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首页深度解析:人工智能与智能计算的最新进展、挑战与全球视野
本文是一篇深度探讨人工智能与智能计算领域的重要综述,由多位顶尖学者共同编撰,旨在梳理当前技术的最新进展,揭示面临的挑战,并对未来发展趋势进行权威预测。作者涵盖了来自全球多所知名学术机构和研究单位的专家,如浙江大学、维也纳工业大学、索邦大学、帝国理工学院、曼尼托巴大学等,他们的跨学科视角为读者提供了全面深入的理解。 在文章中,首先概述了智能计算的定义及其在信息技术中的核心作用,强调了它在人机融合中的关键地位,包括在自动化、决策支持、数据分析和模式识别等领域的广泛应用。作者们分享了他们在算法优化、机器学习、深度学习等方面取得的突破性成果,这些成果推动了人工智能的进步,如基于神经网络的模型、强化学习以及自然语言处理技术的革新。 然而,尽管智能计算取得了显著进展,文中也指出了若干挑战。这包括数据隐私和安全问题,如何确保算法的透明度和可解释性,以及计算效率与能源消耗之间的平衡。此外,随着技术的快速发展,伦理道德和法律规范的制定也日益重要,例如AI的责任归属和决策公正性等。 未来展望部分,学者们预测智能计算将在物联网、自动驾驶、医疗健康、智慧城市等领域发挥更大的影响力。同时,他们呼吁进一步加强跨学科研究,推动理论与实践的深度融合,以及国际间的知识共享和标准制定,以促进全球智能计算的健康发展。 这篇综述不仅提供了对当前人工智能和智能计算现状的详尽解读,还为行业从业者和政策制定者描绘了一幅关于技术发展路径的蓝图,对于推动人工智能技术的发展和应对其中的挑战具有重要意义。通过阅读这篇论文,读者将能够深入了解这一领域的前沿动态,以及如何在学术和工业界寻找新的机遇和应对策略。
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Zhu et al. 2023 | https://doi.org/10.34133/icomputing.0006 8
ability that simulates the evolutionary process of organisms
in nature, where the machines learn from the environment
and subsequently make self- adjustments to adapt to the
environment.
· High computing capability and high energy efficiency.
Aiming to exceed the traditional von Neumann’s architecture,
intelligent computing evolves to new computing architectures
concerning processing-in-memory, heterogeneous integration,
and wide-area collaboration. High computing power refers to the
computing capability that meets the needs of an intelligent society
and serves as infrastructures like water and electricity. Moreover,
high energy eciency aims to maximize computing eciency and
reduce energy consumption as much as possible to ensure ecient
processing of big data with large-scale characteristics, complex
structure, and sparse value.
· Security and reliability. Intelligent computing supports
cross-domain trust and security protection for large-scale
ubiquitous interconnected computing systems. It establishes
independent and controllable trusted security technology
and support systems, realizing data fusion, sharing, and opening.
High trust refers to the trust of identity, data, computing
process, and computing environment through trusted hardware,
operating system, soware, network, and private computing.
Particularly, high security means network security, storage
security, content security, and circulation security of computing
systems that can be guaranteed by integrating various privacy
protection technologies.
· Automation and precision. Intelligent computing is task
oriented; it matches computing resources and realizes automatic
demand calculation and precise system reconstruction. e
system architecture is constantly adjusted to the task execution.
Directed coupling reconstruction is performed at the soware
and hardware levels. Automation of the computing process
includes automatic resource management and scheduling,
automatic service creation and provision, and automatic
management of the task life cycle, which is the key to evaluating
the friendliness, availability, and service of intelligent
computing. e precision of computing results anchors
computing services; besides, it solves diculties, including
fast processing of computing tasks and timely matching of
computing resources.
· Collaboration and ubiquity. Intelligent computing integrates
existing techno logies to promote the penetration and integration
of the physical, information, and social space using the various
perception ability of heterogeneous elements, complementary
computational resources, and the collaboration and competition
of computational node functions. Cooperation between humans
and machines improves intelligence levels in intelligent tasks,
and ubiquity enables computing to be conducted everywhere by
combining intelligent computing theoretical methods, architectural
systems, and technical approaches.
Fusion of intelligence and computation
Intelligent computing includes 2 essential aspects: intelligence
and computation, which complement each other. Intelligence
facilitates the development of computing technologies, while
computation is the foundation of intelligence. e paradigm
of high-level intelligence technologies that improve the perform-
ance and eciency of computing systems is “computing by
intelligence.” e paradigm of ecient and powerful compu-
tational technologies that support the development of com-
puter intelligence is “computing for intelligence.” e 2 basic
paradigms are innovated from 5 aspects to improve computing
power, energy eciency, data usage, knowledge expression,
and algorithm capabilities and achieve ubiquitous, transparent,
reliable, real-time, and automatic services.
· The paradigm of computing by intelligence. e computing
power demand of complex models has exceeded that of
general computers by 1 or 2 orders of magnitude. Moreover,
there is a considerable gap between the underlying computing
mechanism of traditional computers and the computing mode
of intelligent models, resulting in low computing eciency.
e paradigm of computing by intelligence includes new
models, support, paradigms, mechanisms, and synergy that
utilize intelligent approaches to improve computing capability
and eciency.
Currently, intelligent systems can only handle specic tasks
in a closed environment since they lack common sense, intui-
tion, and imagination. Research on neuromorphic computing,
graph computing, biological computing, and other new com-
puting models is conducted to analyze the human brain, biolog-
ical, and knowledge computing mechanisms. ese new models
can eectively improve cognitive understanding and reasoning
learning abilities, adaptability, and the generalization eect for
intelligent algorithms.
Due to the limitations of computing system architecture and
lack of end-to-end computational capacity, the computing and
response speed of the current computing system needs further
improvement. Intelligent computing can improve the real-time
performance of the computing system by utilizing new computing
Fig.5.Features of intelligent computing.
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Zhu et al. 2023 | https://doi.org/10.34133/icomputing.0006 9
support technologies, e.g., processing-in-memory, edge com-
puting, and online learning. Moreover, new technologies, such
as the fusion of perception and computation and processing
localization, have also become promising research hotspots.
e deep integration of the triple space leads to the diversities
of computing tasks, so the computing scenarios and data are
more unstructured, and the solutions of the tasks are more com-
plex and challenging. us, the new computing paradigm ena-
bles the analysis and modeling of unstructured scenes and the
adaptive processing of unstructured data. It achieves transpar-
ency computing through an automatic and intelligent process
that concludes tasks understanding, decomposition, solving,
and resource allocation.
Intelligent computing explores new computing mechanisms,
such as hardware and soware refactoring and cooperative
evolution, to deal with dierent types of tasks. During the exe-
cution of intelligent processes, the new mechanism congures
the hardware through the organization of computing resources
with dierent granularity and functions. e new mechanism
will form an automatic computing system with autonomous
learning and evolutionary iteration applying intelligent tech-
nologies, including elasticity design of soware and hardware,
exible cooperation of algorithms and models, and adaptive
allocation of data and resources.
New synergy computing architectures, such as human–
computer interaction, swarm intelligence, and human-in-loop,
Fig.6.Technology architecture of graph computing.
Fig.7.Structure of a typical neuron and structure of an artificial neuron.
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