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从基础研究浅析人工智能技术发展趋势
2020年电子技术应用第10期
李美桃
国家工业信息安全发展研究中心人工智能所,北京100040
摘要: 近六十多年来,人工智能在算法、算力和数据的共同驱动下,获得了飞速发展,但仍处于弱人工智能阶段。重点分析了人工智能算法和算力方面的基础研究现状和发展趋势,弱人工智能迈向强人工智能亟待基础研究上的革命性突破。算法层面,深度学习算法模型缺乏可释性和可泛化性,在基础理论上遇到瓶颈,亟待基础理论上的突破;算力层面,因集成电路工艺制程逼近微观物理极限导致摩尔定律失效和电子芯片算力增长趋缓,通用计算芯片架构受制于冯诺依曼瓶颈,以神经形态芯片为代表的人工智能芯片方兴未艾;数据层面,细分领域的高质量数据集匮乏制约人工智能技术应用发展,未来高质量数据集将不断构建。总之,人工智能底层技术将在未来相当长时间内缓慢前进,但产业化应用正在蓬勃发展。
中图分类号: TP301
文献标识码: A
DOI:10.16157/j.issn.0258-7998.200346
中文引用格式: 李美桃. 从基础研究浅析人工智能技术发展趋势[J].电子技术应用,2020,46(10):29-33,38.
英文引用格式: Li Meitao. Analysis of the trend of artificial intelligence technology on basic research[J]. Application of Electronic Technique,2020,46(10):29-33,38.
Analysis of the trend of artificial intelligence technology on basic research
Li Meitao
National Industrial Information Security Development Research Center,Beijing 100040,China
Abstract: During the past sixty years, artificial intelligence(AI) has achieved rapid development jointly promoted by algorithms, computing power, and big data, but it is still in the stage of artificial narrow intelligence. The status and trends of basic research in AI algorithms and computing power are analyzed. The evolution of artificial narrow intelligence to artificial general intelligence will depend on breakthrough in AI basic theory research. On the aspect of AI algorithms, the deep learning algorithm model lacks interpretive reasoning and generalizability. AI encounters bottlenecks in basic theory and urgently needs a breakthrough. On the aspect of computing power, due to the CMOS physical limits the Moore′s law is approaching failure and the growth of computing power is slowing down, the general computing chip architecture is limited by Feng Neumann′s bottleneck and AI chips represented by neuromorphic chips are in the ascendant. On the aspect of data, the lack of high-quality data sets in specific area restricts AI technology application and more high-quality data sets will be continuously constructed in the short future. In short, the basic AI technology will slowly advance for a long time in the future, but the AI applications are booming from right now.
Key words : artificial intelligence;basic research;development trend;algorithm;computing power

0 引言

    人工智能(Artificial Intelligence,AI)是计算机技术发展到高级阶段的复杂技术体系,综合了计算机、数学、逻辑、信息论、控制论、认知科学和伦理学等多种学科。人工智能于1956年在达特茅斯学院的一次学术会议上被提出,可分为三个发展阶段:弱人工智能(Artificial Narrow Intelligence,ANI)、强人工智能(Artificial General Intelligence,AGI)和超人工智能(Artificial Super Intelligence,ASI)。ANI是在限定条件下的人工智能,目前掌握的人工智能技术处于该阶段,是没有理解和推理的感知智能;AGI是能理解、推理和解决问题的机器智能,有知觉和自我意识,属于认知智能;ASI是在几乎所有领域都比最聪明的人类大脑都聪明的机器智能,是人工智能技术发展的终极目标。

    过去六十多年来,三大基石即算法算力和数据,共同驱动着人工智能技术快速发展。本文概述了弱人工智能的发展历程,即初始时期、知识驱动时期和数据驱动时期,重点梳理了算法和算力的前沿基础研究进展和面临的挑战,阐明了大数据在数据驱动时期对人工智能发展的巨大推动作用,最后从算法、算力、数据集和产业化应用四个方面浅析了人工智能技术的发展趋势




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作者信息:

李美桃

(国家工业信息安全发展研究中心人工智能所,北京100040)

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