In 1992 Tom Gruber proposed the following definition "An ontology is a specification of a conceptualization" [4]. Several variants exist that usually add adjectives further describing the specification (e.g., "formal", "explicit") or the conceptualization (e.g., "shared") (see discussion of related work in Section 5). These definitions are not helpful because they violate one of the basic rules for good definitions: the defining statement (the definiens) should be clearer than the term that is defined (the definiendum). As long as "conceptualization" is murkier than "ontology", any attempt of defining "ontology" as a kind of "specification of a conceptualization" is an intellectual placebo: it makes us feel like it provides a better grasp of the nature of ontologies, but there is no intellectual progress, because it lacks explanatory value (see Section 2 for details). Given the difficulties in defining "ontology" one may come to the conclusion that a proper definition is not really needed.

New developments in Artificial Intelligence are transforming the world, from science and industry to government administration and finance. Modern data analysis produces significant outcomes that have real life consequences for people in employment, housing, credit, commerce, and criminal sentencing. Many of these techniques are entirely opaque, leaving individuals unaware whether the decisions were accurate, fair, or even about them. We propose these Universal Guidelines to inform and improve the design and use of AI. The Guidelines are intended to maximize the benefits of AI, to minimize the risk, and to ensure the protection of human rights.

Robots could bring about a four-day working week in Britain as automation and artificial intelligence increase workplace efficiency, a new study has revealed. If new technologies were passed on to staff, they would be able to generate their current weekly economic output in just four days. Even relatively modest gains from using robots and AI had the potential to give British workers Scandinavian levels of leisure time, according to research done by the cross-party Social Market Foundation (SMF) thinktank. The research will boost John McDonnell's plans to reduce hours in the working week The conclusions of the study will come as a boost to John McDonnell, the shadow chancellor, who wants to look at reducing hours in the working week. TUC general secretary Frances O'Grady used her speech to the organisation's annual gathering last month to call for a four-day working week, saying that it should be achievable by the end of the century.

In spite of the fact that the idea of Artificial Intelligence has been around for a long time, it is just in the most recent years that it has gotten on the tech charts and is trending in each and every industry conceivable. Getting to be noticeably extraordinary compared to other cherished techs among the ingenious minds all over the world, Artificial Intelligence demands a mix of computer science, mathematics, cognitive psychology, and engineering. There is no doubt about that soon the demand for experts prepared in Artificial Intelligence would beat supply. In spite of the fact that there is some overlap of Artificial Intelligence with analytics, a capable Artificial Intelligence expert would have profound knowledge on spheres like computer vision, natural language processing, robotics automation, and machine learning. Artificial Intelligence education is still in its youthful days.

Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural networks (CNN) have recently been applied for automatic feature selection and market prediction. However, in experiments reported so far, less attention has been paid to the correlation among different markets as a possible source of information for extracting features. In this paper, we suggest a CNN-based framework with specially designed CNNs, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets. The suggested framework has been applied for predicting the next day's direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of initial features. The evaluations show a significant improvement in prediction's performance compared to the state of the art baseline algorithms.

In this chapter we discuss soft concept analysis, a study which identifies an enriched notion of "conceptual scale" as developed in formal concept analysis with an enriched notion of "linguistic variable" as discussed in fuzzy logic. The identification "enriched conceptual scale" = "enriched linguistic variable" was made in a previous paper (Enriched interpretation, Robert E. Kent). In this chapter we offer further arguments for the importance of this identification by discussing the philosophy, spirit, and practical application of conceptual scaling to the discovery, conceptual analysis, interpretation, and categorization of networked information resources. We argue that a linguistic variable, which has been defined at just the right generalization of valuated categories, provides a natural definition for the process of soft conceptual scaling. This enrichment using valuated categories models the relation of indiscernability, a notion of central importance in rough set theory. At a more fundamental level for soft concept analysis, it also models the derivation of formal concepts, a process of central importance in formal concept analysis. Soft concept analysis is synonymous with enriched concept analysis. From one viewpoint, the study of soft concept analysis that is initiated here extends formal concept analysis to soft computational structures. From another viewpoint, soft concept analysis provides a natural foundation for soft computation by unifying and explaining notions from soft computation in terms of suitably generalized notions from formal concept analysis, rough set theory and fuzzy set theory.

The European Union needs to brace itself for the prospect of a new wave of technology and productivity resurgence in ten to fifteen years' time, spearheaded by the implementation of AI and robotics innovations. This was the message introduced by Stefan Crets, Executive Director of CSR Europe, who, opening the high-level event, said that digitisation required a new agility in the workplace and a new way of collaborating. "The future of work is determined by the actions we take today and the choices we make. What we want to do is exchange experience, pull the expertise together and find the best practices." Economist Mirko Draca, co-author of a London School of Economics (LSE) report commissioned by Huawei, entitled'The evolving role of ICT in the economy,' said that a new wave of automation had started.

Efforts to develop artificial intelligence (AI) are increasingly being framed as a global race, or even a new Great Game. In addition to the race between countries to build national competencies and establish a competitive advantage, firms are also in a contest to acquire AI talent, leverage data advantages, and offer unique services. In both cases, success will depend on whether AI solutions can be democratized and distributed across sectors. The global AI race is unlike any other global competition, because the extent to which innovation is being driven by the state, the corporate sector, or academia differs substantially from country to country. On average, though, the majority of innovations so far have emerged from academia, with governments contributing through procurement, rather than internal research and development.

The late Stephen Hawking did not believe in an afterlife, but he has one all the same. He has appeared as a co-author in two posthumous research papers since he died in March. One takes a fresh look at the problem of just how complex the universe far beyond our horizon could be; the other returns to the intractable but apparently not entirely insoluble problem of what happens to information once it falls into a black hole. This second paper is a response to a paradox that concerns only theoretical physicists but the first addresses the machinery of creation that seems to have needed no creator. Not surprisingly, he returns to both themes and many more in what his publishers call his final thoughts.