Deep Learning Summit Boston 2015 schedule

Deep Learning for Formal Reasoning

Deep learning has transformed machine perception in the past five years. However, recognizing patterns is a crucial feature of intelligence in general. Here we give a short overview on how deep learning can be utilized for formal reasoning, especially for reasoning in large mathematical theories. The fact that pattern recognition capabilities are essential for these tasks has wider implications for other tasks like software synthesis and long term planning in complicated environments. Here is will give a short overview on some methods that leverage deep learning for such tasks.

Christian Szegedy is research scientist at Google, working on deep learning for computer vision, including image recognition, object detection and video analysis. He is the designer of the Inception architecture which set new state of the art on the latest ImageNet benchmark in the latest Large Scale Visual Recognition Competition. Before joining Google in 2010, he was scientist at Cadence Research Laboratories in Berkeley devising algorithms for chip design. His background is discrete mathematics and mathematical optimization. Christian got his PhD from the University of Bonn in applied mathematics in 2005.

Alejandro (Alex) Jaimes is CTO & Chief Scientist at Acesio. Acesio focuses on Big Data for predictive analytics in Healthcare to tackle disease at worldwide scale, impacting individuals and entire populations. We use Artificial Intelligence to collect and analyze vast quantities of data to track and predict disease in ways that have never been done before- leveraging environmental variables, population movements, sensor data, and the web. Prior to joining Acesio, Alex was CTO at AiCure and prior to that he was Director of Research/Video Product at Yahoo where he led research and contributions to Yahoo's video products, managing teams of scientists and engineers in New York City, Sunnyvale, Bangalore, and Barcelona. His work focuses on Machine Learning, mixing qualitative and quantitative methods to gain insights on user behavior for product innovation. He has published widely in the top-tier conferences (KDD, WWW, RecSys, CVPR, ACM Multimedia, etc), has been a visiting professor (KAIST), and is a frequent speaker at international academic and industry events. He is a scientist and innovator with 15+ years of international experience in research leading to product impact (Yahoo, KAIST, Telefonica, IDIAP-EPFL, Fuji Xerox, IBM, Siemens, and AT&T Bell Labs). He has worked in the USA, Japan, Chile, Switzerland, Spain, and South Korea, and holds a Ph.D. from Columbia University.

Semantic Image Segmentation Using Deep Learning & Graphical Models

Kevin Murphy is a research scientist at Google in Mountain View, California, where he works on AI, machine learning, computer vision and NLP. Before joining Google in 2011, he was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada. Before starting at UBC in 2004, he was a postdoc at MIT. Kevin got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He has published over 80 papers in refereed conferences and journals, as well as an 1100-page textbook called "Machine Learning: a Probabilistic Perspective" (MIT Press, 2012), which was awarded the 2013 DeGroot Prize for best book in the field of Statistical Science. Kevin is also the (co) Editor-in-Chief of JMLR (the Journal of Machine Learning Research).

Dr. Andrew Ng is Chief Scientist at Baidu. He leads Baidu Research, which comprises three interrelated labs: the Silicon Valley AI Lab, the Institute of Deep Learning and the Big Data Lab. The organization brings together global research talent to work on fundamental technologies in areas such as image recognition and image-based search, speech recognition, natural language processing and semantic intelligence. In addition to his role at Baidu, Dr. Ng a faculty member in Stanford University's Computer Science department, and Chairman of Coursera, an online education platform that he co-founded. Dr. Ng is the author or co-author of over 100 published papers in machine learning, robotics and related fields. He holds degrees from Carnegie Mellon University, MIT and the University of California, Berkeley.

Multimodal Question Answering for Language and Vision

Deep Learning has made tremendous breakthroughs possible in visual understanding and speech recognition. Ostensibly, this is not the case in natural language processing (NLP) and higher level reasoning. However, it only appears that way because there are so many different tasks in NLP and no single one of them, by itself, captures the complexity of language. I will talk about dynamic memory networks for question answering. This model architecture and task combination can solve a wide variety of visual and NLP problems, including those that require reasoning.

Richard Socher is Chief Scientist at Salesforce. He was previously the CEO and founder of MetaMind, a startup that seeked to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng and won the best Stanford CS PhD thesis award. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision.

He was awarded the Distinguished Application Paper Award at the International Conference on Machine Learning (ICML) 2011, the 2011 Yahoo! Key Scientific Challenges Award, a Microsoft Research PhD Fellowship in 2012 and a 2013 "Magic Grant" from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.

How AI and Deep Learning Transform 800 Million items in 190 Countries: eBay’s Approach

In today’s global ecommerce landscape, borders are artificial, and consumers expect to transact anytime, anywhere. The challenge for eBay – one of the world’s largest online marketplaces – is enabling the best-possible commerce experience across 800 million listings from 25 million sellers in 190 countries and multiple languages. Find out how eBay’s product experience team is leveraging AI and deep learning to improve the user experience for today’s global consumer – and how they’re building machine translation and information extraction engines specific to commerce at scale.

With nearly 20 years of expertise in language technologies, Hassan Sawaf is well-recognized in the field of machine translation. As senior director of machine translation at eBay, Hassan develops tools and services in language technology to open new markets and enable more opportunities for eBay customers around the world. In his role, Hassan focuses on eBay’s efforts in emerging markets and cross border trade, helping the company develop new ways to solve the language challenge inherent in global online commerce. For the past year, Hassan and his team have developed and implemented language solutions that enable eBay shoppers to more easily access the items they need and love in their native languages, and offer seamless translation options for businesses selling on eBay. Prior to joining eBay in April 2013, Hassan held management roles with several companies in the language solutions space, including AIXPLAIN AG, AppTek Inc., Net2Voice and SAIC. Hassan speaks fluent English, German and Arabic, holds two patents, is a member of the board of directors at idenTV LLC and BlueOSS, and continues to be active in the Angel investor community. He holds a Master of Science degree in computer science from Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, and attended the university for his Ph.D. studies.

Deep Reinforcement Learning Advancements and Applications

Recent advances in Deep Reinforcement Learning have captured the imagination of both the AI researchers and the general public. Combining the latest Deep Learning technology with Reinforcement Learning techniques has led to stunning breakthroughs, surpassing human level performances at Atari games and the game of Go. Furthermore, Deep RL is being successfully adopted in a variety of fields such as robotics, control systems, translation, dialogue systems, and others. This talk will explore the intuitions, algorithms, and theories that have led to the recent success of Deep RL. A survey of exciting Deep RL applications and tough challenges ahead will also be discussed.

Charlie obtained his PhD in 2015 in Machine Learning from the University of Toronto, advised by Geoffrey Hinton and Ruslan Salakhutdinov. His thesis focused on various aspects of Deep Learning technology. Charlie also holds a Bachelors in Mechatronics Engineering and Masters in Computer Science from the University of Waterloo. After his PhD, along with Ruslan Salakhutdinov and Nitish Srivastava, Charlie co-founded a startup focused on the application of Deep Learning based vision algorithms. Currently, Charlie is a research scientist at Apple Inc. Charlie's research interests include Deep Learning, Vision, Neuroscience and Robotics. He is one of the few competitors to have reached the #1 ranking on Kaggle.com, a widely popular machine learning competition platform. Charlie is also a Canadian national chess master.

Vision AI for Augmented Human Machine Interaction

This session will unveil the latest vision AI technologies that ensure safe and efficient human machine interactions in the industrial automation context. Today’s human-facing industrial AI applications lack a key element for Human Behavior Understanding (HBU) that is critical for augmented safety and enhancing productivity. The second part of this session will detail how real-world applications can benefit from a comprehensive suite of visual behavior analytics that are readily available today.

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

Using Artificial Intelligence to Improve the Quality of Care

Every year 125,000 people die in the US because medications are not taken properly. Globally, this contributes to tens of millions of preventable deaths. In clinical research, pharmaceutical companies have no idea if subjects are actually taking the experimental drugs that are being tested.

Adam Hanina, CEO of AiCure will be presenting the first artificial intelligence platform focused towards accurately monitoring, interacting, predicting and intervening with patients in real-time. The National Institutes of Health stated that AiCure “has the ability to significantly impact drug research and therapy” through the ubiquitous mobile phone platform that exists today. In this session, Adam will overview the technology, its impact, and the importance of understanding and designing adaptive human machine interfaces.

Adam Hanina is co-founder and CEO of AiCure, an artificial intelligence company that has developed facial recognition and motion-sensing technology to confirm medication ingestion by clinical trial participants and high-risk patients in real time. He is a passionate advocate for the use of healthcare software as a population health tool and has directed much of his previous work to this end. He has acted as a subject-matter expert on medication adherence technologies for the National Institutes of Health (NIH) and is currently a principal investigator for multiple NIH innovation grants.

Patrick Ehlen, PhD, is a cognitive scientist and Head of Deep Learning at Loop AI Labs. He specializes in representation learning for semantics, pragmatics, and concept acquisition. He developed natural language and context resolution technologies at AT&T Labs, and worked on methods to extract concepts and topics from ordinary spontaneous conversations among people as part of the DARPA CALO project at CSLI/Stanford. He has produced 45 research publications in the areas of computational semantics, cognitive linguistics, psycholinguistics, word sense disambiguation, and human concept learning. He joined Loop AI Labs to help usher in a new era of cognitive computing services.

Multichannel Signal Processing with Deep Neural Networks for Automatic Speech Recognition

Automatic Speech Recognition systems commonly separate speech enhancement, including localization, beamforming and postfiltering, from acoustic modeling. In this talk, we perform multichannel enhancement jointly with acoustic modeling in a deep neural network framework. Overall, we find that such multichannel neural networks give a relative word error rate improvement of more than 5% compared to a traditional beamforming-based multichannel ASR system and more than 10% compared to a single channel model.

I received my PhD in Electrical Engineering and Computer Science from MIT in 2009. The main focus of my PhD work was in acoustic modeling for noise robust speech recognition. After my PhD, I spent 5 years at the Speech and Language Algorithms group at IBM T.J. Watson Research Center, before joining Google Research. I have co-organized a special session on Sparse Representations at Interspeech 2010 in Japan. I have also organized a special session on Deep Learning at ICML 2013 in Atlanta. In addition, I am a staff reporter for the IEEE Speech and Language Processing Technical Committee (SLTC) Newsletter. My research interests are mainly in acoustic modeling, including deep neural networks, sparse representations and adaptation methods.

How AI and Deep Learning Transform 800 Million iIems in 190 Countries: eBay’s Approach

In today’s global ecommerce landscape, borders are artificial, and consumers expect to transact anytime, anywhere. The challenge for eBay – one of the world’s largest online marketplaces – is enabling the best-possible commerce experience across 800 million listings from 25 million sellers in 190 countries and multiple languages. Find out how eBay’s product experience team is leveraging AI and deep learning to improve the user experience for today’s global consumer – and how they’re building machine translation and information extraction engines specific to commerce at scale.

As Senior Director Global Product Experience, Marc Delingat is responsible for the global customer experience outside of the United States. Marc is focused on building innovative products for eBay’s buyers and sellers, to break down language and market barriers in global commerce, and expand eBay’s geographic footprint.

In this role, Marc looks for new opportunities to research, develop, and bring to market new product solutions for emerging markets and improve cross border trade to open up the world’s selection of merchandise. His organization includes product management, applied research, and engineering in human language technology, localization, global product experience, global architecture, and internationalization for all of eBay’s web and mobile products. A proficient English and German speaker, Marc has a keen interest in combining design, technology, science, and language, to build products customers across the world love. Beginning in 2013, he formed the eBay Human Language Technology Research team with the charter to bridge the language gap in global commerce. In 2014 he led the acquisition of parts of AppTek to build out the team and acquire key technologies and intellectual property to further eBay’s leadership position in this area. As a result, eBay is a oft-cited leader in machine translation innovations specific to the field of commerce.

Claudia Perlich currently acts as Chief Scientist at Dstillery and designs, develops, analyzes and optimizes the machine learning that drives digital advertising. An active industry speaker and frequent contributor to academic and industry publications, Claudia was recently named winner of the Advertising Research Foundation’s (ARF) Grand Innovation Award, was selected as member of the Crain’s NY annual 40 Under 40 list, WIRED’s Smart List, and FastCompany’s 100 Most Creative People. She has published over 50 scientific articles, and holds multiple patents in machine learning. Claudia has a PhD in Information Systems from NYU and worked in the Predictive Modeling Group at IBM’s Watson Research Center, concentrating on data analytics and machine learning for real-world applications. She also teaches in the NYU Stern MBA program.

Lauren Berkowitz is a leader in launching and commercializing emerging technologies. Prior to joining audio artificial intelligence company, One Llama Labs, where she was SVP of Product and Business Development, Lauren led major technology divisions for companies including Bertelsmann, Sony and EMI and consulted with numerous clients including Comcast and the American Red Cross on the optimum use and timing of integrating emerging technologies into core business. Having recently left One Llama Labs, Lauren is currently working on a stealth bio-inspired multi-modal AI project. Lauren holds JD and MBA degrees from Suffolk University and a BS from Boston University.

Patrick Ehlen, PhD, is a cognitive scientist and Head of Deep Learning at Loop AI Labs. He specializes in representation learning for semantics, pragmatics, and concept acquisition. He developed natural language and context resolution technologies at AT&T Labs, and worked on methods to extract concepts and topics from ordinary spontaneous conversations among people as part of the DARPA CALO project at CSLI/Stanford. He has produced 45 research publications in the areas of computational semantics, cognitive linguistics, psycholinguistics, word sense disambiguation, and human concept learning. He joined Loop AI Labs to help usher in a new era of cognitive computing services.

Industry-Grade Deep Learning

Today’s AI is arming humans with superpowers — from aiding doctors to make better diagnoses to helping the public move around safely. Entire industries are being redefined, and new ones are emerging as well. AI, today mostly powered by Deep Learning, is a powerful tool, but one that is not trivial to master and integrate into existing industry workflows. NVIDIA has enabled the current AI boom by providing critical compute power necessary for scientists to solve a wide range of AI problems, specifically challenging perception problems. Today NVIDIA is investing in higher-level abstractions to solve even more complex innovations, like self-driving cars, and help other industries leverage Deep Learning. In this talk, you’ll learn how Deep Learning has evolved over the past 10 years, how we enabled this field, and continue to do so; what we are doing to get to fully autonomous cars; and how we are building platforms to enable anyone to create value with Deep Learning. I will also talk about research at NVIDIA and how we operate a fast-moving R&D team to rapidly transfer research into products.

Clement Farabet is VP of AI Infrastructure at NVIDIA. His team is responsible for building NVIDIA’s next-generation AI platform, to enable a broad range of applications, from autonomous cars to healthcare. Clement received a PhD from Université Paris-Est in 2013, while at NYU, co-advised by Laurent Najman and Yann LeCun. His thesis focused on real-time image understanding, introducing multi-scale convolutional neural networks and a custom hardware arch for deep learning. He cofounded Madbits, a startup focused on web-scale image understanding, sold to Twitter in 2014. He cofounded Twitter Cortex, a team focused on building Twitter’s deep learning platform for recommendations/search/spam/nsfw/ads.

Beyond the Headless Map

Many of today’s maps are traditional, giving you a full picture of a particular space, like your current location on a Google Map. But there’s a growing trend of headless maps. Consider Tinder which is a giant map of potential partners where you never see a map. With IoT we can imagine going beyond the headless map into a world of no maps at all. For example, you say “ok car, let’s go to LA” and it maps out the best way to get there from streams of real-time sensor data from roads, satellites, traffic, weather, etc. In this talk we explore a future where we might stop looking at our maps altogether, because IoT is the map.

Aaron Steele is Sr. Vice President of Technical Business Development at CartoDB where he thinks creatively about the future and is responsible for company growth. Aaron holds a degree in Computer Science from the University of California, Berkeley. Before CartoDB, Aaron was CTO and Principal Software Engineer at World Resources Institute. His career started in academia, bridging gaps between biodiversity science, big data, and the web.

Bart is the Co-Founder, CTO and Chief Scientist of Loop AI Labs, which develops technology that helps machines understand the human world. His current focus is on extracting markers of a person’s interests, personas, and life context from social data and other unstructured data. For the past 12 years Bart has led and contributed to several projects related to personalization and learning, including Ph.D. research on personalized robotics for older adults, a calendaring system that learns its user's preferences, and a collaborative task management tool that learns how individuals interact with people and web services.

Katherine Gorman is the Executive Producer of the Talking Machines podcast and the Creative Director of Tote Bag Productions. After a decade as a daily news producer for public radio, she pursued her passion for science reporting and launched the podcast with host Harvard Professor Ryan Adams. Through clear explanations of fundamental concepts and in depth interviews with those at the forefront of research, Talking Machines introduces the reality of machine learning to a wide audience. Early in its first season, the show has already become one of the most popular tech news podcasts on the global iTunes charts. http://www.thetalkingmachines.com/

Scalable Data Science and Deep Learning with H2O

In a world where Data Science has become a driving force for innovation and profitability, organizations are in an arms race to leverage Machine Learning as a competitive differentiator. H2O is a leading open-source in-memory Machine Learning platform designed for distributed compute clusters. H2O’s vision is to democratize scalable Machine Learning with its open-source Java code base that integrates with everyday tools such as R, Python, Hadoop and Spark. H2O’s friendly User Interface makes it easy for data scientists to train models and share results with business stakeholders. Models can easily be put into production with auto-generated Java scoring code. This talk will spotlight H2O Deep Learning and its ease of use and scalability on large real-world datasets and showcase its versatility across multiple applications.

Arno is the Chief Architect of H2O, a distributed and scalable open-source machine learning platform. He is also the main author of H2O's Deep Learning. Before joining H2O, Arno was a founding Senior MTS at Skytree where he designed and implemented high-performance machine learning algorithms. He has over a decade of experience in HPC with C++/MPI and had access to the world’s largest supercomputers as a Staff Scientist at SLAC National Accelerator Laboratory where he participated in US DOE scientific computing initiatives and collaborated with CERN on next-generation particle accelerators.

Arno holds a PhD and Masters summa cum laude in Physics from ETH Zurich, Switzerland. He has authored dozens of scientific papers and is a sought-after conference speaker. Arno was named “2014 Big Data All-Star” by Fortune Magazine. Follow him on Twitter: @ArnoCandel.

Turning Everyday Devices into Health Sensors

Today's electronics have very sensitive optical and motion sensors. These can captures subtle signals resulting from cardiorespiratory activity. I will present how webcam(s) can be used to measure important physiological parameters without contact with the body. In addition, I will show how an ordinary smartphones can be turned into a continuous physiological monitors. Both of these techniques reveal the surprising power of devices with around us all the time. I will show how deep learning are helping us create highly scalable and low-cost applications based on these sensor measurements.

Daniel McDuff is Principal Research Scientist at Affectiva. He is building and utilizing scalable computer vision and machine learning tools to enable the automated recognition and analysis of emotions and physiology. At Affectiva Daniel is building state-of-the-art facial expression recognition software and leading analysis of the world's largest database of human emotions (currently with 8B+ data points). Daniel completed his PhD in the Affective Computing Group at the MIT Media Lab in 2014 and has a B.A. and Masters from Cambridge University. His work has received nominations and awards from Popular Science magazine as one of the top inventions in 2011, South-by-South-West Interactive (SXSWi), The Webby Awards, ESOMAR and the Center for Integrated Medicine and Innovative Technology (CIMIT). His work has been reported in many publications including The Times, the New York Times, The Wall Street Journal, BBC News, New Scientist and Forbes magazine. Daniel is also a Research Affiliate at the MIT Media Lab.