Surveying Safety-relevant AI Characteristics

[Otros] The current analysis in the AI safety literature usually combines a risk or safety issue (e.g., interruptibility) with a particular paradigm for an AI agent (e.g., reinforcement learning). However, there is currently no survey of safety-relevant characteristics of AI systems that may reveal neglected areas of research or suggest to developers what design choices they could make to avoid or minimise certain safety concerns. In this paper, we take a first step towards delivering such a survey, from two angles. The first features AI system characteristics that are already known to be relevant to safety concerns, including internal system characteristics, characteristics relating to the effect of the external environment on the system, and characteristics relating to the effect of the system on the target environment. The second presents a brief survey of a broad range of AI system characteristics that could prove relevant to safety research, including types of interaction, computation, integration, anticipation, supervision, modification, motivation and achievement. This survey enables further work in exploring system characteristics and design choices that affect safety concernsFMP and JHO were supported by the EU (FEDER) and the Spanish MINECO under grant TIN 2015-69175-C4-1-R, by Generalitat Valenciana (GVA) under grant PROME-TEOII/2015/013 and by the U.S. Air Force Office of Scientific Research under award number FA9550-17-1-0287. FMP was also supported by INCIBE (Ayudas para la excelencia de los equipos de investigacion avanzada en ciberseguridad), the European Commission, JRC¿s Centre for Advanced Studies, HUMAINT project (Expert Contract CT-EX2018D335821-101), and UPV PAID-06-18 Ref. SP20180210. JHO was supported by a Salvador de Madariaga grant (PRX17/00467) from the Spanish MECD for a research stay at the Leverhulme Centre for the Future of Intelligence (CFI), Cambridge, and a BEST grant (BEST/2017/045) from GVA for another research stay also at the CFI. JHO and SOH were supported by the Future of Life Institute (FLI) grant RFP2-152. SOH was also supported by the Leverhulme Trust Research Centre Grant RC2015-067 awarded to the Leverhulme Centre for the Future of Intelligence, and a a grant from Templeton World Charity FoundationHernández-Orallo, J.; Martínez-Plumed, F.; Avin, S.; Heigeartaigh, SO. (2019). Surveying Safety-relevant AI Characteristics. CEUR Workshop Proceedings. 1-9. http://hdl.handle.net/10251/146561S1

Drones in Railways

Evidence shows that the applications of drones are increasing quickly in many industries. Railways are no exception. Due to fast advances in technology, drones are on the verge of breakthroughs that will affect future applications, implementations, and their consequences. Looking ahead, we elaborate on the potential for drones in railways. We use scenario planning and combine it with the findings of an action research project, which we conducted with Swiss Federal Railways (SBB). First, we explore the applications and future trends of drone use in railway operations. Second, based on seven identified factors that may affect the future of drones in railways by 2030, we develop three future scenarios: pessimistic, realistic, and optimistic. The study results help practitioners make informed decisions regarding future drone programs in railways. We also contribute theoretical insights into how high-potential technologies can unleash new capabilities in railway operations

A knowledge based system for valuing variations in civil engineering works: a user centred approach

There has been much evidence that valuing variations in construction projects can lead to conflicts and disputes leading to loss of time, efficiency, and productivity. One of the reasons for these conflicts and disputes concerns the subjectivity of the project stakeholders involved in the process. One way to minimise this is to capture and collate the knowledge and perceptions of the different parties involved in order to develop a robust mechanism for valuing variations. Focusing on the development of such a mechanism, the development of a Knowledge Based System (KBS) for valuing variations in civil engineering work is described. Evaluation of the KBS involved demonstration to practitioners in the construction industry to support the contents of the knowledge base and perceived usability and acceptance of the system. Results support the novelty, contents, usability, and acceptance of the system, and also identify further potential developments of the KBS

Redevelopment or rehabilitation?

Thesis (B.Sc)--University of Hong Kong, 2007.Includes bibliographical references (p. 76-82).published_or_final_versio

Machine Learning and Deep Learning for the Built Heritage Analysis: Laser Scanning and UAV-Based Surveying Applications on a Complex Spatial Grid Structure

The reconstruction of 3D geometries starting from reality-based data is challenging and time-consuming due to the difficulties involved in modeling existing structures and the complex nature of built heritage. This paper presents a methodological approach for the automated segmentation and classification of surveying outputs to improve the interpretation and building information modeling from laser scanning and photogrammetric data. The research focused on the surveying of reticular, space grid structures of the late 19th–20th–21st centuries, as part of our architectural heritage, which might require monitoring maintenance activities, and relied on artificial intelligence (machine learning and deep learning) for: (i) the classification of 3D architectural components at multiple levels of detail and (ii) automated masking in standard photogrammetric processing. Focusing on the case study of the grid structure in steel named La Vela in Bologna, the work raises many critical issues in space grid structures in terms of data accuracy, geometric and spatial complexity, semantic classification, and component recognition

SURVEY, HBIM AND CONSERVATION PLAN OF A MONUMENTAL BUILDING DAMAGED BY EARTHQUAKE

open4Oreni, D.; Brumana, R.; Della Torre, S.; Banfi, F.Oreni, Daniela; Brumana, Raffaella; DELLA TORRE, Stefano; Banfi, Fabrizi

A practical tool for evaluating fire induced failure probability of steel columns designed based on U.S. prescriptive standards

Previous research has investigated and developed systematic probabilistic models for parameters involved in determining the reliability of a structure under fire. The established models have been summarized and applied in this paper to quantify and compare the reliability of steel columns protected based upon the US prescriptive approach. A set of columns with a range of section factors are selected to study the influence of utilization ratio, restraint conditions, and fuel load density on the probability of failure under fire. The results show a relatively large variation in the value of probability of failure for columns with similar fire protection rating but different section factors and utilization ratio. The influence of fuel load density is presented in the form of fragility functions, where the probabilities of failure for expected fuel load density values are discussed. In addition, the probability of failure for columns across different stories of a building is calculated, leading to the conclusion that further harmonization of safety levels can be achieved if reliability-based quantifications are introduced in the design process