Non—cryptographic methods for improving real time transmission security and integrity

In this paper we present a few non cryptographic methods for improving the security, integrity and reliability of real time services. The methods presented in this paper apply to real time transmitting systems, which are based on the Peer-to-Peer (P2P) model. A basic idea of the first technique is to use agents for detecting steganographic content in packet headers, so packets with suspicious entries in the IP header fields will be blocked or the fields will be erased. The two other presented techniques are based on reputation and trust systems, so trust and reputation basic definitions, types and modelling methods are shown. Also a simple design scheme of using these mechanisms in a P2P real-time data transmitting infrastructure is presented. Additionally, we describe an idea of path selecting technique, which can be used to avoid paths that are susceptible to eavesdropping

Reframing superintelligence: comprehensive AI services as general intelligence

Studies of superintelligent-level systems have typically posited AI functionality that plays the role of a mind in a rational utility-directed agent, and hence employ an abstraction initially developed as an idealized model of human decision makers. Today, developments in AI technology highlight intelligent systems that are quite unlike minds, and provide a basis for a different approach to understanding them: Today, we can consider how AI systems are produced (through the work of research and development), what they do (broadly, provide services by performing tasks), and what they will enable (including incremental yet potentially thorough automation of human tasks). Because tasks subject to automation include the tasks that comprise AI research and development, current trends in the field promise accelerating AI-enabled advances in AI technology itself, potentially lead- ing to asymptotically recursive improvement of AI technologies in distributed systems, a prospect that contrasts sharply with the vision of self-improvement internal to opaque, unitary agents. The trajectory of AI development thus points to the emergence of asymptotically comprehensive, superintelligent-level AI services that— crucially—can include the service of developing new services, both narrow and broad, guided by concrete human goals and informed by strong models of human (dis)approval. The concept of comprehensive AI services (CAIS) provides a model of flexible, general intelligence in which agents are a class of service-providing products, rather than a natural or necessary engine of progress in themselves. Ramifications of the CAIS model reframe not only prospects for an intelligence explosion and the nature of advanced machine intelligence, but also the relationship between goals and intelligence, the problem of harnessing advanced AI to broad, challenging problems, and fundamental considerations in AI safety and strategy. Perhaps surprisingly, strongly self-modifying agents lose their instrumental value even as their implementation becomes more accessible, while the likely context for the emergence of such agents becomes a world already in possession of general superintelligent-level capabilities. These prospective capabilities, in turn, engender novel risks and opportunities of their own. Further topics addressed in this work include the general architecture of systems with broad capabilities, the intersection between symbolic and neural systems, learning vs. competence in definitions of intelligence, tactical vs. strategic tasks in the context of human control, and estimates of the relative capacities of human brains vs. current digital systems

Engage D3.10 Research and innovation insights

Engage is the SESAR 2020 Knowledge Transfer Network (KTN). It is managed by a consortium of academia and industry, with the support of the SESAR Joint Undertaking. This report highlights future research opportunities for ATM. The basic framework is structured around three research pillars. Each research pillar has a dedicated section in this report. SESAR’s Strategic Research and Innovation Agenda, Digital European Sky is a focal point of comparison. Much of the work is underpinned by the building and successful launch of the Engage wiki, which comprises an interactive research map, an ATM concepts roadmap and a research repository. Extensive lessons learned are presented. Detailed proposals for future research, plus research enablers and platforms are suggested for SESAR 3