In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes. Over the last decade, there has been steady progress towards the automation of business processes under the umbrella of ``robotic process automation'' (RPA). However, we are currently at an inflection point in this evolution, as a new paradigm called ``Intelligent Process Automation'' (IPA) emerges, bringing machine learning (ML) and artificial intelligence (AI) technologies to bear in order to improve business process outcomes. The purpose of this paper is to provide a survey of this emerging theme and identify key open research challenges at the intersection of AI and business processes. We hope that this emerging theme will spark engaging conversations at the RPA Forum.

One of the primary goals of the mathematical analysis of algorithms is to provide guidance about which algorithm is the "best" for solving a given computational problem. Worst-case analysis summarizes the performance profile of an algorithm by its worst performance on any input of a given size, implicitly advocating for the algorithm with the best-possible worst-case performance. Strong worst-case guarantees are the holy grail of algorithm design, providing an application-agnostic certification of an algorithm's robustly good performance. However, for many fundamental problems and performance measures, such guarantees are impossible and a more nuanced analysis approach is called for. This chapter surveys several alternatives to worst-case analysis that are discussed in detail later in the book.

One-stop-shop to get information into the history, development and potential of GPT-3. Julien Lauret's article is a comprehensive summary of the journey taken so far to create GPT-3. Julien has managed to summarize years of development and introductions of methodology and techniques to model language and solve natural language processing into several small, concise paragraphs. As well as providing the reader with a background of GPT-3, Julien also gives a somewhat diplomatic answer to the question as to whether GPT-3 is AGI. His response truly reflects the nature of the question itself, in that the question is subjected to the definition of intelligence by whoever poses the question.

You thought AI is intelligent? Well, it has a long road ahead. Without a doubt, artificial intelligence is one of the most revolutionary technologies being developed. Though, as powerful as it is, there still are a lot of basic problems it is yet to become competent to solve. David Cox, Director of MIT-IBM Watson AI lab says, "It's time to reinvent artificial intelligence."

The rapidly growing computational demands of deep neural networks require novel hardware designs. Recently, tunable nanoelectronic devices were developed based on hopping electrons through a network of dopant atoms in silicon. These "Dopant Network Processing Units" (DNPUs) are highly energy-efficient and have potentially very high throughput. By adapting the control voltages applied to its terminals, a single DNPU can solve a variety of linearly non-separable classification problems. However, using a single device has limitations due to the implicit single-node architecture. This paper presents a promising novel approach to neural information processing by introducing DNPUs as high-capacity neurons and moving from a single to a multi-neuron framework. By implementing and testing a small multi-DNPU classifier in hardware, we show that feed-forward DNPU networks improve the performance of a single DNPU from 77% to 94% test accuracy on a binary classification task with concentric classes on a plane. Furthermore, motivated by the integration of DNPUs with memristor arrays, we study the potential of using DNPUs in combination with linear layers. We show by simulation that a single-layer MNIST classifier with only 10 DNPUs achieves over 96% test accuracy. Our results pave the road towards hardware neural-network emulators that offer atomic-scale information processing with low latency and energy consumption.

Knowledge Graphs are increasingly becoming popular for a variety of downstream tasks like Question Answering and Information Retrieval. However, the Knowledge Graphs are often incomplete, thus leading to poor performance. As a result, there has been a lot of interest in the task of Knowledge Base Completion. More recently, Graph Neural Networks have been used to capture structural information inherently stored in these Knowledge Graphs and have been shown to achieve SOTA performance across a variety of datasets. In this survey, we understand the various strengths and weaknesses of the proposed methodology and try to find new exciting research problems in this area that require further investigation.

The almost overnight shift of resources toward remote work has introduced the need for far more flexible, dynamic and seamless end-to-end applications, putting us on a path that requires autonomous capabilities using AIOps – Artificial Intelligence for IT Operations. It’s the topic that the SNIA Cloud Storage Technologies Initiative is going to cover on August 25, 2020 at our live webcast, “IT Modernization with AIOps: The Journey.” Our AI expert, Parviz Peiravi, will provide an overview of concepts and strategies to accelerate the digitalization of critical enterprise IT resources, and help architects rethink what applications and underlying infrastructure are needed to support an agile, seamless data centric environment. This session will specifically address migration from monolithic to microservices, transition to Cloud Native services, and the platform requirements to help accelerate AIOps application delivery within our dynamic hybrid and multi-cloud world.     Join this webcast to learn:  • Use cases and design patterns: Data Fabrics, Cloud Native and the move from Request Driven to Event Driven  •    Foundational technologies supporting observability: how to build a more consistent scalable framework for governance and orchestration  •    The nature of an AI data centric enterprise: data sourcing, ingestion, processing, and distribution  This webcast will be live, so please bring your questions. We hope to see you on August 25th. Register today.

In this paper, we provide a philosophical account of the value of creative systems for individuals and society. We characterize creativity in very broad philosophical terms, encompassing natural, existential, and social creative processes, such as natural evolution and entrepreneurship, and explain why creativity understood in this way is instrumental for advancing human well-being in the long term. We then explain why current mainstream AI tends to be anti-creative, which means that there are moral costs of employing this type of AI in human endeavors, although computational systems that involve creativity are on the rise. In conclusion, there is an argument for ethics to be more hospitable to creativity-enabling AI, which can also be in a trade-off with other values promoted in AI ethics, such as its explainability and accuracy.

Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a key challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This paper presents a survey of the integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps. First, we systematically cover approaches to dynamics model learning, including challenges like dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. Second, we present a systematic categorization of planning-learning integration, including aspects like: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. After these two key sections, we also discuss the potential benefits of model-based RL, like enhanced data efficiency, targeted exploration, and improved stability. Along the survey, we also draw connections to several related RL fields, like hierarchical RL and transfer, and other research disciplines, like behavioural psychology. Altogether, the survey presents a broad conceptual overview of planning-learning combinations for MDP optimization.

Thanks to advancements in deep learning & artificial neural networks, computer vision is increasingly capable of mimicking human vision & is paving the way for self-driving cars, medical diagnosis, scanning recorded surveillance, manufacturing & much more. In this introductory workshop, Sage Elliot will give an overview of deep learning as it related to computer vision with a focused discussion around image classification. You will also learn about careers in computer vision & who are some of the biggest users of this technology. About Your Instructor: Sage Elliott is a Machine Learning Developer Evangelist for Sixgill with about 10 years of experience in the engineering space. He has passion for exploring new technologies & building communities.