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.

Using Deep Learning To Reverse the Resolution-Degrading Effects of Conventional Video Capture

Entropix patented technology reconstructs video to 9x it’s captured pixel density while dramatically reducing the bandwidth and storage requirements to only the parts that matter. Our technology is easily integrated with an array of intelligent video analytics and security and public safety video management systems.

Ten years of hardware and software design and innovations in the enterprise HD surveillance market help Nathan shape Entropix as a software product. He is also the Founder and Chairman of Network Optix, an enterprise video management software company listed as the 7th fastest growing software company in the US. (2016, Inc. 5000).

Business Production AI on Cloud and Edges, along with Case Studies

When AI technology is implemented as a business solution, preparation of data, pipeline and algorithm are required, along with peripheral systems to manage GPUs. We have created its own success AI approach featuring all functions needed by implementation: Data Collection, Model Deployment & Training, Model Management, GPU Edge Device Management and many more. This method is already in use by 100 companies such as Manufacture (preventive malfunction, visual inspection, auto picking), Retail (VMD optimization, measure the effect of marketing), Logistics (auto picking, delivery optimization) and etc, with over 450 case studies. 

Born in 1988. Started programming from 10-year-old. Majored in computer graphics in high school and awarded a prize from Minister of Education, Culture, Sports, Science and Technology. In college, researched 3-dimensional computer graphics using Graphical Processing Unit with Compute Unified Device Architecture and proposed some paper in various international conferences. Focused on solving the distributed physical simulation. After that, joined small venture company to manage a multi-million business. In 2011 stayed at Silicon Valley and impressed artificial intelligence technology on research stage. Backed Japan, Started ABEJA as CEO / CTO.

Your Voice is Pure Gold: Understand Speech Data with Deep Learning

With deep learning, businesses are quickly cashing in on the promise of big data. It’s now true that you can record the data, build the model, and get results. Alexa and Siri have given you a glimpse of the new gold -- it’s your speech data. Recorded phone calls and meetings are full of rich information that can now be extracted to help your business. We’ll discuss this new wave of tech and how to deploy AI Speech Products into your business.

Dr. Scott Stephenson is a dark matter physicist turned Deep Learning entrepreneur. He earned a PhD in particle physics from University of Michigan where his research involved building a lab two miles underground in China to detect dark matter. Scott left his physics post-doc research position to co-found Deepgram, a San Francisco-based Speech AI company. Deepgram participated in YCombinator in 2016 and leads private companies in research and deployment for deep learning based Speech AI.

How Deep Learning Enables Autonomous Vehicles

Self-driving vehicles will transform how we work and play. Today, thirty percent of the cars on the road at any given time are trying to park. We spend 500 million hours each day driving to and from the grocery store. The impact of automating these tasks and more is huge. Marble is building self-driving delivery vehicles to give you back this time. I'll talk about why delivery is a good application of robotics, and how deep learning enables us to automate driving.

Co-Founder and software lead, Kevin Peterson, is responsible for the software that enables Marble’s robots to seamlessly navigate the city and efficiently route to their destination. Before Marble, Kevin developed perception systems for the first self-driving car and led development of a lunar lander for the Google Lunar XPRIZE. He has developed robots to chase submarines, rove the Moon, and clean up unexplored ordnance.

End-to-End Deep Learning for Detection, Prevention, and Classification of Cyber Attacks

With more than a million new malicious files created every single day, it is becoming exceedingly difficult for currently existing malware detection methods to detect most of these new sophisticated attacks. In this talk, we describe how Deep Instinct uses an end-to-end deep learning based approach to effectively train its brain on hundreds of millions of files, and thus providing by far the highest detection and prevention rates in the cybersecurity industry today. We will additionally explain how deep learning is employed for malware classification and attribution of attacks to specific entities.

Dr. Eli David is a leading 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, in addition to supervising the research of graduate students in these fields. He 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 based on genetic algorithms and deep learning. The program reached the second place in World Computer Speed Chess Championship. He 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 the Best Paper Award in 2016 International Conference on Artificial Neural Networks. Currently Dr. David is the co-founder and CTO of Deep Instinct, the first company to apply deep learning to cybersecurity. Recently Deep Instinct was recognized by Nvidia as the "most disruptive AI startup".

Machine Learning: A New Approach to Drug Discovery

Modern medicine has given us effective tools to treat some of the most significant and burdensome diseases. At the same time, it is becoming consistently more challenging to develop new therapeutics: clinical trial success rates hover around the mid-single-digit range; the pre-tax R&D cost to develop a new drug (once failures are incorporated) is estimated to be greater than $2.5B; and the rate of return on drug development investment has been decreasing linearly year by year, and some analyses estimate that it will hit 0% before 2020. A key contributor to this trend is that the drug development process involves multiple steps, each of which involves a complex and protracted experiment that often fails. We believe that, for many of these phases, it is possible to develop machine learning models to help predict the outcome of these experiments, and that those models, while inevitably imperfect, can outperform predictions based on traditional heuristics. The key will be to train powerful ML techniques on sufficient amounts of high-quality, relevant data. To achieve this goal, we are bringing together cutting edge methods in functional genomics and lab automation to build a bio-data factory that can produce relevant biological data at scale, allowing us to create large, high-quality datasets that enable the development of novel ML models. Our first goal is to engineer in vitro models of human disease that, via the use of appropriate ML models, are able to provide good predictions regarding the effect of interventions on human clinical phenotypes. Our ultimate goal is to develop a new approach to drug development that uses high-quality data and ML models to design novel, safe, and effective therapies that help more people, faster, and at a lower cost.

Daphne Koller is the CEO and Founder of insitro, a startup company that aims to rethink drug development using machine learning. She is also the Co-Chair of the Board and Co-Founder of Coursera, the largest platform for massive open online courses (MOOCs). Daphne was the Rajeev Motwani Professor of Computer Science at Stanford University, where she served on the faculty for 18 years. She has also been the Chief Computing Officer of Calico, an Alphabet company in the healthcare space. She is the author of over 200 refereed publications appearing in venues such as Science, Cell, and Nature Genetics. Daphne was recognized as one of TIME Magazine’s 100 most influential people in 2012 and Newsweek’s 10 most important people in 2010. She has been honored with multiple awards and fellowships during her career including the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the MacArthur Foundation Fellowship in 2004, and the ACM Prize in Computing in 2008. Daphne was inducted into the National Academy of Engineering in 2011 and elected a fellow of the American Academy of Arts and Sciences in 2014 and of the International Society of Computational Biology in 2017. Her teaching was recognized via the Stanford Medal for Excellence in Fostering Undergraduate Research, and as a Bass University Fellow in Undergraduate Education.

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.

Democratizing Data Science and Machine Learning to the End-User

Machine learning techniques represent powerful tools and has resulted in hypotheses that we use to focus and test the innovative concepts behind these solutions: (1) measurable benefits (efficiency, accuracy, and discovery) to end-users; (2) including machine learning models improves speed and accuracy; (3) comparing traditional data science against new machine learning techniques will identify changes in data science productivity. We funded investments against real-world problems using algorithmic solutions including deep learning, predictive analytics, translation, classification & clustering, and facial/object recognition. The results of this effort answer the hypothesis and determine strengths and weaknesses of exposing algorithms directly to non-expert end-users.

Nina is a Computer Science Software R&D Advisor from the DOE Sandia National Laboratories, detailed as a contractor to JIDO for over ten years. Her multi-disciplinary research background covers applied agent-based and artificial intelligence in diverse domains such as distributed computing, enterprise systems, advanced analytics, frameworks for cognitive encapsulation, computational terrorist recruitment models, video processing, wireless smart sensor, and pervasive computing devices. She provides JIDO with technical guidance for selecting software analytics and architectures used to detect and understand the Counter Threat Network domains, by modeling disparate big data to extract, integrate, visualize, mine, and fuse.

Deep Defense: Using Deep Learning to Fight off Uber Fraudsters

Fraud models are generally based on narrow data streams processed by traditional machine learning models such as gradient boosted machines. Our talk will cover how Uber improved on this by applying deep learning to extract complex feature relationships from high-dimensional datasets such as tapstream and location data. We will cover the lessons we learned while applying deep learning to three fraud use cases: Finding anomalous trip locations based on all Uber trip history; Using tap streams to model normal vs fraud app usage; Computer vision for validating credit cards and IDs.

Karthik is a senior data scientist at Uber focusing on solving fraud problems using machine learning. He builds advanced machine learning models like semi-supervised and deep learning models to detect account takeovers and stolen credit cards. Before Uber, Karthik was a co-founder of his company LogBase where he worked on real-time analytics infrastructure and building models to rate drivers based on their driving behavior. Prior to founding LogBase, he was a founding member of the LinkedIn security team where he developed various security products, with a particular focus on anti-automation efforts.

Application of Deep Neural Networks (DNN) for Modern Data with two examples; Recommender Systems and User Recognition

For almost two decades, we have been dealing with “Modern Data” as the prevalent type of data in many areas of science and technology. “Modern Data” has unique characteristics such as, extreme sparsity, very high correlation, massive size and high dimensionality. A major difficulty is that many of the old machine learning models cannot be applied on Modern Data. In this talk, we show our deploying DNN models in dealing with modern data effectively as , among other advantages, DNN models do not rely on many of the assumptions and abstractions the traditional models depend on.

Kourosh has completed his graduate studies at Stanford (MS, PhD). His PhD dissertation, “A local regularization method using multiple regularization levels“, focused on developing new modeling approaches for a major issue in AI and Machine Learning, regularization. His PhD advisor was Gene Golub. He has been working as entrepreneur and also as Sr AI- Machine Learning scientist at Adobe where he has been the founder and manager of Adobe AI-Machine Learning group. At Adobe, Kourosh has been leading many projects such as "AI-based Recommender Systems", “Automated, Insightful and Interpretable Clustering” and "AI-based User Comprehensive View – Connection of users cross devices, venues and channels".

Campaign Representation Learning in Advertisement

I'll present our work on training deep learning models in conversion rate (CVR) prediction in online advertisement. Specifically, I would like to describe an attention joint embedding model to simultaneously learn heterogeneous sources of information for ads campaigns. The campaign representations were able to capture similarities on ad contents, conversion rules and targeting user groups. Extensive experiments on real-world dataset were conducted to show the effectiveness of the proposed attention campaign embedding model, in comparison with different baselines in CVR prediction.

Key Takeaways:

• Representation learning with heterogeneous information
• Conversion rate prediction
• Cold-start Campaign

Miao is a research scientist from Yahoo Research, working on Native/Display/Search Ads Recommendation and Forecasting, leading the corporate traffic and revenue forecasting projects. He has strong interdisciplinary background in Statistics, Machine Learning and Data Mining, with wide applications in biomedical science and internet technology. Before joining Yahoo, he obtained a PhD / MS in statistics from University of Virginia, and a BS in statistics from Zhejiang University.

Personalization using LSTMs

Personalization is a common theme in social networks and e-commerce businesses. However, personalization at Uber will involve understanding of how each driver/rider is expected to behave on the platform. One way to quantify future behavior is to understand the amount of trips a driver/rider will do. In this talk, I will present our work on training LSTMs for short term trip predictions (4-6 weeks) of each driver on the platform. Specifically, I would like to describe how we combine past engagement data of a particular driver with incentive budgets and use a custom loss function (i.e. zero inflated poisson) to come up with accurate trip predictions using LSTMs. Predicting rider/driver level behaviors can help us find cohorts of high performance drivers, run personalized offers to retain users, and deep dive into understanding of deviations from trip forecasts.

I am a data scientist at Uber focusing on solving forecasting problems using deep learning methods. Previously I worked in the finance industry using machine learning for researching quantitative trading strategies. I hold a Masters from UC Berkeley and a Masters from NYU.

Is Your Organization Ready for ML?

As data science has become increasingly popular, many organizations rush to hire ML experts without laying the proper foundation to ensure their success, including creating proper database architecture, building out essential data science technology, establishing data governance, and instilling data-driven decision-making throughout the organization. Absent these elements, many ML experts join companies excited to deploy their data science expertise only to end up marred in data cleaning or lobbying for tech resources. In this presentation, I discuss how organizations can prepare their organization for success, as well as how candidates can diagnose whether the organization is truly ready for ML.

Dr. Gershkoff is Chief Data Officer for Ancestry, which specializes in genealogy and consumer genomics. Previously, she was Chief Data Officer at Zynga. During her career, she has led the Customer Analytics & Insights team at eBay, served as Chief Data Scientist at WPP, and was Head of Media Planning at Obama for America, where she designed the campaign’s advertising and analytics strategy. Gershkoff was named one of America’s “40 Under 40” leading entrepreneurs, one of the Top 50 Women to Watch in Tech, and one of San Francisco's Most Influential Women in Business. She holds a Ph.D. from Princeton University.

On the Human Element in Building Trustworthy Consumer-Centric AI Products in Banking

As the demand for personalized AI-empowered assistants along the value chain in banking is growing, the reliability and interpretability of their underlying models become paramount to build trust and sustainable value for consumers. This presentation focuses on human-augmented training of domain-specific neural networks and discusses their use cases and recent advances in methods to address model transparency, adversarial robustness, algorithmic bias and fairness in the context of the current state and perspectives on the evolution of the global regulatory landscape around AI.

Manuel is currently Lead Data Scientist and Head of Predictive Analytics in Banking Products at UBS. Previously, he's been a senior advisor and AI cloud platform lead at Ernst & Young, developed numerous AI-driven business solutions for international organizations, and held managing roles in cross-border audit & advisory engagements and leading international research collaborations with contributions to AI research, Cognitive Systems and Particle Physics.

Are you new to the field of deep learning? This interactive session will be lead by AI experts to provide you with the opportunity to ask any questions you have regarding the current technological landscape; key investors in the sector; business applications; integration challenges; and what challenges can it solve.

The Commercialization of Deep Learning

The world has not seen a more disruptive and powerful technology since the inception of the internet itself. Deep Learning is going to transform every single industry that it touches. At Sightline Innovation, our goal is to make this technology accessible to industry and immediately applicable without the need for data scientists or Phds. Sightline Innovation has designed a machine learning as a service platform designed to address problems facing industry today. We are at the forefront of commercializing machine learning applications on our unique technology platform, mlCortex (TM).

The world has not seen a more disruptive and powerful technology since the inception of the internet itself. Deep Learning is going to transform every single industry that it touches. At Sightline Innovation, our goal is to make this technology accessible to industry and immediately applicable without the need for data scientists or Phds. Sightline Innovation has designed a machine learning as a service platform designed to address problems facing industry today. As Founder and COO at Sightline Innovation, Maithili is in charge of ensuring smooth delivery of their solutions into a customer’s organization. Maithili is a long time entrepreneur and leverages decades of engineering management experience to ensure customers can harness the power of deep learning and achieve immediate gains.

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.

Practical Safety Issues with AI Products

The Consortium for Safer AI is a membership based organization with the common cause to increase the shared knowledge on the practical safety issues of AI enabled products. The Consortium directly funds research that helps support this overall goal.

Mahmood Tabaddor is founder of the Consortium for Safer AI with UL LLC as one of its founding sponsors. His main focus has been on increasing the responsible use of predictive modeling and analytics tools in the context of product failure analysis. Mahmood has his PhD in nonlinear system theory from Virginia Tech.

Getting ROI from ML - the Good, the Bad, the Ugly

The hype surrounding AI and machine learning can make you think it can do anything with ease. All too often organizations find limitations long after budgets are exhausted. In this workshop will examine how to match machine learning capacities with your organization to ensure ROI. We will also look at how to match a team and machine learning framework to fit well with the organization's goals. Plan to leave with a better understanding of the practical side of machine learning and how to monetize it in your organization.

Rix Ryskamp is the founder and CEO of useAIble, which developed the Ryskamp Machine Learning System. This alternative paradigm for machine learning solves more types problems and does it faster, all while providing transparency at every step of the learning cycle. The system is tailor-made to deliver business value. Rix has been a serial entrepreneur for 20 years and has sold several software and technology businesses. In every venture he’s undertaken, Rix has relied on his uncanny ability to see problems in a unique light and innovative new and exciting solutions.

How machine learning applications can deliver a superior customer support experience

In this interactive roundtable discussion, we will explore: What are useful criteria to make buy v. build decisions for key applications of AI and ML technologies?; Are large data sets intrinsically necessary to build compelling AI and ML applications?; What are best practices to measure the business impact of AI and ML applications? Are they any different than for previous generation applications?; What are the key trends in the C-suite when it comes to driving efficiencies from these new technologies? Who is doing it well?; How do you distinguish 'thin layer' applications of AI and ML from more meaningful and deep applications? How do you apply this to vendor/partner selection?

Mahesh Ram is the CEO of Solvvy. Mahesh has been a founder or early employee of three cloud-based software companies in his career. Two were successful exits (after creating new categories), and acquired by large public companies (most recently GlobalEnglish acquired by Pearson PLC in 2012).

How can we apply deep learning and AI to solve some of the biggest environmental challenges of the 21st Century? How can deep learning and satellite imagery be used to map land use and better conservation of natural resources? Can we use AI to create a digital dashboard for our planet to help us to monitor environmental systems?

The opportunity for startups to meet with leading AI investors, VCs and industry experts to pitch ideas, and gain advice on their business ideas and strategies. View the VC information here.

Building Customer Service Virtual Agents Faster and Smarter by Analyzing Human-to-human Interactions with ML

Innovative companies are modernizing their contact centers by adopting AI-powered virtual agents to improve customer satisfaction by providing helpful service 24/7, globally. But for brands building a virtual agent, the critical questions are: What are the best use cases for the virtual agent? What questions do customers ask and how do they phrase them (including follow-ups)? How to ensure virtual agents responds helpfully? What is the optimal time to do a hand-off to a live agent? Particularly for companies with large customer service operations, answering the questions above manually is slow, based on guesswork, and creates gaps that put customer satisfaction at risk. In this session, attendees will learn how machine learning techniques can unlock actionable insights from human-to-human interactions in contact center chat and call logs -- making development much faster, and virtual agents much smarter.

Ofer Ronen is the general manager of Chatbase, a conversational analytics service brought to you by Area 120 (a products incubator operated by Google). Previously, he was CEO of Pulse.io, an app performance monitoring service (acquired by Google), and CEO of ad network Sendori (acquired by IAC). Ofer is a startup mentor at Stanford and an angel investor in Lyft, Palantir, and Klout. He holds an MS in artificial intelligence from Michigan and an MBA from Cornell.

What are the best system infrastructures for AI and machine learning software? What are the key challenges of running machine learning algorithms? How can you maintain AI practicality in your business? How can we best address scalability issues? Join in this interactive breakout session to lear more.

Panelists: 1. Ashmeet Sidana, Founder & Managing Partner, Engineering Capital @ashmeetsidana 2. Evan R. Sparks, Co-Founder & CEO, Determined AI @evanrsparks 3. Eli David, CTO, Deep Instinct @drelidavid 4. Moderator: Manuel Proissl, Senior Quant & Data Scientist, EY @ey_us

Image creation using Generative Adversarial Networks (GANs)

This workshop will guide you through the process of training a Generative Adversarial Network (GAN) to generate image contents in DIGITS. You will learn how to: Use Generative Adversarial Networks (GANs) to create handwritten numbers; Visualize the feature space and use attribute vector to generate image analogies; Train a GAN to generate images with set attributes. Upon completion of this workshop, you will be able to use GANs to generate images by manipulating feature space.

David Nola is a Deep Learning Solutions Architect at NVIDIA specializing in computer vision workflows and time series problems. He works with professionals in Healthcare, the Industrial Internet of Things, and Financial Services to GPU accelerate their Data Science processes and provide education and proof of concepts for deep learning projects. He got his Master's degree from UCLA in Computer Science where he specialized in Artificial Intelligence, Deep Learning, and Hyperparameter Optimization.