As artificial intelligence and machine learning algorithms make further inroads into society, calls are increasing from multiple stakeholders for these algorithms to explain their outputs. At the same time, these stakeholders, whether they be affected citizens, government regulators, domain experts, or system developers, present different requirements for explanations. Toward addressing these needs, we introduce AI Explainability 360 (http://aix360.mybluemix.net/), an open-source software toolkit featuring eight diverse and state-of-the-art explainability methods and two evaluation metrics. Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability. For data scientists and other users of the toolkit, we have implemented an extensible software architecture that organizes methods according to their place in the AI modeling pipeline. We also discuss enhancements to bring research innovations closer to consumers of explanations, ranging from simplified, more accessible versions of algorithms, to tutorials and an interactive web demo to introduce AI explainability to different audiences and application domains. Together, our toolkit and taxonomy can help identify gaps where more explainability methods are needed and provide a platform to incorporate them as they are developed.

Who We Are: ShopperTrak is on a mission: we are revolutionizing the brick and mortar retail world with innovative products that fuel their success. Our path forward includes active exploration of IoT, Computer Vision, Machine (Deep) Learning and other technologies. We believe this is a thrilling journey and providing clarity on our future and empowering people to do their best work is the key to our success. We are inspired by people who are passionate, curious and want to push the envelope on innovation. Around here, our motto is "build great stuff" that helps solve problems for our customers and great things will follow.

PLEASE NOTE THE NEW ADDRESS OF MORSE BARNES-BROWN & PENDLETON at 480 Totten Pond Road. Artificial intelligence technologies are threatening to take over many decision-making tasks humans perform at work and in personal life. AI systems are already making critical decisions in areas previously thought to be the exclusive domain of humans: driving cars, reviewing job applications, underwriting loans, and even endeavoring to create patentable innovation and recommending sentencing in the criminal justice system. What does this rapid and seemingly unstoppable development in artificial intelligence mean for the legal profession? In his talk, Joe Barkai will provide an overview of key AI technologies.

Matthew is one of the founders of the World Futures Forum (WFF) and will be one of our highly anticipated speakers at WFF on 24th September 2019. The inaugural 2019 World Futures Forum Summit, being held in London this September is your opportunity to discover and discuss the exponential technologies and megatrends that are shaping our world, and driving the new global economy. It's where business, culture, and technology converge, to give you access to some of the most innovative companies and people in the world.

Artificial Intelligence is basically the hypothesis and practice with regards to building machines equipped for performing tasks that seem to require intelligence. At present, cutting edge technologies in this paradigm include machine learning, artificial neural networks, and deep learning. In the meantime, blockchain is basically another documenting framework for computerized data which stores information in an encrypted, distributed ledger format. Since the information is encrypted and distributed across many different computers, it empowers the making of carefully designed, exceptionally robust databases which can be read and updated only by those with permission. It goes without saying that every innovation has its own individual level of complexity, however, the combination of the two might be advantageous to both.

Historically, we carried out content moderation using third party vendors, but with the increasing volume of the images (and text content) we started to automate as much of this work as possible with the help of machine learning models. In the next few sections, we will provide an overview of our modeling framework, data collection, and evaluation frameworks. One challenge we faced when we started this project was the lack of enough labeled data with granular categories for user generated content. In the past, Expedia teams labeled content using crowd-sourcing, but in many cases we found that images had only been labeled as approved or rejected without specifying the reason. This meant we lacked the training data to inform models why an image was rejected (an image can be rejected because it had low quality, or because it contains identifiable children, or for many other reasons).

Robotic Process Automation (RPA) accelerates the digital transformation of business processes by automatically replicating tedious actions that have no added value. SAP Intelligent Robotic Process Automation is a complete automation suite where software robots are designed to mimic humans by replacing manual clicks, interpreting text-heavy communications, or making process suggestions to end users for definable and repeatable business processes. The course will give an introduction to RPA as an industry standard, introduce the SAP solution with its individual cloud and on-premise components, show an example of an automated scenario with SAP Intelligent RPA, and explain the business value of the solution as well as key differentiators. After attending this course, participants will be able to understand what RPA is and how the SAP solution functions. Moreover, they'll learn how to rate use cases for RPA and understand the value of it for today's businesses.

In this paper we consider the basic version of Reinforcement Learning (RL) that involves computing optimal data driven (adaptive) policies for Markovian decision process with unknown transition probabilities. We provide a brief survey of the state of the art of the area and we compare the performance of the classic UCB policy of \cc{bkmdp97} with a new policy developed herein which we call MDP-Deterministic Minimum Empirical Divergence (MDP-DMED), and a method based on Posterior sampling (MDP-PS).

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games. OpenSpiel supports n-player (single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and sequential, strictly turn-taking and simultaneous-move, perfect and imperfect information games, as well as traditional multiagent environments such as (partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel also includes tools to analyze learning dynamics and other common evaluation metrics. This document serves both as an overview of the code base and an introduction to the terminology, core concepts, and algorithms across the fields of reinforcement learning, computational game theory, and search.

The program opens with four days of tutorials that will provide an introduction to major themes of the entire program and the four workshops. The goal is to build a foundation for the participants of this program who have diverse scientific backgrounds. The tutorials will focus on the theoretical and conceptual foundations of machine learning, as well as several of the application areas that will be discussed during the program. For those participating in the long program, please plan to attend Opening Day on September 4, 2019, as well. Others may participate in Opening Day by invitation from the organizing committee.