Associate Professor of Computer Science at the University of Illinois Urbana-Champaign. His company creates AI and analytics for IoT sensors, primarily focusing on automobile and mobility needs. In his scientific research Eyal focuses on merging common sense reasoning and AI with machine learning and probabilistic reasoning. He is the winner of a number of awards from Stanford University, IEEE, and the National Science Foundation. He shares his time between San Francisco, California and Munich, Germany.
Matthew Taylor
Matthew Taylor
OS Flag-Bearer, Numenta
Matt manages Numenta's open source projects, helps the HTM Community, and produces educational videos about HTM.
Deepak Agarwal
Deepak Agarwal
VP Engineering, LinkedIn
Deepak Agarwal is a vice president of engineering at LinkedIn where he is responsible for all AI efforts across the company. He is well known for his work on recommender systems and has published a book on the topic. He has published extensively in top-tier computer science conferences and has coauthored several patents. He is a Fellow of the American Statistical Association and has served on the Executive Committee of Knowledge Discovery and Data Mining (KDD). Deepak regularly serves on program committees of various conferences in the field of AI and computer science. He is also an associate editor of two flagship statistics journals.
Michael Arthur Bucko
Michael Arthur Bucko
Co-Founder, Deckard AI
Co-founder of Deckard.ai. Product-oriented software engineer & ML hacker enabling machines to learn how to boost software teams.
Dawn Song
Dawn Song
Professor, UC Berkeley
Dawn Song is a Professor in the Department of Electrical Engineering and CS at UC Berkeley. Her research interest lies in deep learning and security. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, database security, distributed systems security, applied cryptography, to the intersection of ML and security. She is the recipient of various awards including the MacArthur Fellowship, the Guggenheim Fellowship, the NSF CAREER Award, the Alfred P. Sloan Research Fellowship, the MIT Technology Review TR-35 Award, the George Tallman Ladd Research Award, the Okawa Foundation Research Award, the Li Ka Shing Foundation Women in Science Distinguished Lecture Series Award, the Faculty Research Award from IBM, Google and other major tech companies, and Best Paper Awards from top conferences. She obtained her Ph.D. degree from Cal. Prior to joining Cal, as a faculty, she was an Assistant Professor at CMU from 2002 to 2007.
John Whitbeck
John Whitbeck
Tech Lead, Liftoff
John has been building business-critical machine learning pipelines for the past 5 years, first at Criteo, and now at Liftoff where he currently leads the machine learning team. He holds a PhD in Computer Science from the Sorbonne University in Paris.
Adji Bousso Dieng
Adji Bousso Dieng
PhD Student, Columbia
Adji is a PhD candidate in the department of Statistics at Columbia University where she is being advised by David Blei and John Paisley. Her research focus is on machine and deep learning, probabilistic modeling, and variational methods with applications to natural language processing. She holds a double degree from Telecom ParisTech in France and Cornell University. Learn more on her homepage http://stat.columbia.edu/~diengadji/
Bhawna Shiwani
Bhawna Shiwani
Research Engineer, Delsys Inc
Bhawna Shiwani is a Research Engineer at Delsys Inc. She received her B.E. in Electronics and Communication Engineering from National Institute of Technology, Jalandhar, India in 2012 and she is currently pursuing her M.S. in Robotics Engineering at Worcester Polytechnic Institute, Worcester, MA. She was previously employed by Cadence Design Systems, Noida, India where she worked as a Member of Technical Staff. Her current research interests include Robotics, Artificial Intelligence and Machine Learning.
Hobson Lane
Hobson Lane
Founder, Total Good
Lead Data Scientist a [Talentpair] where we use machine intelligence to pair talented people with their dream job. Cofounder of [Total Good], a nonprofit helping aspiring developers & data scientists contribute to the greater good. Author of "Natural Language Processing in Action" coming out this summer. Dedicated contributor to open science, open data, and open source projects.
Tanmay Bakshi
Tanmay Bakshi
Darwin Ecosystem
Tanmay, 13, a Software/Cognitive Developer, Algorithm-ist, Honorary IBM Cloud Advisor, IBM Champion for Cloud, and an Author; shares his knowledge with the world through his YouTube channel; host of an IBM Facebook Live series “Watson Made Simple with Tanmay”; has taken a resolve to help 100,000 kids and beginners on their journey to innovate through coding. He began coding when he was 5. At 12, he presented his algorithm, AskTanmay, the world’s first web-based NLQA System to be powered by IBM Watson, at IBM InterConnect 2016. He's focussed on technologies such as AI and neural networks, modelling the human brain and nervous system.
Charles Ollion
Charles Ollion
CTO, Heuritech
Charles Ollion is the co-founder and head of Research at Heuritech, a company focusing on image and text analysis on Fashion posts for product and trend detection. Charles also holds a PhD in Machine Learning and is a lecturer in Deep Learning at Ecole Polytechnique Master 2 program, Paris Saclay Datascience. Charles will present with Hedi Ben Younes, PhD in Machine Learning at LIP6/Heuritech.
Pedro Alves
Pedro Alves
CEO, Ople
Pedro has experience in predicting, analyzing and visualizing data in the fields of: genomics, gene networks, cancer metastasis, insurance fraud/costs, hospital readmissions, soccer strategies, joint injuries, social graphs, human attraction, spam detection, topic modeling and computer vision among others. Pedro is incredibly passionate about all aspects of data science and is constantly creating new techniques and algorithms to suit the problems at hand. Pedro also has a strong attraction to the basics, which can be forgotten easily these days, such as the scientific method and just looking at the data. Recently his efforts were geared towards detecting and interpreting everything that is happening in the world in real-time, from major concerts and sporting events to major and minor news using deep learning. Currently Pedro has decided to begin a startup with the main goal of helping machine learning deliver on its promises.
Karmel Allison
Karmel Allison
Engineering Manager, Quora
Karmel Allison is an Engineering Manager at Quora, and has spent the last decade working with big data and machine learning. She received her PhD in Bioinformatics from the University of California, San Diego, and previously worked to design algorithms for DNA sequencing.
Jordi Torras
Jordi Torras
CEO and Founder, Inbenta
In 2005, Founder and CEO Jordi Torras started Inbenta, a company that specializes in natural language processing and artificial intelligence to help businesses improve online relationships with their customers. Inbenta’s AI-powered search, chatbots and e-commerce search applications help to greatly reduce incoming customer service emails, live chats and calls to call centers for industry-leading companies including Ticketmaster, Groupon and CA Technologies.
Ciprian Chelba
Ciprian Chelba
Staff Research Scientist, Google
Ciprian Chelba is a research scientist at Google. Previously he worked as a researcher in the speech technology group at Microsoft. His research interests are in statistical modeling of natural language and speech. Ciprian got his Phd in Electrical and Computer Engineering from The Johns Hopkins University.
Alex Champandard
Alex Champandard
Founder, AiGameDev.com
Alex is the co-founder of creative.ai, a company that's building AI-assisted tools for artists and designers in creative industries. His experience spans from industry to research, in both classical AI techniques such as hierarchical planners (applied to real-time simulations) as well as deep learning including projects such as Neural Doodle and Neural Enhance. Alex enjoys bringing technology to creative audiences, including via games where he worked as a Lead AI programmer as well as social media projects like @DeepForger. He's also the conference director for nucl.ai, the largest worldwide event dedicated to AI in creative industries.
Vincent Delaitre
Vincent Delaitre
Co-founder & CTO, Deepomatic
Vincent Delaitre is the co-founder and CTO of Deepomatic, a company dedicated to providing computer vision based software solutions and services to businesses. He previously combined his passions for AI, robotics, programming and computer graphics into a PhD in computer vision at Ecole Normale Supérieure in Paris on modeling human, objects and scene interactions. He also loves to share his interest for machine learning and worked for a while as a self-employed consultant.
Pierre-Edouard Lieb
Pierre-Edouard Lieb
Partnerships Manager, Recast.AI
After graduating from Ecole 42 in Paris Pierre-Edouard joined Recast.AI in 2015 and is now managing partnerships. Building your bot in one hour with Recast.AI 101. This workshop will teach you how to use the Recast.AI platform and the basics of chatbot building. You will also learn how to connect your bot to a messaging platform using the bot connector.
Kamelia Aryafar
Kamelia Aryafar
Senior Data Scientist, Etsy
Kamelia Aryafar, Ph.D. is a senior data scientist with Etsy's Data Science team since 2013. She works on building scalable machine learning and computer vision tools to curate a personalized experience for Etsy users. Prior to Etsy she was doing a Ph.D. in computer science and machine learning in Drexel University, building large-scale music classification models.
Adam Gibson
Adam Gibson
CTO & Founder, Skymind
Jaya Kawale
Jaya Kawale
Researcher, Netflix
Jaya Kawale is a senior research scientist at Netflix working on various problems related to recommendation and targeting. Before that she was a senior research scientist at Adobe Research and did her PhD in Computer Science from University of Minnesota
Vikas Agrawal
Vikas Agrawal
Senior Data Scientist, Oracle
Dr. Vikas Agrawal works as a Senior Principal Data Scientist in the area of Cognitive Computing for Advanced Analytics for Oracle Corporation. Vikas has created activity context-aware Virtual Personal Assistants for insurance, pharma, retail and investment banks, Real-Time Asset Management systems for Internet of Things (IoT) in mining and production systems, Reliability Risk Management and Predictive Maintenance systems while at Intel Corporation, Infosys Limited and Oracle Corporation. Vikas received a BTech in Electrical Engineering from the Indian Institute of Technology, New Delhi, an MS in Computer Science and a PhD in Computational Modeling from University of Delaware (Newark, DE), and conducted post-doctoral research at California Institute of Technology (CalTech, Pasadena, CA) in PDE-based modeling of population development and differentiation with researchers from NASA's Jet Propulsion Labs (JPL) for NSF's FIBR.
Behnoush Abdollahi
Behnoush Abdollahi
ML Scientist, Uni. of Louisville
Behnoush is currently Ph.D. Research Assistant at the Knowledge Discovery and Web Mining Lab, University of Louisville. Her research has been mainly on machine learning and personalization techniques that incorporate large amounts of heterogeneous data available on the internet. As part of her research, Behnoush has developed three new approaches to building interpretable recommender systems that exceed leading existing approaches based on experiments so far. In 2015, Behnoush joined a startup company Gen Nine, Inc. as Machine Learning Software Engineer, where she helped build multiple data products and applications to study user patterns using wearable technology in the health domain. Previously, she worked at the BioImaging Lab, University of Louisville, where she performed research on the problem of image segmentation in medical scan images of anonymous patients with lung cancer. She successfully developed a high performance computing model for the segmentation and detection of lung/chest regions in medical images using Parallel Programming and GPU computing.
Andres Rodriguez
Andres Rodriguez
Sr Tech Lead Deep Learning, Intel
Andres Rodriguez is a senior technical lead with Intel Nervana where he designs deep learning solutions for Intel’s customers and provides technical leadership across Intel for deep learning products. He has 13 years of experience working in artificial intelligence. Andres received his Ph.D. from Carnegie Mellon University for his research in machine learning, and prior to joining Intel, he was a research scientist and principal investigator for deep learning with the Air Force Research Laboratory and adjunct professor at Wright State University. He holds over 20 peers-reviewed publications in journals and conferences and a book chapter on machine learning.
Mark Schmidt
Mark Schmidt
Chair of ML, UBC
Mark Schmidt has been an assistant professor in the Department of Computer Science at the University of British Columbia since 2014, and a Canada Research Chair since 2016. His research focuses on developing faster algorithms for large-scale machine learning, with an emphasis on methods with provable convergence rates and that can be applied to structured prediction problems. From 2011 through 2013 he worked at the cole normale supÈrieure in Paris on inexact and stochastic convex optimization methods. He finished his M.Sc. in 2005 at the University of Alberta working as part of the Brain Tumor Analysis Project, and his Ph.D. in 2010 at the University of British Columbia working on graphical model structure learning with L1-regularization. He has also worked at Siemens Medical Solutions on heart motion abnormality detection, with Michael Friedlander in the Scientific Computing Laboratory at the University of British Columbia on semi-stochastic optimization methods, and with Anoop Sarkar at Simon Fraser University on large-scale training of natural language models.
Keith Butler
Keith Butler
Researcher, Uni Washington
PI for AHRQ research on health IT, former Principle Research Scientist at University of Washington, Director of Future Products & Architecture at Microsoft Global Services Automation, Technical Fellow for human-computer interaction in Boeing Math & Computing Technology, and lead author of the ISO standard for usability now adapted for Federal certification of electronic medical record systems. He also serves on NASA's standing research review panel for the Orion Project, and as Univ of WA representative to the Object Management Group.
Jun Yan Zhu
Jun Yan Zhu
Berkeley AI Research Lab
Jun-Yan is a Ph.D. student at Berkeley AI Research Lab. Before coming to Berkeley, he was a Ph.D. student at CMU. Jun-Yan is now working on computer vision, graphics, and machine learning with Professor Alexei A. Efros. His research goal is to build machines capable of visual creativity. Jun-Yan is currently supported by a Facebook Fellowship. He received his B.E from Tsinghua University in 2012.
Jean-Baptiste Guignard
Jean-Baptiste Guignard
AI Mentor, IBM Watson
Jean-Baptiste Guignard is a Senior Cognitive Scientist of the UTC-Sorbonne Universités, where he supervises the work of several Computer Vision and/or AI PhD candidates. Former Invited Scholar of Princeton University [Green Hall], CTO & Head of Research of CLAY – SDK solutions for gesture recognition on smartphones – he is also a mentor for IBM Watson XPrize.
Siraj Raval
Siraj Raval
The Siraj Raval Company
Data Scientist, Bestselling Author, Youtube Star
Dimitri Kanevsky
Dimitri Kanevsky
Research Scientist, Google
Dimitri Kanevsky is a Research Scientist at Google, developing a YouTube speech recognition system. Prior to Google, Dimitri was a research staff member in the Speech and Language Algorithms Department at IBM Watson Research Center. Dimitri was responsible for developing the first Russian automatic speech recognition system, as well as key projects for embedding speech recognition in automobiles and broadcast transcription systems. Dimitri was an invited scientist at top math centers, including Weizmann Institute of Science, Max Planck Institute and the IAS. He currently holds 257 US patents and was granted the title of Master Inventor at IBM in 2002, 2005 and 2010. His conversational biometrics based security patent was recognized by MIT Tech Review, as one of five most influential patents for 2003. His contributions on Extended Baum-Welch algorithms for speech, embedding speech recognition in automobiles and his work on conversational biometrics were recognized as science accomplishments in 2002, 2004 and 2008 by the Director of Research at IBM. In 2005 Dimitri Kanevsky received an Honorary Degree (Doctor of Laws, honoris causa) from CBU. He was elected as a member of the WTN in 2004 and was a Chairperson of the IT Software Technology session at the WTN Summit, 2005, SF. In 2012, he organized a special session on Large Scale Optimization at ICASSP, organized a NIPS workshop on log-linear models, was honored at the White House as a Champion of Change for his efforts to advance access to STEM, and received the Tan Chin Tuan Exchange Fellowship award from NTU Singapore for development of sparse optimization methods and its application in speech. In 2003, he was the lead guest editor for the Special Issue on Large-Scale Optimization for IEEE Trans. ASLP.
Olaf Witkowski
Olaf Witkowski
Research Scientist, Tokyo Institute of Technology
Dr. Olaf Witkowski, Research Scientist at the Earth-Life Science Institute in Tokyo, Visiting Member at the Institute for Advanced Study in Princeton and Chief Architect at YHouse Inc. in New York.
Michael Galvin
Michael Galvin
Executive Director, Metis
Michael Galvin is the Executive Director of Data Science at Metis. He came to Metis from General Electric where he worked to establish their data science strategy and capabilities for field services and to build solutions supporting Global operations, risk, engineering, sales, and marketing. Prior to GE, Michael spent several years as a data scientist working on problems in credit modeling at Kabbage and corporate travel and procurement at TRX. Michael holds a Bachelor's degree in Mathematics and a Master's degree in Computational Science and Engineering from the Georgia Institute of Technology where he also spent 3 years working on machine learning research problems related to computational biology and bioinformatics. Additionally, Michael spent 12 years in the United States Marine Corps where he held various leadership roles within aviation, logistics, and training units.
Peter Gloor
Peter Gloor
Research Scientist, MIT Center for Collective Intelligence
Peter A. Gloor is a Research Scientist at the Center for Collective Intelligence at MIT's Sloan School of Management where he leads a project exploring Collaborative Innovation Networks. He is also Founder and Chief Creative Officer of software company galaxyadvisors, a Honorary Professor at University of Cologne, Distinguished Visiting Professor at P. Universidad Católica de Chile and Honorary Professor at Jilin University, Changchun, China. Earlier he was a partner with Deloitte and PwC, and a manager at UBS. He got his Ph.D in computer science from the University of Zurich and was a Post-Doc at the MIT Lab for Computer Science.
İlker	Köksal
İlker Köksal
Co-founder, Botanalytics
As an Ex-Googler, Ilker is the co-founder & CEO of Botanalytics which is a conversational analytics & engagement tool for bots based in San Francisco. Botanalytics helps bot makers to enhance the human-bot communication on their bots. Also, Botanalytics is backed by 500 Startups. Ilker has a BSc in Computer Science and an MBA degree. He exited his first startup while studying at college. He’s a doer and professional tennis player.
Roni Wiener
Roni Wiener
Founder, Keotic
Founder of Keotic, a Core AI company. Roni blends his 15 years of experience in building complex systems and algorithms, with a business oriented strategic state of mind. As a former AOL employee, he explored new ways to tackle real life disambiguation problems, which eventually led to AOL’s entity resolver. Roni’s areas of interest include: Deep Learning, Artificial Intelligence, Cognitive Science, Economic Science, Natural Language Processing, Machine Learning and Data Science. Alongside his role at Keotic, he is serving as an advisor to various companies in these fields.
Jason Yosinski
Jason Yosinski
Research Scientist, Uber AI Labs
Jason Yosinski is a machine learning researcher and founding member of Uber AI Labs, where he uses neural networks and machine learning to build more capable and more understandable AI. He suspects scientists and engineers will build increasingly powerful AI systems faster than we can understand them, motivating much of his work on what has been called "AI Neuroscience" -- an emerging field that may become increasingly important in the next several years. Mr. Yosinski was previously a PhD student and NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, the Caltech Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, and on the BBC.
Daniel Krasner
Daniel Krasner
CEO, Merriam Tech
Daniel Krasner is the Founder/CEO of Merriam Tech, which focuses on intelligent, AI driven document management systems solutions, and the Director of Data Science in eDiscovery at Paul Hastings, where he brings the latest developments in statistical engineering to the legal world. In addition, he is the co-Founder of KFit Solutions, a data science consulting firm, that has created data science solutions across various sectors (financial, commerce, media, news, startup, legal). Over the past year Daniel has also been the technology lead with the Columbia University History Lab which focuses on building archival document management systems and analyzing large collections of textual data. His current interests and work focus on high performance statistical solutions in text and natural language processing. Previously, Daniel was the chief data scientist at Sailthru, an email and behavioral analytics platform, a senior researcher at Johnson Research Labs, a lecturer at the London School of Economics and a professor at Columbia University statistics department. Prior to entering the world of data science, Daniel Krasner was a researcher at the Mathematical Sciences Research Institute in Berkeley and an assistant professor of mathematics at UCLA. He holds a PhD in mathematics from Columbia University.
Markus Schatten
Markus Schatten
Head of AI Lab, FOI
Assistant professor Markus Schatten got his masters degree at the Faculty of organization and informatics in 2008. and his doctoral degree at the same faculty in 2010. He has been working at the Faculty of organization and informatics from 2006. He has been teaching several courses related to database systems, programming and artificial intelligence at doctoral, graduate, undergraduate and professional level at the Faculty of Organization and Informatics in Varazdin, at the Faculty of Information Studies in Novo Mesto in Slovenia and at the University of the People, USA. He launched, and is currently the head of the Laboratory for Artificial Intelligence at the Faculty of organization and informatics. From 2009. to 2014. was a board member of MLAZ (Network of Young Scientists), and from 2014 to today is a board member of ITS (Intelligent Transport Systems). Assistant professor Markus Schatten, PhD is the author and co-author of many scientific and professional articles (over 80). He is a mentor of a number of undergraduate and graduate theses (over 50) and a mentor or co-mentor of 5 doctoral dissertations.
Nathan Wilson
Nathan Wilson
CTO & Co-Founder, Nara Logics
Nathan Wilson is a scientist and entrepreneur who is focused on actualizing powerful new models of brain-based computation. After many years at MIT working on the mathematical logic of neural circuits, Nathan co-founded Nara Logics, a Cambridge, MA artificial intelligence company building a novel type of neural network that automatically finds and refines connections in raw data for large enterprises to guide decisions. Nara Logics exemplifies how breakthroughs in neuroscience are poised to transform computer science. Nathan holds many patents in AI and his research has been featured in top journals including Nature, Science, Proceedings of the National Academy of Sciences, Neuron, and the MIT Press. An enthusiastic writer and teacher who routinely appears in the popular press on current topics in AI, Nathan now works to guide advancements at Nara Logics as CTO.
Steven Gustafson
Steven Gustafson
Chief Scientist, Maana
Steven Gustafson is Chief Scientist overseeing research and data science at Maana. Previously, he spent over 10 years at General Electric's Global Research Center, New York, developing analytical solutions across the finance, healthcare, and industrial sectors. Steven drove company-wide strategies for Big Data, Semantics, and Artificial Intelligence. He founded GE's Knowledge Discovery Lab and co-founded the journal of Memetic Computing, as Technical Editor-in-Chief. He has chaired conferences and program committees, and serves on several editorial boards. Steven was awarded the IEEE Intelligent System's “AI's 10 to Watch,” holds over 10 patents, and has authored over 40 peer reviewed articles. Steven earned a PhD in Computer Science at University of Nottingham, UK, and was a research fellow in the Automated Scheduling, Optimization and Planning Research Group. He received his BS and MS in Computer Science from Kansas State University, and was a research assistant in the Knowledge Discovery in Databases Laboratory.
Eli David
Eli David
Co-Founder & CTO, Deep Instinct
Dr. Eli David is an expert in the field of computational intelligence, specializing in deep learning (neural networks) and evolutionary computation. He has published more than thirty papers in leading artificial intelligence journals and conferences, mostly focusing on applications of deep learning and genetic algorithms in various real-world domains. For the past ten years, he has been teaching courses on deep learning and evolutionary computation at Bar-Ilan University, in addition to supervising the research of graduate students in these fields. Dr. David has also served in numerous capacities successfully designing, implementing, and leading deep learning based projects in real-world environments. Dr. David is the developer of Falcon, a grandmaster-level chess playing program, which automatically learns by processing datasets of chess games. The program reached the second place in World Computer Speed Chess Championship 2008 relying solely on machine learning for its performance. Dr. David received the Best Paper Award in 2008 Genetic and Evolutionary Computation Conference, the Gold Award in the prestigious "Humies" Awards for Human-Competitive Results in 2014, and recently the Best Paper Award in 2016 International Conference on Artificial Neural Networks.
Aude Billard
Aude Billard
Professor, LASA
Satya Gautam	Vadlamundi
Satya Gautam Vadlamundi
Lead Data Scientist, Capillary Technologies
Satya Gautam Vadlamudi received the B.Tech.(Hons.) and Ph.D. degrees in Computer Science and Engineering from the Indian Institute of Technology Kharagpur (IIT), Kharagpur, India, in 2008 and 2014, respectively. He is currently a Principal Data Scientist at Capillary Technologies, Bengaluru. Prior to Capillary, he has spent time as a researcher at ASU, Intel Labs, IIT Kharagpur (working in collaboration with General Motors India Science Lab and with Xerox Research Centre India), Google and Georgia Tech, predominantly working on various AI related technologies among other things. Dr. Vadlamudi was the recipient of several honors, including the Pratibha Award from the Government of Andhra Pradesh and SAP Labs Doctoral Fellowship.
Luka Bradeško
Luka Bradeško
Founder, Curious Cat Company
Luka Bradesko is a computer science PhD candidate from Artificial Intelligence Lab at Jozef Stefan Institute, Slovenia. His research interests and PhD topic are in Natural Language Processing, Logical Inference and Knowledge Extraction. From 2008 to 2013 he also worked as a principal software engineer for Cycorp Europe, which was at the time an EU branch of the American AI company Cyc Inc. During these years, he worked on an EU project developing distributed large scale inference engine (LarKC), and also on an AI assistant startup build on top of Cyc (Curious Cat). The AI assistant that was part of the startup is core of his PhD topic: Knowledge Acquisition through Natural Language Conversation and Crowdsourcing, which is at the moment being under minor revision review in the Transactions on Information Systems Journal. After 2013, when the startup was not succeeding Luka continued his PhD research activities and also other research projects. Some of the recent projects include a concept of an intelligent motorhome (reasoning engine software interacting with sensors and actuators) for a European motorhome producer (Adria Mobil) and Named Entity Disambiguation algorithm which is a work in progress in a collaboration with US Company Bloomberg L.P.
Tuomas Sandholm
Tuomas Sandholm
Professor, Carnegie Mellon University
Jeremy Howard
Jeremy Howard
Founder & Researcher, Fast.ai
Jeremy Howard is an entrepreneur, business strategist, developer, and educator. Jeremy is a founding researcher at fast.ai, a research institute dedicated to making deep learning more accessible. He is also 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. Jeremy’s most recent startup, Enlitic, was the first company to apply deep learning to medicine, and has been selected one of the world’s top 50 smartest companies by MIT Tech Review two years running. He was previously the President and Chief Scientist of the data science platform Kaggle, where he was the top ranked participant in international machine learning competitions 2 years running. He was the founding CEO of two successful Australian startups (FastMail, and Optimal Decisions Group–purchased by Lexis-Nexis). Before that, he spent 8 years in management consulting, at McKinsey & Co, and AT Kearney. Jeremy has invested in, mentored, and advised many startups, and contributed to many open source projects. He has many television and other video appearances, including as a regular guest on Australia’s highest-rated breakfast news program, a popular talk on TED.com, and data science and web development tutorials and discussions.
Piero Molino
Piero Molino
Research Scientist, Uber AI
Machine learning researcher with focus on language. Completed a PhD on question answering at the University of Bari, Italy, were he founded a startup, QuestionCube. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning and then joined Geometric Intelligence, where he worked on grounded language understanding. He is now a research scientist at Uber AI Labs in San Francisco.
Marlene Jia
Marlene Jia
Head of Revenue, TOPBOTS
Marlene is the Head of Revenue at TOPBOTS, a strategy and research firm for enterprise AI and bots. Fortune 500 clients include L’Oreal, WPP, and PayPal. She specializes in enterprise implementations and taking B2B strategies to market at former companies including Wizeline and Ustream (acq. by IBM). Marlene has a degree in Economics from Northwestern University.
Shashi Kant
Shashi Kant
Founder, Netra Inc.
Shashi Kant is the founder and CTO of Netra, a company that empowers brands to engage with imagery and social networks. Shashi earned his Master’s in Engineering & Management from MIT and MIT Sloan School of Management. His research in “Semantics” of Artificial Intelligence at the MIT Computer Science & Artificial Intelligence Laboratory was the genesis of Netra's technology. His career has spanned Schlumberger, Absolute Software, NASA-Ames and “big data” consulting with multiple Fortune-100 companies. He holds a Bachelor’s degree in Engineering from India and was an ASP Fellow at MIT.
Steven Hamblin
Steven Hamblin
Co-founder & CTO, Shoal
Steven Hamblin is an academic by background, having done his PhD and postdocs in computational evolutionary biology and biostatistics. He's also been programming since he was a kid and seems to be unable to give it up. More recently he's transitioned into industry, first as the head of the artificial intelligence team at babylon health, where he built the team from scratch to recognition as one of the health industry's leading AI efforts, and more recently as CTO of Shoal, a new early-stage startup aimed at making insurance better.
Albert Bifet
Albert Bifet
Big Data Scientist, SAMOA
Hedi	Ben Younes
Hedi Ben Younes
R&D engineer, Heuritech
Hedi received his diploma from Centrale-Supelec French engineering school. His work is related to deep learning for multimodal representations - combining text and visual information. He notably worked on Visual Question Answering. He works jointly as a deep learning scientist at Heuritech, and as a PhD at Université Pierre and Marie Curie (LIP6).
Davide Venturelli
Davide Venturelli
Ops Manager, NASA Ames Lab
Venturelli graduated from Ecole Normale Superieure de Lyon and obtained his Ph.D. in Numerical Simulations of the Condensed Matter at the International School for Advanced Studies in Trieste and in Nanophysics at the Universite de Grenoble.Venturelli is currently Science Operations Manager at the Quantum Artificial Intelligence Laboratory at NASA Ames Research Center, invested for the Research Institute of Advanced Computer Science (RIACS) in research projects dealing with the non-equilibrium physics of statistical models relevant for quantum annealing and quantum computation, and how these models reflect practically across different implementation technologies or computational strategies. His applied focus in quantum optimization is in complex scheduling, telecommunication networks, and robotics/distributed AI, also in collaborations with academia and the private sector. He is also in the Faculty of Singularity University, and the CEO and co-founder of Archon Dronics, a robotics service platform to use ground and flying robots for inspection and security in remote regions of the planet.
Javier Gonzalez Helly
Javier Gonzalez Helly
co-CEO & Founder, Botfuel
Javier is the co-founder at Botfuel, speaker in the area of chatbots and AI and founder of Chatbots Paris.He previously co-founded Pledg - a Fintech offering crowd-guarantees as as service and Drivoo - the French leader in crowdsourced last mile logistics. In a past life, he was the Managing Director of the BNP Paribas Bank M&A practice.
Yan Georget
Yan Georget
Co Founder, Botfuel
Yan is currently Co Founder at Botfuel. Previously, he held CTO positions at Ogury, Shotgun and Come&Stay and was Vice President Research & Development at Criteo. In a first part of his career he worked for several software companies (Trilogy, Business Objects, SAP). He co-founded: Botfuel, Skincar, Shotgun and Koalog. Yan graduated from Ecole Polytechnique and holds a PhD in Computer Science.
Dr Marco A. V. Bitetto
Dr Marco A. V. Bitetto
Senior Roboticist, Robotronics LLC
I am blind and also a professional scientist by training. I have been teaching graduate level engineering courses since I first graduate from the university at age 24. I have also been both peer reviewer on numerous governmental and private sector based scientific and engineering research funding committees. In addition to writing numerous successful scientific and engineering grant proposals that have funded my research throughout the years. I have a PhD. in Robotics and a B.S. degree in Computer Systems Engineering from the State University of New York at Stoney Brook. I presently am a professor emeritus and teach as an adjunct professor at several local colleges and universities. I teach at both the undergraduate and graduate levels. Presently, I am a Co-Advisor on the doctoral committee of a foreign PhD. candidate in Robotics and Artificial Intelligence. This candidate is doing additional work on hardware based adaptive neural networks for machine vision applications and his work is based in large part upon my own research on the NERVOTRON that was done in 1994. Also, I am a scientific peer reviewer on the IEEE-WCCS (Institute of Electrical and Electronic Engineers - World Conference on Complex Systems) research paper acceptance committee and I also do contract based jobs in robotics and artificial intelligence based systems design.
Amit Kumar Pandey
Amit Kumar Pandey
Chief Scientist, Aldebaran
Dr. Amit Kumar Pandey is the Head Principal Scientist (Chief Scientist) at SoftBank Robotics (formerly Aldebaran Robotics), Paris, France, also serving as the scientific coordinator (R&D) of its various collaborative projects. Earlier for 6 years he worked as researcher in Robotics and AI at LAAS-CNRS (French National Center for Scientific Research), Toulouse, France. His Ph.D. thesis in Robotics (title: Towards Socially Intelligent Robots in Human Centered Environment), is the second prize winner (tie) of the prestigious Georges Giralt Award for the best Ph.D. Thesis in Robotics in Europe, awarded by euRobotics (the European Union Robotics Community). His current research interest includes Socially Intelligent Robots, Human Robot Interaction (HRI), Robot’s Cognitive Architecture and Lifelong Learning. On these aspects, he has been actively contributing as principal investigator, researcher, and industrial scientific coordinator in various national and European Union (EU) projects, as well as involved in their design and proposal. Among other responsibilities, he is the founding coordinator of Socially Intelligent Robots and Societal Applications (SIRo-SA) Topic Group (TG) of euRobotics, and an active contributor in the Multi-Annual Roadmap (MAR) and Strategic Research Agenda (SRA) of euRobotics, which aim to shape the future of robotics in Europe in collaboration with European Commission (EC) through PPP SPARC (the largest civilian-funded robotics innovation programme in the world). He is also the recipient of Pravashi Bihari Samman Puruskar 2014 (Non Residential Bihari Honour Award), for Science, Technology and Education, one of the highest level civilian honors, awarded by the state of Bihar, India.
John Bocharov
John Bocharov
Director of Engineering, VoiceBase
Fernanda Viégas
Fernanda Viégas
Research Scientist, Google Brain
Fernanda Viégas and Martin Wattenberg are the leaders of Google’s “Big Picture” data visualization group, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. Viégas holds a Ph.D. from the MIT Media Lab; Wattenberg has a Ph.D. in mathematics from U.C. Berkeley. Their visualization-based artwork has been exhibited worldwide, and is part of the permanent collection of Museum of Modern Art in New York.
Martin Wattenberg
Martin Wattenberg
Research Scientist, Google
Fernanda Viégas and Martin Wattenberg are the leaders of Google’s “Big Picture” data visualization group, part of Google Brain. Their work in machine learning focuses on transparency and interpretability, as part of a broad agenda to improve human/AI interaction. Viégas holds a Ph.D. from the MIT Media Lab; Wattenberg has a Ph.D. in mathematics from U.C. Berkeley. Their visualization-based artwork has been exhibited worldwide, and is part of the permanent collection of Museum of Modern Art in New York.
Gaurav Kumar Singh
Gaurav Kumar Singh
ML & DL Researcher, Ford
Gaurav Kumar Singh is a Machine and Deep Learning Researcher at Research and Advanced Engineering at Ford Motor Company, located in Dearborn, Michigan. He has over 6 years of research experience ranging from Control Systems to Machine Learning and Data Science. His side gigs involve consulting friends in ways to utilize machine learning techniques in their startups. He has served as project reviewer and mentor for Machine Learning and Self Driving Car Nanodegree at Udacity as well. Gaurav graduated with a Masters’ degree in Electrical and Computer Engineering from University of Michigan, Ann Arbor in December 2015. He received his Bachelors of Technology (B.Tech) degree from National Institute of Technology, Trichy, India in 2014.
Tejaswini Ganapathi
Tejaswini Ganapathi
Senior Data Scientist, Salesforce
Tejaswini Ganapathi currently is a Senior Data Scientist at Salesforce's Infrastructure Division. She works on machine learning solutions to optimize data transfer over the network edge. Prior to joining Salesforce, she was a Data Scientist at Twin Prime Inc, a mobile performance startup which Salesforce acquired, where a bulk of this technology was built. She is passionate about developing AI technology and applying AI to newer areas and problems.
Jérôme Selles
Jérôme Selles
Director of Data Science, Turo
Jérôme leads data-related initiatives at Turo. From data-driven strategic decision making to design and implementation of user-facing data features like dynamic pricing or search ranking, data is widely used at Turo and highly contributed to 4 years of hyper-growth for the company. Jérôme joined as the first data scientist in the company and has built a 15-person team. Prior to Turo, Jérôme worked as data scientist at AgilOne, a marketing analytics startup and at SAP Labs in Palo Alto. He holds a MSc. in Applied Mathematics from ENSTA Paritech and a MSc. in Telecommunications from UPC Barcelona. When not shaping new datasets, Jérôme enjoys kitesurfing in the Bay Area.
Xiaohui Hu
Xiaohui Hu
Principal Data Scientist, GE Digital
Xiaohui Hu is currently principal data scientist at GE Digital. He is working in asset performance management (APM) group to develop new advanced analytic solutions for industry customers. APM is a suite of software and service solutions to help asset-centric industrial companies increase uptime, decrease costs, and reduce operational risks. Xiaohui received his PhD in Electrical and Computer Engineering from Purdue University and his Bachelor degree from Tsinghua university, China respectively. His main research interests are machine learning/computational intelligence, data modeling, and prognostics and health management. He is a senior member of IEEE.
Pavel Machalek
Pavel Machalek
Co-Founder, Spaceknow
Pavel is a Co-Founder and CEO of Spaceknow Inc, a satellite imagery analytics VC-funded company based in San Francisco, CA. Previously Pavel was Head of Remote Sensing at the Climate Corporation, which was acquired by Monsanto Corp for over $1bn. Throughout his career he has worked with numerous NASA great observatories including Spitzer, Hubble and Kepler Space Telescopes as Principal Investigator in search of planets in our galaxy and characterizing their atmospheres. Pavel holds a PhD in Physics and Astronomy from Johns Hopkins University.
Fred Sadaghiani
Fred Sadaghiani
CTO, Sift Science
Fred is CTO of Sift Science -- a ML-based Fraud Fighting company that protects thousands of web sites from bad actors. Before Sift Science, he was a tech lead at Google, Principal Engineer at Jambool, CTO at Teachstreet, product lead at Zillow and engineer at Amazon.
Get to know the speakers
Presenting companies include
deepomatic
Spaceknow
Darwin Ecosystem
Berkeley AI Research Lab
GE Digital
Inbenta
Turo
Softbank
MIT Media Lab
VoiceBase
Telecom ParisTech
Shoal
Sorbonne
Salesforce
Topbots
Ford
Ople
x.ai
Utah State
Uni N Carolina Chapel Hill
University of Minnesota
Berkley
Uber AI Labs
UBC
Track
Total Good
Tokyo Inst of Tech
STM
Stanford University
Delsys
Google Research
Quora
Park Nav
Penn State
Recast.ai
Paddle Paddle
ORacle
Nuclai
NYU
NASA
NARA
MIT
NASA
Merriam Tech
MEtis
Maana
LynxCare
LinkedIn
LiftOff
LASA
Keotic
Intel
Heuritech
HealthiHabits
Gen 9
DeepMind
Lenovo
Deep Vu
Fast AI
Deep Instinct
Curious Cat
CSC
Columbia University
Carnegie Mellon University
Capillary Technologies
Bot Analytics
BigML
Booz Allen Hamilton
ASI
Baidu
Netflix
Ericsson
Etsy
Google
Y Combinator

Agenda

9am-6pm EST // 6am-3pm PST // 3pm-12am CEST // 9pm-6am GMT+8
MACHINE LEARNING RESEARCH
COMPUTER VISION, NLP & CHATBOTS
STARTUPS, INDUSTRY & SOCIETY
DEMOS, TUTORIALS & applied AI
9:00 am
9:40 am
Fernanda Viégas and Martin Wattenberg
Research Scientists, Google Brain & Google
Fairness in Machine Learning

As machine learning finds a place in high-stakes arenas such as finance and criminal justice, it's critical to make sure the people it affects are treated fairly.  Meeting this challenge is a complex and important endeavor that combines computer science, ethics, and mathematics in sometimes surprising ways. We'll talk about recent results on fairness in ML that range from theoretical to practical, including constructive steps developers can take.

ML, Keynote Speaker
9:40 am
10:20 am
Nicolas Papernot & Patrick McDaniel
Graduate Research Assistant & Director of INSR, Penn State
Adversarial Examples in Machine Learning

Machine learning models, including deep neural networks, were shown to be vulnerable to adversarial examples—subtly (and often humanly indistinguishably) modified malicious inputs crafted to compromise the integrity of their outputs. Adversarial examples thus enable adversaries to manipulate system behaviors. Potential attacks include attempts to control the behavior of vehicles, have spam content identified as legitimate content, or have malware identified as legitimate software. // In fact, the feasibility of misclassification attacks based on adversarial examples has been shown for image, text, and malware classifiers. Furthermore, adversarial examples that affect one model often affect another model, even if the two models are very different. This effectively enables attackers to target remotely hosted victim classifiers with very little adversarial knowledge.

ML, Neural Networks
Dennis Mortensen
CEO & founder, x.ai
The Emergence of Bring Your Own Agent (BYOA)

Scheduling meetings, booking travel, managing your receipts, and repetitive sales tasks; these are some of the many chores we must do everyday. But they’re not core to our jobs and often distract us from the high value tasks, like cultivating a lead or sharpening our analysis of our customers. Over the next half decade, as more AI intelligent agents come to market, employees will increasingly deploy a suite of agents to get their job done, and port agents from one job to the next. Much like Bring Your Own Device (BYOD), this new paradigm—Bring Your Own Agent (BYOA)—will likely change the nature of work.

Agents
Feiyu Xu
VP of Lenovo, Head of AI lab
The Migration of AI from Laboratories into Everyday Life

Today, Artificial intelligence is not science fiction anymore. The availability of big data together with big-data analytics platforms, advanced machine learning methods, high-speed internet, and global open-source R&D communities have enabled powerful AI applications such as intelligent web search, machine translation, smart interactive assistants and business intelligence software. In my talk, I will sketch the transdisci­plinary applied research at the German Research Center for Artificial Intelligence to illustrate the wide range of tomorrow’s artificial intelligence applications. I will also describe transfer channels from research laboratories to products and their comer­cialization that have proven effective. But it is not only the channels but also a com­mercialization-driven methodology that shortens the way from the researcher to the customer. Some central methods will be outlined in the presentation ranging from the integration of “design thinking” into the first steps of the research process all the way to combining research and product use.  Then I will zoom into two areas of language technology and explain their applications: 1) big textual data analytics, 2) smart conversational agents.

Big Data, Machine Learning, Society
Pierre-Edouard Lieb
Partnerships Manager, Recast.ai
Building your bot in one hour with Recast.AI 101

This workshop will teach you how to use the Recast.AI platform and the basics of chatbot building. You will also learn how to connect your bot to a messaging platform using the bot connector.

Demo/Tutorial, Chatbots
10:20 am
11:00 am
Vincent Delaitre
Co-founder & CTO, deepomatic
Deep Learning is for everyone: A top notch but accessible deep learning solution to solve businesses custom needs

Although deep learning is now widely known in the data science community, it still remains underused by big players to solve concrete business cases for three main reasons: deep learning is annotation hungry, it is hard to build data-science teams with the required expertise and because deploying deep learning models on GPUs can be tricky. We intend to change this. In this talk we will see how deep learning can easily be applied to solve image-based problems for businesses. We will demo our self-service/user friendly/accessible ? deep learning platform that allows users to easily annotate and make use of of unannotated data, train the best possible models and deploy them in the cloud or on premise(ça me parait un peu bizarre, je mettrais plutôt « on site » ou « locally », après si c’est un terme technique, oublie ça) . This demo will also be the occasion to unveil mathematical, algorithmic and technical details about how our product works under the hood, so be sure to join us for a comprehensive tour of applied deep learning.

deep learning, GPU
Michael Galvin
Executive Director, Metis
Natural Language Processing and Text Mining in Python

The session will cover NLP and text mining using Python and offer several examples of real world applications. Participants will be introduced to various text processing techniques and learn more about text classification, clustering, and topic modeling. By the end of the workshop, participants will be able to use Python to explore and build their own models on text data.

NLP, Computer Vision, Python
Andrew Cozier
Data Engineer, ASI Data Science
What does your Postcode say about you? - A Technique to Understand Rare Events Based on Demographics

It is widely accepted that where you live says a lot about who you are, demographically speaking. At the same time, many companies are desperate to find out more about their customers in order to better understand them. By knowing where they live however, many companies are sitting on an extremely rich data set from which they could learn a lot about their customers. Furthermore, this data can be used to optimise their marketing strategy and help them expand their customer base. The technique we have developed enriches a customer data set using UK census data and then applies a novel, tree-based unsupervised learning algorithm to extract differentiating demographic features and identify high-value postcodes. Our algorithm allows us to avoid performing anomaly detection on the entirety of the UK population. Furthermore, the method we have developed is not restricted to the field of marketing; it also applies to rare events. Fires or A&E admissions are relatively rare events where one would like to avoid having to perform anomaly detection on the entire UK population or all UK households. Likewise we look to future applications of our algorithm involving enriching data with data sets other than the census.

Demographics, Society, Data, Algorithms
Michael Bucko
Co-Founder, Deckard A.I.
Working with a machine on a software team

Imagine a machine that simultaneously looks at each single developer on a software team, the entire team, the code and all other data sources, and uses this knowledge to make developers smarter, teams more effective and code better. Welcome to the Deckard's world.

Demo/Tutorial, Dev, Efficiency
11:00 am
11:40 am
Danko Nikolic
Lead Data Scientist, CSC
Why deep neural nets cannot ever match biological intelligence and what to do about it?

The recent introduced theory of practopoiesis offers an account on how adaptive intelligent systems are organized. According to that theory biological agents adapt at three levels of organization and this structure applies also to our brains. This is referred to as tri-traversal theory of the organization of mind or for short, a T3-structure. To implement a similar T3-organization in an artificially intelligent agent, it is necessary to have multiple policies, as usually used as a concept in the theory of reinforcement learning. These policies have to form a hierarchy. We define adaptive practopoietic systems in terms of hierarchy of policies and calculate whether the total variety of behavior required by real-life conditions of an adult human can be satisfactorily accounted for by a traditional approach to artificial intelligence based on T2-agents, or whether a T3-agent is needed instead. We conclude that the complexity of real life can be dealt with appropriately only by a T3-agent. This means that the current approaches to artificial intelligence, such as deep architectures of neural networks, will not suffice with fixed network architectures. Rather, they will need to be equipped with intelligent mechanisms that rapidly alter the architectures of those networks.

Tri-transversal theory, Neural networks, ML
Bruno Gonçalves
Data Scientist, New York University
word2vec and friends

Word embeddings have received a lot of attention since some Googlers published word2vec in 2013 and showed that the embeddings that the neural network learned by "reading" a large corpus of text preserved semantic relations between words. As a result, this type of embedding started being studied in more detail and applied to more serious NLP and IR tasks such as summarization, query expansion, etc... In this talk we will cover the implementation and mathematical details underlying tools like word2vec and some of the applications word embeddings have found in various areas. An overview of the emerging field of "<anything>2vec" (phrase2vec, doc2vec, dna2vec, node2vec, etc...) methods that use variations of the word2vec neural network architecture will also be presented.

NLP, IR, Word embeddings
Steven Hamblin
CTO, Shoal
Lessons from the frontline: building real AI chatbot systems

Chatbots and conversational interfaces are a topic of significant interest, but the space is oddly divided in current thinking, ranging along a gradient from "chatbots are an essential and complex AI component of new tech" to "chatbots are quickly becoming a shallow tech commodity". The truth is, as usual, somewhere in between: it is easy to underestimate the effort and resources needed to create a conversational interface with real value, but the field is exploding with new tools that will help. In this talk I will present practical learnings and advice from my last two years of work, first at babylon health developing one of the leading medical chatbots acting as an interface for what is becoming a true AI doctor, and lately as the CTO of an insurtech startup aiming to improve the insurance experience with AI technology. I will cover when and why conversational interfaces are an appropriate choice, the pitfalls many attempts face when creating their own, thoughts on the best approaches and views on upcoming trends.

Chatbots, Startup
Steven Gustafson
Chief Scientist, Maana
Humans Required, Future of AI and Knowledge-centric Technology

Artificial intelligence is more than a game of man vs. machines. For AI to have a valuable impact, it requires a symbiosis with humans. The presentation will explore real-world examples of how core AI ingredients such as knowledge representation and reasoning, learning, and decision-making requires human engagement. Attendees will learn how this synergy can lead business leaders to:1. Utilize the right data (structured and unstructured) from across silos and organizations; 2. Accelerate knowledge discovery within the enterprise; 3.Reduce capital expenditures; 4.Optimize assets and processes through operationalization; 5. Increase profitability The session will conclude with a look at a new category of technology being driven in part by AI algorithms, machine learning, semantic search, and an enterprise knowledge graph, called Knowledge-centric technology.

Demo/Tutorial, Data, ML, Knowledge-centric
11:40 am
12:20 pm
Peter Gloor
Research Scientist, MIT Center for Collective Intelligence
Measuring the Collective Mind

In a parallel to quantum physics this talk introduces social quantum physics, defining four key principles of social quantum physics that help build collective consciousness of swarms: empathy leading to entanglement, and reflection leading to reboot and refocus. The collective mind is measured through a collaboration scorecard made up of six key variables – “honest signals” – drawn from communication on Twitter and the Web, from e-mail inside large companies and in small teams from smartwatches and sociometric badges. The “six honest signals of collaboration” are strong leadership, balanced contribution, rotating leadership, responsiveness, honest sentiment, and shared context. I will illustrate these “honest signals of collaboration” using numerous examples ranging from biotech startups to innovation teams at the R&D departments of Fortune 500 firms to teams of Healthcare researchers and patients. Read more in the two new books by Peter Gloor: “Sociometrics and Human Relationships: Analyzing Social Networks to Manage Brands, Predict Trends, and Improve Organizational Performance” and “Swarm Leadership and the Collective Mind: Using Collaborative Innovation Networks to Build a Better Business” which will both come out with Emerald Publishers in April 2017.

Collective mind, Quantum Physics, Communication
Charles Ollion & Hedi Ben younes
CTO & R&D engineer, Heuritech
Image Understanding through weak supervision: application to Fashion domain

Deep Learning for Image analysis is now widely spread among academia as well as business use cases. In most cases, the amount of quality labeled data needed as well as the definition itself of the labels is problematic. On the other hand, image data associated with raw text is omnipresent on internet. Using this weak supervision, we will show how we can leverage huge amounts of data for image understanding, and show the pertinence of the method on visual fashion analysis. This work is made and presented by both Charles Ollion as well as Hedi Ben Younes, PhD in Machine Learning at LIP6/Heuritech.

Deep Learning, Analysis, Fashion
Yi Wang
Software Engineer, PaddlePaddle; Baidu
Deep Learning Fundamentals: from modeling strategies up to a complete solution

Many open source deep learning frameworks are competing for the position that eases programming the most. This helps researchers with faster iterations. But for the industry, the next question is -- how to run the programs. AI depends on big data, which come from Web servers and in the form of log messages, or from crawlers and in the form of external datasets. In the industry, we need a complete solution that covers the collecting of data, learning from data, and feedback the models to the business. This talk explains lessons we learned from PaddlePaddle, a recently open sourced deep learning platform which has been widely used in Baidu for four years.

Deep Learning, Big Data, Open Source, Baidu
Javier Gonzalez & Yan Georget
Co-CEO and Founder & Co-founder, Botfuel
AI for chatbots

To what extent do chatbots use AI? How can it be efficiently used? What’s the right data for chatbots? What kind of machine learning is best suited?

Chatbots, Efficiency, Data, ML
12:20 pm
1:00 pm
Matt Taylor
OS community flag-bearer, Numenta
The Biological Path Towards Strong AI

Strong AI is a common goal of many computer scientists. So far, machine learning techniques have created amazing results in narrow fields, but haven’t produced something we could all call “intelligent”. Given recent advances in neuroscience research, we know a lot more about how neurons work together now than we did when ANNs were created. We believe systems with a more realistic neuronal model will be more likely to produce Strong AI. Hierarchical Temporal Memory is a theory of intelligence based upon neuroscience research. The neocortex is the seat of intelligence in the brain, and it is structurally homogeneous throughout. This means a common algorithm is processing all your sensory input, no matter which sense. We believe we have discovered some of the foundational algorithms of the neocortex, and we’ve implemented them in software. I’ll show you how they work with detailed dynamic visualizations of Sparse Distributed Representations, Spatial Pooling, and Temporal Memory.

Strong AI, HTM, ML, Hierarchal Temporal Memory, Algorithms
Eli David
Co-Founder and CTO, deepinstinct
Applying Deep Learning AI to Cybersecurity: Outsmarting Hackers with an Artificial Brain

Join our session on the first application of deep learning to cybersecurity. Dr Eli David, one of the leading global experts on deep learning, co-founder and CTO of Deep Instinct, will cover the evolution of artificial intelligence, from old rule-based systems to conventional machine learning models until current state-of-the-art deep learning models. Deep learning is a novel branch of artificial intelligence inspired by the brain’s ability to learn: once a brain learns to identify an object, its identification becomes second nature. Similarly, as a deep learning-based artificial brain learns to detect any type of cyber threat, its prediction capabilities become instinctive. As a result, the most evasive and unknown cyber-attacks are immediately detected and prevented. In this webinar, Dr Eli David will present the ground-breaking results exhibited by deep learning when applied to computer vision, speech, text understanding and for the first time to cybersecurity.

Deep Learning, Security
Andres Rodrigues
Senior Tech Lead Deep Learning, Intel
Distributed deep learning training

Supervised deep learning networks require significant computational resources to train. In order to reduce the total time to train, it is advantageous to distribute the workloads across several compute nodes. In this lecture we will discuss the algorithmic challenges of distributed training and methods to alleviate some of these challenges.

Training, Supervised Learning, Demo/Tutorial
1:00 pm
1:40 pm
Ian Goodfellow
Research Scientist, Google Brain
Machine learning privacy and security

As machine learning algorithms become more widely used, it is important to ensure that they provide the privacy and security guarantees. In this talk, I outline some of the kinds of attacks that adversaries can make against machine learning models, and some of the defenses that we can use in response, like adversarial training and differential privacy. This talk is a high-level overview of this area to whet your appetite; AI With the Best also features detailed talks by Nicolas Papernot, Patrick McDanel and Dawn Song zooming into detail on some of these subjects.

Security, Privacy, ML
1:40 pm
2:20 pm
Behnaz Abdolahi
Machine Learning Engineer
Machine Learning in Practice

Machine learning algorithms are categorized into supervised, unsupervised and semi-supervised. This presentation will discuss how to analyze a given dataset and applying an appropriate model. The steps are: getting and cleaning data, extracting and selecting features and finally developing an appropriate classifier. Some of the popular classifiers such as Naïve Bayes, SVM and Neural Network will be discussed. Generalizing the algorithm on test dataset and calculating error rate is an important part in developing a robust model on any given dataset. The algorithms will be discussed briefly with some practical examples.

ML, Algorithms
Kirk Borne
Principal Data Scientist, Booz Allen Hamilton
AI for Social Good

I will review some of the opportunities, applications, and challenges of using AI and machine learning for societal good. I will also summarize briefly this year's $1Million Data Science Bowl competition, hosted by Kaggle and sponsored by Booz Allen Hamilton.

Society
Tanmay Bakshi
Software/Cognitive Developer
Building a Deep Learning Image Classifier using custom CNNs & Datasets in Keras

In this session, you’ll learn how you can buid a custom image classifier powered by your own CNN architectures in Keras, and also learn to use the trained models and run predictions against them.

Deep Learning, Demo/Tutorial, Keras
2:20 pm
3:00 pm
Kamelia Aryafar
Senior Data Scientist, Etsy
Learning to Rank in e-commerce

Search is an important problem for modern e-commerce platforms such as Etsy. As a result, the task of ranking search results automatically or the so-called learning to rank is a multibillion dollar machine learning problem.In this talk, we first review Etsy's approach to learning to rank using a few hand-constructed features based on the Etsy listing's text-based representation. We then discuss a multimodal learning to rank model that combines these traditional text-based features with visual semantic features transferred from a deep convolutional neural network. We show that a multimodal approach to learning to rank can improve the quality of ranking in an experimental setting. Reference: http://www.kdd.org/kdd2016/subtopic/view/images-dont-lie-transferring-deep-visual-semantic-features-to-large-scale-m

E-commerce, Search, Etsy, Multimodal, ML
Jordi Torras
CEO & Founder, Inbenta
Chatbots for the Enterprise - NLP vs ML

When you think of virtual assistants, what comes to mind? Likely your experiences with Alexa, Google Home or Siri. However, chatbots in the enterprise have often failed to provide a similar quality experience. Yet chat remains the number one way customers want to talk to brands, and they often want to talk when customer service teams are offline. What are the criteria needed to deploy an enterprise-class chatbot, one that yields effective business results? And what is the role that natural language processing technology and machine learning play in the technology powering chatbots? Why is NLP sometimes superior to ML and vice versa? We'll also explore how chatbots platforms like Inbenta are leveraging both technologies to deliver the best conversational experience.

NLP, ML, Chatbots
Roni Wiener
Founder, Keotic
From Data to Information - An Intuitive View of Neural Networks and Embedded Spaces

Deep Neural Networks can be viewed as a mechanism for modeling information. In this talk we will share an intuitive view of deep neural network and embedded spaces in terms of the information they hold. The talk will not involve complex mathematical explanations, rather, only intuitions that should help simplify and clarify the process of solving real life problems via neural networks.

Deep Neural Networks, Real-world, NLP
Deepak Agarwal
VP AI, LinkedIn
AI that Creates Professional Opportunities at Scale

Professional opportunities can manifest itself in several ways like finding a new job, enhancing or learning a new skill through an online course, connecting with someone who can help with new professional opportunities in the future, finding insights about a lead to close a deal, sourcing the best candidate for a job opening, consuming the best professional news to stay informed, and many others. LinkedIn is the largest online professional social network that connects talent with opportunity at scale by leveraging and developing novel AI methods. In this talk, I will provide an overview of how AI is used across LinkedIn and the challenges thereof. The talk would mostly emphasize the principles required to bridge the gap between theory and practice of AI, with copious illustrations from the real world.

LinkedIn, Demo/Tutorial
3:00 pm
3:40 pm
Jason Yosinski
ML Research Scientist, Uber AI Lab
A Little Fun with Image Gradients

Computing activation gradients in image space is a basic tool for visualizing individual neuron function in neural nets. First popularized by Erhan et al. (2009), the method without any tweaks usually produces noisy, unrecognizable results. However, with a few tricks, this family of approaches can be made to produce crisp results useful not only for visualizing neural function, but for creating a flexible class of generative models.

ML, Image Gradients
Pedro Alves
Founder and CEO, Ople
Data Science to Industry: It's not you, it's me

Machine learning and data science have taken Silicon Valley by storm with virtually every company creating positions in the field. Currently, it appears to be a general consensus that machine learning as it is being employed in industry is not living up to its promises. I want to take a deeper look into the state of data science in industry. This talk will address some of the problems and challenges that data science has, how it can help industries when it is working properly, and how to help get data science from where it is today to where it has the potential to be more quickly.

ML, Data Science
Jeremy Howard
Founder & Researcher, FastAI
GPU accelerate your algorithms with Pytorch

Most developers are aware that some algorithms can be run on a GPU, instead of a CPU, and see orders of magnitude speedups. However, many people assume that:

1. Only specialist areas like deep learning are suitable for GPU 

2. Learning to program a GPU takes years of developing specialist knowledge.

It turns out that neither assumption is true! Nearly any non-recursive algorithm that operates on datasets of 1000+ items can be accelerated by a GPU. And recent libraries like Pytorch make it nearly as simple to write a GPU accelerated algorithm as a regular CPU algorithm. In this talk we'll write an algorithm first in python (with numpy), and will then show how to port it to Pytorch, and will show how to get a 20x performance improvement in the process. Familiarity with Python and numpy is assumed. No previous pytorch experience is necessary.

GPU, Pytorch, Algorithms, Demo/Tutorial
3:40 pm
4:20 pm
Gerald Friedland
Adjunct Assistant Professor, UC Berkeley
Beyond Deep Learning: The Future of AI

Deep Learning and other artificial neural networks in combination with big data have brought a renaissance to AI and led to a torrent of new applications. Continued advances promise to produce autonomous systems that will perceive, learn, decide, and maybe even act on their own. However, the effectiveness of AI systems is still limited in many ways. For example, one of the biggest challenges is that increasingly intelligent machines need to be accountable for their decisions and, ideally, even explain their reasoning to human users. This talk will present an overview of approaches to address these and other current AI problems.

Deep Learning, Big Data
Mohammed Abdoolcarim
Co-Founder, Vahan
Designing an AI-driven simulator inside of WhatsApp for learning new skills

We will look at conversational design tips and tricks to deliver effective learning and engagement through messaging services such as WhatsApp.  

WhatsApp, Simulator
Jun-Yan Zhu
Ph.D Student, Berkeley AI Research Lab
Building Creative Machines via Generative Adversarial Networks

Humans are consumers of visual content. Everyday, people watch videos, play digital games and share photos on social media, but there is still an asymmetry, in which not that many of us are creators. In this talk, we aim to build machines capable of visual creativity, and use the “creative machines” as training wheels for visual content creation, with the goal of making people more visually literate.  We will present three projects based on Generative Adversarial Networks (GANs). First, we propose to directly model the natural image manifold via GANs, and constrain the output of an image editing tool to lie on this manifold.Then, we present a general image-to-image translation framework, “pix2pix”, where a network is trained to map user inputs directly to the final results. Finally, we present a new algorithm that can learn image-to-image translation even when paired training data is not available. See more details at https://github.com/junyanz.

Computer Vision, ML
4:20 pm
5:00 pm
Behnoush Abdollahi
Machine Learning Engineer, Gen Nine
Explainability in Recommender Systems

Explanations have been shown to increase the user’s trust in the recommender system in addition to providing other benefits such as scrutability, meaning the ability to verify the validity of recommendations. Most explanation methods are designed for classical neighborhood-based Collaborative Filtering (CF) or rule-based methods. However, most accurate recommender systems are black-box models, that have difficulty explaining the reasoning behind their recommendations. Therefore, there is a gap between accuracy and transparency or explainability of the models. This talks gives an overview of the main streams of research in the field of explainable models in recommender systems.

ML, Recommender system, CF
Francisco Martin
Co-Founder & CEO, BigML, Inc
Real-world Machine Learning: A View From the Trenches!

The Artificial Intelligence (AI) hype machine has been running at full throttle for the last few years.  Machine learning, one of the most rigorously researched AI subfields, has had a few high-profile successes in that period, and that is all it has taken to drive the imaginations of many observers into science fiction.  In fact, many of those success stories were built on decades-old techniques that have only now become feasible thanks to the availability of large-scale computation at low costs. The sources of this frenzied perception of machine learning are varied and many; from journalists seeking sensationalist angles, to professors that now make football player salaries, to venture capitalists pouring unprecedented amounts of money into untested, unproven, and often bizarre AI approaches. However, headlines do not make science fiction into fact, neither human nor robotic neurons are amplified by dollars, and general intelligence will not be created in the lifespan of a venture fund. The human involvement needed to develop any basic application that shows a minimum level of intelligence is still huge. When we see a new “AI” beating the best humans at Go or Poker, we rarely get a detailed account of the arduous tasks and enormous amount of grunt work behind the scenes that make these applications really work.  Usually, these efforts involve dozens or hundreds of hours collecting and preparing data, meticulous tweaking and fine-tuning of algorithms (that took academia years to invent and perfect), and finally preparing and deploying the infrastructure necessary to transform the data seamlessly into a computer program that can take comprehensible actions.  The resulting system often still requires specialized hardware, is useless without significant human interaction, and rarely generalizes beyond the very specific problem it was designed to solve. While the increased attention and investment will help accelerate some research, it will certainly help rediscover that we are further than we believe from producing truly intelligent, general purpose applications at massive scale or with some more general intelligence on them anytime soon. In this talk, I will try to provide a grounded view of what it takes to build an end-to-end machine learning-based application, as well as some evidence on how far AI is from threatening our world.

ML, Data, Algorithms, Real-world
John Bocharov
Director of Engineering, VoiceBase, Inc.
Speech analytics with VoiceBase: in near-real-time, at cloud scale, in the real world

We will demonstrate how VoiceBase use speech analytics to deliver critical insight into business calls, recordings, and videos. We will show the three layers of insight: speech-to-text transcripts, semantic keywords and topics, and business outcome predictions. We will outline the analytical infrastructure that makes the insight possible at a scale of 10,000 compute cores and growing. Finally, we will dive into key AI lessons of the deep learning, NLP, and ML that powers VoiceBase.

Analytics, Deep Learning, NLP, ML
5:00 pm
5:40 pm
Adji Bousso
PhD Candidate, Columbia University
New developments in neural language modeling

Language modeling is crucial to many NLP tasks. Applications include machine translation and speech recognition. Traditional n-gram and feed-forward neural network language models fail to capture long-range word dependencies in a block of text. Previous work by Mikolov et al. has shown that adding context to a Recurrent Neural Network (RNN) language model solves this dependency problem and yields lower perplexity scores. I will briefly review traditional language models before diving into the more recent contextual RNN-based language models. In particular, I will discuss the TopicRNN model, a RNN-based language model that captures long-range semantic dependencies using latent topics. I will also highlight some results on word prediction and sentiment analysis using the TopicRNN model. This is joint work with Chong Wang, Jianfeng Gao, and John Paisley.

NLP, Language modeling, ML
Ciprian Chelba
Staff Research Scientist, Google
Language Modeling in The Era of Abundant Data

The talk presents an overview of statistical language modeling as applied to real-word problems: speech recognition, machine translation, spelling correction, soft keyboards to name a few prominent ones. We summarize the most successful estimation techniques, and examine how they fare for applications with abundant data, e.g. voice search. We conclude by highlighting a few open problems: getting an accurate estimate for the entropy of text produced by a very specific source, e.g. query stream); optimally leveraging data that is of different degrees of relevance to a given "domain"; does a bound on the size of a "good" model for a given source exist?

Language modeling, Data
Hobson Lane
Founder, Total Good
Deterministic Online Embedding for Semantic Indexing

An important component of conversational AI is efficient natural language semantic processing and search. Latent Semantic Indexing (LSI) enables the extraction of semantic features but doesn't quite live up to its "Indexing" name. The dense, continuous, high-dimensional topic vectors required to characterize the meaning of natural language documents with LSI are not indexable or searchable with anything other than an "index scan." At Talentpair we used to search for "pairings" within a large database of 200-dimensional topic vectors (for resumes and job descriptions) with a brute force O(N^2) computation (O(N) for a single query). Incremental isometric feature mapping (Isomap) and Incremental Locally Linear Embedding (ILLE) offered the promise of reducing our feature space sufficiently (from 200-D to 3-D or less) to enable indexing using mature GIS database technology. However, mature implementations are not available for our backend technology (Python, PostgreSQL, Elasticsearch). In addition, the run-once T-Distributed Stochastic Neighbor Embedding algorithm offers a higher fidelity embedding that preserves more of the structure relevant to our problem--but TSNE does not allow online (incremental) embedding. We recently discovered a straightforward way to approximate some TSNE mappings with a multivariate polynomial regression using an off-the-shelf open source machine learning package (Scikit-Learn). This is a game changer for our semantic search problem, enabling us to perform semantic queries on a large database of documents (finding the best pairs for a job or candidate) in constant time. It may also be effective for more general natural language semantic search problems, such as those in conversational AI.

LSI, Open Source, ML, Semantic Indexing,
Jerome Selles
Director of Data Science and Analytics, Turo
Implementing AI: from data collection to production, lifecycle of a ML model - example of ranking relevance

With hundreds of thousands of cars available on Turo, how can relevance be defined? What are the best cars to show? This talk will cover the full lifecycle of a Machine Learning model from data collection to its deployment in production.

ML, Demo
5:40 pm
6:20 pm
Siraj Raval
AI Educator, The Siraj Raval Company
Learning to Learn

I'll discuss strategies that let models learn the optimal hyper-parameters for themselves and potential applications

Learning, ML
Keith Butler
Human factors research, NASA
Conceptual Work Products of Socio-Technical Systems

Advances in declarative knowledge modeling can represent the conceptual work of socio-technical systems for rigorous analysis and design. These systems can have complex combinations of multiple users, a variety of computing devices, with information that is used and changed as it flows between activity in the physical world and processing in the digital world. This complexity can overwhelm conventional design methods, which has led to some serious, negative impacts. Clarity and rigor of their design, however, can now be achieved by explicitly representing the products of conceptual work with declarative models, which also enable powerful model checking for design verification. The new techniques will be illustrated with examples chosen from clinical health care, aerospace, and online tech support.

NASA, Healthcare, Aerospace, Online Tech Support, Analysis, Human factors research
Fred Sadaghiani
CTO, Sift Science
Fighting fraud at scale with Machine Learning

Over $60 billion is spent annually fighting fraud and abuse. Fraudsters are highly motivated, skilled and organized. As the value and volume of online transactions increase, the need to address fraud in a robust, scalable manner becomes indispensable. This talk covers how Sift Science applies large-scale machine learning to protect thousands of web sites from fraud and abuse. By combining models specific to an online business with those learned by mixing data from across its network of customers, Sift Science uses a combination of various ML approaches to prevent fraud.

ML, Demo, Security
MACHINE LEARNING RESEARCH
COMPUTER VISION NLP / CHATBOTS
APPLIED AI: STARTUPS, INDUSTRY & SOCIETY
DEMOS & TUTORIALS
9:00 am
9:40 am
Yoshua Bengio
Professor, University of Montreal
Deep Generative Models

One of the biggest challenges for research in artificial intelligence is unsupervised learning. Current industrial success with deep learning relies heavily on supervised learning, where humans are needed to categorize data and define high-level abstractions which we want the computer to know about. However, humans are able to discover many aspects of the world without a teacher telling them anything about it, and this ability to autonomously learn to make sense of the world is something that needs to be further developed for computers. The deep learning approach to unsupervised learning centers on the question of learning representations, and different algorithms define an objective function which leads the learner to capture essential aspects of the data distribution along with a new space in which to represent data. Deep generative models can demonstrate their understanding of the data by generating novel examples which nonetheless look like those which were used to train the model. Many of these models are related to the old idea of auto-encoder, with an encoder function mapping data to representation and a decoder (or generator) mapping the abstract representation to the raw data space. The talk will focus in particular on the family of generative adversarial networks (GANs), which question the established approaches based on maximum likelihood and probability function estimation and bring us into the realm of game theory and novel ways of comparing different distributions against each other, as well as with very impressive generation of images.

Deep Learning, Unsupervised Learning, Algorithms
9:40 am
10:20 am
Aude Billard
Professor, LASA
Machine learning for robots to think fast in the face of unexpected events

The next generation of robots will soon get out of the secure and predictable environment of factories and will face the complexity and unpredictability of our daily environments. To avoid that robots fail lamely at the task they are programmed to do, robots will need to adapt on the go. I will present techniques from machine learning to allow robots to learn strategies to enable them to react rapidly and efficiently to changes in the environment. Learning the set of feasible solutions will be preferred over learning optimal controllers. I will review methods we have developed to allow instantaneous reactions to perturbation, leveraging on the multiplicity of feasible solutions. I will present applications of these methods for compliant control during human-robot collaborative tasks and for performing fast motion in sport, such as when playing golf with moving targets. The talk will conclude with examples in which robots achieve super-human capabilities for catching fast moving objects with a dexterity that exceeds that displayed by human beings.

ML, Robots
Dimitri Kanevsky
Research Scientist, Google
AI and Machine Learning: Solving problems, spawning new inventions

In this talk, I will discuss inventions and patents that are derived from AI. This is not a talk on how machines can invent or write patents. Instead, it will focus on how ideas derived from AI can ultimately lead to new inventions and patents.  I will give examples of this creative process. Specifically, I will show how we can think of the problems that surround us in life not just as "nuisances." Instead, problems can be seen as challenges to be resolved with machine learning methods. When the resulting solutions are sufficiently novel, they can lead to new patents.

Inventions, ML, Patents
Luka Bradesko
Co-Founder, Curious Cat Company
Making a Deep Inference AI Assistant and why it was not successful as a startup

In order for an AI to be able to assist and help its users, it needs to know them and their world first. For this reason, one of the crucial parts of an AI assistant is acquisition of the knowledge about the world around and the users. In this talk, Luka Bradesko will present a context aware knowledge acquisition system that simultaneously satisfies users’ immediate information needs while extending its own knowledge using crowd-sourcing. The focus is on knowledge acquisition on a mobile device, which makes the approach practical (also partially available as a library to other developers). The viability of the approach was tested experimentally with real users around the world, and an existing large source of common sense background knowledge (Cyc). The experiments show that the approach is promising if it could be successfully brought from a research prototype into a full scale product. The talk will also include some thoughts on why the previous attempt of productivisation attempt was not successful.

Demo, AI
10:20 am
11:00 am
Olaf Witkowski
Research Scientist, Tokyo Institute of Technology
Information Flows, Connectionist Learning and the Transition to Collective Cognition

After life originated on Earth, the next important transition was the emergence of cognitive life, in which simple organisms self-organized into dynamical networks to compress and express complex information in the environment about their own preservation. Cognition is better understood as the information flow between single agents, implementing a dynamical way to compress relevant information for their own survival, and enabling them to make predictions about their environment on much shorter timescales than Darwinian evolution. In this talk, Dr. Witkowski will present the contribution of artificial life tools, information theory and connectionist machine learning, to our understanding of the transition to cognitive life. Just as life can be formulated computationally as the search for sources of free energy in an environment to maintain its own persistence, cognition is better understood as finding efficient encodings and algorithms to make this search probable to succeed. Cognition then becomes the “abstract computation of life”, with the purpose to make the unlikely likely for the sake of survival.

Cognition, Information Flow, ML
Christian Guttmann
Innovation Director, Healthi Habits
How is Artificial Intelligence shaping the future of health?

How is Artificial Intelligence (AI) used in today’s digitization of health, and how will it shape our future of health? AI is becoming increasingly important in digitizing many areas of health care, medicine, and life sciences. Indeed, AI is already key in approaches of digital health, such as digital medicine, digital diagnostics and digital therapeutics. A main driver for this development is that AI can efficiently personalize health services for many people. Key stakeholders in health care, e.g. health insurers, hospitals and doctors, believe that AI is a scalable approach to achieve better health outcomes at lower costs. In short, “automated” personalization improves value in public health. For example, a person can improve long term health when he/she knows the practical meaning of his/her genetic and behavioral background in daily life situations. This presentation focuses on how AI (and related concepts, such as Machine Learning and Data Science) addresses today’s challenges of health, and considers theoretical and technical requirements and limitations. Recent technologies are reviewed in more depth, such as advanced and predictive analytics, and social and mobile health.

Digital Health, Analytics
Satya Gautam Vadlamundi
Lead Data Scientist, Capillary Technologies
Problem Solving with AI: Search and its Applications

Any autonomous agent/system would have to face unseen problems during its lifetime and be able to solve them on its own in order to sustain. Problem solving is an area of artificial intelligence that studies the frameworks and methods related to accomplishing non-trivial tasks, with the given capabilities of an agent. In this talk, we will introduce you to state-space search approach which is a well known problem solving technique in AI and is state-of-the-art for arriving at solutions in various game environments, optimization settings, and robotics. We discuss the combinatorial explosion of states involved, how to handle it, and some applications that guide you on to applying the search techniques in new contexts on your own.

Problem solving, Demo/Tutorial
11:00 am
11:40 am
Xiaohui Hu
Principal Data Scientist, GE Digital
Machine learning challenges in industrial internet

Data is the crucial part of any AI  and machine learning applications. the presentation discusses the data in industrial world and how it produces challenges to machine learning algorithms.

Data, Machine Learning
Vlad Lata
CTO & Co-Founder, Konux
The right AI approach to unlocking a new level of asset performance

The industrial world is changing; - From AI theory to industry changing products; - Engineering AI with KONUX to unlock a new level of asset performance in the rail industry

Rail Industry
Albert Bifet
Big Data Scientist, Télécom ParisTech
Massive Online Analytics for the Internet of Things (IoT)

Big Data and the Internet of Things (IoT) have the potential to fundamentally shift the way we interact with our surroundings. The challenge of deriving insights from the Internet of Things (IoT) has been recognized as one of the most exciting and key opportunities for both academia and industry. Advanced analysis of big data streams from sensors and devices is bound to become a key area of data mining research as the number of applications requiring such processing increases. Dealing with the evolution over time of such data streams, i.e., with concepts that drift or change completely, is one of the core issues in stream mining. In this talk, I will present an overview of data stream mining, and I will introduce some popular open source tools for data stream mining.

IoT, Big Data, Demo/Tutorial, Open Source
11:40 am
12:20 pm
Deborah Hanus
NSF Fellow, Harvard University
Challenges in precision medicine: Making machine learning work for healthcare

From helping doctors prescribe medications to spotting abnormalities in medical images, there are many ways that machine learning can assist in building better precision medicine. However, many people who know machine learning still struggle when working with medical data. This talk will mention some ways to get the most out of your medical data, with a focus on dealing with missing data.

ML, Heathcare
Gaurav Kumar Singh
ML and Deep Learning Researcher, Ford
Gaming at Work: Synthesizing data for Autonomous Driving Research – Why and How?

Development of Autonomous driving capabilities through machine and deep learning requires training upon huge annotated data. Obtaining such training data requires a lot of efforts, not to mention the large time required to do so. This talk will explore the possibility of accelerating autonomous driving research by training machine and deep learning models upon objects in a rich virtual world. The talk will briefly comment on how models, trained on simulated data, perform when tested with the real world driving data.

ML, Deep Learning, Data, Gaming
Markus Schatten
AI Head of Lab, FOI
ModelMMORPG - Agent-based Models of Massively Multi-Player On-Line Role Playing Games

Massively Multi-Player On-Line Role Playing Games provide us with an excellent opportunity to study and implement multi-agent systems. At one hand various in game characters (including mobs, monsters, non-playing characters - NPCs etc.) can be modelled and implemented as (intelligent) agents thus facilitating interesting game-play. On the other hand, the behaviour of players can be analyzed using agent based models based on big data analytics. These models can then be put to use to implement bots (artificial players) for automated testing of such games. Herein results from the ModelMMORPG project will be presented that investigated both aspects mentioned above.

Gaming, Big Data, Multi-Agent Systems
Jean-Baptiste Guignard
AI Mentor, IBM Watson
AI as Human-Augmentation – How smartphone VR is a game-changer for Weak AI and Touchless Technology.

AI has largely been mystified and is commonly viewed as self-thinking “strong” IA (the Ghost in the Shell Fantasy), when operational AI is and remains probability tools and/or classifiers, no matter how complex or bio-inspired they are. The three waves of AI each carry specific assets and liabilities, but endlessly turn out to be “weak”, instructional, predetermined and purpose-oriented. Heuristics, fuzzy logics, expert systems, NN, RNN and their newly- patented architectures are as old in the history of Computer Science as relevant in today’s entrepreneurial world. I shall rather argue for a prosthetic conception of AI – if human cognition is embodied, distributed and non-computational, then AI has to be a tool for human augmentation (not imitation or full substitution), the way an instrument is for a musician. As such, it is an entire constituent of his/her understanding of daily patterns and routines – one may not see the world if he/she glares at his/her glasses, but only perceives it once looking through them. The metaphor extends to VR where feeling immersed rests on the (prosthetic) tools you appeal to for adequate perception. Clay VR is a SDK for gesture recognition on smartphones from any embedded lens (no need for additional hardware). It is designed for uses in VR and distal control to drastically enrich the user’s interaction possibilities – it displays one’s own hands contoured in a virtual environment. Such a touchless experience is control without the hardware, touch, pinpoint or remote-control pains, ensures immersion by preserving self-perception, but implies massive technical difficulties. Because it is constrained by the device limitations, the approach has to be minimal and data, often, has to be rebuilt or inferred. In that, it centrally resorts to Computer Vision and AI. Computer Vision for real-time image interpretation, and AI for learning (from the ever-changing capture environments) and automation (scoring, gesture validation, depth processing, etc.). The overall architecture is, indeed, a multi-image/feature- fed RNN that, in turn, nourishes a heuristics-jugulated expert system, but most of the intelligence provided only serves poise, both technically and for the user’s perceptual loops that the AI-boosted external system provides. It (AI) therefore becomes a constituent of human distributed cognition processes, augments the user’s capacities, but never replaces intellection itself.

VR, Human Augmentation
12:20 pm
1:00 pm
Konstantina Christakopoulou
Ph.D. Student, University of Minnesota
Recommendation under Constraints

Recommendation systems that help users navigate through information by delivering the right content at the right time, are a part of our every day lives. Although a lot of progress has happened regarding the development of recommendation systems for unconstrained offline settings, there are still challenges when deploying such systems in constrained interactive settings. This talk will begin by reviewing the state-of-the-art in offline unconstrained recommendation. Then it will discuss methods for the particular constrained settings of (i) limited screen size of the devices, and (ii) limited capacities of the candidate items for recommendation. The talk will continue with a benchmarking study comparing the proposed methods with the state-of-the-art.

ML, Recommendation
Vikas Agrawal
Senior Principal Data Scientist, Oracle
Amplifying Human Decision-Makers with Internet of Things Driven Intelligent Context-aware Systems

We need to amplify the efficiency of the human experts using Internet of Things with Machine-to-Machine and Machine-to-Human Networks to create intelligent context-aware systems for solving the following three grand challenge problems: N=1 Personalization What if we present information and enable actions relevant to the context (location, role, social circumstance, access level) of the users? What if we can detect user intent and direct the user along personally and commercially valuable paths of action? Can we provide adaptive security to learn from daily behaviors, and detect the unusual through novel signals? Can we minimize loss and optimize usage by predicting when certain patterns are needed? Zero Down Time,  Zero Intrusion, Zero Loss Can we provide highly reliable temporally relevant information in people's work context on their mobile devices today? What if we can prevent equipment failure and down-time by predicting maintenance or replacement needs? Can we enable predictive maintenance in mines, factories and optimize equipment usage through stream data analytics and prediction? What if we can give pre-summarized predictions and recommendation with seamless data provenance to technicians? What if we can predict risk for a fire by connecting the condition of a boiler, simulating it with the ambient? Zero Waste, Zero Delay Can sensors in the supply chain prevent food waste, Rx/Dx misuse, detect counterfeiting? Can we predict manufacturing requirements and changes from orders placed and cancelled online? Can we propagate the requirements up the supply chain all the way to suppliers? We will describe how to build smart digital workspaces that know [the context] all the while observing, recording this context of work in episodic memory and generalizations in semantic memory. What am I doing?[Activity Structure, Context, Goals] How am I doing it? [Best Methods] What resources am I using? [Allocation, Discovery] When and where am I doing it? [Time and Place] Who am I, what is my role? [Responsibility, Profile] Who are my collaborators? [Social Network] What is the device doing? Current Action in Business Process, Goals Is it Down or Active? How efficient or inefficient is it? What is the condition of the device? State (Past, Present, Future) Temperature, Pressure, Vibration, Dust, Humidity, Leaks, Fatigue/Stress How does it compare to normal operating ranges? What resources does the device depend on? Device and Human Dependencies Where is it located? When is it needed? Place, Motion What is the function of the device in the business process? Is the current activity expected according to the business process? What is the full downstream cost impact of the device going down (criticality)? What devices are its neighbors? Edge Intelligence The smart workspace will: Let devices solve routine problems automatically depending on risk using edge intelligence Anytime, Anywhere, On the Edge Selected problems that can be automated with very high confidence Let us seamlessly know device states and predictions by presenting information about operational inefficiency, risk of failure, cost of replacement, opportunity costs of switching devices, in-context of our roles To quickly find directly related information and answers to questions based on what we mean, in the context we need it, with access to the source, quality and how the information was derived, connecting us to insights of experts within the organization and beyond Context Semantics Driven Guidance Proactively show us steps others have taken in meaningfully similar situations before, helping us reason and decide faster, with greater confidence. Social Collaboration and Decision Support

Machine to human, Human to machine, Oracle
Nathan Wilson
CTO & Co-Founder, nara logics
Brain-Based Learning Algorithms for Recommendations

The recent surge of interest in brain-based learning algorithms has improved artificial "sensory" capabilities including image recognition and speech processing, and "motor" activities as with robotics and navigation.  A focused handle for addressing the "cognitive" capabilities that might intermediate between the two is recommendations, which deals with how to select between numerous available options based on underlying data, with consideration to desired effects.  Brain-based learning algorithms are now beginning to meaningfully impact results here, and in turn benefit from original research in this area.  We will review major approaches that are improving recommendations, including deep learning, Bayesian methods, reinforcement learning, and how to evaluate and ensemble them, in data-rich and "cold start" conditions alike.  This is an exciting time for developing recommendations that really work, and for exploring cognitive technologies in general, when cleaner and more ubiquitous data can begin to inform more meaningful decisions.

Deep Learning, Recommendations, Data
1:00 pm
1:40 pm
Sam Altman
President, Ycombinator
Societal Impacts of AI

In this talk, I will talk about various paths for the arrival of strong AI, and the potential impacts on society.

Society, Impact
1:40 pm
2:20 pm
Alex Champandard
Co-Founder, creative.ai
Generative Pipelines

The field of Creative A.I. is blossoming as many companies, hobbyists and research labs turn their attention to promising new applications in creativity. From novelty search that helps designers explore options, to generative models that empowers artists to produce more artifacts at higher quality, there's a lot going on! In this talk, you'll hear how both classical artificial intelligence and machine learning can benefit creative applications. In practice, deep learning faces many problems when applied in this generative space and what you'll see can be done about it. You'll discover concept of generative pipelines and how they have been used over the years, and why they are now more relevant than ever as we enter the Generative Age.

ML, Deep Learning
Natalie Stanley
Ph.D. Student, University of North Carolina
Prediction and Modeling with Networks

Networks are a useful data structure for encoding relational information between a set of entities and appear in a variety of fields, from biology to social science. The use of principled statistical, computational and mathematical tools is crucial for the understanding of the structural and functional relationships encoded in the network. In this talk we will summarize 3 important areas of network science, including, 1) link prediction, 2) anomaly detection, and 3) community detection. We will discuss the practical concerns for the implementation of the state-of-the-art tools in each of the 3 areas. Finally, we will discuss the computational challenges in handling large networks.

Stats, NLP, Networks
Shashi Kant
Founder, Netra
What makes an image click

Everyday, billions of images and videos are uploaded to social media sites, a number that is growing exponentially. It is challenging for brands to reach customers with their content, and hence they are seeking a “viral” message that resonates with their audience, is shared widely and rapidly, and provides audience engagement. Tools for automating assessment of image “virality” based on both content and context hold significant value for marketers. Based on analysis of tens of million of images from social media, we show how deep neural networks can be used to predict image virality. Our analysis shows that image content such as human presence, their emotions, pets as well as objects such as cars, impact the potential virality of an image, as do more abstract concepts such as color, background, theme and composition. For example, images of puppies and babies are more likely to be popular. Our findings further indicate that these attributes apply in a certain context, such as current political, sports and entertainment events. We also find that the social context of the image i.e. the original poster, their network, and their engagement level also impact the potential virality. We used a combination of Deep neural networks and probabilistic models to analyze tens of millions of images from multiple social media sources to identify contextual and content variables that are correlated with a higher image engagement, and to predict the normalized views of images. Using exemplar images, we present a deep-dive into our approach and key findings.

Social media, Images, Deep Neural Networks, Analysis
Amit Kumar Pandley
Head Principal Scientist
The new era of Robotic Revolution: Social Robots. You can play a role, for sure.

We are evolving, so as our society, lifestyle and the needs. AI has been with us for decades, and now penetrating more in our day-to- day life so as the robots. But, where are all these converging together? Towards creating a smarter eco-system of living, where robots will coexist with us in harmony, for a smarter, healthier, safer and happier life. How? Social Intelligence (SI) of such consumer Robots will be the key technology and the next big R&amp;D challenge. SI will enable such robots to behave in socially expected and accepted manners. The talk will reinforce that robots have a range of potential societal applications, and that as a robotics industry, SoftBank Robotics’ R&amp;D and Innovation is around the centrality of wellbeing of people. The time has arrived, when social robots have started to be deployed, evaluated and available for practical purposes outside automation industry. For example, Pepper robot from SoftBank Robotics, which is mass produced and already being used in thousands of homes, and at public places; the Romeo humanoid robot companion for everyday life of people needing assistance; the Nao robot as teaching assistant. The first part of the talk will illustrate some of the use cases, market analysis and potential applications for such intelligent humanoid robots, grounded with some key European Union Projects. The second part will present the feedback and needs from the real users. This will help to highlight some of the immediate R&amp;D challenges from industrial perspective in the third part of the talk. Hence the young graduates will know the must/should have skills to be the part of this next generation of robotics revolution: the socially intelligent robots. The talk will conclude with some open and grand challenges ahead, including social and ethical issues.

Robots, SI, Softbank, Social and Ethical Issues
2:20 pm
3:00 pm
Jaya Kawale
Sr Research Scientist, Netflix
Combining Bandits with Matrix Factorization Techniques for Online Recommendation

Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems. However, the problem of finding an optimal trade-off between exploration and exploitation (otherwise known asthe bandit problem), a crucial problem in collaborative filtering from cold-start, has not been previously addressed. I will present a novel algorithm for online MF recommendation that automatically combines finding the most relevant items with exploring new or less-recommended items.

Netflix, MF, Algorithms, ML
Daniel Krasner
Founder/CEO, Merriam Tech
HCI products - AI technology

While AI/Machine Learning systems are becoming more “intelligent” and ever-present in today’s products, our approach to interacting with these systems is in its infancy and we have a lot to learn. The value of such systems is directly correlated to the end-user product utility, and the underlying dynamics. This situation beckons new forms of human computer interaction capable of addressing the following questions: How will the end-user interact with the system? How will the system interact with other applications? How should these influence the overall approach to the architecting the solution?  Can we formulate the underlying problem and then design the relevant statistical and engineering frameworks to support, and scale, such a solution in the first place?  We will explore the evolution of human-computer interaction, it’s influence on product design and architecture, as well as ramifications in the current AI-driven environment.

ML, HCI
Bhawna Shiwani
Research Engineer, Delsys Inc
Customizing the Learning Algorithms

In this talk I will discuss some of the insights behind the learning algorithms. The selection criterion of machine learning algorithms depending on the nature of problem to be solved. Methods to tune these algorithms and most importantly the methods to infer the errors to direct the algorithm design in the right direction. With this practical guidance, we can customize and tune our learning algorithms to provide more informed results.

Algorithms, ML, Demo/Tutorial
3:00 pm
3:40 pm
Mark Schmidt
Chair of ML, UBC
There Are Much Better Algorithms For Machine Learning On Huge Datasets

In nearly all fields of science and engineering, the amount of data available is growing at unprecedented rates. Applications no longer produce data sizes from megabytes to gigabytes, but rather from terabytes to petabytes (and beyond). Machine learning is one the key tools we use to make sense of these ever-growing quantities of data. We now use machine learning methods every day; they are behind software for e-mail spam filtering, product and advertisement recommendation systems, Microsoft's Kinect, Google Translate, speech recognition on phones, and now self-driving cars. The successes and potential of machine learning are driving the need to develop techniques that can consider even larger datasets and more complicated models. A major challenge is that the "learning" in most machine learning models involves solving a numerical optimization problem, and standard numerical optimization codes are simply not up for the task of fitting very-complicated models to huge data sets. The default way to address this challenge is to use "stochastic gradient" methods. Instead of repeatedly going through your entire dataset between each model update, these methods alternate between looking at small *random* parts of the data and updating the model. These methods have been enormously successful, but they are enormously frustrating to use: it can be very hard to tune their parameters, to decide when to stop running them, and even if you address these issues they still converge very slowly. In this talk I'll give an overview of these methods, and then discuss a revolution that is happening in numerical optimization and machine learning with the development of a new class of stochastic gradient methods in 2012. Not only do the new methods make tuning parameters and deciding when to stop much easier, but these algorithms are dramatically faster than the old algorithms both in theory and practice.

Algorithms, ML, Data
Aerin Kim
Co-Founder, BYOR Lab
Deep NLP Essentials in 30 Mins

Companies ranging from Google to Facebook to Amazon are on the hunt for scientists to build language intelligence. Learn the essential skills to work in one of the most exciting fields of Artificial Intelligence! This is a sequel of Aerin's first talk "Phrase2Vec in practice"

NLP
Eyal Amir
CEO & Chief Data Scientist, Parknav
Common Sense for Cars

Cars increasingly are equipped with sensors that can sense their surroundings and insides. These sensors are about to be connected to the cloud in an online fashion, promising to deliver a new era of connectivity and information flow between cars. Cars of the future increasingly would enable autonomous driving, and so need to know and understand more than before. Common sense, such as understanding the meaning of a ball stuck under a parked car, or the possible intentions of a kid hiding behind that parked car, is traditionally easy for people and hard for computers and artificial intelligence. This kind of understanding and commonsense is necessary to ensure safe driving and safe co-existence of humans with those AI cars. In this presentation, I will describe the tools already developed in AI and those that still need to be developed to reach this goal of common sense for cars.

Safety, Cars, Autonomous
Tejaswini Ganapathi
Senior Data Scientist, Salesforce
Learning Solution to Optimize Mobile Network Performance

In this work, we apply machine learning to the problem of optimizing data transfer over mobile networks. We developed an adaptive learning framework to optimize TCP parameters of congestion control and concurrent connections. This generates custom TCP strategies for various geographies, origin servers and wireless networks, taking into account change in network quality over time.

ML, Data
3:40 pm
4:20 pm
Carlos Azevedo
Researcher Machine Intelligence, Ericsson
The Rise of Anticipatory Multi-Objective Machine Learning

This talk will cover algorithmic design principles for intelligent systems exhibiting anticipatory, flexible, autonomous, and sustainable behavior. In particular, you’ll be exposed to anticipatory multi-objective machine learning strategies for automating the resolution of conflicts in sequential decision-making under multiple, noisy, and cost-adjusted optimization criteria. The goal of anticipatory machine learning is to improve decision processes by taking advantage of predictive modeling, data-driven simulation, and prescriptive analytics. You’ll thus realize how anticipating multiple conflicting scenarios contributes for preserving the decision maker future freedom of action, as preferences are learned and refined over time.​ You’ll then be in a good position to understand how an anticipatory hypervolume-based multi-objective Bayesian metaheuristic can incorporate meta-preferences to improve financial portfolio selection in real and simulated markets. In addition, you’ll learn about connections between conditional future hypervolume maximization and the causal entropic principle proposed by Wissner-Gross and Freer (2013). Finally, you’ll be stimulated to engage in a discussion about the relevance of the anticipatory multi-objective approach to artificial general intelligence. I hope you join us for an exciting discussion!

ML, Algorithms, Data, Autonomous
Anna Quach
Ph.D. Student, Utah State University
Tips and Tricks to Building a Random Forests Classifier

In this talk I will show how Random Forests is built and optimized for the best results in R.

Stats, Algorithms
John Whitbeck
Tech Lead, Liftoff
Production-grade Machine Learning

You've just fine-tuned your strongest model, demonstrated uncanny accuracy on your test set, and the board now wants you to build a product on top of it. Congratulations, now the real work begins! In this talk, I will share lessons learned from building business-critical machine learning systems that reliably meet product requirements in the face of unexpected data distribution changes, rapid code iteration, large infrastructure changes, and loss of shared context within growing teams.

ML, Data, Industry
Davide Venturelli
Research Scientist, NASA
Practical Quantum Computing: Status and Opportunities

We are going to discuss the status of quantum computing in terms of potential as well as architectures and cloud services that will become available in the near-term, and their usefulness and programmability challenges. This includes an introduction to Quantum Annealing and the D-Wave machines. A good part will be devoted to questions and answers (write to dventurelli@usra.edu)

Quantum Computing, Cloud, Demo/Tutorial
4:20 pm
5:00 pm
Marco Bitetto
Research Scientist in Residence, Institute of Cybernetics Research, Inc
BRAIN vs NANO-CHIP

The purpose of this paper is to present a brief history of the field of cybernetics that is concerned with the simulation of biological brains for the purposes of controlling both machines and industrial processes. This paper will also discuss the current understanding of how biological brains function and learn. It will then discuss the current state of the art in neuromorphic technology design and where it falls short of actually faithfully functioning like a biological brain. It will then discuss how primarily hardware based computational modeling methods can far more rapidly and faithfully model the functionality of biological brains. Finally, it shall conclude with a discussion of the advantages of hardware based computational design over both conventional digital hardware implementation and the ROM-ified code versions of transforming software based ANN&rsquo;s (Attractor Neural Networks) into hardware. It then discusses some hardware examples of each of the analogs to these biological neural systems that have been discussed and concludes with the advantages of this design approach over the conventional approaches used in neuromorphic hardware design.

Cybernetics, Simulation, ML
Ilker Köksal
Co-founder, Botanalytics
Automated analytics for more engaged bots

With the rise of bots, bot user data has become crucial to extract meaningful insights from. Thus, the automation of analytics systems, will help bot makers to analyze their data by segmenting user conversations, understanding the sentiments of users, and just let the system take the action on behalf of data scientists. As we're experiencing a breakthrough, humans and automation systems will be in charge together unlike it was used to be..

Automation, Bots
Marlene Jia
Head of Revenue, Topbots
AI strategy for executives

The theme of the presentation is AI strategy for executives and goes into the following:

1. Recent breakthroughs in AI;

2. How it applies to business applications;

3. Things to consider when planning your organization's AI strategy;

4. Common risks and mistakes

Strategy, Executives
5:00 pm
5:40 pm
Dawn Song
Professor, UC Berkeley
AI and Security

In this talk, I will discuss recent advances and key questions and challenges at the intersection of AI and Security: how AI and deep learning can enable better security, and how Security can enable better AI.

Security, Deep Learning, ML
Piero Molino
Machine Learning Scientist, Uber AI Lab
Word Embeddings: History, Present and Future

Word Embeddings are both a hot research topic and a useful tool for NLP practitioners, as they provide representations that are useful in many intermediate tasks, like part-of-speech tagging, syntactic parsing or named entity recognition, as well as end to end tasks like text classification, sentiment analysis and question answering. The recent attention to the topic started in 2013 when the original word2vec paper was published at NIPS and alongside with an efficient and scalable implementation, but much research was carried out on the topic since the '50s' in fields like computer science, cognitive science, and computational linguistics. The Historical part of the talk will focus on this body of work, with the aim of distilling ideas and learned lessons, of which many practitioners and machine learning researchers are unaware of. The second part of the talk will focus on recent developments and novel methods, highlighting interesting directions that are being explored in the last couple years, like the role of syntax in learning embeddings, the compositionally of meaning and how to learn representations of knowledge graphs.

Word Embeddings, NLP, Learning
Karmel Allison
Engineering Manager, Quora
Machine Learning at Quora: Helping us share and grow the world’s knowledge

I will give a talk on the many applications of ML to the rich datasets we have at Quora— touching on recommendation engines, NLP with neural nets, and the difference that scale makes

ML, NLP
Pavel Machalek
CEO, Spaceknow
Analyzing satellite imagery with Deep Neural Networks
5:40 pm
6:20 pm
Adam Gibson
Founder, Skymind
Deep Learning in Production: What does it take to build a production deep learning system?

Deep Learning today is made up of large research body with a large focus on innovation. It's still seen as a complex subject out of the reach of practicioners. In this talk we will cover the requirements for building a production deep learning system as well as some of the problems when running a deep learning application at scale.

Deep Learning, Production

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