This is especially true when AI systems tackle difficult problems whose solution cannot be accurately defined by a traditional rule-based approach but require the data-driven and/or learning approaches increasingly being used in AI. Indeed, data-driven AI systems, such as those using machine learning, are very successful in terms of accuracy and flexibility, and they can be very "creative" in solving a problem, finding solutions that could positively surprise humans and teach them innovative ways to resolve a challenge. However, creativity and freedom without boundaries can sometimes lead to undesired actions: the AI system could achieve its goal in ways that are not considered acceptable according to values and norms of the impacted community. Thus, there is a growing need to understand how to constrain the actions of an AI system by providing boundaries within which the system must operate. This is usually referred to as the "value alignment" problem, since such boundaries should model values and principles required for the specific AI application scenario.

I don't know about you, I've never been comfortable with the use of the term Artificial Intelligence (AI) by mainstream media, and the way the ubiquitous sales and marketing folk have recently begin to fling it around only add to my general angst over it all. When we talk about Artificial Intelligence, to many, this conjures up thoughts of killer robots, a dystopian future and being enslaved to mega-corporations (gulp, is that bit already here?!). Apart from all that, there is a general misconception that you need a Ph.D. to be able to use these technologies, let alone understand them. To all of the above, I simply say - balderdash good sir! balderdash! I have recently taken a deep dive into the field of AI by involving myself (yet again) in some further university study.

AI for business is an incredibly helpful tool for enterprises when used correctly. Just take a look at some numbers recently published in a Forbes Magazine article: 38% of 235 enterprises the NBRI looked at are already using AI for a variety of tasks; and more importantly, 62% of these enterprises expect to be using AI by 2018. But here's the rub: AI is a massively broad catch all term. Over the last few years, people have termed all sorts of machine coding techniques as'AI;' in fact, saying that your business uses AI is kind of like saying your garden has plants. In other words, AI is an umbrella for a whole host of technologies.

Despite improvements in technology, implementation of master data management (MDM) solutions have long been a known pain for many organizations pushing to improve data quality and competency. The source of this pain is often due to the fact that traditional MDM solutions solve the data mastering problem using deterministic, rule-based approaches that do not easily accommodate nor scale for the increasing flow of messy, diverse data coming from disparate data systems. Faster technology has not been able to remove this pain, but it can be relieved with a fresh approach to MDM. In previous blog posts, my colleagues have examined in detail this new approach while explaining the need for organizations to adopt anagile approach to the data mastering problem, as well as why this approach is critical to anorganization's digital transformation. Tamr's API-driven, machine learning capability makes agile data mastering possible as it fundamentally changes the way we can tackle the data mastering problem.

This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.

Businesses have long recognized that the best and most innovative ideas usually come from the front lines – originating from the individuals directly involved in manufacturing products, executing processes, and overseeing operations. For large organizations, however, the task of soliciting, reviewing, and considering massive volumes of ideas quickly becomes overwhelming. Specifically, how do you separate the truly creative and inspired proposals from the crowd of also-rans? And, once identified, how do you ensure that the best ideas get implemented quickly? Online idea generation platforms that allow users and managers to evaluate innovation proposals can help.

With the rapid development of digital information, the data volume generated by humans and machines is growing exponentially. Along with this trend, machine learning algorithms have been formed and evolved continuously to discover new information and knowledge from different data sources. Learning algorithms using hyperboxes as fundamental representational and building blocks are a branch of machine learning methods. These algorithms have enormous potential for high scalability and online adaptation of predictors built using hyperbox data representations to the dynamically changing environments and streaming data. This paper aims to give a comprehensive survey of literature on hyperbox-based machine learning models. In general, according to the architecture and characteristic features of the resulting models, the existing hyperbox-based learning algorithms may be grouped into three major categories: fuzzy min-max neural networks, hyperbox-based hybrid models, and other algorithms based on hyperbox representation. Within each of these groups, this paper shows a brief description of the structure of models, associated learning algorithms, and an analysis of their advantages and drawbacks. Main applications of these hyperbox-based models to the real-world problems are also described in this paper. Finally, we discuss some open problems and identify potential future research directions in this field.

We propose the Relational Tucker3 (RT) decomposition for multi-relational link prediction in knowledge graphs. We show that many existing knowledge graph embedding models are special cases of the RT decomposition with certain predefined sparsity patterns in its components. In contrast to these prior models, RT decouples the sizes of entity and relation embeddings, allows parameter sharing across relations, and does not make use of a predefined sparsity pattern. We use the RT decomposition as a tool to explore whether it is possible and beneficial to automatically learn sparsity patterns, and whether dense models can outperform sparse models (using the same number of parameters). Our experiments indicate that---depending on the dataset--both questions can be answered affirmatively.

This book explains how human learning is promoted by applying artificial intelligence to education. Before that, let's first look back on how information technology including artificial intelligence contributed to education. Various technologies have been developed to make it easier for learners to learn and to create an environment where teachers can more easily teach. An example of this is called e-learning or intelligent tutoring systems (ITS). ITS was developed using a rule-based system which is an initial result of artificial intelligence. In the process, user models for learners called learner models and educational contents have been improved.

Previous research on quantum computing / mechanics and the arts has usually been in simulation. The small amount of work done in hardware or with actual physical systems has not utilized any of the advantages of quantum computation: the main advantage being the potential speed increase of quantum algorithms. This paper introduces a way of utilizing Grover's algorithm - which has been shown to provide a quadratic speedup over its classical equivalent - in algorithmic rulebased music composition. The system introduced - qgMuse - is simple but scalable. It lays some groundwork for new ways of addressing a significant problem in computer music research: unstructured random search for desired music features. Example melodies are composed using qgMuse using the ibmqx4 quantum hardware, and the paper concludes with discussion on how such an approach can grow with the improvement of quantum computer hardware and software.