Applied AI Virtual Summit schedule

Why AI Ethics Matter

I am the Head, Artificial Intelligence and Machine Learning, at the Center for the Fourth Industrial Revolution at the World Economic Forum. Prior to taking this position I was Executive Director of AI-Global and led the Ethics Advisory Panel of Lucid.ai.I have worked for the past three decades as a barrister, mediator, arbitrator, business owner, professor and judge in the United Kingdom. In the United States, I have taught at the undergraduate and law school levels and worked as a professional lecturer. I am a humanitarian with a strong sense of social justice and have advanced degrees in Law and International Relations. In my role as Chief officer of the EAP I have advised governments, think tanks and non-profits about artificial intelligence. I am co-founder of the Consortium for Law and Policy of Artificial Intelligence and Robotics at the Robert E. Strauss Center, University of Texas and teach a course at the UT Law School for the Consortium: "Artificial Intelligence and emerging technologies: Law and Policy". Additionally, I am a Distinguished Scholar of the Robert E Strauss Center at the University of Texas and Vice Chair of the IEEE Industry Connections "Global Initiative for Ethical Considerations in the Design of Autonomous Systems.

Jun Jia is a Senior Staff Software Engineer in the Artificial Intelligence team at LinkedIn where he works on developing state-of-the-art algorithms to improve LinkedIn's Search and Recommendation systems. He has extensive experience in R&D and software engineering and numerous publications in top-tier journals and conferences. Prior to LinkedIn, he obtained his MS and PhD in CS and Math from the University of North Carolina at Chapel Hill and worked as a technical staff member at ORNL.

Sandeep Jha is a Staff Technical Program Manager in the Artificial Intelligence group at LinkedIn, where he leads programs that empowers LinkedIn's Search and Recommendation systems. Before LinkedIn, he was a Sr. Technical Program Manager in Search Science and AI team at Amazon, where he worked on improving the search result in the first page of Amazon worldwide. Before that, he worked at Facebook, where he led initiatives to enhance ad quality and launch of multiple commerce products such as Facebook Marketplace and Instagram Shopping.

Deep Learning for Recommendations

Simon is a senior data scientist at The Home Depot where he works on personalization and ranking for the core recommendations team. In his time at Home Depot, he has built a number of different recommender systems including a personalized deals recommender, and a system for recommending how to guides for customers working on home improvement projects. Simon has a PhD in Machine Learning and Natural Language Processing from DePaul University, over 6 years’ experience working in data science, and 15 years’ experience in software development.

Applied Machine Learning in Early-Stage Start-Ups

Applying machine learning in an early-stage start-up is particularly challenging. Unlike an established business where optimization objectives are well-defined, a "Day-One" company may change the target metrics rapidly to tackle the challenge of growth, which leaves training data small and noisy. Instead of waiting for big data to accumulate, we leverage multiple machine learning techniques, including Ensemble Learning, Deep Learning, and Causal Inference, to jointly improve search quality and help drive growth.

In this talk, we will investigate factors needed to be considered when we are designing a search ranking model in an early-stage start-up environment. We will walk through examples of designing machine learning algorithms for different types of applications, and introduce how an optimization objective can be selected, mathematically defined, and optimized with a machine learning framework. Part of the presentation is based on Airbnb Experiences, the second start-up of Airbnb focusing on recommending what to do when people travel.

Liang Wu is a machine learning data scientist working in the Search and Relevance team at Airbnb, focusing on core search ranking and recommendation of local tours and activities for Airbnb's second startup - Experiences. He previously worked on product search and web search at Etsy and Microsoft Research. He serves as a program committee member for major AI conferences such as AAAI, SIGIR, KDD and WSDM. Liang received his PhD from Arizona State University. During his academic career, he published over 30 papers, authored 2 book chapters, achieved 3rd place of KDD Cup, and received 3 patent awards that have been cited by Alibaba, Baidu, Microsoft, Tencent, The Fourth Paradigm, etc. His thesis research concentrated on building robust machine learning models with noisy data and inaccurate labels.

Inventory Level Analysis using Artificial Intelligence

With the current trend in retail industry, everything is being automated. The effort is put in the same direction to do stock level analysis using a depth sensing device. In retail we observe that most of the times, things are always picked up from the front of a bin. On basis of that surmise, we can use deep learning to scan the space using a depth sensing device and come up with a very good approximation of its volumetric space. The module can be plugged into an AGV/Drone which will scan the bin area and update the backend with inventory details.

Ravi Dalal is a Computer Vision Engineer III who helps Sam’s Club to solve Club Operation problems using Artificial Intelligence. Before starting as a CV engineer at WalMart in 2018, Ravi was pursing his Masters from Carnegie Mellon University and graduated in Fall 2017 with a degree in Information Systems having concentration in Machin Learning and Data Science. And in a short span of 2 years while working at Sam’s; he has filed 5 patents in the domain of automating mundane tasks using AI/CV. Ravi loves Basketball and his all-time favorite athlete is Michel Jordan.

AI for Earth: Conservation by Acoustic Surveillance

Rainforest Connection (RFCx) is an innovative nonprofit startup at the forefront of conservation technology committed to protecting the planet’s precious, ancient forests and wildlife. RFCx listens to the threatened ecosystems remotely, using commonplace mobile tech and existing telecommunications infrastructure, and transforms these audio streams into an automatic understanding of the soundscape, rooting out any threats using AI/ML. Over the last year, Rainforest Connection has expanded its vision: the RFCx Bioacoustics Platform brings scientists and data scientists together to use AI/ML models to monitor for endangered and threatened species health and catalyze new ecological discoveries.

A long-time veteran of the tech industry, Bourhan has over fifteen years’ experience in building and leading large-scale Manufacturing and Engineering teams in several Bay Area companies including Powis and Zazzle, as well as the leading Dubai-based e-commerce company MarkaVIP, where he served as long-time COO before joining RFCx.

Machine Learning: Build, Deploy, Monitor at Scale

How do you deploy and manage not just 1, but 100’s of machine learning models? What changes when you move from batch processing to synchronous calls? When your algorithms become mission critical, what changes? What is seemingly easy when working for a small company, with lower volumes or with batch operations becomes exponentially more difficult at scale. Off the shelf development and deployment platforms have existed for years, but use cases are becoming broader and corporations are starting to customize and invest internally. In this presentation, I will discuss the infrastructure and processes necessary to operationalize data science at scale.

Tom is an experienced data scientist specializing in development and deployment of algorithms into real time, mission critical, applications. Through the years, his work has ranged from ground up infrastructure buildouts in smaller startups to more mature large corporate America installations. He currently leads the data science team at eBay responsible for payments and risk, specializing in fraud detection and otherwise creating a safe platform for buyers and sellers. Previous roles included risk assessment and customer acquisition related to automated, high risk, online lending. Outside of work he spends most of his time on two wheels, racing mountain biking around central Texas.

Mohamed Fawzy is a senior manager at Facebook. In his six years at the company, he’s worked on its distributed storage system and was part of the team that developed cold storage, Facebook’s exabyte archiver storage system that keeps your memories safe. Mohamed started the Distributed AI Group to build large-scale distributed training infrastructure for deep learning and support all use cases within the company including large scale ranking and recommendation, computer vision, machine translation and speech.

Large scale distributed training has become an essential element to scaling the productivity for ML engineers. Today, ML models are getting larger and more complex in terms of compute and memory requirements. The amount of data we train on at Facebook is huge. In this talk, we will learn about the Distributed Training Platform to support large scale data and model parallelism. We will touch base on Distributed Training support for PyTorch and how we are offering a flexible training platform for ML engineers to increase their productivity at facebook scale.

Dwarak Rajagopal is a Senior Engineering Manager and Technical Lead in AI Infrastructure at Facebook. He currently leads the core development of PyTorch 1.0, an open source deep learning platform and the center of Facebook's effort to scale Research to Production in deep learning. Prior to Facebook, as the head of Core Platforms in Uber ATG, he led the Onboard Infra, ML and Data Platforms for the self driving software stack and built out the engineering team in SF.