DEEP LEARNING ADVANCES
Deep Learning: Putting Theory into Practice
DEEP LEARNING OPTIMIZATION
Brandon Rohrer - Facebook
What Do Your Neural Networks Learn? A Peek Inside the Black Box
Deep neural networks are famously difficult to interpret. We'll take a tour of their inner workings to build an intuition of what's inside the black box and how all those cogs fit together. Then we'll use those insights as we step through a image processing problem with deep learning, showing at every step what the neural network is "thinking".
Brandon love solving puzzles and building things. Applied machine learning gives him the opportunity to do both in equal measure. He started by studying robotics and human rehabilitation at MIT (MS '99, PhD '02), moved on to machine vision and complex system modeling at Sandia National Laboratories, then to predictive modeling of agriculture DuPont Pioneer, and cloud data science at Microsoft. At Facebook he works to get internet and electrical power to those in the world who don't have it, using deep learning and satellite imagery, and to do a better job identifying topics reliably in unstructured text. In his spare time he likes to rock climb, write robot learning algorithms, and go on walks with his wife and our dog, Reign of Terror.
Jason Yosinski - Uber AI Labs
AI Neuroscience: Can we Understand the Neural Networks we Train?
Deep neural networks have recently made a bit of a splash, enabling machines to learn to solve problems that had previously been easy for humans but difficult for computers, like playing Atari games or identifying lions and jaguars in photos. But how do these neural nets actually work? What concepts do they learn en route to their goals? We built and trained the networks, so on the surface these questions might seem trivial to answer. However, network training dynamics, internal representations, and mechanisms of computation turn out to be surprisingly tricky to study and understand, because networks have so many connections — often millions or more — that the resulting computation is fundamentally complex.
This high fundamental complexity enables the models to master their tasks, but we find now that we need something like neuroscience just to understand the AI that we’ve constructed! As we continue to train more complex networks on larger and larger datasets, the gap between what we can build and what we can understand will only grow wider. This gap both inhibits progress toward more competent AI and bodes poorly for a society that will increasingly be run by learned algorithms that are poorly understood. In this talk, we’ll look at a collection of research aimed at shrinking this gap, with approaches including interactive model exploration, optimization, and visualization.
Jason Yosinski is a machine learning researcher, founding member of Uber AI Labs, and scientific adviser to Recursion Pharmaceuticals. His work focuses on building 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 of study that investigates fundamental properties and behaviors of AI systems. Dr. Yosinski was previously a PhD student and NASA Space Technology Research Fellow working at the Cornell Creative Machines Lab, the University of Montreal, Caltech/NASA Jet Propulsion Laboratory, and Google DeepMind. His work on AI has been featured on NPR, Fast Company, the Economist, TEDx, and on the BBC. Prior to his academic career, Jason cofounded two web technology companies and started a program in the Los Angeles school district that teaches student algebra via hands-on robotics. In his free time, Jason enjoys cooking, sailing, reading, paragliding, and sometimes pretending he's an artist.
David Bau - MIT CSAIL
Visualizing and Understanding Generative Adversarial Networks
The remarkable success of Generative Adversarial Networks in generating nearly photorealistic images leads to the question: how do they work? Are GAN just memorization machines, or do they learn semantic structures? What do these networks learn? I introduce the method of Network Dissection to test the semantics captured by neurons in the middle layers of a network, and show how recent state-of-the-art GANs learn a remarkable amount of structure. Even without any labels in the training data, neurons in a GAN trained to draw scenes will separately code for objects such as trees, furniture, and other meaningful objects. The causal effects such neurons are strong enough that we can add and remove objects and paint pictures directly by manipulating the neurons of a GAN. These methods provide insights about the a GAN's errors as well as the contextual relationships learned by a GAN. By cracking open the black box, we can see how deep networks learn meaningful structure, and we can gain understandable insights about a network’s inner workings.
David Bau is a PhD student at MIT CSAIL, advised by Professor Antonio Torralba. David previously worked at Google and Microsoft where he has contributed to several widely used products including Google Image Search and Microsoft Internet Explorer. David believes that complex systems should be built to be transparent, and his research focuses on the interpretability of deep networks.
Akram Bayat - University of Massachusetts Boston
Deep Learning Based Visual Scene and Object Recognition in Machine and Human Visual Systems
In this talk, we present deep learning solutions for three visual scene perception and object recognition problems. The goal is to investigate to which extent deep convolutional neural networks resemble the human visual system for scene perception and object recognition: (1) classification of scenes based on their global properties, (2) deploying multi-resolution technique for object recognition, and (3) evaluating the influence of the high-level context of scene grammar for object and scene recognition. The first problem proposes to drive global properties of a scene as high-level scene descriptions from deep features of convolutional neural networks in scene classification tasks. The second problem shows that fine-tuning the Faster-RCNN to multi-resolution data inspired by human multi-resolution visual system improves the network performance and robustness over a range of spatial frequencies. Finally, the third problem studies the effects of violating the high level scene syntactic and semantic rules on human eye-movement behavior and deep neural scene and object recognition networks.
Akram Bayat is a Research Assistant at the University of Massachusetts Boston where she also received her Ph.D. in computer Science at the Visual Attention Laboratory of the advised by Professor Marc Pomplun. Akram received both the master of Electrical Engineering and the master of Computer Science prior to joining Ph.D. program. She is currently working on how to apply human attentional mechanism to deep neural network for the scene and object recognition. Akram has conducted several projects on Human activity recognition and eye-movement based user classification. She is also interested in computer vision, machine learning, data mining, and human-user interface design.
Zoya Bylinskii - Adobe Research
Predicting What Drives Human Attention in Photographs, Visualizations, and Graphic Designs
Knowing where a person looks in an image can provide us with important clues about what captures their attention and what may eventually enter their memory. Aggregating the attention patterns of a group of people can help us make conclusions about the effectiveness of a design. Computational models of attention help guide image processing algorithms like automatic image resizing and thumbnailing, they can direct a model to compose more meaningful image captions, and they can be used to provide feedback within graphic design tools. In this talk, I will cover what we know about human attention and how we capture human attention and interest in images at a large data scale using novel crowdsourcing interfaces. I will then demonstrate how we use this data to build computational models of attention for photographs, visualizations, and graphic designs, along with the applications that these models make possible.
Zoya Bylinskii is a Research Scientist in the Creative Intelligence Lab at Adobe Research in Cambridge and an Associate of the Institute of Applied Computational Science at Harvard University. She received a Ph.D. and an M.Sc. in Computer Science from the Massachusetts Institute of Technology in 2018 and 2015, respectively, and an Hon. B.Sc. in Computer Science and Statistics from the University of Toronto in 2012. Zoya is a 2018 Rising Star in EECS, a 2016 Adobe Research Fellow, a 2014 NSERC Postgraduate Scholar, a 2013 Julie Payette Research Scholar, and a 2011 Anita Borg Scholar. Zoya works at the interface of human vision, computer vision, and human-computer interaction.
Kelly Davis - Mozilla
Childhood's End: Maturation of Deep Speech and Common Voice
We’ll talk about the blossoming of Deep Speech, an open deep learning based speech-to-text engine, and Common Voice, an open crowd-sourced speech corpora. We will cover recent Deep Speech advancements (streaming, small platform support, and product integrations) as well as Common Voice advancements (multi-language support, multi-language corpora, and profiles). Also we’ll give a overview of future plans and how to get involved.
Kelly Davis has many irons in the fire. He studied Mathematics and Physics at MIT, then went on to do graduate work in Superstring Theory/M-Theory. He then jumped ship, coding at a startup that eventually went public in the late 90's. When the bubble burst, he jumped back into an academic setting and joined the Max Planck Institute for Gravitational Physics where he worked on software systems used to help simulate black hole mergers. Jumping ship yet again, he went back into industry, writing 3D rendering software at Mental Images/NVIDIA. When that lost its charm, he founded a NLU at a startup, 42, that created a system, based off of IBM'S Watson, able to answer general knowledge questions. After a brief stint as the Director of Machine Learning at another Berlin startup, he joined Mozilla where he now leads the machine learning group.
QUANTUM MACHINE LEARNING
Jonathan Mailoa - Robert Bosch RTC
Neural Network Force Field for Molecular Dynamics of Multi-Element Atomistic System
Neural network-based force field has recently emerged as a way to bypass expensive quantum mechanics calculation in molecular dynamics simulation, which enables us to study material properties and physical mechanisms at the atomistic level. Despite fundamental advances in rotation-invariant symmetry function “fingerprint” data representation, the derivative fingerprints required for the atomic force calculation significantly increases the training and execution runtime required in this approach. In this talk, we present an algorithm to bypass the need for fingerprint derivatives and perform direct atomic force prediction which significantly reduces the computation efforts required for training and executing the neural network force field for molecular dynamics simulations.
Jonathan Mailoa is currently a research engineer at Bosch Research and Technology Center, where he works on atomistic computational material science simulation of materials relevant for energy applications such as batteries and fuel cells. Prior to that, he completed his PhD in Electrical Engineering and Computer Science at MIT, developing novel tandem solar cell device architectures. He is currently interested in developing molecular dynamics force field based on machine learning methods.
Stefanos Nikolaidis - University of Southern California
Robot Learning via Human Adversarial Games
Much work in robotics has focused on “human-in-the-loop” learning techniques that improve the efficiency of the learning process. However, these algorithms have made the strong assumption of a cooperating human that assists the robot. In reality, people tend to act also in an adversarial manner towards deployed robotic systems. We show that this can in fact improve the robustness of the learned models by proposing a physical framework that leverages perturbations applied by a human adversary, guiding the robot towards more robust models. In a manipulation task, we show that grasping success improves significantly when the robot trains with a human adversary as compared to training in a self-supervised manner. This work opens a range of exciting potential applications in other domains as well, such as in autonomous driving.
Stefanos Nikolaidis is an Assistant Professor of Computer Science at the University of Southern California, where he directs the Interactive and Collaborative Autonomous Robotic Systems (ICAROS) Lab. Research in ICAROS spans the whole spectrum of human-robot interaction science: from distilling the fundamental mathematical principles that govern interactive behaviors, to developing approximation algorithms for deployed robotic systems and testing them "in the wild" with actual end users. Previously, Stefanos completed his PhD at Carnegie Mellon's Robotics Institute and received his MS from MIT. He has also a MEng from the University of Tokyo and a BS from the National Technical University of Athens. Stefanos has worked as a research associate at the University of Washington, as a research specialist at MIT and as a researcher at Square Enix in Tokyo. He has received a Best Enabling Technologies Paper Award from the IEEE/ACM International Conference on Human-Robot Interaction in 2015, a best paper nomination from the same conference in 2018 and was a best paper award finalist in the International Symposium on Robotics 2013.
David Held - Robotics Institute, CMU
Robot Learning through Motion and Interaction
Robots today are typically confined to operate in relatively simple, controlled environments. One reason for these limitations is that current methods for robotic perception and control tend to break down when faced with occlusions, viewpoint changes, poor lighting, unmodeled dynamics, and other challenging but common situations that occur when robots are placed in the real world. I argue that, in order to handle these variations, robots need to learn to understand how the world changes over time: how the environment can change as a result of the robot’s own actions or from the actions of other agents in the environment. I will show how we can apply this idea of understanding changes to a number of robotics problems, such as object tracking and safe robot learning. By learning how the environment can change over time, we can enable robots to operate in the complex, cluttered environments of our daily lives.
David Held is an assistant professor at Carnegie Mellon University in the Robotics Institute. His research focuses on robotic perception for autonomous driving and object manipulation. Prior to coming to CMU, David was a post-doctoral researcher at U.C. Berkeley, and he completed his Ph.D. in Computer Science at Stanford University where he developed methods for perception for autonomous vehicles. David also has a B.S. and M.S. in Mechanical Engineering at MIT. David is a recipient of the Google Faculty Research Award in 2017.
NATURAL LANGUAGE UNDERSTANDING
Latent Models (Shallow and Deep) for Recommender Systems
Davis Addy - Chick-fil-A
How Chick-fil-A uses AI to Spot Food Safety Trends in Social Media
Social media is an amazing way for companies to connect directly to their customers. At Chick-fil-A, it’s also one of many tools we use to help improve food safety at more than 2,000 locations around North America. To derive food safety insights from the hundreds of customer reviews received each day, we had to address a number of challenges inherent in analyzing social media data. After all, social media often contains broken grammar, mixed sentiments, and off-topic musings. To address these challenges, we developed a cloud-based service that uses artificial intelligence to help spot potential food safety issues from restaurant level customer review data. In this presentation, we’ll cover how the service works, using natural language processing (NLP) in combination with common serverless cloud computing tools. We’ll also cover how the data collected from social media can be used with other data sets (e.g. health department data) to help show possible correlations to better identify food safety trends.
Davis Addy is the Principal Technology Leader for Food Safety & Product Quality at Chick-fil-A. His team is responsible for internal Restaurant assessment and evaluation platforms in addition to developing digital solutions that leverage AI, advanced analytics, and connected devices in the Restaurant. Prior to joining Chick-fil-A in 2016, Davis spent eight years with General Electric (GE) supporting enterprise application rollouts across North America, Europe, and Asia. Davis holds a BS in Computer Engineering from the University of Florida and a MS in Information Systems Management from Georgia State University. He currently resides in Atlanta, GA with his wife Kim and their two daughters Brooklyn (6) and Hollyn (2).
DEEP LEARNING & SOCIETAL IMPACTS
Rana el Kaliouby - Affectiva
The New Social Contract - Humanizing Artificial Intelligence
Artificial Intelligence is quickly becoming mainstream, engrained in the fabric of our lives, acting on our behalf – helping us get things done faster, more efficiently, giving us deeper insights, maybe even helping us be happier and healthier. AI is taking on tasks that were traditionally done by humans – from acting as our personal assistants and hiring our next co-worker, to driving our cars and assisting with our healthcare. But AI today has high IQ but no EQ, no emotional intelligence. We’re forging a new kind of partnership with technology. A new social contract that is based on mutual trust. In this talk, Dr. el Kaliouby will discuss the 5 tenets of this new social contract including how to build AI that has empathy, the ethical considerations of AI and the importance of guarding against data and algorithmic bias.
A pioneer in artificial emotional intelligence (Emotion AI), Rana el Kaliouby, PhD, is co-founder and CEO of MIT spinoff and category-defining company Affectiva. Rana led the innovation of the company’s award-winning technology, which uses deep learning and massive amounts of data to analyze complex and nuanced emotions and cognitive states from face and voice, for industries like automotive, market research, HR video recruitment, and mental health. Rana is now paving the way for Human Perception AI: software that can detect all things human, from nuanced human emotions and complex cognitive states, to behaviors, activities and the objects people use. Rana is one of few women leading a disruptive AI company. A Muslim-American and passionate advocate, she frequently speaks in press and on stage about innovation, women in technology, ethics in AI and leadership. Forbes recently included Rana in their list of America’s Top 50 Women in Tech, Fortune Magazine included her in their 2018 40 under 40 and she was named one of the three Global Business pioneers by Bloomberg in 2017. Rana is also a member of the World Economic Forum’s Young Global Leaders and a member of the Partnership on AI. Rana holds a BSc and MSc in Computer Science from the American University in Cairo, a PhD from the Computer Laboratory at the University of Cambridge and a Post Doctorate at MIT.
PANEL: Is the Biggest Challenge Facing AI an Ethical One?
Conversation & Drinks
DEEP LEARNING IMPLEMENTATION IN BUSINESS
Building a Foundation for Machine Learning
Matthew Mattina - Arm
ML on the Edge: Hardware and Models for Machine Learning on Constrained Platforms
Deep neural networks are a key technology at the core of advanced audio and video applications. As these applications begin to migrate from large servers executing in the cloud to mobile and embedded platforms, they place significant demands on the underlying hardware platform. This talk will review the key properties of these models and how these properties are leveraged to deliver efficient inference on energy, compute, and space constrained platforms.
Matthew Mattina is Senior Director of Machine Learning & AI Research at Arm, where he leads a team of researchers developing advanced hardware, software, and algorithms for machine learning. Prior to joining Arm in 2015, Matt was Chief Technology Officer at Tilera, responsible for overall company technology, processor architecture and strategy. Prior to Tilera, Matt was a CPU architect at Intel and invented and designed the Intel Ring Uncore Architecture. Matt has been granted over 30 patents relating to CPU design, multicore processors, on-chip interconnects, and cache coherence protocols. Matt holds a BS in Computer and Systems Engineering from Rensselaer Polytechnic Institute and an MS in Electrical Engineering from Princeton University.
Gautam Shroff - Tata Consultancy Services
Deep Learning For the Future Enterprise
The initial wave of deep learning breakthroughs led to an explosion of exciting new applications based on superior solutions to problems in vision, speech and text. However, the initial solutions were largely focused on end user applications such as music recommendations and photo tagging, and the impact did not immediately translate to applications in Banking, Healthcare, Retail, and Manufacturing. There are multiple challenges that impede the effective application of deep learning to real world problems such as machine health monitoring, container stowage planning and healthcare recommendations. Insufficient and noisy data, compliance with privacy regulations, interpretability requirements on predictions, incorporation of domain knowledge during learning, contingency planning for model failure and an inflexible enterprise culture are some of the prominent obstacles that deep learning has to overcome for effective application to enterprise problems. Slowly but surely, companies are finding solutions to these problems and the impact of deep learning is now percolating to enterprises in many different sectors. As one of the worlds largest IT consulting firms, TCS has nurtured a dedicated team of deep learning researchers to provide solutions to these problems for use cases across sectors ranging from manufacturing and shipping to healthcare and finance.
Dr. Gautam Shroff is a Vice President and Chief Scientist in Tata Consultancy Services and heads TCS Research. He has published 90 research papers in the areas of computational mathematics, parallel computation, distributed systems, software architecture, software engineering, big data, information fusion, virtual reality as well as artificial intelligence including machine learning, deep learning, Bayesian inference and natural language processing. He has written two books “Enterprise Cloud Computing” published by Cambridge University Press, UK, in October 2010, and “The Intelligent Web”, published by Oxford University Press, UK, in 2013 (paperback ed. 2015). Prior to joining TCS in 1998, Dr. Shroff had been on the faculty of the California Institute of Technology, Pasadena, USA (1990 - 91) and thereafter of the Department of Computer Science and Engineering at Indian Institute of Technology, Delhi, India (1991 - 1997). He has also held visiting positions at NASA Ames Research Center in Mountain View, CA, and at Argonne National Labs in Chicago. He completed his B.Tech degree in Electrical Engineering from IIT Kanpur in 1985, and Ph.D in Computer Science from Rensselaer Polytechnic Institute Troy, NY in 1990.
PANEL: Operational Machine Learning: What Do We Need to Consider?
APPLICATION OF DEEP LEARNING IN ENTERPRISE
Jay Baxter - Twitter Cortex
Applications of Deep Learning to New User Recommendations at Twitter
The cold start problem for new users is a classic challenge for recommender systems. In this talk, I will discuss some deep learning approaches that can be used to address this problem, including using neural networks to train co-embeddings of new users and items, and serving them in an efficient way at runtime via approximate nearest neighbor algorithms like LSH or HNSW. I will also touch on some of the difficulties of evaluating such models both offline and online in the context of A/B tests.
Jay Baxter is a Senior Machine Learning Engineer at Twitter Cortex, where he works on applying scalable machine learning methods to improve Twitter's recommendations and conversational health. Previously, he had worked on a variety of software and machine learning projects, ranging from book search and alerts at Google to entity coreference resolution at Diffeo. He received his M.Eng. and S.B. in Computer Science from MIT, where he led development on a probabilistic database system called BayesDB.
Michael Sollami - Salesforce
Building Visual Search at Salesforce
Fine-grain recognition remains an unsolved problem at in the general case, indeed, it may even be as difficult as self-driving cars. There are many technical challenges in achieving accurate production-level image retrieval at web scale (handling catalogs of tens of millions of items). This talk details the steps and highlights the hurdles in building such a search platform. At Commerce Cloud Einstein, we have developed a custom multi-stage pipeline of deep metric learning models for product detection and recognition. Our networks are trained to discover a manifold representing the space of all consumer products. We will present the current architectures in our embedding networks, i.e. the mapping from consumer images to the product feature space, as well as the most promising research directions. Implementation level details will be covered insofar as they make efficient fine-grain retrieval possible, and performance evaluation (both statistical as well as qualitative) measures will be described.
Michael received a doctorate in mathematics from the University of Wyoming. Since 2012 he has led research and development teams at a number of successful Boston-based startups. Currently a lead data scientist on Salesforce's Einstein team, he enoys designing and building deep learning systems with applications to e-commerce and computer vision.
End-to-End Conversational System for Customer Service Application
APPLICATIONS OF DEEP LEARNING IN INDUSTRY
Andrei Polzounov - Blue River Technology
Deep Learning in Precision Agriculture for Reducing Herbicide
The use of herbicide in agriculture has skyrocketed in the past few decades. This trend has largely been caused by new genetically modified, herbicide resistant crops. Combating the ecological side-effects of chemical overspray as well as easing the economic burden of costly herbicides is where John Deere's Blue River Technology comes in. Blue River’s flagship product is See & Spray. An intelligent machine that utilizes deep learning to automatically detect and classify crops and weeds on-the-fly and uses precision sprayers to selectively spray weeds, saving vast quantities of chemicals in the process. This presentation will cover pixelwise semantic segmentation of imagery collected real-time in the field by See & Spray machines and how that information feeds is used for targeted spraying of unwanted weeds. On the fly detection of spray allows for a closed feedback loop control system where a GPU accelerated semantic autoencoder model works in tandem with the mechanically actuated sprayer system to achieve precision farming.
Andrei is a senior research scientist at Blue River Technology. He is focused on deep learning and computer vision for perception for smarter agricultural machines. Previously, Andrei worked on processing Airbus’ satellite imagery, drones for Lockheed Martin and text localization and semantic understanding of text for Singapore’s Agency for Science Technology and Research. In his spare time Andrei enjoys skiing and hiking.
Cheng Zhan - Anadarko Petroleum
Application of Machine Learning for Oil Production Forecasting
One of the central questions in science is forecasting: based on the past history, how well can we predict the future? In many domains with complex multivariate correlation structures and nonlinear dynamics, forecasting is highly challenging. In the oil and gas industry, conventional approaches such as the modified hyperbolic method, have been utilized to analyze the production decline curve. Forecasting decline curves is an important component for E&P companies in business planning, asset evaluation, and decision making. Here we introduce a machine learning approach to tackle the problem, and to be more specific, an LSTM approach (LSTM stands for Long Short Term Memory, which is one kind of recurrent neural network). Compared with the hyperbolic approach, where the problem has been reduced to an over-simplified curve and essentially determined by a global curvature structure, the LSTM model is more dynamic and has a better chance of capturing non-linear events. In time series prediction, one main difficulty is how to stabilize the solution, as the error can easily accumulate over time. One way to make the algorithm more robust is through feature engineering, and here we leverage historical data from other wells, which improves our prediction significantly. We also build the prediction model from the accumulated curve domain, and eventually ensemble multiple models to reduce the variance. Given the fact that the model is only trained on the first 3 months of data (around 10% of the data), the oil rate prediction for the first 2 years shows great promise.
Cheng Zhan is a Senior Data Scientist at Anadarko Petroleum, where he works on field development optimization and long-term production forecasting. He focuses on building machine learning algorithms to create strategic and financial impact for the company. Prior to his current role, he worked as a Geophysicist at TGS and CGG, utilizing seismic data and inversion methods to help operators make better decisions in exploration. He holds a PhD in mathematics from University of Houston, and a B.S. in Mathematics from Sun Yat-sen University.
Deep Learning in Financial Markets
Transforming Transportation With Machine Learning
Arnie Kravitz - Advanced Robotics for Manufacturing (ARM)
Optimizing the Manufacturing Process
Arnie will discuss a concept for a program/manufacturing line managers decision and reporting aid. The concept involves natural language spoken interfaces, machine learning, and inference engines to accomplish the collection, discussion, analysis, and reporting of weekly management metrics. During the analysis, trends, anomalies, and similarities are identified analyzed, subjects of concern, inferred causes, and suggestions for remediation. The goal of the system is to enable managers to have more time (and funding for staff) to spot problems as they evolve to prevent or solve them.
Arnie Kravitz drives ARM’s technical vision. He helps it fulfill its mission of increasing U.S. global competitiveness by accelerating innovative technologies that make robots more accessible to U.S. manufacturers. With over 37 years designing, developing, and manufacturing a large portfolio of products Arnie draws from a broad portfolio of experiences. These include; expert, imagery interpretation, and automatic target recognition systems; self-learning inference engine applications, aquatic robots, autonomous vehicles, robotics, augmented, and virtual reality vision systems, commercial consumer electronics, and cryptographic devices. He has taught as an Adjunct Professor at The Johns Hopkins University and has significant experience exploiting emerging tools and techniques to rapidly transform new ideas into manufactured products. Arnie holds a master’s degree in Electrical Engineering from Rensselaer Polytechnic Institute.
END OF SUMMIT
Demystifying AI Terms & Tools - Introductory Overview to AI Key-Terms
An introduction and overview to some of the key AI terms which you can expect to hear throughout the 2-Day Summit
Jane Hung - Uber AI Labs
BOB-ROS: A Deep RL Simulation Environment for ROS and Gazebo
Deep Reinforcement Learning (Deep RL) is presently one of the hottest and fastest-paced application areas of deep learning and machine learning as a whole. This intense focus is driven by the eye-watering potential of importing the ground-breaking accuracy improvements of deep neural networks seen in large-scale supervised learning benchmarks, into the world of optimal decision making and control. Arguably one of the most important components of Deep RL is the simulation environment. Simulation environments play the key role of providing a benchmarking platform for comparing the cornucopia of different RL algorithms, hence giving researchers and practitioners crucial feedback on how effective their ideas are. To date, most simulation environments have been focused on gaming environments, due to their fast physics engines, semi-realistic rendering pipelines and the ease with which one can use their points systems as an out-of-the-box reward function, which typically isn't too delayed. Ultimately, the end goal of RL research is to build agents/robots which can interact effectively within real-world environments. The applications are limitless, ranging from autonomous vehicles to drones which can deliver packages. When viewed from this lens, the gaming-as-a-benchmark approach suffers from several shortcomings. The first issue is somewhat obvious: a game environment, by design, has its own rules and isn't completely governed by the rules of the physical world a robot must operate in, and as such, has limited applicability. We argue that the physical world presents plenty of highly challenging environments to navigate, and there simply is no need to use the additional rules imposed by games to test RL systems, and, indeed these additional rules can be a distraction from building agents which are actually useful for society. The second is the lack of realistic agent-centric input channels. Almost all real-world agents have multiple sensors recording stimuli in parallel. A good example is an autonomous vehicle which receives, in real-time, data from odometric sensors, GPS, IMU, LIDAR, RADAR, SONAR and cameras. Data from all these sensors need to be fused effectively to form a state representation useful for the task at hand. Gaming agents have a very limited (if any) array of sensors: one simply learns from pixels showing the state of the environment and the agent form a third-person point of view.
To address these shortcomings we introduce a new RL benchmarking tool: the Benchmark Of Behavior in the Robot Operating System (BOB-ROS). BOB-ROS is a simulation environment made for the sole purpose of actually building robots. Therefore, it is a highly pertinent simulation environment for deep RL systems in order to determine their utility for robotics specifically.
We present results of using several standard deep RL tools for the purpose of training a drone to fly in a maze-like office environment from A to B without hitting any obstacles. We will present the challenges in using deep RL in a slower and more complex simulation environment like those built upon ROS, and the solutions we used to overcome these challenges.
Prerequisites: Basics of Reinforcement Learning fundamentals, Basic knowledge of probability and statistics, (Optional) Some familiarity with OpenAI gym.
Jane received her PhD from MIT and the Broad Institute in Anne Carpenter’s Imaging Platform. While applying deep learning-based computer vision models to biological problems like malaria detection, she became interested in software that bridges the gap between research and real world use cases. At Uber AI Labs, she has been working with product teams like Elevate on machine learning models for improved planning and Freight for improved price forecasting. She is also working on combining reinforcement learning algorithms with realistic simulations.
Lunch & Learn - WORKSHOP
Join industry leaders including Google Cloud, WekaIO and Nephos Technlogies for Lunch!
Cansu Canca - AI Ethics Lab
Cansu is the founder and director of the AI Ethics Lab, where she leads teams of computer scientists and legal scholars to provide ethics analysis and guidance to researchers and practitioners. She has a Ph.D. in philosophy specializing in applied ethics. She works on ethics of technology and population-level bioethics with an interest in policy questions. Prior to the AI Ethics Lab, she was a lecturer at the University of Hong Kong, and a researcher at the Harvard Law School, Harvard School of Public Health, Harvard Medical School, Osaka University, and the World Health Organization.
Talent & Talk - NETWORKING
Are you recruiting and want to share positions with leading minds in AI? Or looking for a new role and want to explore the options? This quick pitch sessions allows you 1-Minute to share details of what/who you are looking for or skills you have to share and network with the right people. Interested? Email email@example.com to sign up!
Beyond the Hype: The Real Value of AI in Enterprise - Case Studies & Ask the Experts
How can you differentiate between real value added and technology hype? Which AI tools are essential to successful implementation?
Choosing Which Deep Learning Method is the Right Tool to Use - Case Studies & Best Practices
10 Top Questions Your Startup Should Have Answers For - Presentation & Open Floor Q&A
What are investors looking out for? Hear from leading investors on their key considerations when looking to invest, why these are important and if there are any you are missing.
Lunch & Learn - WORKSHOP
Join industry leaders including Google Cloud, WekaIO and Nephos Technlogies for Lunch!
Rising Stars - Presentations from the next generation of AI Pioneers
Learn more from young prospectives leaders of the AI world as they present their latest research