A solution to the hyper complex, cross domain reality of artificial intelligence: The hierarchy of AI

Artificial Intelligence (AI) is an umbrella term used to describe machine-based forms of learning. This can encapsulate anything from Siri, Apple's smartphone-based assistant, to Tesla's autonomous vehicles (self-driving cars). At present, there are no set criteria to classify AI. The implications of which include public uncertainty, corporate scepticism, diminished confidence, insufficient funding and limited progress. Current substantial challenges exist with AI such as the use of combinationally large search space, prediction errors against ground truth values, the use of quantum error correction strategies. These are discussed in addition to fundamental data issues across collection, sample error and quality. The concept of cross realms and domains used to inform AI, is considered. Furthermore there is the issue of the confusing range of current AI labels. This paper aims to provide a more consistent form of classification, to be used by institutions and organisations alike, as they endeavour to make AI part of their practice. In turn, this seeks to promote transparency and increase trust. This has been done through primary research, including a panel of data scientists / experts in the field, and through a literature review on existing research. The authors propose a model solution in that of the Hierarchy of AI

Customer emotions in service failure and recovery encounters

Emotions play a significant role in the workplace, and considerable attention has been given to the study of employee emotions. Customers also play a central function in organizations, but much less is known about customer emotions. This chapter reviews the growing literature on customer emotions in employee–customer interfaces with a focus on service failure and recovery encounters, where emotions are heightened. It highlights emerging themes and key findings, addresses the measurement, modeling, and management of customer emotions, and identifies future research streams. Attention is given to emotional contagion, relationships between affective and cognitive processes, customer anger, customer rage, and individual differences

Eight new T4.5-T7.5 dwarfs discovered in the UKIDSS large area survey data release 1

The definitive version is available at www.blackwell-synergy.com Copyright Blackwell Publishing DOI : 10.1111/j.1365-2966.2007.12023.xPeer reviewe

Direct imaging and new technologies to search for substellar companions around MGs cool dwarfs

We describe here our project based in a search for sub-stellar companions (brown dwarfs and exo-planets) around young ultra-cool dwarfs (UCDs) and characterise their properties. We will use current and future technology (high contrast imaging, high-precision Doppler determinations) from the ground and space (VLT, ELT and JWST), to find companions to young objects. Members of young moving groups (MGs) have clear advantages in this field. We compiled a catalogue of young UCD objects and studied their membership to five known young moving groups: Local Association (Pleiades moving group, 20–150 Myr), Ursa Mayor group (Sirius supercluster, 300 Myr), Hyades supercluster (600 Myr), IC 2391 supercluster (35 Myr) and Castor moving group (200 Myr). To assess them as members we used different kinematic and spectroscopic criteria