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
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. In this 2 day training, Wenming Ye (AWS) and Miro Enev (Nvidia) offer a practical next step in DL learning with instructions, demos, and hands-on labs. Read more.
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9:00am–5:00pm Monday, 09/09/2019
Training
Robert Schroll (The Data Incubator)
The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. This training will introduce TensorFlow's capabilities in Python. It will move 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
Dylan Bargteil (The Data Incubator), Michael Li (The Data Incubator)
This course is a non-technical overview of AI and data science. You’ll learn common techniques, how to apply them in your organization, and common pitfalls to avoid. Though this course, you’ll pick up the language and develop a framework to be able to effectively engage with technical experts and utilize 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
Amir Issaei (Databricks)
The course covers the fundamentals of neural networks and how to build distributed Keras/TensorFlow models on top of Spark DataFrames. Throughout the class, you will use Keras, TensorFlow, Deep Learning Pipelines, and Horovod to build and tune models. You will also use MLflow to track experiments and manage the machine learning lifecycle. NOTE: This course is taught entirely in Python. 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 users 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. This course will introduce the PyTorch workflow and demonstrate how to use it. Students will be equipped with the knowledge to build deep learning models using real-world datasets. 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 with deep learning, walking you through neural network architectures and NLP tasks and teaching you how to apply these architectures for those tasks. 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 and Biases)
Introduction to building and deploying LSTMs, 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
Ira Cohen (Anodot)
The goal of the tutorial is to learn and experience what it takes to be a manage machine learning (ML ) based products. In the tutorial we will go through the cycle of developing machine learning 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–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
Tutorial
Implementing AI
Paris Buttfield-Addison (Secret Lab), Tim Nugent (lonely.coffee), Mars Geldard (University of Tasmania)
Are you a scientist who wants to test a research problem without building costly and complicated real-world rigs? A self-driving car engineer who wants to test their AI logic in a constrained virtual world? A data scientist who needs to solve a thorny real-world problem without touching a production environment? Have you considered simulation-driven ML problem solving with a game engine? Read more.
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9:00am–12:30pm Tuesday, 09/10/2019
Tutorial
Implementing AI
In this workshop, you will get hands-on experience in developing intelligent AI assistants based entirely on machine learning and using only open source tools - Rasa NLU and Rasa Core. You will 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–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. In this hands-on session, we will learn about the Kubeflow components and how they interact with Kubernetes. We explore the machine learning lifecycle from model training to model serving. 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, MLSListings, the World Bank, Baosight, and Midea/KUKA. Read more.
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9:00am–5:00pm 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.

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)
This tutorial will briefly discuss what modern neural NLP looks like, after which we'll train some models, write some code, and learn 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
Chris Butler (IPsoft)
Purpose, a well-defined problem, and trust from people 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–5:00pm Tuesday, 09/10/2019
Tutorial
Implementing AI
Robert Nishihara (UC Berkeley), Philipp Moritz (UC Berkeley), Ion Stoica (UC Berkeley)
Ray is a general purpose framework for programming your cluster. We will lead a deep dive into Ray, walking you through its API and system architecture and sharing application examples, including several state-of-the-art AI algorithms. Read more.
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1:30pm–5:00pm Tuesday, 09/10/2019
Neil Conway (Determined AI), Yoav Zimmerman (Determined AI)
Success with deep learning requires understanding more than just TensorFlow or Keras. In this tutorial, we will describe a range of practical problems faced by DL practitioners and the software tools and techniques needed to address them, including data prep/augmentation, GPU scheduling, hyperparameter tuning, distributed training, metrics management, deployment, and mobile/edge optimization. Read more.
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1:30pm–5:00pm Tuesday, 09/10/2019
Tutorial
Implementing AI
Boris Lublinsky (Lightbend), Dean Wampler (Lightbend)
This hands-on tutorial examines production use of ML in streaming data pipelines; how to do periodic model retraining and low-latency scoring in live streams. We'll discuss Kafka as the data backplane, pros and cons of microservices vs. systems like Spark and Flink, tips for Tensorflow and SparkML, performance considerations, model metadata tracking, and other techniques. Read more.
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1:30pm–5:00pm Tuesday, 09/10/2019
Tutorial
Mo Patel (Independent)
This tutorial will focus on all aspects of the PyTorch lifecycle via hand on examples such as image classification, text classification, and linear modeling. Other aspects of machine learning such as transfer learning, data modeling and deploying to production will be covered via immersive labs. Read more.

3:00pm

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

5:00pm

5:00pm–7:00pm Tuesday, 09/10/2019
TBC

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 (1h)

8:15am

Add to your personal schedule
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–10:35am Wednesday, 09/11/2019
Keynote
Keynote lineup to come. Read more.

10:35am

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

11:05am

11:05am–11:45am Wednesday, 09/11/2019
TBC
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11:05am–11:45am Wednesday, 09/11/2019
Nicole Eagan (Darktrace)
While nearly every firm is impacted by a wide variety of external factors, the most robust businesses are recognizing the need to first learn about themselves. Organizations are increasingly deploying self-learning AI. Able to learn how a company functions from the inside, and evolve with changes, this AI is enabling businesses to detect vulnerabilities, improve processes, and continue to grow. Read more.
11:05am–11:45am Wednesday, 09/11/2019
TBC
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11:05am–11:45am Wednesday, 09/11/2019
Session
Implementing AI
Jasjeet Thind (Zillow)
Advances in AI & deep learning are enabling new technologies to mimic how the human brain interprets scenes, objects & images. This progress has major implications for businesses that need to extract meaning from overwhelming quantities of unstructured data. In this session, learn how implementing computer vision based in deep neural networks allows machines to “see” images in an entirely new way. Read more.
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11:05am–11:45am Wednesday, 09/11/2019
mayukh bhaowal (Salesforce)
With the shift from the digital revolution to the AI revolution, the old product management workflow and frameworks are crumbling down. How do you manage AI products, how are AI executive roles different and what toolbox do they 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
Danielle Dean (Microsoft), Wee Hyong Tok (Microsoft)
In this session, you will learn best practices and reference architectures (which have been validated in real-world AI/ML projects for customers globally) for implementing AI. Join us in this session as Wee Hyong and Danielle share the lessons distil from working with large global customers on AI/ML projects, and the challenges that they overcome. Read more.
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11:05am–11:45am Wednesday, 09/11/2019
Session
Implementing AI
Huaixiu Zheng (Uber)
In this talk, I will cover how Uber applies Deep Learning in the domain of NLP and Conversational AI. In particular, I will go into details of how we implement AI solutions in a real-world environment, as well as cutting edge research we are doing in end-to-end dialogue systems. 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. Is it possible to avoid duplicating the effort? Further, can NLU-dependent applications be developed language-agnostically (write once, applicable to multiple languages)? We will show a vision to answering yes to both questions. Read more.
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11:55am–12:35pm Wednesday, 09/11/2019
Bahman Bahmani (Rakuten)
We will provide a framework and present 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
Nagendra Shishodia (EXL), Solmaz Torabi (EXL Service), Chaithanya Manda (EXL Service)
Every NLP based document processing solution depends on converting scanned documents/ images to machine readable text using an OCR solution. However, accuracy of OCR solutions is limited by quality of scanned images. We show that generative adversarial networks can be used to bring significant efficiencies in any document processing solution by enhancing resolution and de-noising scanned images. Read more.
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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 will describe the design and modeling methodologies used to build this platform-as-a-service. 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 potential to create substantial value for business and the global economy. What’s less well understood is how it can be used to address some of the world’s biggest societal challenges. Michael Chui will discuss the ethical implications of AI and how executives can leverage the technology for good while considering its 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
Michael Bauer (Sylabs, Inc.)
Containerization technology can be used to 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, we demonstrate the approach by treating a container as a Decoupled Neural Interface to enable novel applications for neural networks which were previously impractical. Read more.
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11:55am–12:35pm Wednesday, 09/11/2019
Session
Implementing AI
Hagay Lupesko (Amazon Web Services)
In this session, you will learn how Lex, Amazon's cloud-based AI-powered chatbot service, was architected, built and deployed. You will learn practical considerations for deploying and maintaining deep learning models in production, and how Lex used Apache MXNet and MXNet Model Server to build and scale the successful 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)
We have developed a system that is capable of detecting, classifying and automatically correcting for manufacturing defects in a multinodal process Read more.
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1:45pm–2:25pm Wednesday, 09/11/2019
Haixun Wang (WeWork)
The AI advancements in the cyber world far surpass those in the physical world. This discussion will outline how WeWork aims to change this by discussing the approaches the company is taking to bring AI to the real world, ranging from modeling a neighborhood to creating digital twins of a building, and how AI can make businesses more efficient and improve people’s quality of life. Read more.
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1:45pm–2:25pm Wednesday, 09/11/2019
Session
Implementing AI
Akhilesh Kumar (Adobe)
Photographic defects such as noise, exposure(underexposure/overexposure), blur can ruin the perfect shot. We have developed a solution based on GAN that can identify the region of defectiveness in images and fix these defective images. This solution is better than traditional algorithms. It can also be applied to fix videos. No more spending hours of time manually editing the images. Read more.
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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 has been increasingly becoming mainstream in the recent years. In particular, it has been leveraged for applications such as, speech recognition, language translation and question answering. we shall walk through how S2S modeling can be leveraged for the aforementioned use cases, viz., real-time anomaly detection and forecasting. Read more.
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1:45pm–2:25pm Wednesday, 09/11/2019
Yi Zhang (Rulai & University of California Santa Cruz)
This talk will present the predictions represent our thoughts on how conversational technology will evolve from its current state in 2019. Common misunderstandings about the technologies and case studies in several industries will be presented and discussed.  Read more.
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1:45pm–2:25pm Wednesday, 09/11/2019
Session
Implementing AI
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, 3D) daily. This talk walks through the challenges we 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
Paige Bailey (Google)
TensorFlow 2.0 has landed! During this session, you will learn all about TensorFlow 2.0's new features, usability enhancements, performance increases, and focus on developer productivity. We will 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.

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 inc)
Machine learning has enabled a move away from manually programming robots to allowing machines to learn/adapt to changes in the environment. We will discuss how AI-enabled robots are currently used in warehouse automation. We will describe recent progress in DRL, imitation learning, etc. and discuss real world requirements for various industrial problems, pipelined versus end to end systems. Read more.
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2:35pm–3:15pm Wednesday, 09/11/2019
Wei Cai (Cox Communication )
Real-time traffic volume prediction plays a vital role in proactive network management, and many forecasting models have been proposed to address this issue in the literature. However, most of them suffer from the inability to fully use the rich information in traffic data to generate efficient and accurate traffic predictions for a longer term (i.e., 7 day predictions at a 5-min interval). Read more.
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2:35pm–3:15pm Wednesday, 09/11/2019
Mark Weber (MIT-IBM Watson AI Lab)
Organized crime inflicts human suffering on a genocidal scale: upwards 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. A new field of AI called graph convolutional networks can help. Read more.
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2:35pm–3:15pm Wednesday, 09/11/2019
Peter Bailis (Stanford University)
Despite a meteoric rise in data volumes within modern enterprises, enabling non-technical users to put this data to work in diagnostic and predictive tasks remains a fundamental challenge. Peter Bailis details 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
Evan Sparks (Determined AI)
We describe 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. We discuss 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. Read more.

3:15pm

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

4:00pm

4:00pm–4:40pm Wednesday, 09/11/2019 TBC
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4:00pm–4:40pm Wednesday, 09/11/2019
Dexter Hadley (University of California, San Francisco)
We will demonstrate how we use natural language processing techniques to curate routine clinical data for over 1M mammograms, and how we use deep learning, blockchain, and other approaches to realize AI that translates this valuable data into precision oncology to better characterize breast cancer and improve patient outcomes. Read more.
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4:00pm–4:40pm Wednesday, 09/11/2019
Li Erran Li (Pony.ai)
Tremendous progresses have been made in applying machine learning to autonomous driving. I will present recent advances in applying machine learning to solving the perception, prediction, planning and control problems of autonomous driving. I will discuss key research challenges. Read more.
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4:00pm–4:40pm Wednesday, 09/11/2019
Stacy Ashworth (SelectData), Alberto Andreotti (John Snow Labs)
A lot of business data is still scanned or snapped documents. This is a real-world case study on reading, understanding, classifying, and acting on facts extracted from such image files - using state-of-the-art, open source, deep learning based OCR, NLP, and forecasting libraries at scale. Read more.
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4:00pm–4:40pm Wednesday, 09/11/2019
Jana Eggers (Nara Logics)
Though we don’t always follow them, we have developed great best practices for designing, developing and delivering great software. What changes when we start adding AI to that great software? This talk will cover six key features of software dev that are similar when adding AI, and six that are different, and how to adjust for those. Read more.
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4:00pm–4:40pm Wednesday, 09/11/2019
Session
Implementing AI
Ashish Bansal (Twitter)
This talk gives insight into unique recommendation system challenges at Twitter’s scale and what makes this a fun and challenging task. Read more.
4:00pm–4:40pm Wednesday, 09/11/2019
TBC

4:50pm

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4:50pm–5:30pm Wednesday, 09/11/2019
Dylan Glas (Futurewei Technologies), Phoebe Liu (Figure Eight)
How do we develop social behavior for robots? 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 is often difficult to quantify human social behavior with algorithms. We highlight some of our researches in this field to enable human-robot interaction. Read more.
4:50pm–5:30pm Wednesday, 09/11/2019
TBC
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4:50pm–5:30pm Wednesday, 09/11/2019
Sijun He (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 system at Twitter and discusses the challenges we face 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
Implementing AI
Anuradha Gali (Uber)
Learn how Uber is leveraging AI to automate their business model via their unique platform. You'll hear about their technology that evolves based on current market insights and dynamically adjusts for the future. Anu Gali, Engineering Leader of this platform will discuss best practises and the architecture that enables organizations like Uber grow and scale rapidly. Read more.
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4:50pm–5:30pm Wednesday, 09/11/2019
Mazin Gilbert (AT&T Research)
This presentation will provide a technical and a market landscape overview of how AI is creating the 5G world. It will highlight how recent developments in AI are helping to accelerate widespread adoption of 5G-based applications for consumers and enterprises. We will discuss 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
TBC
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4:50pm–5:30pm Wednesday, 09/11/2019
Session
Implementing AI
Siddha Ganju (Nvidia), Meher Kasam (Square)
Optimizing deep neural nets to run efficiently on mobile devices. 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 (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–10:35am Thursday, 09/12/2019
Keynote
Keynote lineup to come. 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)
We discuss the use of deep learning models to perform natural language inference, a fundamental task in natural language processing. We introduce a recently released dataset for this task in the clinical domain, describe state of the art models and what can be done to adapt these into the healthcare domain, and finally discuss 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. Unity then invited the public to attempt to solve the challenge. Find out what the company learned from the contest and understand the real-world impact that can result from observing behaviors of multiple AI agents in a simulated virtual environment. Read more.
11:05am–11:45am Thursday, 09/12/2019
TBC
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11:05am–11:45am Thursday, 09/12/2019
Session
Implementing AI
Chiranjeet Chetia (Visa Inc.), Carolina Barcenas (Visa)
Artificial intelligence has revolutionized the way we live, work and play. Payments is no exception. With the help of AI, electronic payments have become more secure and convenient for consumers globally — regardless of currency or form factor. In this talk, we 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 Thursday, 09/12/2019
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 shares 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
Tzvika Barenholz (Intuit)
We will describe Intuit’s efforts to deploy Homomorphic Encryption (FHE) in Production, allowing models to be trained and run on encrypted data, and supporting Intuit’s commitment to the highest standard in data stewardship. This session will cover 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), Wee Hyong Tok (Microsoft)
Automated machine learning (AutoML) enables both data scientists and domain experts (with limited machine learning training) to be productive and efficient. AutoML is seen as a fundamental shift in which organizations can approach making machine learning. In this talk, you will learn how to use AutoML to automate selection of machine learning models and automate tuning of hyperparameters. Read more.

11:55am

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11:55am–12:35pm Thursday, 09/12/2019
Dejan Milojicic (Hewlett Packard Laboratories)
We developed a software stack for the special purpose machine learning accelerator. Software stack improves usability and programmability of the accelerator, making it accessible from common Machine Learning frameworks. 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
Michael Radwin (Intuit)
Design thinking is a methodology for creative problem solving developed at Stanford University 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. In this session, Michael Radwin, VP of Data Science at Intuit, will offer a recipe for how to apply design thinking to the development of AI/ML products. 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 to improve classical classification or detection tasks are few and far between. In this talk, I will share a technique and our experiences of applying D-RL on improving the distribution input datasets to achieve state of the art performance, specifically on object detection tasks. 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 has been enormous interest in AI for healthcare applications. In this talk I present both how to more effectively develop AI algorithms for these settings and present the novel prediction challenges and successes arising from the interaction of AI algorithms and human experts. Read more.
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11:55am–12:35pm Thursday, 09/12/2019
The use of AI is growing rapidly and expanding into applications that impact people’s lives. Researchers have an obligation to consider the impact of intelligent applications. Assumptions made in the design process can be based on subjective value judgments. PARC is therefore leading an industry initiative to build ethical frameworks into the research and design of decision-making processes in AI. Read more.
11:55am–12:35pm Thursday, 09/12/2019
TBC
11:55am–12:35pm Thursday, 09/12/2019
TBC

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
Holden Karau (Google), Trevor Grant (IBM)
Modeling is easy- productizing models, less so. Distributed training? forget about it. Hellllllloooo Kubeflow- a system that makes it easy for data scientists who know how to containerize their models, to train and serve on Kubernetes. Read more.
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1:45pm–2:25pm Thursday, 09/12/2019
Enhao Gong (Subtle Medical), Greg Zaharchuk (Stanford University)
Subtle Medical provides AI solutions, cleared by FDA and powered by industry framework, such as Intel OpenVINO, to 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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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 several days, weeks or months performing algorithm search, model architecture search, hyperparameter search etc. In this session, we’ll discuss you 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
Implementing AI
Vijay Agneeswaran (Publicis Sapient), Abhishek Kumar (Publicis Sapient)
We illustrate how capsule networks can be industrialized: 1. Overview of capsule networks and how they help in handling spatial relationships between objects in an image. We also learn about how they can be applied to text analytics. 2. We show an implementation of recurrent capsule networks, which are useful in text analytics, especially for some tasks such as summarization or classification. Read more.
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1:45pm–2:25pm Thursday, 09/12/2019
Yael Gozin (Pfizer)
The process of matching and verifying a data point in a table cell with its accurate source(s) is one of the main challenges associated with automating data quality checks. Pfizer in partnership with Beaconcure developed 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)
What is the role of experts and creatives in a world dominated by intelligent machines? In this presentation we will try to answer to this question by bridging the gap between the research on complex systems and tools for creativity, discussing what we believe to be the key design principles and perspective on the making of intelligent tools for creativity and for experts in the loop. Read more.
1:45pm–2:25pm Thursday, 09/12/2019
TBC

2:35pm

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2:35pm–3:15pm Thursday, 09/12/2019
Session
Implementing AI
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. We discuss key methods that enterprise AI teams can leverage with regards to driving revenue including A/B testing, data pipelines, reproducibility. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
This session is a result of lessons learnt from productizing enterprise ML services across Vision, Language, Recommendations, Anomaly Detection over the last 5+ years. You will walk away with an actionable framework to bootstrap & scale a machine learning function. We highlight this via a real product journey involving deep learning that we productized in record speed in-spite of no dataset. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Anusua Trivedi (Microsoft)
In this session, we capture a comprehensive study of existing text transfer learning literature in the research community. We explore popular Machine Reading Comprehension (MRC) algorithms. We evaluate and compare the performance of transfer learning approach for creating a QA system for a book corpus using the pretrained MRC models. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Session
Case Studies
Sanji Fernando (OptumLabs)
Sanji Fernando, Vice President, OptumLabs,will share 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 will also share key learnings and opportunities for improvement with deep learning models in health care. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
David Castillo (Capital One)
This talk will review Capital One's approach to explainable AI, with particular focus on fairness in automated decisioning. I will share our key learnings on best practices in implementing fair and responsible AI systems, as well as the challenges we have faced along the way and the research efforts we’ve initiated to overcome them. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Alejandro Saucedo (The Institute for Ethical AI & Machine Learning)
In this talk we demystify AI explainability through a practical hands-on case study. Our objective will be to automate a loan approval process by building and evaluating a deep learning model. We'll introduce motivations through the practical risks that arise with "undesired bias" & "black box models", and we will show tackle these challenges using tools from latest research and domain knowledge. Read more.
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2:35pm–3:15pm Thursday, 09/12/2019
Session
Implementing AI
Brennan Saeta (Google)
Swift for TensorFlow is a next-generation machine learning and differential programming framework that unlocks new domains and applications. This talk will dance through the motivations for Swift, the benefits of this toolchain, and how to use Swift for TensorFlow in your projects. 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 (Microsoft (BING)), Yuqi Wang (Microsoft), Bin Wang (Microsoft)
FrameworkLauncher is built to orchestrate all kinds of workloads on YARN through the same interface without making changes to the workload themselves. These workloads include but not limited to: Large-Scale Long-Running Services (DeepLearning Serving, HBase, Kafka, etc), Batch Jobs (DeepLearning Training, KDTree Building, etc) and Streaming Jobs (Data Processing, Dynamic Rendering, etc). 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 1000 projects delivered and a broad view across real use cases in multiple industries, I will present a organizational and technical framework for using AI to drive true business impact regardless of where an organization is starting from Read more.
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4:00pm–4:40pm Thursday, 09/12/2019
Session
Implementing AI
Jisheng Wang (Mist Systems)
Increased complexity and business demands continue to make enterprise network operation more challenging. In this talk, we will introduce the architecture of the first autonomous network operation solution together with two examples of ML-driven automated actions. We also share experiences and lessons learned applying ML/DL and AI to the development of SaaS-based enterprise solutions. Read more.
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4:00pm–4:40pm Thursday, 09/12/2019
Shourabh Rawat (Trulia)
360-degree images have become ubiquitous in industries ranging from real estate to travel. They enable an immersive experience that benefits consumers but creates a challenge for businesses: how do you direct viewers to the most important parts of the scene? In this session, attendees will learn to identify and extract engaging static 2D images using specific algorithms and deep learning methods. Read more.
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4:00pm–4:40pm Thursday, 09/12/2019
Shelley Zhuang (11.2 Capital)
How is artificial intelligence is transforming drug discovery and development Read more.
4:00pm–4:40pm Thursday, 09/12/2019
TBC
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4:00pm–4:40pm Thursday, 09/12/2019
Session
Implementing AI
Ting-Fang Yen (DataVisor)
We describe a monitor for production machine learning systems that handle billions of requests daily. Our approach discovers detection anomalies, such as spurious false positives, as well as gradual concept drifts when the model no longer captures the target concept. This session presents new tools for detecting undesirable model behaviors early in large-scale online ML systems. Read more.

4:50pm

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4:50pm–5:30pm Thursday, 09/12/2019
Session
Implementing AI
Jonathan Peck (Algorithmia)
We’ll look at why Machine Learning is a natural fit for serverless computing, discuss a general architecture for scalable ML, and cover issues we ran into when implementing our own 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)
The session will discuss a machine learning solution that is enabling the Puerto Rico Science, Technology and Research Trust to identify and classify disease-carrying mosquitoes. The presenter will outline the challenges, strategy and technologies utilized, the results achieved to date and 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 embeddings to capture semantics, even that of polysemous words, presents an opportunity for capturing access semantics to model user behavior. With strong embeddings as a foundation, behavioral use-cases could be mapped to NLP models of choice. Read more.
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4:50pm–5:30pm Thursday, 09/12/2019
Session
Implementing AI
Tianchu Liang (American Tire Distributors)
Deep Learning has been a sweeping revolution in the world of AI and machine learning. But how does this new, hot, technology help a legacy business everyday? In this talk, I will go over a warehouse staffing solution we deployed in 140 distribution centers, where I implemented LSTM recurrent neural network model to generate staffing level forecasts and to optimize staffing schedules. Read more.
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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. This talk, geared towards management and executives, will describe 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)
Are you a software engineer or scientist who wants to test a research problem without building costly and complicated real-world rigs? A self-driving car engineer who wants to test AI logic in a constrained virtual world? A data scientist who needs to solve a thorny real-world problem without touching a production environment? Have you considered AI using game engines? No? We'll teach you how. Read more.

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