Presented By
O’Reilly + Intel AI
Put AI to Work
April 15-18, 2019
New York, NY
Discover opportunities for applied AI
Organizations that successfully apply AI innovate and compete more effectively. How is AI transforming your business?
Be a part of the program—apply to speak by October 16.

Schedule: Text, Language, and Speech sessions

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9:00am - 5:00pm Monday, April 15 & Tuesday, April 16
Implementing AI, Models and Methods
Location: Midtown Suite
Delip Rao (R7 Speech Science)
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.
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9:00am12:30pm Tuesday, April 16, 2019
Location: Sutton South
Garrett Hoffman (StockTwits)
Garrett Hoffman walks you through deep learning methods for natural language processing and natural language understanding tasks, using a live example in Python and TensorFlow with StockTwits data. Methods include word2vec, recurrent neural networks and variants (LSTM, GRU), and convolutional neural networks. Read more.
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1:45pm5:15pm Tuesday, April 16, 2019
Implementing AI
Location: Mercury Rotunda
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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1:00pm1:40pm Wednesday, April 17, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Joanna Chavez (Booz Allen Hamilton)
Cognitive Solutions, the application of intelligent technology and services to empower the user to draw insights from data using natural human interaction, is a disruptive force in the US Federal market and is changing the way citizens engage with data. Read more.
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1:00pm1:40pm Wednesday, April 17, 2019
Case Studies, Machine Learning
Location: Sutton South
Twitter is a company with massive amounts of data. Thus, it is no wonder that the company applies machine learning in myriad of ways. In this session, we are going to describe, in depth, one of those use cases: Timeline Ranking. From modeling to infrastructure our goal is to share some of the optimizations that this team have made in order to have models that are both expressive and efficient. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Implementing AI
Location: Mercury Rotunda
Jeremy Lewi (Google), Hamel Husain (GitHub)
In this talk, we will use the example of a search engine for code using natural language to illustrate how Kubeflow and Kubernetes can be used to build and deploy ML products. Read more.
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1:50pm2:30pm Wednesday, April 17, 2019
Case Studies, Machine Learning
Location: Sutton South
Maryam Jahanshahi (TapRecruit)
Word embeddings such as word2vec have revolutionized language modelling. In this talk I will discuss exponential family embeddings, which apply probabilistic embedding models to other data types. I describe how we implemented a dynamic embedding model to understand how tech skill-sets have changed over 3 years. The key takeaway is that these models can enrich analysis of specialized datasets. Read more.
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2:40pm3:20pm Wednesday, April 17, 2019
Implementing AI
Location: Mercury Rotunda
Sumeet Vij (Booz Allen Hamilton)
The session describes the innovative application of Deep Learning to power Cognitive Conversational Agents. By leveraging Transfer Learning and deep pre-trained models for NLP, we show how Chatbots can overcome limitations of limited training datasets. In addition, we will demonstrate how Machine Learning can advances Robotic Process Automation (RPA) from “robotic” to “cognitive” automation Read more.
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2:40pm3:20pm Wednesday, April 17, 2019
Case Studies, Machine Learning
Location: Sutton South
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. 3. We also benchmark the RCN with capsule networks with dynamic routing on text analytics tasks. Read more.
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4:05pm4:45pm Wednesday, April 17, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
Kyle Hoback (WorkFusion), Mikhail Abramchik (WorkFusion)
Using AI to combat financial crime is more than strong fraud detection models monitoring transactions. Banks follow significant Anti-Money Laundering (AML) and Know-Your-Customer (KYC) laws and procedures, wrought with growth chained to cost and requiring auditable automation. This session will walk-through a series of case studies that utilize AI-powered RPA that address AML and KYC. Read more.
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4:05pm4:45pm Wednesday, April 17, 2019
Case Studies, Machine Learning
Location: Sutton South
Tom Sabo (SAS), Qais Hatim (Center for Drug Evaluation and Research, U.S. Food and Drug Administration)
Drug adverse event narratives contain a wealth of information that is laborious to assess using manual methods. To improve FDA Pharmacovigilance, we apply rule-based text extraction to generate training data for deep learning models. These models improve the identification of adverse events from narrative data, enhance time-to-value, and refine sources of medical terminology. Read more.
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1:00pm1:40pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Regent Parlor
Chang Ming-Wei (Google)
We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language representation models, BERT is designed to pre-train deep bidirectional representations by jointly conditioning on both left and right context in all layers. Read more.
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2:40pm3:20pm Thursday, April 18, 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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2:40pm3:20pm Thursday, April 18, 2019
Machine Learning, Models and Methods
Location: Grand Ballroom West
Anoop Katti (SAP)
We address understanding documents with 2D layout using machine learning. Examples of such documents are invoices, resumes, presentations etc. (in contrast to plain text documents like tweets, articles and reviews). We explore the shortcomings of the existing techniques and discuss a processing pipeline for 2D documents – the chargrid - pioneered by data scientists at SAP Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Case Studies, Machine Learning
Location: Sutton South
Chakri Cherukuri (Bloomberg LP)
In this talk we will see how machine learning and deep learning techniques can be applied in the field of quantitative finance. We will look at a few use-cases in detail and see how machine learning techniques can supplement and sometimes even improve upon already existing statistical models. We will also look at novel visualizations to help us better understand and interpret these models. Read more.
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4:05pm4:45pm Thursday, April 18, 2019
Implementing AI
Location: Rendezvous
Jaewon Lee (LINE Corp.), Jihoon Kang
"Until when are you going to cluster queries by yourself to manage large data corpus?" "Until when are you going to tune model hyper parameters by yourself?" I would like to introduce how to implement self-trained dialogue model by using AutoML in Chatbot within our Chatbot Builder Framework. Read more.
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4:55pm5:35pm Thursday, April 18, 2019
AI Business Summit, Case Studies
Location: Sutton North/Center
A case study that details lights-out automation and how DCL uses AI to transform massive volumes of confidential disparate data into searchable and structured information. Considerations for architecting a solution that processes a continuous flow of 5M+ “pages” of complex work units. Read more.