Sep 9–12, 2019

Schedule: Text, Language, and Speech sessions

9:00am - 5:00pm Monday, September 9 & Tuesday, September 10
Location: Santa Clara Room (Hilton)
Delip Rao (AI Foundation), Brian McMahan (Wells Fargo)
Average rating: ****.
(4.50, 4 ratings)
Delip Rao and Brian McMahan explore natural language processing using a set of machine learning techniques known as deep learning. They walk you through neural network architectures and NLP tasks and teach you how to apply these architectures for those tasks. Read more.
9:00am12:30pm Tuesday, September 10, 2019
Location: 230 B
Average rating: *****
(5.00, 1 rating)
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.
9:00am12:30pm Tuesday, September 10, 2019
Location: LL21 E/F
Lukas Biewald (Weights & Biases)
Average rating: ****.
(4.25, 4 ratings)
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.
1:30pm5:00pm Tuesday, September 10, 2019
Location: LL21 E/F
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; 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.
11:05am11:45am Wednesday, September 11, 2019
Location: Expo Hall 3
Huaixiu Zheng (Uber)
Average rating: ****.
(4.33, 3 ratings)
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.
11:55am12:35pm Wednesday, September 11, 2019
Location: Expo Hall 3
Hagay Lupesko (Facebook)
Average rating: ****.
(4.33, 6 ratings)
Hagay Lupesko explores AI-powered personalization at Facebook and the challenges and practical techniques it applied to overcome these challenges. You'll learn about deep learning-based personalization modeling, scalable training, and the accompanying system design approaches that are applied in practice. Read more.
11:55am12:35pm Wednesday, September 11, 2019
Location: 230 B
Huaiyu Zhu (IBM Research - Almaden), Dulce Ponceleon (IBM Research - Almaden), Yunyao Li (IBM Research - Almaden)
Average rating: **...
(2.00, 1 rating)
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.
11:55am12:35pm Wednesday, September 11, 2019
Location: LL21 C/D
Joy Rimchala (Intuit), TJ Torres (Intuit), Xiao Xiao (Intuit), Hui Wang (Intuit)
Average rating: *****
(5.00, 1 rating)
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.
1:45pm2:25pm Wednesday, September 11, 2019
Location: LL21 A/B
Yi Zhang (Rulai | University of California, Santa Cruz)
Average rating: ****.
(4.43, 7 ratings)
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.
2:35pm3:15pm Wednesday, September 11, 2019
Location: Expo Hall 3
Joseph Spisak (Facebook), Hao Lu (Facebook)
Average rating: *****
(5.00, 1 rating)
Joseph Spisak and Hao Lu lead a deep dive into 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.
4:00pm4:40pm Wednesday, September 11, 2019
Location: 230 A
Ashish Bansal (Twitter)
Average rating: ****.
(4.50, 2 ratings)
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.
4:00pm4:40pm Wednesday, September 11, 2019
Location: Expo Hall 3
Average rating: *****
(5.00, 1 rating)
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.
4:00pm4:40pm Wednesday, September 11, 2019
Location: LL21 E/F
Dexter Hadley (University of California, San Francisco)
Average rating: *****
(5.00, 1 rating)
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 translate this into precision oncology. Read more.
4:00pm4:40pm Wednesday, September 11, 2019
Location: LL21 A/B
David Talby (Pacific AI)
Average rating: ***..
(3.33, 6 ratings)
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.
4:00pm4:40pm Wednesday, September 11, 2019
Location: LL21 C/D
Stacy Ashworth (SelectData), Alberto Andreotti (John Snow Labs)
Much business data still exists as challenging scanned or snapped documents. Stacy Ashworth and Alberto Andreotti explore a real-world case of 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.
4:50pm5:30pm Wednesday, September 11, 2019
Location: 230 B
Dylan Glas (Futurewei Technologies), Phoebe Liu (Figure Eight)
Average rating: *****
(5.00, 1 rating)
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.
4:50pm5:30pm Wednesday, September 11, 2019
Location: 230 C
Sijun He (Twitter), Ali Mollahosseini (Twitter)
Average rating: ****.
(4.00, 1 rating)
Twitter is what’s happening in the world right now. To connect users with the best content, Twitter needs to build a deep understanding of its noisy and temporal text content. Sijun He and Ali Mollahosseini explore the named entity recognition (NER) system at Twitter and the challenges Twitter faces to build and scale a large-scale deep learning system to annotate 500 million tweets per day. Read more.
4:50pm5:30pm Wednesday, September 11, 2019
Location: Santa Clara Room (Hilton)
Yishay Carmiel (IntelligentWire)
One of the most important tasks of AI has been to understand humans. People want machines to understand not only what they say but also what they mean and to take particular actions based on that information. This goal is the essence of conversational AI. Yishay Carmiel explores the latest breakthroughs and revolutions in this field and the challenges still to come. Read more.
11:05am11:45am Thursday, September 12, 2019
Location: 230 B
Chaitanya Shivade (IBM Research)
Average rating: ****.
(4.00, 1 rating)
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.
1:45pm2:25pm Thursday, September 12, 2019
Location: 230 A
Vijay Srinivas Agneeswaran (Walmart Labs), Abhishek Kumar (Publicis Sapient)
Vijay Agneeswaran and Abhishek Kumar explore multilabel text classification problems, where multiple tags or categories have to be associated with a given text or documents. Multilabel text classification occurs in numerous real-world scenarios, for instance, in news categorization and bioinformatics (such as the gene classification problem). Read more.
1:45pm2:25pm Thursday, September 12, 2019
Location: Expo Hall 3
Stef Nelson-Lindall (Facebook)
Average rating: **...
(2.00, 2 ratings)
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.
2:35pm3:15pm Thursday, September 12, 2019
Location: 230 C
Anusua Trivedi (Microsoft)
Average rating: **...
(2.33, 3 ratings)
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.
4:00pm4:40pm Thursday, September 12, 2019
Location: 230 C
Jisheng Wang (Mist Systems)
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 details some of his experiences and the lessons he learned applying ML, DL, and AI to the development of SaaS-based enterprise solutions. Read more.
4:50pm5:30pm Thursday, September 12, 2019
Location: LL21 A/B
Mayank Kejriwal (USC Information Sciences Institute)
Average rating: ****.
(4.67, 3 ratings)
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.
4:50pm5:30pm Thursday, September 12, 2019
Location: 230 C
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.

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