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.

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.

DeepBrew – AI at Starbucks

DeepBrew is in the news and on the front lines for Starbucks now. But – several years ago, Artificial Intelligence at Starbucks was considered an impossible task. In this talk, we will cover what happened in the journey to stand up the first artificial intelligence application at Starbucks – and discuss the implications for any firm that wants to enter this new space. Lessons learned, common misconceptions, taking risks, and stepping out of swimlanes – all the in name of supporting a project – will be discussed.

I lead the team that built DeepBrew - the AI and Machine Learning Platform at Starbucks. My small team built this from the ground up and is responsible for all aspects of performance - from revenue impact to uptime. This platform (and the models) is at the heart of significant change at Starbucks.

Solving Hard Problems in Computer Vision With Synthetic Data

We make the case that a necessary step towards the advancement of computer vision will be to solve the bottlenecks of data curation and annotation. Here we show how synthetic data can solve both of these issues while providing additional advantages over real-world data. The talk will conclude by discussing real-world case studies for orbital insights, retail, and agriculture that are currently being solved using synthetic data from AI.Reverie.

Daeil Kim is co-founder and CEO of AI.Reverie, a startup specializing in creating high quality synthetic data to train computer vision algorithms. Daeil received his Ph.D in Computer Science from Brown University focusing on scalable machine learning algorithms. He is excited about building tools that will help advance machine learning progress and considers synthetic data to be a core element towards advancing that field.

Anomaly Detection in Retail

Tian received her bachelor degree from Peking University (China), finished master degree at Heidelberg University (Germany) and obtained Ph.D. from Yale University.

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.

Deep Learning for Recommendations

Complimentary recommendations are an important form of recommendations in retail and e-commerce. When a customer purchases a product, such as a patio table, they usually also need to purchase complimentary products, such as patio chairs, or an umbrella. We developed a hybrid content-based complimentary recommendation system that combines textual and visual features to produce recommendations that are complimentary in both style and function. I describe how this model is powered by a combination of Siamese Neural Network and a simple visual similar model. Finally, I will describe how we scale this model to produce recommendations for 4 million different products.

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.

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 Senior Computer Vision Engineer 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.

How Robots Are Being Used For COVID-19 (and Ebola)

Dr. Robin R. Murphy is the Raytheon Professor of Computer Science and Engineering at Texas A&M University, a director of the Center for Robot-Assisted Search and Rescue, and an ACM and IEEE Fellow. She specializes in human-robot interaction and human-centered AI for ground, air, and marine robots. She is a TED speaker and the author of over 150 papers and four books including the award-winning Disaster Robotics. She has deployed robots to over 29 disasters in five countries including the 9/11 World Trade Center, Fukushima, and Hurricane Harvey. She blogs at RoboticsThroughScienceFiction.com.

Building the Ideal Infrastructure for AI Workloads

What’s the “right” way to build your infrastructure stack for deep learning? Today, GPU infrastructure for deep learning is mostly built on bare metal, and resources are allocated to researchers in a static way. Hear from Omri Geller, CEO and co-founder of Run:AI about the three steps you can take to optimize, share resources, and ultimately free data scientists from AI infrastructure management hassles. Learn about:

1. Tips for scheduling workloads with Kubernetes,
2. Orchestration – bringing concepts from the world of HPC to AI for better management of expensive resources
3. Scale - from fractional GPUs to multiple nodes of GPUs, for distributed training using batch workloads

Omri Geller co-founded Run:AI in 2018 in order to build a virtualization layer for AI workloads – essentially abstracting jobs from compute power in order to pool and dynamically share expensive compute resources. Omri leads all of Run:AI’s day to day activities as well as strategic direction. Prior to Run:AI, Omri served in an elite technological unit of the Israeli Prime Minister’s Office as part of the “Academic Atuda” program of the Israeli Defense Forces. By training, Omri is an algorithm engineer focused specifically on High Performance Computing algorithms. In 2015, he received an award for outstanding contribution to Israel’s defense. He is also the recipient of the Tel Aviv University award for excellence in M.Sc. studies (2015) and the Israel Defense Forces award for excellence in academic studies (2010).

Automated Model Exploration

The Machine Learning modeling work has a lot of repetitive steps, and we can automate some of those to speed up the path to make an impact. In this talk, we will provide a brief overview of LinkedIn's initiative on machine learning model exploration and dive deep into automating standard model training practices such as feature selection, hyperparameter tuning, and model selection.

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.

Automated Model Exploration

The Machine Learning modeling work has a lot of repetitive steps, and we can automate some of those to speed up the path to make an impact. In this talk, we will provide a brief overview of LinkedIn's initiative on machine learning model exploration and dive deep into automating standard model training practices such as feature selection, hyperparameter tuning, and model selection.

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.

Training Models at Facebook Scale with PyTorch

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.

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.

Training Models at Facebook Scale with PyTorch

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.

Making Happy Modelers: Build and Maintain Your Data Warehouse with AWS Redshift and Airflow

To apply AI effectively in the business setting, and to get the optimal benefit for business decisionmaking, data needs to be ready to use and easy to access for data science teams. Journera (a travel industry data startup) has built our data warehouse using Airflow and AWS Redshift, and we're using it to access and analyze hundreds of millions of records on the fly. This talk will share an introduction to each tool, walk through the pipeline that can be built from any data store to the Redshift platform, discuss the architecture of a relational data warehouse in Redshift, and give tips on how to avoid mistakes we made in our own process.

Stephanie Kirmer is a Senior Data Scientist at Journera, an early stage startup that helps companies in the travel industry use data efficiently and securely to create better travel experiences for customers. Previously she worked as a Senior Data Scientist at Uptake, where she developed tools for analyzing diesel engine fuel efficiency, and constructed predictive models for diagnosing and preventing mechanical failure. Before joining Uptake, she worked on data science for social policy research at the University of Chicago and taught sociology and health policy at DePaul University.

Risto Miikkulainen is a Professor of Computer Science at the University of Texas at Austin and Associate VP of Evolutionary AI at Cognizant. He received an M.S. in Engineering from Helsinki University of Technology (now Aalto University) in 1986, and a Ph.D. in Computer Science from UCLA in 1990. His current research focuses on methods and applications of neuroevolution, as well as neural network models of natural language processing and vision; he is an author of over 430 articles in these research areas. At Cognizant, and previously as CTO of Sentient Technologies, he is scaling up these approaches to real-world problems. Risto is an IEEE Fellow; his work on neuroevolution has recently been recognized with the IEEE CIS Evolutionary Computation Pioneer Award, the Gabor Award of the International Neural Network Society and Outstanding Paper of the Decade Award of the International Society for Artificial Life.

Natalia Konstantinova is a great enthusiast with over 10 years' experience in the application of Natural Language Processing, Artificial Intelligence, IT and machine learning technologies to real world problems. She is currently an AI Lead Architect at BP and her role is to lead the design and delivery of AI Solution Patterns, develop standards and best practices to accelerate the adoption and implementation of AI enabled solutions, and doing so with the right level of compliance and standards within BP. Natalia got her PhD from the University of Wolverhampton and worked in various fields such as machine translation, ontologies, information extraction, dialogue systems and chat bots. Natalia is a strong believer that modern technology can transform businesses and our everyday life.

Recognised as Top 100 influential Leaders in Asian Analytics Industry and Top 40 under 40 Data Scientists in India, Anish is a professional with over 19 years of experience with exceptional expertise of delivering value through innovative use of data across a spectrum of industry verticals. An informed opinion Leader and the go-to person in the field of Data Analytics, Data Visualisation, Strategy Design, Thought Leadership, Financial Modelling, Artificial Intelligence, Machine Learning, Natural Language Processing (NLP) and Robotics Process Automation (RPA). He is on panel of several industry conferences and has authored several papers on various subjects related to Artificial Intelligence and advanced Data & Analytics.

Dr Loubna Bouarfa is a machine learning scientist turned entrepreneur. She is the founder and CEO of OKRA Technologies - an artificial intelligence data analytics company for healthcare. OKRA allows healthcare professionals to combine all their data in one place and generate actionable, evidence-based insights in real time, to save and improve human lives. Loubna is currently a member of the European Union High-Level Expert Group on Artificial Intelligence, where she is particularly focused on healthcare and achieving competitive business impact with AI. She was named an MIT Technology Review Top Innovator Under 35, a Forbes 50 Top Women In Tech, and won several prizes, including CEO of Year 2019 at the Cambridge Independent Science and Technology Awards and Best Female-Led Startup at the StartUp Europe Awards. On a personal level, she is a strong advocate for diversity, women and challenging the status quo.