Extreme 13C depletion of CCl2F2 in firn air samples from NEEM, Greenland

A series of 12 high volume air samples collected from the S2 firn core during the North Greenland Eemian Ice Drilling (NEEM) 2009 campaign have been measured for mixing ratio and stable carbon isotope composition of the chlorofluorocarbon CFC-12 (CCl2F2). While the mixing ratio measurements compare favorably to other firn air studies, the isotope results show extreme 13C depletion at the deepest measurable depth (65 m), to values lower than d13C = -80‰ vs. VPDB (the international stable carbon isotope scale), compared to present day surface tropospheric measurements near -40‰. Firn air modeling was used to interpret these measurements. Reconstructed atmospheric time series indicate even larger depletions (to -120‰) near 1950 AD, with subsequent rapid enrichment of the atmospheric reservoir of the compound to the present day value. Mass-balance calculations show that this change is likely to have been caused by a large change in the isotopic composition of anthropogenic CFC-12 emissions, probably due to technological advances in the CFC production process over the last 80 yr, though direct evidence is lacking

Explainable AI: The new 42?

Explainable AI is not a new field. Since at least the early exploitation of C.S. Pierce’s abductive reasoning in expert systems of the 1980s, there were reasoning architectures to support an explanation function for complex AI systems, including applications in medical diagnosis, complex multi-component design, and reasoning about the real world. So explainability is at least as old as early AI, and a natural consequence of the design of AI systems. While early expert systems consisted of handcrafted knowledge bases that enabled reasoning over narrowly well-defined domains (e.g., INTERNIST, MYCIN), such systems had no learning capabilities and had only primitive uncertainty handling. But the evolution of formal reasoning architectures to incorporate principled probabilistic reasoning helped address the capture and use of uncertain knowledge. There has been recent and relatively rapid success of AI/machine learning solutions arises from neural network architectures. A new generation of neural methods now scale to exploit the practical applicability of statistical and algebraic learning approaches in arbitrarily high dimensional spaces. But despite their huge successes, largely in problems which can be cast as classification problems, their effectiveness is still limited by their un-debuggability, and their inability to “explain” their decisions in a human understandable and reconstructable way. So while AlphaGo or DeepStack can crush the best humans at Go or Poker, neither program has any internal model of its task; its representations defy interpretation by humans, there is no mechanism to explain their actions and behaviour, and furthermore, there is no obvious instructional value.. the high performance systems can not help humans improve. Even when we understand the underlying mathematical scaffolding of current machine learning architectures, it is often impossible to get insight into the internal working of the models; we need explicit modeling and reasoning tools to explain how and why a result was achieved. We also know that a significant challenge for future AI is contextual adaptation, i.e., systems that incrementally help to construct explanatory models for solving real-world problems. Here it would be beneficial not to exclude human expertise, but to augment human intelligence with artificial intelligence

Interaction Design of Augmented Education Environments - Augmented and Mixed Reality for performance and training support of Aviation / Automotive Technicians.

"Augmented reality (AR),Mixed Reality (MR) and their mix and combination with other disruptive technologies offer an enormous potential for supporting instructors and trainees in modern education and working environments such as of aircraft maintenance technicians or automotive service technicians. In this paper we investigate and show some examples on how the performance and training of such instructors and trainees can be actively supported. Furthermore we will discuss the new challenges for training designers. The augmentation of the physical world with interactive, context-aware information (e.g. 2D and 3D content) provides multifaceted possibilities, on various ubiquitous and pervasive computing environments. While there is still the broad opinion that these concepts are just situated in the world of science fiction (SciFi) and SciFi movies, we will relate these techniques to existing technologies and prototypes in research. Terms like outernet, print + or 2.0, augmented goggles, wearable technology are not just remaining pure buzzwords anymore. We will demonstrate how different prototypes applying low cost rapid prototyping methods can be applied as powerful performance assistance and training support instruments, whereby discussing the requirements and user-needs analysis phases as well as human–computer interaction and interaction design issues, user modelling, usability engineering, prototyping and evaluation issues. Different scenarios are possible and provide the basis to generate storyboards. One of the key factors is hereby to analyse typical tasks and activities of users and utilize familiar user interaction paradigms for accessing information, such as using a book or assisting the work with task sheets. For example by augmenting the material that is printed in the book with additional graphical 3D interactive information which can be viewed and manipulated by the instructor and/or trainee, one can provide a link between traditional learning and technology-enhanced learning. Basing on theoretical and empirical research it is possible then to design via first moodboards and scribbles relevant prototypes. A qualitative and quantitative analysis can be used to define a basic design process for such new environments and settings. Moreover, MR and AR along with Mobile Tagging (MT) combined with Pervasive Computing provide the possibility to realize a Physical World Connection (PWC) between Reality and Virtuality. In the field of aviation and automotive industry, this offers manifold possibilities for maintenance and service personnel to get access to assistive technologies in a very intuitive way to enhance their operation, work, training, and knowledge. Assistance for the large variety of job tasks can be provided to a certain extent by offering augmentation of the different senses like vision and audition, providing a media-rich interface. Although the roots of Mixed Reality and Augmented Reality are based on prototype applications in the aircraft industry in the early 1990s, the impact of these emerging technologies on special target groups has not yet been investigated and validated by many research groups. With a specific focus on these user communities, applications are considerably more influenced by both usefulness and usability of technology. Consequently, it is argued that key issues in developing such applications are the tracking methodology, the display technology, interaction (devices and framework) and most of all ensuring good usability. In this paper, a concrete example in a aviation and automotive environment will be presented as a case study for investigating and validating these key issues. Preliminary results of semi-structured interviews and observations in real training and work settings indicate a lack of information concerning existence of such technologies and environments, but show big interest and potential for such educational and workplace innovations, while concrete visions or user requirements for future augmented education environments remain open and are subject of our further research steps

An App to Support Yoga Teachers to Implement a Yoga-Based Approach to Promote Wellbeing Among Young People: Usability Study

Many young people suffer from chronic stress and other issues that inhibit the functioning and development of the prefrontal cortex, and this also affects their intrinsic motivation to engage in any activity. In short, unless their well-being is addressed, they cannot engage effectively. The HIPPOCAMPUS project aims to address these issues by promoting the well-being of young people through the practice of a range of techniques derived from yoga. Yuva Yoga app is part of the approach to support the yoga-based practices with young people. It is a multiplatform mobile app developed as Backend as a Service both for Android and iOS. The first public version of the mobile app is part of the pilots implemented in the schools involved in the project, but there is not a special focus on the usability of the app. This work presents the heuristic evaluation of Yuva Yoga for iOS carried out by four experts as part of a major usability study that combines heuristic techniques, both iOS and Android, and empirical methods with users. Some problems were detected during the evaluation, but more of the problems have a low priority rating. They are mainly cosmetic problems that do not need to be fixed unless extra time is available on the project, or minor usability problems. The results have provided an important input to develop a new minor version of the mobile app, in order to improve the user experience in the pilots at schools

On the challenges and opportunities in visualization for machine learning and knowledge extraction: A research agenda

We describe a selection of challenges at the intersection of machine learning and data visualization and outline a subjective research agenda based on professional and personal experience. The unprecedented increase in the amount, variety and the value of data has been significantly transforming the way that scientific research is carried out and businesses operate. Within data science, which has emerged as a practice to enable this data-intensive innovation by gathering together and advancing the knowledge from fields such as statistics, machine learning, knowledge extraction, data management, and visualization, visualization plays a unique and maybe the ultimate role as an approach to facilitate the human and computer cooperation, and to particularly enable the analysis of diverse and heterogeneous data using complex computational methods where algorithmic results are challenging to interpret and operationalize. Whilst algorithm development is surely at the center of the whole pipeline in disciplines such as Machine Learning and Knowledge Discovery, it is visualization which ultimately makes the results accessible to the end user. Visualization thus can be seen as a mapping from arbitrarily high-dimensional abstract spaces to the lower dimensions and plays a central and critical role in interacting with machine learning algorithms, and particularly in interactive machine learning (iML) with including the human-in-the-loop. The central goal of the CD-MAKE VIS workshop is to spark discussions at this intersection of visualization, machine learning and knowledge discovery and bring together experts from these disciplines. This paper discusses a perspective on the challenges and opportunities in this integration of these discipline and presents a number of directions and strategies for further research