The Role Of Technology and Innovation In The Framework Of The Information Society

The literature on the information society indicates that it is a still-developing field of research. It can be explained by the lack of consensus on basic definitions and research methods. There are also different judgments on the importance and the significance of the information society. Some social scientists write about a change of era, others emphasize parallelism with the past. There are some authors who expect that the information society will solve the problems of social inequalities, poverty and unemployment, while others blame it on the widening social gap between the information haves and have-nots. Various models of the information society have been developed so far and they are so different from country to country that it would be rather unwise to look for a single, all-encompassing definition. In our time a number of profound socio-economic changes are underway. Almost every field of our life is affected by the different phenomena of globalization, beside the growing role of the individual; another important characteristic of this process is the development of an organizing principle based on the free creation, distribution, access and use of knowledge and information. The 1990s and the 21st century is undoubtedly characterized by the world of the information society (as a form of the post-industrial society), which represents a different quality compared to the previous ones. The application of these theories and schools on ICT is problematic in many respects. First, as we stated above, there is not a single, widely used paradigm which has synthesized the various schools and theories dealing with technology and society. Second, these fragmented approaches do not have a fully-fledged mode of application to the relationship of ICT and (information) society. Third, SCOT, ANT, the evolutionary- or the systems approach to the history of technology – when dealing with information society – does not take into account the results of approaches (such as information science or information systems literature or social informatics, information management and knowledge management, communication and media studies) studying the very essence of the information age: information, communication and knowledge. The list of unnoticed or partially incorporated sciences, which focuses on the role of ICT in human information processing and other cognitive activities, is much longer

Cognition as management of meaningful information. Proposal for an evolutionary approach.

Humans are cognitive entities. Our behaviors and ongoing interactions with the environment are\ud threaded with creations and usages of meaningful information, be they conscious or unconscious.\ud Animal life is also populated with meaningful information related to the survival of the individual\ud and of the species. The meaningfulness of information managed by artificial agents can also be\ud considered as a reality once we accept that the meanings managed by an artificial agent are\ud derived from what we, the cognitive designers, have built the agent for.\ud This rapid overview brings to consider that cognition, in terms of management of meaningful\ud information, can be looked at as a reality for animal, humans and robots. But it is pretty clear\ud that the corresponding meanings will be very different in nature and content. Free will and selfconsciousness\ud are key drivers in the management of human meanings, but they do not exist for\ud animals or robots. Also, staying alive is a constraint that we share with animals. Robots do not\ud carry that constraint.\ud Such differences in meaningful information and cognition for animal, humans and robots could\ud bring us to believe that the analysis of cognitions for these three types of agents has to be done\ud separately. But if we agree that humans are the result of the evolution of life and that robots are a\ud product of human activities, we can then look at addressing the possibility for an evolutionary\ud approach at cognition based on meaningful information management. A bottom-up path would\ud begin by meaning management within basic living entities, then climb up the ladder of evolution\ud up to us humans, and continue with artificial agents.\ud This is what we propose to present here: address an evolutionary approach for cognition, based\ud on meaning management using a simple systemic tool.\ud We use for that an existing systemic approach on meaning generation where a system submitted\ud to a constraint generates a meaningful information (a meaning) that will initiate an action in order\ud to satisfy the constraint [1,2]. The action can be physical, mental or other.\ud This systemic approach defines a Meaning Generator System (MGS). The simplicity of the MGS\ud makes it available as a building block for meaning management in animals, humans and robots.\ud Contrary to approaches on meaning generation in psychology or linguistics, the MGS approach is\ud not based on human mind. To avoid circularity, an evolutionary approach has to be careful not to\ud include components of human mind in the starting point.\ud The MGS receives information from its environment and compares it with its constraint. The\ud generated meaning is the connection existing between the received information and the\ud constraint. The generated meaning is to trigger an action aimed at satisfying the constraint. The\ud action will modify the environment, and so the generated meaning. Meaning generation links\ud agents to their environments in a dynamic mode. The MGS approach is triadic, Peircean type.\ud The systemic approach allows wide usage of the MGS: a system is a set of elements linked by a\ud set of relations. Any system submitted to a constraint and capable of receiving information from\ud its environment can lead to a MGS. Meaning generation can be applied to many cases, assuming\ud we identify clearly enough the systems and the constraints. Animals, humans and robots are then\ud agents containing MGSs. Similar MGSs carrying different constraints will generate different\ud meanings. Cognition is system dependent.\ud We first apply the MGS approach to animals with “stay alive” and “group life” constraints. Such\ud constraints can bring to model many cases of meaning generation and actions in the organic\ud world. However, it is to be highlighted that even if the functions and characteristics of life are well\ud known, the nature of life is not really understood. Final causes are difficult to integrate in our\ud today science. So analyzing meaning and cognition in living entities will have to take into account\ud our limited understanding about the nature of life. Ongoing research on concepts like autopoiesis\ud could bring a better understanding about the nature of life [3].\ud We next address meaning generation for humans. The case is the most difficult as the nature of\ud human mind is a mystery for today science and philosophy. The natures of our feelings, free will\ud or self-consciousness are unknown. Human constraints, meanings and cognition are difficult to\ud define. Any usage of the MGS approach for humans will have to take into account the limitations\ud that result from the unknown nature of human mind.\ud We will however present some possible approaches to identify human constraints where the MGS\ud brings some openings in an evolutionary approach [4, 5]. But it is clear that the better human\ud mind will be understood, the more we will be in a position to address meaning management and\ud cognition for humans. Ongoing research activities relative to the nature of human mind cover\ud many scientific and philosophical domains [6].\ud The case of meaning management and cognition in artificial agents is rather straightforward with\ud the MGS approach as we, the designers, know the agents and the constraints. In addition, our\ud evolutionary approach brings to position notions like artificial constraints, meaning and autonomy\ud as derived from their animal or human source.\ud We next highlight that cognition as management of meaningful information by agents goes\ud beyond information and needs to address representations which belong to the central hypothesis\ud of cognitive sciences.\ud We define the meaningful representation of an item for an agent as being the networks of\ud meanings relative to the item for the agent, with the action scenarios involving the item.\ud Such meaningful representations embed the agents in their environments and are far from the\ud GOFAI type ones [4]. Meanings, representations and cognition exist by and for the agents.\ud We finish by summarizing the points presented and highlight some possible continuations.\ud [1] C. Menant "Information and Meaning" http://cogprints.org/3694/\ud [2] C. Menant “Introduction to a Systemic Theory of Meaning” (short paper)\ud http://crmenant.free.fr/ResUK/MGS.pdf\ud [3] A. Weber and F. Varela “Life after Kant: Natural purposes and the autopoietic foundations of\ud biological individuality”. Phenomenology and the Cognitive Sciences 1: 97–125, 2002.\ud [4] C. Menant "Computation on Information, Meaning and Representations. An Evolutionary\ud Approach" http://www.idt.mdh.se/ECAP-2005/INFOCOMPBOOK/CHAPTERS/10-Menant.pdf\ud http://crmenant.free.fr/2009BookChapter/C.Menant.211009\ud [5] C. Menant "Proposal for a shared evolutionary nature of language and consciousness"\ud http://cogprints.org/7067/\ud [6] Philpapers “philosophy of mind” http://philpapers.org/browse/philosophy-of-min

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

Steps towards operationalizing an evolutionary archaeological definition of culture

This paper will examine the definition of archaeological cultures/techno-complexes from an evolutionary perspective, in which culture is defined as a system of social information transmission. A formal methodology will be presented through which the concept of a culture can be operationalized, at least within this approach. It has already been argued that in order to study material culture evolution in a manner similar to how palaeontologists study biological change over time we need explicitly constructed ‘archaeological taxonomic units’ (ATUs). In palaeontology, the definition of such taxonomic units – most commonly species – is highly controversial, so no readily adoptable methodology exists. Here it is argued that ‘culture’, however defined, is a phenomenon that emerges through the actions of individuals. In order to identify ‘cultures’, we must therefore construct them from the bottom up, beginning with individual actions. Chaîne opèratoire research, combined with the formal and quantitative identification of variability in individual material culture behaviour allows those traits critical in the social transmission of cultural information to be identified. Once such traits are identified, quantitative, so-called phylogenetic methods can be used to track material culture change over time. Phylogenetic methods produce nested hierarchies of increasingly exclusive groupings, reflecting descent with modification within lineages of social information transmission. Once such nested hierarchies are constructed, it is possible to define an archaeological culture at any given point in this hierarchy, depending on the scale of analysis. A brief example from the Late Glacial in Southern Scandinavia is presented and it is shown that this approach can be used to operationalize an evolutionary definition of ‘culture’ and that it improves upon traditional, typologically defined technocomplexes. In closing, the benefits and limits of such an evolutionary and quantitative definition of ‘culture’ are discussed

‘The uses of ethnography in the science of cultural evolution’. Commentary on Mesoudi, A., Whiten, A. and K. Laland ‘Toward a unified science of cultural evolution’

There is considerable scope for developing a more explicit role for ethnography within the research program proposed in the article. Ethnographic studies of cultural micro-evolution would complement experimental approaches by providing insights into the “natural” settings in which cultural behaviours occur. Ethnography can also contribute to the study of cultural macro-evolution by shedding light on the conditions that generate and maintain cultural lineages

AUGUR: Forecasting the Emergence of New Research Topics

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall

evoText: A new tool for analyzing the biological sciences

We introduce here evoText, a new tool for automated analysis of the literature in the biological sciences. evoText contains a database of hundreds of thousands of journal articles and an array of analysis tools for generating quantitative data on the nature and history of life science, especially ecology and evolutionary biology. This article describes the features of evoText, presents a variety of examples of the kinds of analyses that evoText can run, and offers a brief tutorial describing how to use it

A distributed framework for semi-automatically developing architectures of brain and mind

Developing comprehensive theories of low-level neuronal brain processes and high-level cognitive behaviours, as well as integrating them, is an ambitious challenge that requires new conceptual, computational, and empirical tools. Given the complexities of these theories, they will almost certainly be expressed as computational systems. Here, we propose to use recent developments in grid technology to develop a system of evolutionary scientific discovery, which will (a) enable empirical researchers to make their data widely available for use in developing and testing theories, and (b) enable theorists to semi-automatically develop computational theories. We illustrate these ideas with a case study taken from the domain of categorisation