[1]李修全.当前人工智能技术创新特征和演进趋势[J].智能系统学报,2020,15(2):409-412.[doi:10.11992/tis.202001030]
 LI Xiuquan.Main features and development trend in current artificial intelligence technology innovation[J].CAAI Transactions on Intelligent Systems,2020,15(2):409-412.[doi:10.11992/tis.202001030]
点击复制

当前人工智能技术创新特征和演进趋势(/HTML)
分享到:

《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年2期
页码:
409-412
栏目:
洞见与碰撞
出版日期:
2020-07-09

文章信息/Info

Title:
Main features and development trend in current artificial intelligence technology innovation
作者:
李修全
中国科学技术发展战略研究院, 北京 100038
Author(s):
LI Xiuquan
Chinese Academy of Science and Technology for Development, Beijing 100038, China
关键词:
人工智能技术形态创新特征发展趋势软硬件协同技术融合模型轻量化
Keywords:
Artificial intelligencetechnical forminnovation featuredevelopment trendsoftware-hardware collaborationtechnology fusionlightweight model
分类号:
TP18
DOI:
10.11992/tis.202001030
摘要:
近年来,全球人工智能发展进入新一轮技术创新活跃期,新的理论、模型、算法快速迭代。本文从模型算法、软硬件实现以及技术形态等角度分析了当前全球人工智能技术的主要特征,总结了国内外人工智能前沿研究的一些创新热点和新动向,并从基础理论突破、底层计算模式创新、模型算法演进等方面,展望和探讨了未来人工智能技术进一步突破的几个可能趋势。
Abstract:
In recent years, the development of global artificial intelligence (AI) has entered a new round of active period, with the rapid iteration of new theories, models, and algorithms. This study analyzes the main features of the current AI technology innovation from the perspective of model algorithms, software and hardware implementations, and intelligent system forms. It summarizes some of the innovation hotspots in the domestic as well as international frontier research of AI. Furthermore, in terms of the breakthroughs in basic theory, innovation of underlying computing models, and evolution of model algorithms, several possible trends in the future development of AI technology are discussed.

参考文献/References:

[1] KIM B, WATTENBERG M, GILMER J, et al. Interpretability beyond feature attribution: quantitative Testing with Concept Activation Vectors (TCAV)[C]//Proceedings of the 35th International Conference on Machine Learning 2018. Stockholm, Sweden, 2018: 2673-2682.
[2] BATTAGLIA P W, HAMRICK J B, BAPST V, et al. Relational inductive biases, deep learning, and graph networks[EB/OL]. https://arxiv.org/abs/1806.0126.
[3] KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Doha, Qatar, 2014: 1746-1751.
[4] PETERS M E, NEUMANN M, IYYER M, et al. Deep contextualized word representations[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). Louisiana, USA, 2018.
[5] DEVLIN J, CHANG Mingwei, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL). Minneapolis, MN, USA, 2019.
[6] KRIZHEVSKY A, SUTSKEVER I, HINTON G. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS). Lake Tahoe, USA, 2012: 1097-1105.
[7] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[EB/OL]. https://arxiv.org/abs/1512.03385.
[8] STRUBELL E, GANESH A, MCCALLUM A. Energy and policy considerations for deep learning in NLP[C]//Proceedings of the 57th Conference of the Association for Computational Linguistics. Florence, Italy, 2019.
[9] ABADI M, AGARWAL A, BARHAM P, et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems[EB/OL]. https://arxiv.org/abs/1603.04467v1.
[10] PASZKE A, GROSS S, MASSA F, et al. PyTorch: An imperative style, high-performance deep learning library[EB/OL]. https://arxiv.org/abs/1912.01703?context=cs.LG.
[11] JOUPPI N P, YOUNG C, PATIL N, et al. In-datacenter performance analysis of a tensor processing unit[C]//Proceedings of the 44th Annual International Symposium on Computer Architecture (ISCA). Toronto, Canada, 2017: 1-12.
[12] NVIDIA. GPU-based deep learning inference: a performance and power analysis[EB/OL]. California: NVIDIA, 2015. (2015-11)[2020-03-25]. https://www.nvidia.com/content/tegra/embedded-systems/pdf/jetson_tx1_whitepaper.pdf.
[13] SILVER D, HUANG A, MADDISON C J, et al. Mastering the game of Go with deep neural networks and tree search[J]. Nature, 2016, 529(7587): 484-489.
[14] ULLMAN S. Using neuroscience to develop artificial intelligence[J]. Science, 2019, 363(6428): 692-693.
[15] ROY K, JAISWAL A, PANDA P. Towards spike-based machine intelligence with neuromorphic computing[J]. Nature, 2019, 575(7784): 607-617.
[16] PEI Jing, DENG Lei, SONG Sen, et al. Towards artificial general intelligence with hybrid Tianjic chip architecture[J]. Nature, 2019, 572(7767): 106-111.
[17] YING Mingsheng. Quantum computation, quantum theory and AI[J]. Artificial intelligence, 2010, 174(2): 162-176.

相似文献/References:

[1]李德毅.网络时代人工智能研究与发展[J].智能系统学报,2009,4(01):1.
 LI De-yi.AI research and development in the network age[J].CAAI Transactions on Intelligent Systems,2009,4(2):1.
[2]赵克勤.二元联系数A+Bi的理论基础与基本算法及在人工智能中的应用[J].智能系统学报,2008,3(06):476.
 ZHAO Ke-qin.The theoretical basis and basic algorithm of binary connection A+Bi and its application in AI[J].CAAI Transactions on Intelligent Systems,2008,3(2):476.
[3]徐玉如,庞永杰,甘 永,等.智能水下机器人技术展望[J].智能系统学报,2006,1(01):9.
 XU Yu-ru,PANG Yong-jie,GAN Yong,et al.AUV—state-of-the-art and prospect[J].CAAI Transactions on Intelligent Systems,2006,1(2):9.
[4]王志良.人工心理与人工情感[J].智能系统学报,2006,1(01):38.
 WANG Zhi-liang.Artificial psychology and artificial emotion[J].CAAI Transactions on Intelligent Systems,2006,1(2):38.
[5]赵克勤.集对分析的不确定性系统理论在AI中的应用[J].智能系统学报,2006,1(02):16.
 ZHAO Ke-qin.The application of uncertainty systems theory of set pair analysis (SPU)in the artificial intelligence[J].CAAI Transactions on Intelligent Systems,2006,1(2):16.
[6]秦裕林,朱新民,朱 丹.Herbert Simon在最后几年里的两个研究方向[J].智能系统学报,2006,1(02):11.
 QIN Yu-lin,ZHU Xin-min,ZHU Dan.Herbert Simons two research directions in his lost years[J].CAAI Transactions on Intelligent Systems,2006,1(2):11.
[7]谷文祥,李 丽,李丹丹.规划识别的研究及其应用[J].智能系统学报,2007,2(01):1.
 GU Wen-xiang,LI Li,LI Dan-dan.Research and application of plan recognition[J].CAAI Transactions on Intelligent Systems,2007,2(2):1.
[8]杨春燕,蔡 文.可拓信息-知识-智能形式化体系研究[J].智能系统学报,2007,2(03):8.
 YANG Chun-yan,CAI Wen.A formalized system of extension information-knowledge-intelligence[J].CAAI Transactions on Intelligent Systems,2007,2(2):8.
[9]赵克勤.SPA的同异反系统理论在人工智能研究中的应用[J].智能系统学报,2007,2(05):20.
 ZHAO Ke-qin.The application of SPAbased identicaldiscrepancycontrary system theory in artificial intelligence research[J].CAAI Transactions on Intelligent Systems,2007,2(2):20.
[10]王志良,杨 溢,杨 扬,等.一种周期时变马尔可夫室内位置预测模型[J].智能系统学报,2009,4(06):521.[doi:10.3969/j.issn.1673-4785.2009.06.009]
 WANG Zhi-liang,YANG Yi,YANG Yang,et al.A periodic time-varying Markov model for indoor location prediction[J].CAAI Transactions on Intelligent Systems,2009,4(2):521.[doi:10.3969/j.issn.1673-4785.2009.06.009]

备注/Memo

备注/Memo:
收稿日期:2020-01-21。
作者简介:李修全,研究员,工学博士,硕士生导师,兼任科技部新一代人工智能发展研究中心副主任,主要研究方向为大数据与人工智能技术预测、产业技术路线图、人工智能创新政策研究。主持课题10余项,发表学术论文40余篇。
通讯作者:李修全.E-mail:lixq@casted.org.cn
更新日期/Last Update: 1900-01-01