Super-forecasting the 'technological singularity' risks from artificial intelligence.

IF 2.7 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Evolving Systems Pub Date : 2022-01-01 Epub Date: 2022-06-04 DOI:10.1007/s12530-022-09431-7

Petar Radanliev, David De Roure, Carsten Maple, Uchenna Ani

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引用次数: 7

Abstract

This article investigates cybersecurity (and risk) in the context of 'technological singularity' from artificial intelligence. The investigation constructs multiple risk forecasts that are synthesised in a new framework for counteracting risks from artificial intelligence (AI) itself. In other words, the research in this article is not just concerned with securing a system, but also analysing how the system responds when (internal and external) failure(s) and compromise(s) occur. This is an important methodological principle because not all systems can be secured, and totally securing a system is not feasible. Thus, we need to construct algorithms that will enable systems to continue operating even when parts of the system have been compromised. Furthermore, the article forecasts emerging cyber-risks from the integration of AI in cybersecurity. Based on the forecasts, the article is concentrated on creating synergies between the existing literature, the data sources identified in the survey, and forecasts. The forecasts are used to increase the feasibility of the overall research and enable the development of novel methodologies that uses AI to defend from cyber risks. The methodology is focused on addressing the risk of AI attacks, as well as to forecast the value of AI in defence and in the prevention of AI rogue devices acting independently.

Supplementary information: The online version contains supplementary material available at 10.1007/s12530-022-09431-7.

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超级预测来自人工智能的“技术奇点”风险。

本文从人工智能的“技术奇点”的角度来研究网络安全（和风险）。该调查构建了多个风险预测，这些预测在一个新的框架中进行综合，以抵消人工智能本身的风险。换言之，本文的研究不仅涉及系统的安全，还分析了系统在发生（内部和外部）故障和妥协时的反应。这是一个重要的方法论原则，因为并非所有系统都可以得到保护，完全保护一个系统是不可行的。因此，我们需要构建算法，使系统即使在部分系统受损的情况下也能继续运行。此外，文章预测了人工智能在网络安全中的整合所带来的新的网络风险。基于这些预测，本文专注于在现有文献、调查中确定的数据源和预测之间创造协同效应。这些预测用于提高整体研究的可行性，并有助于开发利用人工智能抵御网络风险的新方法。该方法侧重于解决人工智能攻击的风险，以及预测人工智能在防御和防止人工智能流氓设备独立行动方面的价值。补充信息：在线版本包含补充材料，请访问10.1007/s12530-022-09431-7。

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来源期刊

Evolving Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

7.80

自引率

6.20%

发文量

期刊介绍： Evolving Systems covers surveys, methodological, and application-oriented papers in the area of dynamically evolving systems. ‘Evolving systems’ are inspired by the idea of system model evolution in a dynamically changing and evolving environment. In contrast to the standard approach in machine learning, mathematical modelling and related disciplines where the model structure is assumed and fixed a priori and the problem is focused on parametric optimisation, evolving systems allow the model structure to gradually change/evolve. The aim of such continuous or life-long learning and domain adaptation is self-organization. It can adapt to new data patterns, is more suitable for streaming data, transfer learning and can recognise and learn from unknown and unpredictable data patterns. Such properties are critically important for autonomous, robotic systems that continue to learn and adapt after they are being designed (at run time). Evolving Systems solicits publications that address the problems of all aspects of system modelling, clustering, classification, prediction and control in non-stationary, unpredictable environments and describe new methods and approaches for their design. The journal is devoted to the topic of self-developing, self-organised, and evolving systems in its entirety — from systematic methods to case studies and real industrial applications. It covers all aspects of the methodology such as Evolving Systems methodology Evolving Neural Networks and Neuro-fuzzy Systems Evolving Classifiers and Clustering Evolving Controllers and Predictive models Evolving Explainable AI systems Evolving Systems applications but also looking at new paradigms and applications, including medicine, robotics, business, industrial automation, control systems, transportation, communications, environmental monitoring, biomedical systems, security, and electronic services, finance and economics. The common features for all submitted methods and systems are the evolving nature of the systems and the environments. The journal is encompassing contributions related to: 1) Methods of machine learning, AI, computational intelligence and mathematical modelling 2) Inspiration from Nature and Biology, including Neuroscience, Bioinformatics and Molecular biology, Quantum physics 3) Applications in engineering, business, social sciences.