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Put AI to work
Sep 4-5, 2018: Training
Sep 5-7, 2018: Tutorials & Conference
San Francisco, CA

Leaving no one behind: Make equal access to social good possible with deep learning

Goodman Gu (Cogito)
4:00pm-4:40pm Friday, September 7, 2018
Secondary topics:  Computer Vision, Interfaces and UX

Who is this presentation for?

  • Data scientists, machine learning engineers, and managers and directors of data science and machine learning

Prerequisite knowledge

  • A basic understanding of machine learning

What you'll learn

  • Explore an end-to-end data science and machine learning process and a compelling use case of computer vision and deep learning for social good
  • Learn basic computer vision techniques with OpenCV, how to do deep learning modeling using convolutional neural networks, how to use AWS DeepLens as an edge inference device, and how to use Amazon SageMaker to quickly and easily build, train, optimize, and deploy an ML app at scale


Nowadays, people seem to be more worried about AI replacing jobs than excited about the new opportunities AI creates. However, many of these debates are centered around the “normal” workforce. Over 400M people worldwide have some sort of speech or hearing disorder that precludes them from participating in the job market. Disability should not be a disadvantage, and everyone who wants to work with others should have equal access to do so.

Goodman Gu offers an overview of Stride4All, an initiative created at Atlassian using AI and machine learning technologies to open work up for those with speech and hearing impairments and empower them for teamwork, and showcases a prototype that uses deep learning and computer vision technologies for gesture recognition of American Sign Language. He will then explore other use cases where AI can be a major force for social good. In particular, Goodman explains how to use a Keras frontend, a TensorFlow backend, and convolutional neural networks to build a multiclass classifier for sign language alphabets and convert signs into text and lifelike speech. An early adopter of Amazon Sagemaker, Goodman also shares his experience using this fully managed end-to-end machine learning pipeline, revealing how feature engineering, hyperparameter tuning, and model deployment can be simplified and streamlined to make data scientists’ and machine learning engineers’ lives easier and more productive.

Photo of Goodman Gu

Goodman Gu


Goodman Xiaoyuan Gu is head of machine learning architecture at Boston-based Cogito, where he leads operations of large-scale real-time augmented intelligence platform. Previously, he headed marketing data engineering at Atlassian and was vice president of technology at CPXi, director of engineering at Dell, and general manager at Amazon, where he built marketing, analytics and machine learning applications. He has served on technical program committees of two IEEE flagship conferences and is the author of over a dozen academic publications in high-profile IEEE and ACM journals and conferences. Goodman holds a degree in engineering and management from MIT.