Sep 9–12, 2019

Schedule

Monday, 09/09/2019

8:00am

8:00am–9:00am Monday, 09/09/2019
Morning Coffee (1h)

9:00am

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9:00am–5:00pm Monday, 09/09/2019
Training
Robert Schroll (The Data Incubator)
The TensorFlow library provides computational graphs with automatic parallelization across resources, ideal architecture for implementing neural networks. Robert Schroll walks you through TensorFlow's capabilities in Python from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications. Read more.
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9:00am–5:00pm Monday, 09/09/2019
Training
Delip Rao (AI Foundation)
Delip Rao explores natural language processing (NLP) with deep learning, walking you through neural network architectures and NLP tasks. You'll learn how to apply these architectures for those tasks. Read more.
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9:00am–5:00pm Monday, 09/09/2019
Secondary topics:  Ethics, Security, and Privacy
Nicholas Cifuentes-Goodbody (The Data Incubator)
Dylan Bargteil and Michael Li lead you through a nontechnical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls. You’ll pick up the language and develop a framework to be able to effectively engage with technical experts and use their input and analysis for your business’s strategic priorities and decision making. Read more.
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9:00am–5:00pm Monday, 09/09/2019
Training
Rich Ott (The Data Incubator)
PyTorch is a machine learning library for Python that allows you to build deep neural networks with great flexibility. Its easy-to-use API and seamless use of GPUs make it a sought-after tool for deep learning. Get the knowledge you need to build deep learning models using real-world datasets and PyTorch with Rich Ott. Read more.
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9:00am–5:00pm Monday, 09/09/2019
Training
Amit Kapoor (narrativeVIZ), Bargava Subramanian (Binaize Labs)
Recommendation systems play a significant role—for users, a new world of options; for companies, it drives engagement and satisfaction. Amit Kapoor and Bargava Subramanian walk you through the different paradigms of recommendation systems and introduce you to deep learning-based approaches. You'll gain the practical hands-on knowledge to build, select, deploy, and maintain a recommendation system. Read more.
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9:00am–5:00pm Monday, 09/09/2019
Training
Wenming Ye (Amazon Web Services), Miro Enev (NVIDIA)
Machine learning (ML) and deep learning (DL) projects are becoming increasingly common at enterprises and startups alike and have been a key innovation engine for Amazon businesses such as Go, Alexa, and Robotics. Wenming Ye and Miro Enev detail a practical next step in DL learning with instructions, demos, and hands-on labs. Read more.

10:30am

10:30am–11:00am Monday, 09/09/2019
Morning Break (30m)

12:30pm

12:30pm–1:30pm Monday, 09/09/2019
Lunch (1h)

3:00pm

3:00pm–3:30pm Monday, 09/09/2019
Afternoon Break (30m)

Tuesday, 09/10/2019

8:00am

8:00am–9:00am Tuesday, 09/10/2019
Morning Coffee (1h)

9:00am

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9:00am–12:30pm Tuesday, 09/10/2019
Lukas Biewald (Weights & Biases)
Join Lukas Biewald to build and deploy long short-term memories (LSTMs), grated recurrent units (GRUs), and other text classification techniques using Keras and scikit-learn. Read more.
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9:00am–12:30pm Tuesday, 09/10/2019
Tutorial
Implementing AI
AI assistants are among the most in demand topics in tech. Get hands-on experience with Justina Petraityte as you develop intelligent AI assistants based entirely on machine learning and using only open source tools—Rasa NLU and Rasa Core. You'll learn the fundamentals of conversational AI and the best practices of developing AI assistants that scale and learn from real conversational data. Read more.
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9:00am–5:00pm Tuesday, 09/10/2019
Kristian Hammond (Northwestern Computer Science)
Even as AI technologies move into common use, many enterprise decision makers remain baffled about what the different technologies actually do and how they can be integrated into their businesses. Rather than focusing on the technologies alone, Kristian Hammond provides a practical framework for understanding your role in problem solving and decision making. Read more.
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9:00am–12:30pm Tuesday, 09/10/2019
Skyler Thomas (MapR)
The popular open source Kubeflow project is one of the best ways to start doing machine learning and AI on top of Kubernetes. However, Kubeflow is a huge project with dozens of large complex components. Skyler Thomas dives into the Kubeflow components and how they interact with Kubernetes. He explores the machine learning lifecycle from model training to model serving. Read more.
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9:00am–12:30pm Tuesday, 09/10/2019
Tutorial
Implementing AI
Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee), Mars Geldard (University of Tasmania)
Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Tim Nugent, and Mars Geldard teach you how to use solution-driven ML AI problem solving with a game engine. Read more.
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9:00am–12:30pm Tuesday, 09/10/2019
Secondary topics:  Machine Learning
Ira Cohen (Anodot)
While the role of the manager doesn't require deep knowledge of ML algorithms, it does require understanding how ML-based products should be developed. Ira Cohen explores what it takes to manage ML-based products, the cycle of developing ML-based capabilities (or entire products), and the role of the (product) manager in each step of the cycle. Read more.
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9:00am–12:30pm Tuesday, 09/10/2019
Jason Dai (Intel), Yuhao Yang (Intel), Jiao(Jennie) Wang (Intel), Guoqiong Song (Intel)
Jason Dai, Yuhao Yang, Jennie Wang, and Guoqiong Song explain how to build and productionize deep learning applications for big data with Analytics Zoo—a unified analytics and AI platform that seamlessly unites Spark, TensorFlow, Keras, and BigDL programs into an integrated pipeline—using real-world use cases from JD.com, MLS Listings, the World Bank, Baosight, and Midea/KUKA. Read more.
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10:30am

10:30am–11:00am Tuesday, 09/10/2019
Morning Break (30m)

12:30pm

12:30pm–1:30pm Tuesday, 09/10/2019
Lunch (1h)

1:30pm

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1:30pm–5:00pm Tuesday, 09/10/2019
Tutorial
Implementing AI
Joel Grus (Allen Institute for Artificial Intelligence)
AllenNLP is a PyTorch-based library designed to make it easy to do high-quality research in natural language processing (NLP). Joel Grus explains what modern neural NLP looks like, and you'll get your hands dirty training some models, writing some code, and learning how you can apply these techniques to your own datasets and problems. Read more.
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1:30pm–5:00pm Tuesday, 09/10/2019
Tutorial
Implementing AI
Robert Nishihara (UC Berkeley), Philipp Moritz (University of California, Berkeley), Ion Stoica (UC Berkeley)
Building AI applications is hard, and building the next generation of AI applications, such as online and reinforcement learning (RL), is more challenging. Robert Nishihara, Philipp Moritz, and Ion Stoica lead a deep dive into Ray—a general-purpose framework for programming your cluster—its API, and system architecture and examine application examples, including state-of-the-art algorithms. Read more.
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1:30pm–5:00pm Tuesday, 09/10/2019
Tutorial
Implementing AI
Secondary topics:  Machine Learning
Boris Lublinsky (Lightbend), Chaoran Yu (Lightbend)
Boris Lublinsky and Chaoran Yu examine ML use in streaming data pipelines, how to do periodic model retraining, and low-latency scoring in live streams. Learn about Kafka as the data backplane, the pros and cons of microservices versus systems like Spark and Flink, tips for TensorFlow and SparkML, performance considerations, metadata tracking, and more. Read more.
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1:30pm–5:00pm Tuesday, 09/10/2019
Neil Conway (Determined AI), Yoav Zimmerman (Determined AI)
Success with DL requires more than just TensorFlow or Keras. Neil Conway and Yoav Zimmerman detail a range of practical problems faced by DL practitioners and the software tools and techniques you'll need to address the problems, including data prep and augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile and edge optimization. Read more.
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1:30pm–5:00pm Tuesday, 09/10/2019
Tutorial
Implementing AI
Mo Patel (Independent)
PyTorch captured the minds of ML researchers and developers upon its arrival. Now it's matured into a production-ready ML framework with use cases and applications. Mo Patel explores the PyTorch lifecycle via hands-on examples such as image and text classification and linear modeling. You'll cover other aspects of ML such as transfer learning, data modeling, and deploying to production in labs. Read more.
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1:30pm–5:00pm Tuesday, 09/10/2019
Chris Butler (IPsoft)
Purpose, a well-defined problem, and trust are important factors to any system, especially those that employ AI. Chris Butler leads you through exercises that borrow from the principles of design thinking to help you create more impactful solutions and better team alignment. Read more.
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1:30pm–7:30pm Tuesday, 09/10/2019
Event
Join us during the O’Reilly Artificial Intelligence Conference to learn how to deploy enterprise AI solutions with Intel and its partner ecosystem. This event features offerings for a wide variety of industries and AI use cases. Read more.

3:00pm

3:00pm–3:30pm Tuesday, 09/10/2019
Afternoon Break (30m)

5:00pm

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5:00pm–7:00pm Tuesday, 09/10/2019
Event
If you're an AI pioneer, we’d love for you to participate in the Emerging AI Showcase at the O’Reilly AI Conference. Our team of investors, entrepreneurs, and industry experts will review all submissions and invite 10 finalists to present their technologies and tell their stories during the Emerging AI Showcase on Tuesday, September 10. Read more.

7:00pm

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7:00pm–9:00pm Tuesday, 09/10/2019
Event
Get to know your fellow attendees over dinner. We've made reservations for you at some of the most sought-after restaurants in town. Read more.

Wednesday, 09/11/2019

8:00am

8:00am–9:00am Wednesday, 09/11/2019
Morning Coffee (sponsored by Gamalon) (1h)

8:15am

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8:15am–8:45am Wednesday, 09/11/2019
Event
Ready, set, network! Meet fellow attendees who are looking to connect at the AI Conference. We'll gather before Wednesday and Thursday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun. Read more.

8:45am

8:45am–9:00am Wednesday, 09/11/2019
TBC

9:00am

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9:00am–9:05am Wednesday, 09/11/2019
Keynote
Ben Lorica (O'Reilly Media), Julie Shin Choi (Intel AI), Roger Chen (Computable)
Program chairs Ben Lorica, Julie Choi, and Roger Chen open the first day of keynotes. Read more.

9:05am

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9:05am–9:10am Wednesday, 09/11/2019
Keynote
Ben Lorica (O'Reilly Media), Roger Chen (Computable)
Details to come. Read more.

9:10am

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9:10am–9:25am Wednesday, 09/11/2019
Keynote
Eric Gardner (Intel)
Details to come. Read more.

9:25am

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9:25am–9:35am Wednesday, 09/11/2019
Keynote
Dinesh Nirmal examines how, with a unified, prescriptive information architecture, organizations can successfully unlock the value of their data for AI as well as trust and control the business impact and risks of AI while coexisting in a multicloud world. Read more.

9:35am

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9:35am–9:50am Wednesday, 09/11/2019
Keynote
Secondary topics:  Ethics, Security, and Privacy
Sarah Bird (Microsoft)
Researchers and practitioners from different disciplines have highlighted the ethical and legal challenges posed by the use of machine learning in many current and future real-world applications. Sarah Bird outlines her perspective on some of the major challenges in responsible AI development and examines promising new tools and technologies to help enable it in practice. Read more.

9:50am

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9:50am–9:55am Wednesday, 09/11/2019
Keynote
Kurt Muehmel (Dataiku)
With the adoption of AI in the enterprise accelerating, its impacts—both positive and negative—are rapidly increasing. Kurt Muehmel explores why the builders of these new AI capabilities all bear some moral responsibility for ensuring that their products create maximum benefit and minimal harm. Read more.

9:55am

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9:55am–10:10am Wednesday, 09/11/2019
Keynote
Andrew Feldman (Cerebras Systems)
Details to come. Read more.

10:15am

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10:15am–10:30am Wednesday, 09/11/2019
Keynote
Secondary topics:  Machine Learning
Srinivas Narayanan (Facebook)
Srinivas Narayanan takes you beyond fully supervised learning techniques, the next change you're seeing in AI. Read more.

10:30am

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10:30am–10:35am Wednesday, 09/11/2019
Keynote
O'Reilly AI program chairs close the first day of keynotes. Read more.

10:35am

10:35am–11:05am Wednesday, 09/11/2019
Morning Break (sponsored by Dataiku) (30m)

11:05am

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11:05am–11:45am Wednesday, 09/11/2019
Bharath Kadaba (Intuit)
To unleash the full potential of AI, Intuit envisions a future that melds the best capabilities of machines and humans to deliver personalized customer experiences, all on one secure platform. Bharath Kadaba examines how Intuit combines rules-based knowledge engineering with data-driven machine learning and natural language processing to build the human-expert-in-the-loop AI systems of the future. Read more.
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11:05am–11:45am Wednesday, 09/11/2019
Enhao Gong (Subtle Medical), Greg Zaharchuk (Stanford University)
Enhao Gong and Greg Zaharchuk detail AI solutions, cleared by the FDA and powered by industry framework, that deliver 4x–10x faster MRI scans, 4x faster PET scans, and up to 10x dosage reduction. Clinical evaluation at hospitals such as Hoag Hospital, UCSF, and Stanford demonstrates the significant and immediate values of AI to improve the productivity of healthcare workflow. Read more.
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11:05am–11:45am Wednesday, 09/11/2019
Session
Implementing AI
Urs Köster (Cerebras Systems)
Session by Urs Köster, head of machine learning at Cerebras Systems. Read more.
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11:05am–11:45am Wednesday, 09/11/2019
Session
Sponsored
Manish Bhide (IBM Watson), Rohan Vaidyanathan (IBM Watson)
With the potential to transform businesses, AI has become a strategic imperative for most enterprises. A lot of investment is toward machine learning and deep learning models to support business applications. However, as Manish Bhide and Rohan Vaidyanathan explain, these models bring about risks and uncertainties that are difficult to manage. Read more.
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11:05am–11:45am Wednesday, 09/11/2019
mayukh bhaowal (Salesforce)
AI product managers (PMs) are rising. With the shift from the digital revolution to the AI revolution, the old product management workflow and frameworks are crumbling down. Mayukh Bhaowal explores new ways to manage AI products and outlines how AI executive roles are different and what toolbox you'll need to succeed in the era of artificial intelligence. Read more.
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11:05am–11:45am Wednesday, 09/11/2019
Session
Implementing AI
Secondary topics:  Machine Learning
Artificial intelligence has revolutionized the way we live, work, and play. With the help of AI, electronic payments have become more secure and more convenient for consumers globally—regardless of currency or form factor. Chiranjeet Chetia and Carolina Barcenas explore a use case in which data and deep learning converge to root out malicious actors and make the payments ecosystem more secure. Read more.
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11:05am–11:45am Wednesday, 09/11/2019
Session
Implementing AI
Huaixiu Zheng (Uber)
Uber applies natural language processing (NLP) and conversational AI in a number of business domains. Huaixiu Zheng details how Uber applies deep learning in the domain of NLP and conversational AI. You'll learn how Uber implements AI solutions in a real-world environment, as well as cutting-edge research in end-to-end dialogue systems. Read more.
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11:05am–11:45am Wednesday, 09/11/2019
Session
Sponsored
Kurt Muehmel (Dataiku)
We're rapidly closing in on a future where large companies across different sectors will be enriching every business process and decision with AI and gaining a sustained competitive edge as a result. Join Kurt Muehmel on a forward-looking exploration of companies that are already well on their way toward this target. He details Dataiku's vision of the journey ahead. Read more.
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11:05am–11:45am Wednesday, 09/11/2019
Session
Implementing AI
Jasjeet Thind (Zillow)
Advances in AI and deep learning enable new technologies to mimic how the human brain interprets scenes, objects, and images, which has major implications for businesses that need to extract meaning from overwhelming quantities of unstructured data. Jasjeet Thind walks you through how implementing computer vision based in deep neural networks allows machines to see images in an entirely new way. Read more.

11:55am

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11:55am–12:35pm Wednesday, 09/11/2019
Huaiyu Zhu (IBM Research - Almaden), Dulce Ponceleon (IBM Research - Almaden), Yunyao Li (IBM Research - Almaden)
Natural language understanding (NLU) underlies a wide range of applications and services. Rich resources available for English do not exist for most other languages, but the questions of how to expand these resources without duplicating effort and if it's possible to develop language-agnostic NLU-dependent applications remains. Huaiyu Zhu, Dulce Ponceleon, and Yunyao Li believe the answer is yes. Read more.
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11:55am–12:35pm Wednesday, 09/11/2019
Secondary topics:  Design, Interfaces, and UX
Bahman Bahmani (Rakuten)
Developments in ML and DL provided remarkable advances in the predictive capabilities of AI. However, the black box nature of the modern models creates challenges for those looking to adopt these techniques. Bahman Bahmani examines a framework and presents design and operating principles, recommendations, and best practices for human-AI integration in enterprise workflows, products, and services. Read more.
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11:55am–12:35pm Wednesday, 09/11/2019
Session
Implementing AI
Every NLP-based document-processing solution depends on converting documents or images to machine-readable text using an OCR solution, but accuracy is limited by the quality of the images. Nagendra Shishodia, Solmaz Torabi, and Chaithanya Manda examine how generative adversarial networks (GANs) can bring significant efficiencies by enhancing resolution and denoising scanned images. Read more.
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11:55am–12:35pm Wednesday, 09/11/2019
Session
Sponsored
Ramesh Radhakrishnan (Dell Technologies), John Zedlewski (NVIDIA)
Data scientists and machine learning engineers need the flexibility to work in multiple environments without wasting precious time configuring hardware and software and modifying code. Ramesh Radhakrishnan and John Zedlewski walk you through deploying a simple set of technologies for executing end-to-end pipelines entirely on GPUs. Read more.
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11:55am–12:35pm Wednesday, 09/11/2019
Michael Chui (McKinsey Global Institute), James Manyika (McKinsey & Company)
AI has the potential to create substantial value for business and the global economy. It's less well understood how it can address some of the world’s biggest societal challenges. Michael Chui and James Manyika examine the ethical implications of AI and how you can leverage the technology for good while considering the wide-reaching repercussions on business and human society alike. Read more.
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11:55am–12:35pm Wednesday, 09/11/2019
Session
Implementing AI
Secondary topics:  Deep Learning tools
Michael Bauer (Sylabs, Inc.)
Containerization technology can build distributed, scalable, and complex neural networks by leveraging decoupled resource pools—pools that would not traditionally be amenable to such a task. Using Singularity, Michael Bauer demonstrates the approach of treating a container as a decoupled neural interface (DNI) to enable novel applications for neural networks that were previously impractical. Read more.
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11:55am–12:35pm Wednesday, 09/11/2019
Session
Implementing AI
Hagay Lupesko (Facebook)
Hagay Lupesko discusses AI-powered personalization at Facebook: the challenges and practical techniques applied to overcome these challenges. You will learn about deep learning based personalization modeling, scalable training, and the accompanying system design approaches that are applied in practice. Read more.
11:55am–12:35pm Wednesday, 09/11/2019
TBC
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11:55am–12:35pm Wednesday, 09/11/2019
Joy Rimchala (Intuit), TJ Torres (Intuit), Xiao Xiao (Intuit), Hui Wang (Intuit)
Document understanding is a company-wide initiative at Intuit that aims to make data preparation and entry obsolete through the application of computer vision and machine learning. A team of data scientists, Joy Rimchala, TJ Torres, Xiao Xiao, and Hui Wang, detail the design and modeling methodologies used to build this platform as a service. Read more.

12:35pm

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12:35pm–1:45pm Wednesday, 09/11/2019
Event
If you’re looking to make new professional connections and hear ideas for supporting inclusion, come to the diversity networking lunch. Read more.
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12:35pm–1:45pm Wednesday, 09/11/2019
Event
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics. Read more.

1:45pm

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1:45pm–2:25pm Wednesday, 09/11/2019
Vadim Pinskiy (Nanotronics)
Statistical manufacturing has remained largely unchanged since postwar Japan. AI and DL allow for nonlinear feedback and feed-forward systems to be integrated for real-time monitoring and evolution of each part assembly. Vadim Pinskiy explores a system capable of detecting, classifying, and automatically correcting for manufacturing defects in a multinodal process. Read more.
1:45pm–2:25pm Wednesday, 09/11/2019
Secondary topics:  Mobile Computing, IoT, Edge
TBC
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1:45pm–2:25pm Wednesday, 09/11/2019
Session
Implementing AI
Akhilesh Kumar (Adobe)
Photographic defects such as noise, exposure, and blur can ruin the perfect shot. Adobe has developed a solution based on GAN that can identify the defective region images and fix it. Akhilesh Kumar explores how this solution, which can also be applied to fix videos, is better than traditional algorithms and means you won't have to spend hours manually editing the images. Read more.
1:45pm–2:25pm Wednesday, 09/11/2019
TBC
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1:45pm–2:25pm Wednesday, 09/11/2019
Yi Zhang (Rulai | University of California, Santa Cruz)
Consumers want everything now, at their fingertips, with very little effort. To meet these demands and compete, companies need to fundamentally rethink how they operate. Yi Zhang explores some predictions on how conversational technology will evolve from its current state in 2019. She outlines some common misunderstandings about the technologies and provides case studies from several industries. Read more.
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1:45pm–2:25pm Wednesday, 09/11/2019
Session
Implementing AI
Secondary topics:  Computer Vision, Machine Learning
Roshan Sumbaly (Facebook)
There aren't many systems in the world that need to run hundreds of computer vision models (from classification to segmentation) on billions of visual entities (images, videos, 3-D) daily. Roshan Sumbaly walks you through the challenges faced while building such a platform and how, surprisingly, a lot of the answers were found in traditional software engineering best practices. Read more.
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1:45pm–2:25pm Wednesday, 09/11/2019
Session
Implementing AI
Secondary topics:  Deep Learning tools
Paige Bailey (Google)
TensorFlow 2.0 has landed. Paige Bailey walks you through TensorFlow (TF) 2.0's new features, usability enhancements, performance increases, and focus on developer productivity. You'll use the TF 2.0 migration tool to transition a model from TensorFlow 1.x to 2.0 and deploy an end-to-end open source machine learning model. Read more.
1:45pm–2:25pm Wednesday, 09/11/2019
TBC
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1:45pm–2:25pm Wednesday, 09/11/2019
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Sequence to sequence (S2S) modeling using neural networks is increasingly becoming mainstream. In particular, it's been leveraged for applications such as speech recognition, language translation, and question answering. Arun Kejariwal and Ira Cohen walk you through how S2S modeling can be leveraged for the aforementioned use cases, visualization, real-time anomaly detection, and forecasting. Read more.

2:35pm

2:35pm–3:15pm Wednesday, 09/11/2019
TBC
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2:35pm–3:15pm Wednesday, 09/11/2019
Bastiane Huang (OSARO)
Machine learning has enabled the move from manually programming robots to allowing machines to learn and adapt to changes in the environment. Bastiane Huang examines how AI-enabled robots are used in warehouse automation, including recent progress in deep reinforcement learning, imitation learning, and real world requirements for various industrial problems. Read more.
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2:35pm–3:15pm Wednesday, 09/11/2019
Wei Cai (Cox Communications)
Real-time traffic volume prediction is vital in proactive network management, and many forecasting models have been proposed to address this. However, most are unable to fully use the information in traffic data to generate efficient and accurate traffic predictions for a longer term. Wei Cai explores predicting multistep, real-time traffic volume using many-to-one LSTM and many-to-many LSTM. Read more.
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2:35pm–3:15pm Wednesday, 09/11/2019
Session
Sponsored
Pankaj Goyal (Hewlett Packard Enterprise), Nanda Vijaydev (Hewlett Packard Enterprise)
Join Pankaj Goyal and Nanda Vijaydev to learn how HPE put AI into action and helping enterprises unlock the value of their data with a proven, practical approach to AI. Read more.
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2:35pm–3:15pm Wednesday, 09/11/2019
Secondary topics:  Machine Learning
Peter Bailis (Sisu Data | Stanford University)
Despite a meteoric rise in data volumes within modern enterprises, enabling nontechnical users to put this data to work in diagnostic and predictive tasks remains a fundamental challenge. Peter Bailis details the lessons learned in building new systems to help users leverage the data at their disposal, drawing on production experience from Facebook, Microsoft, and the Stanford DAWN project. Read more.
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2:35pm–3:15pm Wednesday, 09/11/2019
Session
Implementing AI
Secondary topics:  Deep Learning tools
Evan Sparks (Determined AI)
Evan Sparks walks you through the current gap between the AI haves (Google, Facebook, Amazon, and Microsoft) and the AI have-nots (the rest of the industry), from the perspective of software infrastructure for model development. You'll learn some of the opportunities for end-to-end system design to enable rapid iteration and scale in AI application development. Read more.
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2:35pm–3:15pm Wednesday, 09/11/2019
Session
Implementing AI
Joseph Spisak (Facebook), Hao Lu (Facebook)
Learn how PyTorch is being used to help accelerate the path from novel research to large-scale production deployment in computer vision, natural language processing, and machine translation at Facebook with Joseph Spisak and Hao Lu. Read more.
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2:35pm–3:15pm Wednesday, 09/11/2019
Session
Sponsored
Moon Soo Lee (Zepl | Apache Zeppelin), Louis Huard (Zepl)
A key step in the data science workflow is rapid model development; however, gaps still exist. Teams are moving from siloed to sharing and reusing models, code, and results. There are also in challenges deploying models into production using tools like Kubeflow and TensorFlow. Louis Huard and Moon Soo Lee examine how leading companies solve these issues, and how you can improve your workflow. Read more.
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2:35pm–3:15pm Wednesday, 09/11/2019
Secondary topics:  Machine Learning
Mark Weber (MIT-IBM Watson AI Lab)
Organized crime inflicts human suffering on a massive scale: upward of 700,000 people per year are "exported" in a $40 billion human-trafficking industry enslaving an estimated 40 million people. Such nefarious industries rely on sophisticated money-laundering schemes to operate. Mark Weber explores how a new field of AI called graph convolutional networks can help. Read more.

3:15pm

3:15pm–4:00pm Wednesday, 09/11/2019
Afternoon Break (sponsored by Dell Technologies) (45m)

4:00pm

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4:00pm–4:40pm Wednesday, 09/11/2019
Secondary topics:  Reinforcement Learning
julien forgeat (Ericsson)
Cell shaping is used to configure the radio antenna parameters to improve the service quality. Julien Forgeat explores a reinforcement learning (RL) approach to configure radio antenna parameters using industry-leading radio simulators from Ericsson and UC Berkeley RISELab's Ray distributed compute framework together with its built-in RL algorithm in RLlib. Read more.
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4:00pm–4:40pm Wednesday, 09/11/2019
Dexter Hadley (University of California, San Francisco)
Typically, large healthcare institutions have large-scale quantities of clinical data to facilitate precision medicine through an AI paradigm. However, this hardly translates into improved care. Dexter Hadley details how UCSF uses NLP to curate clinical data for over 1M mammograms and how deep learning, blockchain, and other approaches translates this into precision oncology. Read more.
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4:00pm–4:40pm Wednesday, 09/11/2019
Li Erran Li (Scale AI | Columbia University)
Tremendous progress has been made in applying machine learning to autonomous driving. Li Erran Li explores recent advances in applying machine learning to solving the perception, prediction, planning, and control problems of autonomous driving as well as some key research challenges. Read more.
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4:00pm–4:40pm Wednesday, 09/11/2019
Session
Sponsored
Margaret Laffan (TalentSeer | BoomingStar Ventures)
With the rapid advancement of AI technology and commercial breakthroughs, building a strong AI team becomes increasingly critical for business success in the high-tech era. Margaret Laffan helps tech and talent leaders to better understand the AI talent market and explores best practices for building, nurturing, and retaining the right team to accelerate their business growth. Read more.
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4:00pm–4:40pm Wednesday, 09/11/2019
Jana Eggers (Nara Logics)
Though Nara Logics doesn't always follow them, it has developed great best practices for designing, developing, and delivering great software. Jana Eggers is here to explore what happens when you start adding AI to great software by covering six key features of software development that are similar when adding AI, six that are different, and how to adjust. Read more.
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4:00pm–4:40pm Wednesday, 09/11/2019
Session
Implementing AI
Ashish Bansal (Twitter)
Twitter has amazing and unique content generated at an enormous velocity internationally in multiple languages. Ashish Bansal provides you with insight into the unique recommendation system challenges at Twitter’s scale and what makes this a fun and challenging task. Read more.
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4:00pm–4:40pm Wednesday, 09/11/2019
Session
Implementing AI
Secondary topics:  Text, Language, and Speech
Moshe Wasserblat demonstrates the challenges and reviews the latest AI solutions in deploying natural language processing (NLP) in commercial environments, specifically dealing with the small amount of data available for training and scaling across different domains. Read more.
4:00pm–4:40pm Wednesday, 09/11/2019
TBC
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4:00pm–4:40pm Wednesday, 09/11/2019
Stacy Ashworth (SelectData), Alberto Andreotti (John Snow Labs)
Much business data is still scanned or snapped documents, which is challenging. Stacy Ashworth and Alberto Andreotti explore a real-world case on reading, understanding, classifying, and acting on facts extracted from such image files using state-of-the-art, open source, deep learning-based optical character recognition (OCR), natural language processing (NLP), and forecasting libraries at scale. Read more.

4:50pm

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4:50pm–5:30pm Wednesday, 09/11/2019
Dylan Glas (Futurewei Technologies), Phoebe Liu (Figure Eight)
Robot technologies are becoming more capable and affordable. Yet even though technologies like natural language processing, mapping, and navigation are becoming more mature and standardized, it's often difficult to quantify human social behavior with algorithms. Dylan Glas and Phoebe Liu highlight some of the ongoing research to enable human-robot interaction. Read more.
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4:50pm–5:30pm Wednesday, 09/11/2019
Juby Jose (Intel), Rohit Israni (Intel)
Across segments, enterprises are exploring novel ways of providing stellar customer service. Conversational AI is delivering just that—high-quality customer service, available 24-7, and in a geography-agnostic manner. Juby Jose and Rohit Israni explore how enterprise customer service is being reimagined with the power of conversational AI. Read more.
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4:50pm–5:30pm Wednesday, 09/11/2019
Sijun He (Twitter), Ali Mollahosseini (Twitter)
Twitter is what’s happening in the world right now. To connect users with the best content, Twitter needs to build up a deep understanding of its noisy and temporal text content. Sijun He provides an overview of the named entity recognition (NER) system at Twitter and explores the challenges Twitter faces to build and scale a large-scale deep learning system to annotate 500 million tweets per day. Read more.
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4:50pm–5:30pm Wednesday, 09/11/2019
Session
Sponsored
Labhesh Patel (Jumio)
Labhesh Patel explores how deep learning informs computer vision through smarter data extraction, fraud detection, and risk scoring. Labhesh details what it takes to put AI in production and how a machine learning infrastructure needs to be fundamentally thought out to allow for better human-in-the-loop AI workflows. Read more.
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4:50pm–5:30pm Wednesday, 09/11/2019
Mazin Gilbert (AT&T Research)
5G promises to change our lives in a big way. Mazin Gilbert provides a technical- and market-landscape overview of how AI creates the 5G world, highlighting how recent developments in AI help accelerate widespread adoption of 5G-based applications for consumers and enterprises. He explores the roles of open source and open platforms as key ingredients of this 5G AI transformation. Read more.
4:50pm–5:30pm Wednesday, 09/11/2019
Session
Implementing AI
Secondary topics:  Hardware
TBC
Add to your personal schedule
4:50pm–5:30pm Wednesday, 09/11/2019
Session
Implementing AI
Siddha Ganju (NVIDIA), Meher Kasam (Square)
Over the last few years, convolutional neural networks (CNNs) have risen in popularity, especially in the area of computer vision. However, CNNs are by nature computationally and memory intensive, making them challenging to deploy on a mobile device. Siddha Ganju and Meher Kasam examine optimizing deep neural nets to run efficiently on mobile devices. Read more.
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4:50pm–5:30pm Wednesday, 09/11/2019
Session
Sponsored
Matt Tharp (Gamalon)
Ben Vigoda explores what you need to be ready for the conversational web. The new AI connects to data by itself and is ready to use out of the box. Companies rely on websites to communicate with existing and potential customers. Chatbots begin to bridge the gaps, but need to be better. Most are unprepared for this and need smart systems in place to understand what customers are saying. Read more.
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4:50pm–5:30pm Wednesday, 09/11/2019
Session
Implementing AI
Anuradha Gali (Uber)
There are 15 million trips a day on the Uber platform. Anu Gali walks you through how Uber leverages AI to automate its business model via its unique platform. You'll learn about technology that evolves based on current market insights and dynamically adjusts for the future. She shares best practices and the architecture that enables organizations like Uber to grow and scale rapidly. Read more.

5:30pm

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5:30pm–6:30pm Wednesday, 09/11/2019
Event
Come enjoy delicious snacks and beverages with fellow AI conference attendees, speakers, and sponsors at the Attendee Reception, happening immediately after the afternoon sessions on Wednesday. Read more.

6:30pm

6:30pm–7:00pm Wednesday, 09/11/2019
TBC

7:00pm

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7:00pm–9:00pm Wednesday, 09/11/2019
Event
Relax and network at AI at Night, happening on Wednesday evening. Read more.

Thursday, 09/12/2019

8:00am

8:00am–9:00am Thursday, 09/12/2019
Morning Coffee (sponsored by PwC) (1h)

8:15am

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8:15am–8:45am Thursday, 09/12/2019
Event
Ready, set, network! Meet fellow attendees who are looking to connect at the AI Conference. We'll gather before Wednesday and Thursday keynotes for an informal speed networking event. Be sure to bring your business cards—and remember to have fun. Read more.

8:45am

8:45am–9:00am Thursday, 09/12/2019
TBC

9:00am

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9:00am–9:05am Thursday, 09/12/2019
Keynote
Ben Lorica (O'Reilly Media), Roger Chen (Computable), Julie Shin Choi (Intel AI)
Program chairs Ben Lorica, Roger Chen, and Julie Choi open the second day of keynotes. Read more.

9:05am

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9:05am–9:20am Thursday, 09/12/2019
Keynote
Secondary topics:  Machine Learning
Michael Jordan (UC Berkeley)
Statistical decisions are often given meaning in the context of other decisions, particularly when there are scarce resources to be shared. Michael Jordan details the aim to blend gradient-based methodology with game-theoretic goals as part of a large "microeconomics meets machine learning" program. Read more.

9:30am

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9:30am–9:40am Thursday, 09/12/2019
Keynote
Ananth Sankar (Intel)
Details to come. Read more.

9:40am

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9:40am–9:55am Thursday, 09/12/2019
Keynote
Sahika Genc (Amazon)
Sahika Genc dives deep into the current state-of-the-art techniques in deep reinforcement learning (DRL) for a variety of use cases. Read more.

9:55am

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9:55am–10:10am Thursday, 09/12/2019
Keynote
Secondary topics:  Ethics, Security, and Privacy
Andrew Zaldivar (Google)
In an effort to encourage responsible transparent and accountable practices, Andrew Zaldivar briefly details some of the existing frameworks technologists can use for ethical decision making in AI. Read more.

10:10am

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10:10am–10:30am Thursday, 09/12/2019
Keynote
Kenneth Stanley (Uber AI Labs | University of Central Florida)
We think a lot in machine learning about encouraging computers to solve problems, but there's another kind of learning, called open-endedness, that's just beginning to attract attention in the field. Kenneth Stanley walks you through how open-ended algorithms keep on inventing new and ever-more complex tasks and solving them continually—even endlessly. Read more.

10:30am

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10:30am–10:35am Thursday, 09/12/2019
Keynote
O'Reilly AI program chairs close the first day of keynotes. Read more.

10:35am

10:35am–11:05am Thursday, 09/12/2019
Morning Break (30m)

11:05am

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11:05am–11:45am Thursday, 09/12/2019
Chaitanya Shivade (IBM Research)
Using deep learning models to perform natural language inference (NLI) is a fundamental task in natural language processing. Chaitanya Shivade introduces a recently released dataset, MedNLI, for this task in the clinical domain, describes state-of-the-art models, explores how to adapt these into the healthcare domain, and details applications that can leverage these models. Read more.
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11:05am–11:45am Thursday, 09/12/2019
Danny Lange (Unity Technologies)
This year, Unity introduced Obstacle Tower, a procedurally generated game environment designed to test the capabilities of AI-trained agents. Then, they invited the public to try to solve the challenge. Danny Lange reveals what Unity learned from the contest and the real-world impact of observing the behaviors of multiple AI agents in a simulated virtual environment. Read more.
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11:05am–11:45am Thursday, 09/12/2019
Session
Implementing AI
Alex Ratner (Snorkel AI)
Alex Ratner explores programmatic approaches to building, managing, and modeling training data for machine learning (ML) using the open source framework Snorkel. Training data is increasingly one of the key bottlenecks to using modern ML, and Alex outlines recent systems and algorithmic and theoretical advances in building and managing training data for ML. Read more.
11:05am–11:45am Thursday, 09/12/2019
TBC
Add to your personal schedule
11:05am–11:45am Thursday, 09/12/2019
Secondary topics:  Text, Language, and Speech
David Talby (Pacific AI)
New AI solutions in question answering, chatbots, structured data extraction, text generation, and inference all require deep understanding of the nuances of human language. David Talby outlines challenges, risks, and best practices for building NLU-based systems, drawing on examples and case studies from products and services built by Fortune 500 companies and startups over the past seven years. Read more.
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11:05am–11:45am Thursday, 09/12/2019
Secondary topics:  Ethics, Security, and Privacy
Tzvika Barenholz (Intuit), Induprakas Keri (Intuit)
Tzvika Barenholz and Induprakas Keri detail Intuit’s efforts to deploy fully homomorphic encryption (FHE) in production, which allows models to be trained and run on encrypted data, and supporting Intuit’s commitment to the highest standard in data stewardship. Take a sneak peak at some of the optimizations and tricks that make FHE practical. Read more.
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11:05am–11:45am Thursday, 09/12/2019
Francesca Lazzeri (Microsoft)
Automated machine learning (AutoML) enables data scientists and domain experts to be productive and efficient. AutoML is seen as a fundamental shift in the way in which organizations can approach machine learning. Francesca Lazzeri outlines how to use AutoML to automate machine learning model selection and automate hyperparameter tuning. Read more.
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11:05am–11:45am Thursday, 09/12/2019
Session
Implementing AI
Mathew Salvaris (Microsoft), Angus Taylor (Microsoft)
Join Danielle Dean and Wee Hyong Tok to learn best practices and reference architectures (which have been validated in real-world AI/ML projects for customers globally) for implementing AI. Wee Hyong and Danielle detail lessons distilled from working with large global customers on AI/ML projects and the challenges that they overcame. Read more.

11:55am

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11:55am–12:35pm Thursday, 09/12/2019
Secondary topics:  Hardware
Dejan Milojicic (Hewlett Packard Laboratories)
Dejan Milojicic examines a software stack designed for the special-purpose machine learning accelerator. The software stack improves usability and programmability of the accelerator, making it accessible from common machine learning frameworks. The software toolchain also exposes the intricacies of the parallelism of the accelerator while hiding its complexities. Read more.
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11:55am–12:35pm Thursday, 09/12/2019
Madhura Dudhgaonkar details lessons learned from productizing enterprise ML services across vision, language, recommendations, and anomaly detection over the last 5+ years. You'll walk away with an actionable framework to bootstrap and scale a machine learning function via a real product journey, involving deep learning that was productized in record speed, in spite of having no dataset. Read more.
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11:55am–12:35pm Thursday, 09/12/2019
Session
Implementing AI
Vijay Gabale (Infilect)
Beyond computer games and neural architecture search, practical applications of deep reinforcement learning (DRL) to improve classical classification or detection tasks are few and far between. Vijay Gabale outlines a technique and some experiences of applying DRL on improving the distribution input datasets to achieve state-of-the-art performance, specifically on object-detection tasks. Read more.
11:55am–12:35pm Thursday, 09/12/2019
TBC
Add to your personal schedule
11:55am–12:35pm Thursday, 09/12/2019
Secondary topics:  Ethics, Security, and Privacy
Raj Minhas (PARC, a Xerox Company)
The use of AI is growing rapidly and expanding into applications that impact people’s lives. Raj Minhas explores how, while researchers are driven by enthusiasm to harness the power of AI, they also have an obligation to consider the impact of intelligent applications. Read more.
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11:55am–12:35pm Thursday, 09/12/2019
Session
Implementing AI
Ting-Fang Yen (DataVisor)
Ting-Fang Yen details a monitor for production machine learning systems that handle billions of requests daily. The approach discovers detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. See new tools for detecting undesirable model behaviors early in large-scale online ML systems. Read more.
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11:55am–12:35pm Thursday, 09/12/2019
Vinay Rao (RocketML), Santi Adavani (RocketML)
Current deep learning approaches require large amounts of labeled data. Creation of labeled data is expensive, error prone, and time consuming. Vinay Rao and Santi Adavani walk you through an effective learning method with minimum labelled data and human intervention. Read more.
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11:55am–12:35pm Thursday, 09/12/2019
Maithra Raghu (Cornell University/Google Brain)
With the fundamental breakthroughs in artificial intelligence and the significant increase of digital health data, there's been enormous interest in AI for healthcare applications. Maithra Raghu examines how to more effectively develop AI algorithms for these settings and the novel prediction challenges and successes arising from the interaction of AI algorithms and human experts. Read more.

12:35pm

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12:35pm–1:45pm Thursday, 09/12/2019
Event
Topic Table discussions are a great way to informally network with people in similar industries or interested in the same topics. Read more.

1:45pm

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1:45pm–2:25pm Thursday, 09/12/2019
Session
Implementing AI
Secondary topics:  Machine Learning
Holden Karau (Google), Trevor Grant (IBM)
Modeling is easy—productizing models, less so. Distributed training? Forget about it. Say hello to Kubeflow with Holden Karau and Trevor Grant—a system that makes it easy for data scientists to containerize their models to train and serve on Kubernetes. Read more.
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1:45pm–2:25pm Thursday, 09/12/2019
Secondary topics:  Ethics, Security, and Privacy
Eloy Avila (Darktrace)
While nearly every firm is impacted by a wide variety of external factors, the most robust businesses recognize the need to first learn about themselves. Nicole Eagan explores how a self-learning AI is able to learn how a company functions from the inside, and evolve with changes; this AI enables businesses to detect vulnerabilities, improve processes, and continue to grow. Read more.
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1:45pm–2:25pm Thursday, 09/12/2019
Session
Implementing AI
Shashank Prasanna (Amazon Web Services)
Machine learning involves a lot of experimentation. Data scientists spend days, weeks, or months performing algorithm searches, model architecture searches, hyperparameter searches, etc. Shashank Prasanna breaks down how you can easily run large-scale machine learning experiments using containers, Kubernetes, Amazon ECS, and SageMaker. Read more.
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1:45pm–2:25pm Thursday, 09/12/2019
Session
Sponsored
Anand Rao (PwC)
Anand Rao provides an overview from the practitioner’s perspective to adapt and action ethics within the business. Anand explores PwC’s responsible AI toolkit, which enables businesses to identify and contextualize relevant ethical AI principles and provides tools for evaluating interpretability of systems. You'll see example applications to illustrate model interpretability and explanations. Read more.
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1:45pm–2:25pm Thursday, 09/12/2019
Yael Gozin (Pfizer)
The size and the complexity of regulatory submissions to health authorities consistently increases, ut the process hasn't changed. The process of matching and verifying a data point in a table cell with its accurate source(s) is one of the main challenges of automating data quality checks. Yael Gozin details an an innovative, highly accurate, and efficient structured data verification method. Read more.
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1:45pm–2:25pm Thursday, 09/12/2019
Alessandro Palladini (Music Tribe)
Alessandro Palladini explores the role of experts and creatives in a world dominated by intelligent machines by bridging the gap between the research on complex systems and tools for creativity, examining what he believes to be the key design principles and perspectives on making intelligent tools for creativity and for experts in the loop. Read more.
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1:45pm–2:25pm Thursday, 09/12/2019
Secondary topics:  Text, Language, and Speech
Stef Nelson-Lindall (Facebook)
PyText is a research to production platform that Facebook has leveraged to quickly develop state-of-the-art natural language processing (NLP) systems and deploy them to critical production use cases. Stef Nelson-Lindall explores several challenges with developing, training, and deploying real production systems with Torch, how to deal with them in NLP use cases, and more. Read more.
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1:45pm–2:25pm Thursday, 09/12/2019
Session
Implementing AI
Vijay Agneeswaran (Walmart Labs), Abhishek Kumar (Publicis Sapient)
Vijay Agneeswaran and Abhishek Kumar explore multi-label text classification problems, where multiple tags or categories have to be associated with given text or documents. Multi-label text classification occurs in numerous real-world scenarios, for instance, in news categorization and in bioinformatics (such as the gene classification problem). Read more.

2:35pm

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2:35pm–3:15pm Thursday, 09/12/2019
Session
Implementing AI
Secondary topics:  Machine Learning
Manasi Vartak (Verta.ai)
Enterprises are investing heavily in integrating AI/ML into their business, and yet it remains challenging to transform these research-oriented initiatives into revenue-driving functions due to a lack of efficient tooling. Manasi Vartak examines key methods that enterprise AI teams can leverage with regard to driving revenue, including A/B testing, data pipelines, and reproducibility. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Secondary topics:  Design, Interfaces, and UX
Michael Radwin (Intuit)
Design thinking is a methodology for creative problem-solving developed at the Stanford d.school. The methodology is used by world-class design firms like IDEO and many of the world's leading brands like Apple, Google, Samsung, and GE. Michael Radwin prepares a recipe for how to apply design thinking to the development of AI/ML products. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Anusua Trivedi (Microsoft)
Modern machine learning models often significantly benefit from transfer learning. Anusua Trivedi details a study of existing text transfer learning literature. She explores popular machine reading comprehension (MRC) algorithms and evaluates and compares the performance of the transfer learning approach for creating a question answering (QA) system for a book corpus using pretrained MRC models. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Session
Sponsored
Arno Candel (H2O.ai)
Driverless AI is H2O.ai’s latest flagship product for automatic machine learning for the enterprise. Arno Candel outlines Driverless AI, explores customer use cases, and performs a live demo with custom recipes to solve a specific machine learning problem. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Paco Nathan (derwen.ai)
Paco Nathan outlines the history and landscape for vendors, open source projects, and research efforts related to AutoML. Starting from the perspective of an AI expert practitioner who speaks business fluently, Paco unpacks the ground truth of AutoML—translating from the hype into business concerns and practices in a vendor-neutral way. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
Alejandro Saucedo demystifies AI explainability through a hands-on case study, where the objective is to automate a loan-approval process by building and evaluating a deep learning model. He introduces motivations through the practical risks that arise with undesired bias and black box models and shows you how to tackle these challenges using tools from the latest research and domain knowledge. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Session
Implementing AI
Secondary topics:  Deep Learning tools
Brennan Saeta (Google)
Swift for TensorFlow is a next-generation machine learning and differential programming framework that unlocks new domains and applications. Brennan Saeta leads you through the motivations for Swift, the benefits of this toolchain, and how to use Swift for TensorFlow in your projects. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Session
Case Studies
Sanji Fernando (Optum)
Sanji Fernando examines his experience building, deploying, and operating a deep learning model that improves hospital revenue cycle management, including business alignment, data preparation, model development, model selection, deployment, and operations. Sanji also details key knowledge and opportunities for improvement with deep learning models in healthcare. Read more.

3:15pm

3:15pm–4:00pm Thursday, 09/12/2019
Afternoon Break (45m)

4:00pm

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4:00pm–4:40pm Thursday, 09/12/2019
Session
Implementing AI
Kai Liu (BING) (Microsoft), Yuqi Wang (Microsoft), Bin Wang (Microsoft)
Bing in Microsoft runs large, complex workflows and services, but no existing solutions met its needs. So it created and open-sourced FrameworkLauncher. Kai Liu, Yuqi Wang, and Bin Wang explore the solution, built to orchestrate workloads on YARN through the same interface without making changes to the workloads, including large-scale long-running services, batch jobs, and streaming jobs. Read more.
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4:00pm–4:40pm Thursday, 09/12/2019
Debo Olaosebikan (Gigster)
As the gap between technology giants and the rest of the enterprise widens, AI-driven transformation has become essential and urgent. From the lens of over 1,000 projects delivered and a broad view across real use cases in multiple industries, Debo Olaosebikan examines an organizational and technical framework for using AI to drive business impact regardless of where an organization starts from. Read more.
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4:00pm–4:40pm Thursday, 09/12/2019
Session
Implementing AI
Jisheng Wang (Mist)
Increased complexity and business demands continue to make enterprise network operation more challenging. Jisheng Wang outlines the architecture of the first autonomous network operation solution along with two examples of ML-driven automated actions. He also shares some of his experiences and the lessons he learned applying ML/DL and AI to the development of SaaS-based enterprise solutions. Read more.
4:00pm–4:40pm Thursday, 09/12/2019
TBC
4:00pm–4:40pm Thursday, 09/12/2019 TBC
Add to your personal schedule
4:00pm–4:40pm Thursday, 09/12/2019
Secondary topics:  Computer Vision
Lindsay Hiebert (Intel)
Join Lindsay Hiebert as he explores the various challenges for developers as they design a product that solves a real-world problem using the power of AI and IoT. To unlock the potential of AI at the edge, Intel launched its Intel AI: In Production ecosystem, to accelerate prototype to production at the edge with Intel and partner offerings. Read more.
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4:00pm–4:40pm Thursday, 09/12/2019
Shourabh Rawat (Trulia)
Lately, 360-degree images have become ubiquitous in industries from real estate to travel. They enable an immersive experience that benefits consumers but creates a challenge for businesses to direct viewers to the most important parts of the scene. Shourabh Rawat walks you through how to identify and extract engaging static 2-D images using specific algorithms and deep learning methods. Read more.

4:50pm

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4:50pm–5:30pm Thursday, 09/12/2019
Session
Implementing AI
Secondary topics:  Machine Learning
Jonathan Peck (Algorithmia)
ML has been advancing rapidly, but only a few contributors focus on the infrastructure and scaling challenges that come with it. Jonathan Peck explores why ML is a natural fit for serverless computing, a general architecture for scalable ML, and common issues when implementing on-demand scaling over GPU clusters, providing general solutions and a vision for the future of cloud-based ML. Read more.
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4:50pm–5:30pm Thursday, 09/12/2019
Leslie De Jesus (Wovenware)
Leslie De Jesus examines a machine learning solution enabling the Puerto Rico Science, Technology & Research Trust to identify and classify mosquitoes that may be carrying diseases such as Zika and dengue fever. She outlines the challenges, strategy, and technologies used, the results achieved to date, and the implications of the AI project in helping to address a global threat. Read more.
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4:50pm–5:30pm Thursday, 09/12/2019
Ramsundar Janakiraman (Aruba Networks, A HPE Company)
While network protocols are the language of the conversations among devices in a network, these conversations are hardly ever labeled. Advances in capturing semantics present an opportunity for capturing access semantics to model user behavior. Ram Janakiraman explains how, with strong embeddings as a foundation, behavioral use cases can be mapped to NLP models of choice. Read more.
4:50pm–5:30pm Thursday, 09/12/2019
TBC
Add to your personal schedule
4:50pm–5:30pm Thursday, 09/12/2019
Mayank Kejriwal (USC Information Sciences Institute)
Embeddings have emerged as an exciting by-product of the deep neural revolution and now apply universally to images, words, documents, and graphs. Many algorithms only require unlabeled datasets, which are plentiful in businesses. Mayank Kejriwal examines what these embeddings really are and how businesses can use them to bolster their AI strategy. Read more.
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4:50pm–5:30pm Thursday, 09/12/2019
Paris Buttfield-Addison (Secret Lab), Mars Geldard (University of Tasmania), Tim Nugent (lonely.coffee)
Whether you're a scientist wanting to test a problem without building costly real-world rigs, a self-driving car engineer wanting to test AI logic in a constrained virtual world, or a data scientist needing to solve a thorny real-world problem without a production environment, Paris Buttfield-Addison, Mars Geldard, and Tim Nugent teach you how to use AI problem-solving using game engines. Read more.
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4:50pm–5:30pm Thursday, 09/12/2019
Session
Implementing AI
Alex (Tianchu) Liang (American Tire Distributors)
Deep learning has been a sweeping revolution in the world of AI and machine learning. But sometimes traditional industries can be left behind. Tianchu Liang details a warehouse staffing solution deployed in 140 distribution centers, where he implemented a long short-term memory (LSTM) recurrent neural network model to generate staffing-level forecasts and optimize staffing schedules. Read more.

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