Deep Learning Summit schedule

Designing Ethical AI Solutions

The imperative for ethical design is clear – but how do we move from theory to practice? In this workshop, Accenture expert and Responsible AI lead, Rumman Chowdhury will lead a design thinking and ideation session to illustrate how AI solutions can imbue ethics and responsibility. This interactive session will ask participants to help design an AI solution and provide guidance for the right kinds of ethical considerations. Each participant will leave with an understanding of applied ethical design.

Rumman is a Senior Principal at Accenture, and Global Lead for Responsible AI. She comes from a quantitative social science background and is a practicing data scientist. She leads client solutions on ethical AI design and implementation. Her professional work extends to partnerships with the IEEE and World Economic Forum. She has been named a fellow of the Royal Society for the Arts and is one of BBC’s 100 most influential women of 2017.

Meta Learning and Self Play

Meta learning is the idea that learning systems can learn to learn fast and well. Self play systems are intriguing because they can lead to immensely complex behavior even in very simple environments. I will present several results in meta learning applied to different domains, self play results applied to simulated physics environments, and discuss the connection between the two.

Ilya Sutskever received his PhD in 2012 from the University of Toronto working with Geoffrey Hinton. After completing his PhD, he cofounded DNNResearch with Geoffrey Hinton and Alex Krizhevsky which was acquired by Google. He is interested in all aspects of neural networks and their applications.

Deep Learning in Production at Facebook

Facebook is powered by machine learning and AI. From advertising relevance, news feed and search ranking to computer vision, face recognition, and speech recognition, we run ML models at massive scale, computing trillions of predictions every day. I'll talk about some of the tools and tricks we use for scaling both the training and deployment of some of our deep learning models at Facebook. I'll also cover some useful libraries that we've open-sourced for production-oriented deep learning applications.

I'm a research engineer at Facebook, working on the Facebook AI Research and Applied Machine Learning teams to drive the large amount of AI applications at Facebook. At Facebook, I've worked on the large scale event prediction models powering ads and News Feed ranking, the computer vision models powering image understanding, and many other machine learning projects. I'm a contributor to several deep learning frameworks, including Torch and Caffe. Before Facebook, I obtained a masters in mathematics from the University of Cambridge, and a bachelors in mathematics from the University of Sydney.

How Deep Learning is Transforming Drug Discovery

By driving cars, beating humans at their own games, transcribing speech and translating text, deep learning is changing the world. However, over the past five years, exponential growth in biomedical datasets has created the perfect opportunity for deep learning to disrupt drug discovery. Already, deep learning systems can identify disease mutations more reliably than human experts, and can predict which compounds will modulate the activity of therapeutic targets. I'll describe different approaches to drug discovery and explain how Deep Genomics is using its machine learning platform to identify therapies for metabolic, neuromuscular and neurodegenerative disorders, and to advance them into clinical trials as fast as possible. I'll also describe our approach to overcoming the so-called 'black box' challenge of establishing trust with stakeholders, regulators, clinicians and patients.

Brendan Frey is internationally recognized as a leader in machine learning and genome biology. His group has published over a dozen papers in Science, Nature and Cell, and their most recent work on using deep learning to identify protein-DNA interactions was highlighted on the front cover Nature Biotechnology. Frey is a Fellow of the Royal Society of Canada, a Fellow of the Institute for Electrical and Electronic Engineers, and a Fellow of the American Association for the Advancement of Science. He has consulted for several industrial research and development laboratories in Canada, the United States and England, and has served on the Technical Advisory Board of Microsoft Research. Most recently, Dr. Frey spun out a company called Deep Genomics.

I'll present our work on training reinforcement learning agents to interact with and complete tasks in web browsers. In the short term our agents are learning to interact with common UI web elements like buttons, sliders and text fields. In the longer term we hope to address more complex tasks, such as achieving competence in interactive online exercises intended for schoolchildren to learn mathematics. I'll use these examples to also give a short but complete Reinforcement Learning tutorial.

Andrej is a 5th year PhD student at Stanford University, studying Deep Learning and its applications to Computer Vision and Natural Language Processing. He is also Head of Artificial Intelligence and Autopilot Vision at Tesla. In particular, his recent work has focused on Image Captioning, Recurrent Neural Network Language Models and Reinforcement Learning. On a side, he enjoys implementing state of the art Deep Learning models in Javascript, competing against Convolutional Networks on the ImageNet challenge, and blogging. Before joining Stanford he completed an undergraduate degree in Computer Science and Physics at the University of Toronto and a Computer Science Master's degree at the University of British Columbia.

Learning Abstractions with Hierarchical Reinforcement Learning

Hierarchical RL has long held the promise of enabling deep RL to solve more complex and temporally extended tasks by abstracting away lower-level details from a higher-level agent. In this talk, we describe how to turn this promise into a reality. We present a hierarchical design in which a higher-level agent solves a task by iteratively directing a lower-level policy to reach certain goals. We describe how both levels may be trained concurrently in a highly-efficient, off-policy manner. Furthermore, we present a provably-optimal technique for learning abstract notions of `goals' without explicit supervision. Our resulting method achieves excellent performance on a suite of difficult navigation tasks.

Ofir Nachum currently works at Google Brain as a Research Scientist. His research focuses on reinforcement learning, with notable work including PCL (path consistency learning) and HIRO (hierarchical reinforcement learning with off-policy correction). He received his Bachelor's and Master's from MIT. Before joining Google, he was an engineer at Quora, leading machine learning efforts on the feed, ranking, and quality teams.

Chelsea Finn is a research scientist at Google Brain and post-doctoral scholar at Berkeley AI Research. Starting in 2019, she will join the faculty in CS at Stanford University. She is interested in how learning algorithms can enable machines to acquire general notions of intelligence, allowing them to autonomously learn a variety of complex sensorimotor skills in real-world settings. She received her PhD in CS at UC Berkeley in 2018 and her Bachelors in EECS at MIT in 2014.

Preferred Networks Inc. (PFN), has been actively working on applications of deep learning on real world problems. In collaboration with leading companies and research institues, PFN has been focusing on deep learning in three domains: Industrial machinery including manufacturing robots, Smart transportation including autonomous driving, and Lifescience including cancer diagnosis and treatment. In December 2016, together with FANUC, a world leader in industrial machinery and industrial robots, we launch the world’s first commercial IoT platform for manufacturing that has Deep Learning technology at its core. In IoT, among other industries, Deep Learning is not only a research topic any more, but an important key technology in driving business.

The dramatic evolvement in the functional capabilities of IoT devices, and the fact that data generated by devices is incomparably larger than that generated by humans, are two particularly important factors contributing to the fast-paced innovation in various industries. Similarly, advancement in Deep Learning research is expanding its applications beyond pure data analysis to device actuation and control in the physical world. However, in order for algorithms to be able to efficiently learn real-time control of real world devices, a combination of advancement in both Deep Learning and computing is essential. That is the concept of Edge-Heavy-Computing where by bringing intelligence close to the network edge devices, the overall system makes it possible for those devices to efficiently learn in a distributed and collaborative manner, while resolving the data communication bottleneck often faced in IoT applications. In this talk, I will introduce some of the work we have been doing at PFN, highlight some results, and also give examples of how new computing boosts the value brought by Deep Learning.

Toru Nishikawa is the president and CEO of Preferred Networks, Inc., a Tokyo-based startup company specialized in applying the latest artificial intelligence technologies to emerging problems in the area of Internet of Things (IoT). He was one of the world finalists of the ACM ICPC (International Collegiate Programming Contest) while he was a graduate student of University of Tokyo. In 2006, he, together with his collegemates and his fellow contenders at ICPC, founded Preferred Infrastructure, Inc., a precursor company. In 2014, Nishikawa founded Preferred Networks with the aim of expanding their businesses into the realization of Deep Intelligence – a future IoT in which all devices, as well as the network itself, are equipped with machine intelligence. He is an ambitious entrepreneur, programmer, and father of a child.

Generative Adversarial Networks

Ian Goodfellow is a Staff Research Scientist at Google Brain. He is the lead author of the MIT Press textbook Deep Learning. In addition to generative models, he also studies security and privacy for machine learning. He has contributed to open source libraries including TensorFlow, Theano, and Pylearn2. He obtained a PhD from University of Montreal in Yoshua Bengio's lab, and an MSc from Stanford University, where he studied deep learning and computer vision with Andrew Ng. He is generally interested in all things deep learning.

Unsupervised Learning in Computer Vision

Computer vision has made great progress through the use of deep learning, trained with large-scale labeled data. However, good labeled data requires expertise and curation and can be expensive to collect. Can one discover useful visual representations without the use of explicitly curated labels? In this talk, I will present several case studies exploring the paradigm of self-supervised learning -- using raw data as its own supervision -- for tasks in computer vision and computer graphics.

Alexei Efros (associate professor, UC Berkeley) works in the area of computer vision and computer graphics, especially at the intersection of the two. He is particularly interested in using data-driven techniques to tackle problems where large quantities of unlabeled visual data are readily available. He is a recipient of NSF CAREER award (2006), Sloan Fellowship (2008), Guggenheim Fellowship (2008), SIGGRAPH Significant New Researcher Award (2010), and the Helmholtz Test-of-Time Prize (2013).

Variational Autoencoders

Current deep learning mostly relies on supervised learning: given a vast amount of examples of humans performing tasks such as labeling images or translation, we can teach computers to mimic humans at these tasks. Since supervised methods only focus on modeling the task directly, however, these methods are not particularly efficient: they need much more examples than humans require for learning new tasks. Enter unsupervised learning, where computers not only model tasks, but also their context, vastly improving data efficiency. We discuss the powerful framework of Variational Autoencoders (VAEs), a synthesis of deep learning and Bayesian methods, as a principled yet practical approach towards unsupervised deep learning. In addition to the underlying mathematics, we discuss current scientific and practical applications of VAEs, such as semi-supervised learning, drug discovery, and image resynthesis.

Diederik (or Durk) Kingma is a Research Scientist at OpenAI, with a focus on unsupervised deep learning. His research carreer started in 2009, while graduated at Utrecht University, working with prof. Yann LeCun at NYU. Since 2013, he pursues a PhD with prof. Max Welling in Amsterdam, focusing on the intersection of deep learning and Bayesian inference. Early in his PhD, he proposed the Variational Autoencoder (VAE), a principled framework for Bayesian unsupervised deep learning. Other well-known work is Adam, a now standard method for stochastic gradient descent.

Your AI is Blind

The final missing piece in AI is Visual Cognition and Understanding. In order for this dream to be realized, it takes more than winning scores at classifying ImageNet. We discuss our 4 years of experience in scaling, quality control, data management, and other important lessons learned in commercializing computer vision in the marketplace, including the procurement of the largest training dataset ever created for Visual Cognition and Understanding through Deep Learning.

Brad Folkens is the co-founder and CTO of CloudSight, where he leads the effort to build the world’s first visual cognition platform to power the future of AI. He earned his Theoretical Computer Science and Mathematics degrees with honors and immediately went to work building profitable companies, first with the award winning PublicSalary.com HR Tool, and later with his co-founder, Dominik, grossing over $1m/year in revenue.

Shivon is a partner and founding member of Bloomberg Beta, a $75 million venture fund backed by Bloomberg L.P. that invests in startups transforming the future of work. Shivon is obsessed with the most important force changing work, machine intelligence. She is known for an annual report that researches thousands of machine intelligence companies and selects the most promising real-world problems they are solving. Bloomberg Beta has invested in more than 25 machine intelligence companies to date. She graduated from Yale, where she was the goalie on the ice hockey team. She is an advisor to OpenAI, a fellow at the Creative Destruction Lab, on the advisory board of University of Alberta's Machine Learning group, and a charter member of C100. She co-hosts an annual conference at the University of Toronto that brings together the foremost authors, academics, founders, and investors in machine intelligence. She was one of Forbes 30 Under 30 in Venture Capital.

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.

Deep Learning for Self Driving Vehicles

In this talk I will cover some of the exciting and innovative deep learning technologies recently developed by the R&D team at the Uber Advanced Technologies Group, based out of Toronto and highlight some of the aspects of the engineering work required to integrate such technology into the vehicle platforms.

Inmar Givoni is an Autonomy Engineering Manager at Uber Advanced Technology Group, Toronto, where she leads a team whose mission is to bring from research and into production cutting-edge deep-learning models for self-driving vehicles. She received her PhD (Computer Science) in 2011 from the University of Toronto, specializing in machine learning, and was a visiting scholar at the University of Cambridge. She worked at Microsoft Research, Altera (now Intel), Kobo, and Kindred at roles ranging from research scientist to VP, applying machine learning techniques to various problem domains and taking concepts from research to production systems. She is an inventor of several patents and has authored numerous top-tier academic publications in the areas of machine learning, computer vision, and computational biology. She is a regular speaker at AI events, and is particularly interested in outreach activities for young women, encouraging them to choose technical career paths. For her volunteering efforts she has received the 2017 Arbor Award from UofT. In 2018 she was recognized as one of Canada’s 50 inspiring women in STEM.

Catalyzing Deep Learning’s Impact in the Enterprise

Deep learning is unlocking tremendous economic value across various market sectors. Individual data scientists can draw from several open source frameworks and basic hardware resources during the very initial investigative phases but quickly require significant hardware and software resources to build and deploy production models. Intel offers various software and hardware to support a diversity of workloads and user needs. Intel Nervana delivers a competitive deep learning platform to make it easy for data scientists to start from the iterative, investigatory phase and take models all the way to deployment. This platform is designed for speed and scale, and serves as a catalyst for all types of organizations to benefit from the full potential of deep learning. Example of supported applications include but not limited to automotive speech interfaces, image search, language translation, agricultural robotics and genomics, financial document summarization, and finding anomalies in IoT data.

Andres Rodriguez is a deep learning solutions architect with Intel Nervana where he designs deep learning solutions for Intel’s customers and provides technical leadership across Intel for deep learning. Andres received his PhD from Carnegie Mellon University for his research in machine learning, and prior to joining Intel, he was a deep learning research scientist with the Air Force Research Laboratory. He holds over 20 peer reviewed publications in journals and conferences, and a book chapter on machine learning.

In-Place Computing: High-Performance Search

This presentation details an in-place associative computing technology that changes the concept of computing from serial data processing—where data is moved back and forth between the processor and memory—to massive parallel data processing, compute, and search in-place directly in the main processing array. This in-place associative computing technology removes the bottleneck at the IO between the processor and memory, resulting in significant performance-over-power ratio improvement compared to conventional methods that use CPU and GPGPU (General Purpose GPU) along with DRAM. Target applications include, convolutional neural networks, recommender systems for e-commerce, and data mining tasks such as prediction, classification, and clustering.

Avidan Akerib is VP of the Associative Computing business unit at GSI Technology. He holds a PhD from the Weizmann Institute of Science where he developed the theory of associative computing and applications for image processing and graphics. Avidan has over 30 years of experience in parallel computing, image processing and pattern recognition, and associative processing. He holds over 20 patents related to parallel computing and associative processing.