Agenda

Senior Scientist Engineer at Microsoft, James McCaffrey and Pranjal Daga, Data Scientist at Cisco, will field questions from the audience of deep learning practitioners about all things deep learning deployment, from initial research and development to championing within the organization to scaling up for production and maintaining.

In the field of Automatic Speech Recognition (ASR), the state of the art for generic conversations have reached super human levels. However, things are not nearly as good in specialized knowledge domains: attempting to transcribe vendor-customer or intra-vendor conversations often results in high double-digit error rates. Considering the low performance of ASR on real data, it becomes imperative to look at the multimodal data of text and images to customize the Language Model, and audio to customize the Acoustic Model. This session will focus on discussing end-to-end Recurrent Neural Network architectures which can learn alphabets just through a sound spectrogram. 

High frequency trading (HFT) has been characterized as an arms race with 'Red Queen' characteristics. And yet, Machine Learning (ML) has barely impacted HFT. Indeed, its application raises more questions than it answers.  How do we integrate an agent's actions into a predictive model? If all agents use ML, then why trade?  This talk demonstrates a novel model for attributing strategy performance to predictive accuracy. Our model quantifies the extent to which an agent's action invalidate the prediction. Moreover, the model sheds light on why a zero-sum game is an unlikely outcome for ML agents.

Long short-term memory (LSTM) deep neural networks have revolutionized natural language and are the foundation of systems like Siri and Alexa. Because LSTM networks maintain state, they can model time series regression problems. In this lively and informal session, Dr. James McCaffrey from Microsoft Research will explain exactly what LSTM networks are, without using (many) Greek letters or annoying math jargon. You'll leave this session with a solid understanding of LSTM networks, know what they can and cannot do, and have all the information you need to implement an LSTM network or communicate with subject matter experts.

Head of Lyft's Machine Learning Platform Gil Arditi and Seagate Technology Senior Data Scientist, Melanie Beck field questions from an audience of deep learning practitioners about all things deep learning deployment, from initial research and development to championing within the organization to scaling up for production and maintaining.

AlphaGo, AlphaGo Zero, Alpha Zero Chess. One might wonder what’s next. AI and Deep Learning (DL) seem more like interesting research topics. Adoption into mainstream enterprise seems some ways out. John Hancock approaches DL as an opportunity to potentially disrupt its traditional model building process wherein the DL algorithms start without any prior business knowledge. In this session, you will learn about the journey of (NLP and) DL using two use cases – to detect fraudulent patterns and in predicting Underwriting Risk Class. We will also discuss a broader range of opportunities for DL application in financial services.  In all cases, our starting point is (text) unstructured data.

With the rise of deep learning as a promising solution in the area of object detection and image recognition, industries are embracing Convolution Neural Networks (CNN) in many applications such as autonomous cars, security surveillance, medical imaging, etc. This presentation will showcase how Seagate is refining its Industrial Internet of Things (IIoT) strategy by developing and deploying CNN algorithms in its factories. From product quality check to security surveillance, deep learning is helping shape the digital transformation of Seagate Technology operations by boosting the performance of deployed machine learning models to unprecedented accuracies.

Like many companies, Northwestern Mutual switched from being paper-based to relying on "electronic forms" for underwriting health surveys.  While this vastly improved underwriting processes, extraction of historical "paper-based" data is still desired to unlock its business value.  Learn how Northwestern Mutual, on its journey to algorithmic underwriting, successfully extracted data from scanned health surveys by leveraging computer vision and deep learning.

As Deep Learning tools and frameworks become more prevalent and lower the complexity of the development process, we see a shift in data-driven software development towards using Deep Learning instead of classical ML or more standard software. This change entails a different software development process that affects the way we frame the problem, iteratively develop the solution, allocate and manage compute and memory resources, and debug our system. The talk will cover the shift in some areas from classical software development to Deep Learning, success stories of employing Deep Learning, the organizational processes and required developer skills needed, and insights on how to best manage the entire model lifecycle - from ideating to deployment and maintenance.

One in six children suffer from developmental delay. In many cases,early screening is possible using behavioral phenotypes. We showcase the use of interactive storytelling sessions on tablets to collect video feeds for automated screeners powered by deep learning.