区块链中的人工智能——提供数字技术

Dziatkovskii Anton
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引用次数: 0

摘要

人工智能技术发展迅速,是计算机科学的一个重要分支。人工智能是建模和扩展人类智能的理论、方法、技术和应用研究和开发的核心。人工智能技术有三个关键方面,即数据、算法和计算能力,因为训练算法生成分类模型需要大量数据,而学习过程需要提高计算能力。在大数据时代,信息可以来自各种来源(如传感器系统、物联网设备和系统以及社交媒体平台)和/或属于不同的利益相关者。这主要导致了许多问题。其中一个关键问题是孤立的数据孤岛,来自单一来源/利益相关者的数据无法提供给其他方或培训人工智能模型,或者为集中处理和培训收集大量分布式数据在财务上困难或不切实际。在集中式体系结构中,也存在成为单点故障的风险,这可能导致数据入侵。此外,来自不同来源的数据可能是非结构化的,质量不同,而且可能很难确定数据的来源和有效性。还存在无效或恶意数据的风险。所有这些限制都可能影响预测的准确性。在实践中,人工智能模型被各种主体创建、训练和使用。学习过程对用户来说是不透明的,用户可能不会完全信任他们正在使用的模型。此外,随着人工智能算法变得更加复杂,人们很难理解训练的结果是如何获得的。因此,最近有一种趋势,从集中的人工智能方法转向分散的人工智能。
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ARTIFICIAL INTELLIGENCE IN BLOCKCHAIN-PROVIDE DIGITAL TECHNOLOGY
Artificial intelligence technologies, today, are rapidly developing and are an important branch of Computer Science. Artificial intelligence is at the heart of research and development of theory, methods, technologies, and applications for modeling and expanding human intelligence. Artificial intelligence technology has three key aspects, namely data, algorithm, and computing power, in the sense that training an algorithm to produce a classification model requires significant data, and the learning process requires improved computing capabilities. In the age of big data, information can come from a variety of sources (such as sensor systems, Internet of Things (IoT) devices and systems, as well as social media platforms) and/or belong to different stakeholders. This mostly leads to a number of problems. One of the key problems is isolated data Islands, where data from a single source/stakeholder is not available to other parties or training an artificial intelligence model, or it is financially difficult or impractical to collect a large amount of distributed data for Centralized Processing and training. There is also a risk of becoming a single point of failure in centralized architectures, which can lead to data intrusion. In addition, data from different sources may be unstructured and differ in quality, and it may also be difficult to determine the source and validity of the data. There is also a risk of invalid or malicious data. All these restrictions may affect the accuracy of the forecast. In practice, artificial intelligence models are created, trained, and used by various subjects. The learning process is not transparent to users, and users may not fully trust the model they are using. In addition, as artificial intelligence algorithms become more complex, it is difficult for people to understand how the result of training is obtained. So, recently there has been a tendency to move away from centralized approaches to artificial intelligence to decentralized ones.
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审稿时长
7 weeks
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