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Quantum Computing in Cybersecurity: An in-Depth Analysis of Risks and Solutions
The world has transformed into an information society that exceedingly depends on data. Since information frameworks create large measures of records each day, consistently, it appears the world is achieving the level of data overload. Big data is used to process the enormous volumes of data into revealing shrouded designs, complex relationships, and other helpful information. This work has done a comprehensive analysis of enormous information investigation in medicinal services. A brief insight into the importance of cognitive computing in healthcare has been presented. The extensive study concludes that the Cognitive computing has more impact on healthcare predictions than the big data analytics.
The conventional dynamic source routing (DSR) standard chooses the path in need of minimal hop counts for communicating the information from the source to the target. Therefore due to node displacements in autonomous self – regulatory ad hoc networks, the path comprising a minimal number of probably increased extent hops do not works well always. Moreover, the presently employed path might split due to the node displacements and the path related data acquired from the path identification might become illogical. For avoiding the probably imprecise nature of the route-related data a fuzzy logic supported scheme is initiated into the routing scheme for evading the control of rough routing related data. Extensively the fuzzy logic supported schemes are probable of operating various inputs which therefore are used for both the anticipated path lifespan and the hop counts as their input thus permitting to combine the physical layer and the network layer into a mutually modeled routing standard. The lifetime of the path is usually minimized based on the increased node displacements. The precise path with increased path constancy is lastly chosen for communicating the data and based on the lifetime of the path the path cache termination time is altered erratically. The illustration is performed for the designed fuzzy based DSR scheme which outdoes the traditional DSR in terms of reasonable network outcomes rather with minimal network governance load. Lastly measuring the influence of the physical layer is performed over the possible behavior of the network layer for various physical layer schemes using NS 2 simulator.
It is the question of determining patterns in big data sets that correlate to helpful data. It involves techniques that are at the confluence of machine learning, statistics, and legacy system, and it is also known as data mining. Machine learning is a branch of artificial intelligence that emerged from the areas of object recognition and artificial intelligence. It is concerned with the research and development of methods that can understand from assessment tools. The study shows financial institutions use of financial data performance and ensure precise management of consumer data in order to identify defaulters, to reduce the number of equipment failures associated, to process transactions quickly and efficiently, to reduce the number of incorrect judgments, to categorize potential customers, and to minimize the wastage of the financial organizations.KeywordsData miningMachine learningFraudRiskFinancial management
The AI system is a better assessment of the situation and more than any other system, allows women to face the tense environment. This scheme could provide women with aid and support and make them safer and even late in the working overtime. It also allows to plan safer protection by identifying various areas of vulnerability from the data collected from the databases and, ideally, substantially mitigating crime against women. The paper introduces the architecture and technologies for women of today's vital challenges through embedded applications and concepts. The framework involves functions such as the activation of tear shocks, shry warnings, streaming video and notifying and exchanging messages. For women, this smart phone app is beneficial. With SWMS, cases of culture may be minimized. Women can get notices regarding uncertain locations with this submission. And she's primed for some scenario now.
There are several reasons for the lack of a consensus definition of (machine) intelligence. The constantly evolving nature and the interdisciplinarity of the Artificial Intelligence (AI) field, together with a historical polarization around what intelligence means, are among the most widely discussed rationalizations, both within the community and outside it. These factors are aggravated by the presence of cognitive biases in subjective reasoning by experts on the definition of intelligence, as we have found in a recent study of experts' opinions across multiple disciplines. In this paper, we show how different cognitive biases can undermine consensus on defining intelligence, and thus how an understanding of intelligence can be substantially affected by these human traits. We also provide general recommendations for tackling these problems. An understanding of intelligence can be achieved by understanding the limits of both human expressiveness and the current discourse around definitions of intelligence within and across the concerned fields.
Given the foreseeable pervasiveness of artificial intelligence (AI) in modern societies, it is legitimate and necessary to ask the question how this new technology must be shaped to support the maintenance and strengthening of constitutional democracy. This paper first describes the four core elements of today's digital power concentration, which need to be seen in cumulation and which, seen together, are both a threat to democracy and to functioning markets. It then recalls the experience with the lawless Internet and the relationship between technology and the law as it has developed in the Internet economy and the experience with GDPR before it moves on to the key question for AI in democracy, namely which of the challenges of AI can be safely and with good conscience left to ethics, and which challenges of AI need to be addressed by rules which are enforceable and encompass the legitimacy of democratic process, thus laws. The paper closes with a call for a new culture of incorporating the principles of democracy, rule of law and human rights by design in AI and a three-level technological impact assessment for new technologies like AI as a practical way forward for this purpose.
This article is part of a theme issue ‘Governing artificial intelligence: ethical, legal, and technical opportunities and challenges’.
We present nine facets for the analysis of the past and future evolution of AI. Each facet has also a set of edges that can summarise different trends and contours in AI. With them, we first conduct a quantitative analysis using the information from two decades of AAAI/IJCAI conferences and around 50 years of documents from AI topics, an official database from the AAAI, illustrated by several plots. We then perform a qualitative analysis using the facets and edges, locating AI systems in the intelligence landscape and the discipline as a whole. This analytical framework provides a more structured and systematic way of looking at the shape and boundaries of AI.
Human intelligence is a quality recognized as showing differences between individuals. For more than 150 years researchers have been studying and continually improving psychometric tests for human intelligence, with a colossal amount of data collected; however, despite many attempts, there remains no clearly agreed upon definition of human intelligence. A lack of a clearly defined definition of human intelligence is a perceived stumbling block in the pursuit of a) understanding intelligence and b) building machines which replicate human intelligence (Brooks, 1991) and c) exceed human intelligence. Seeking to reach consensus on definitions of human and machine intelligence, our current survey research findings indicate that researchers are narrowly in agreement that human intelligence and artificial ‘machine’ intelligence should be defined differently. The evidence from our study suggests that there may be a link between analytical thinking constructs used in written comments about definitions of intelligence and the level of positive agreement with those definitions, even when the comments do not agree with the definitions. Finally, our ongoing work illustrates that intelligent machine which conceptualizes, operationalizes, and exceeds human intelligence should be constructed under the guiding tenet that “[t]he goal of AI systems should be to be useful to humans” (Hutter, 2005). Any definition of an AI that fails to define the ‘what’ of intelligence and more importantly the ‘why’ may be potentially detrimental to society.
Many problems in AI study can be traced back to the confusion of different research goals. In this paper, five typical ways to define AI are clarified, analyzed, and compared. It is argued that though they are all legitimate research goals, they lead the research to very different directions, and most of them have trouble to give AI a proper identity. Finally, a working definition of AI is proposed, which has important advantages over the alternatives.
Reasoning is generally seen as a means to improve knowledge and make better decisions. However, much evidence shows that reasoning often leads to epistemic distortions and poor decisions. This suggests that the function of reasoning should be rethought. Our hypothesis is that the function of reasoning is argumentative. It is to devise and evaluate arguments intended to persuade. Reasoning so conceived is adaptive given the exceptional dependence of humans on communication and their vulnerability to misinformation. A wide range of evidence in the psychology of reasoning and decision making can be reinterpreted and better explained in the light of this hypothesis. Poor performance in standard reasoning tasks is explained by the lack of argumentative context. When the same problems are placed in a proper argumentative setting, people turn out to be skilled arguers. Skilled arguers, however, are not after the truth but after arguments supporting their views. This explains the notorious confirmation bias. This bias is apparent not only when people are actually arguing, but also when they are reasoning proactively from the perspective of having to defend their opinions. Reasoning so motivated can distort evaluations and attitudes and allow erroneous beliefs to persist. Proactively used reasoning also favors decisions that are easy to justify but not necessarily better. In all these instances traditionally described as failures or flaws, reasoning does exactly what can be expected of an argumentative device: Look for arguments that support a given conclusion, and, ceteris paribus, favor conclusions for which arguments can be found.
A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this equation formally captures the concept of machine intelligence in the broadest reasonable sense. We then show how this formal definition is related to the theory of universal optimal learning agents. Finally, we survey the many other tests and definitions of intelligence that have been proposed for machines.
This statement outlines conclusions re-garded as mainstream among researchers on intelligence, in particular, on the nature, ori-gins, and practical consequences of individu-al and group differences in intelligence. Its aim is to promote more reasoned discussion of the vexing phenomenon that the research has revealed in recent decades. The follow-ing conclusions are fully described in the major textbooks, professional journals and encyclopedias in intelligence.
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