《中国工程科学》 >> 2022年 第24卷 第6期 doi: 10.15302/J-SSCAE-2022.07.010
我国企业人工智能应用现状与挑战
1. 华为技术有限公司,广东深圳 518129;
2. 中国石油大学人工智能学院,北京 102249;
3. 中国石油勘探开发研究院,北京 100083
下一篇 上一篇
摘要
深度学习增强了人工智能(AI)算法的通用性,近年来催生了AI产业的快速发展,但实践表明AI技术和算法在产业领域的落地应用依然面临极大困难;企业用上并用好AI、学术界和产业界协同以解决算法落地困难等问题,受到广泛关注。本文着眼我国AI产业的健康可持续发展,从企业AI应用落地的实际案例出发,梳理业界现状、剖析发展挑战、探讨根本原因、提出应对策略。企业AI落地应用的复杂性表现在业务需求、数据、算法、基础设施、配套方案等多个维度,应用成熟度取决于数据的准备程度及治理水平。在国家宏观层面,有必要构建更友好的AI产业生态环境,促进AI全产业链协同发展;以更有力的具体举措支持AI产业的技术研发,特别是全栈AI、AI基础平台及工具体系、AI根技术等,提高我国AI核心技术的自主可控能力;鼓励企业积极实施数字化转型,采用AI技术进行智能化升级,形成AI产业技术研发、企业AI落地创新的强耦合及双向循环。
参考文献
[ 1 ] M Turing A. Computing machinery and intelligence [J]. Mind, 1950, 59: 433‒460.
[ 2 ] LeCun Y, Bengio Y, Hinton G. Deep learning [J]. Nature, 2015, 521(7553): 436‒444.
[ 3 ]
华凌 . 为什么AI很火, 落地却很难 [N]. 科技日报 , 2021-07-26 06.
Hua L . Why is AI popular but difficult to be landed? [N]. Science and Technology Daily , 2021-07-26 06.
[ 4 ]
科技情报大数据挖掘与服务平台 . 2011—2020年人工智能发展报告 [R]. 北京 : 科技情报大数据挖掘与服务平台 , 2020 .
AMiner . Report on artificial intelligence development 2011—2020 [R]. Beijing : AMiner , 2020 .
[ 5 ] Executive Office of the President. Maintaining American leadership in artificial intelligence [EB/OL]. (2019-02-14)[2022-04-15]. https://www.federalregister.gov/documents/2019/02/14/2019-02544/maintaining-american-leadership-in-artificial-intelligence. 链接1
[ 6 ] Commission European. Proposal for a regulation laying down harmonised rules on artificial intelligence [EB/OL]. (2021-04-21)[2022-04-15]. https://digital-strategy.ec.europa.eu/en/library/proposal-regulation-laying-down-harmonised-rules-artificial-intelligence. 链接1
[ 7 ]
胥会云 . 金桥金融科技综合发展指数: 中国区块链、人工智能专利数已超美国 [EBOL]. 2018-06-15 [ 2022-04-15 ]. https:www.yicai.comnews5432412.html .
Xu H Y . Jinqiao financial technology comprehensive development index: China´s blockchain and AI patents have surpassed the United States [J]. 2018-06-15 [ 2022-04-15 ]. https:www.yicai.comnews5432412.html .
链接1
[ 8 ]
王坚 . 数据资源与城市大脑 [J]. 秘书工作 , 2020 1 : 75 ‒ 77 .
Wang J . Data resources and the urban brain [J]. Office Administration , 2020 1 : 75 ‒ 77 .
[ 9 ]
华为技术有限公司 . AI赋能智慧城市白皮书 [R]. 深圳 : 华为技术有限公司 , 2021 .
Huawei Technologies Co. , Ltd . AI-enabled smart city: White paper [R]. Shenzhen : Huawei Technologies Co., Ltd. , 2021 .
[10]
刘合 . 石油勘探开发人工智能应用的展望 [J]. 智能系统学报 , 2021 , 16 6 : 985 .
Liu H . Prospects for the application of artificial intelligence in petroleum exploration and development [J]. CAAI Transactions on Intelligent Systems , 2021 , 16 6 : 985 .
[11] Liu H, Ren Y L, Li X, al et. Rock thin-section analysis and identification based on artificial intelligent technique [J]. Petroleum Science, 2022, 19(4): 1605‒1621.
[12]
龚仁彬 , 李欣 , 李宁 , 等 . 油气人工智能 [M]. 北京 : 石油工业出版社 , 2021 .
Gong R B , Li X , Li N , al e t . Artificial intelligence in oil and gas [M]. Beijing : Petroleum Industry Press , 2021 .
[13]
华为技术有限公司 . 知识计算白皮书 [R]. 深圳 : 华为技术有限公司 , 2022 .
Huawei Technologies Co. , Ltd . Knowledge computing: White paper [R]. Shenzhen : Huawei Technologies Co., Ltd. , 2021 .
[14] Zhang D, Mishra S, Brynjolfsson E, al et. The AI index 2021 annual report [EB/OL]. (2021-03-15)[2022-04-15]. https://arxiv.org/ftp/arxiv/papers/2103/2103.06312.pdf. 链接1
[15] Torralba A, Fergus R, T Freeman W. 80 million tiny images: A large data set for nonparametric object and scene recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(11): 1958‒1970.
[16] Chen T, Kornblith S, Norouzi M, al et. A simple framework for contrastive learning of visual representations [EB/OL]. (2020-05-05)[2022-04-15]. http://proceedings.mlr.press/v119/chen20j/chen20j.pdf. 链接1
[17] Devlin J, Chang M W, Lee K, al et. Bert: Pre-training of deep bidirectional transformers for language understanding [EB/OL]. (2018-10-11)[2022-04-15]. https://arxiv.org/abs/1810.04805. 链接1
[18] Radford A, Kim J W, Hallacy C, al et. Learning transferable visual models from natural language supervision [EB/OL]. (2021-02-26)[2022-04-15]. https://arxiv.org/abs/2103.00020. 链接1
[19]
肖立志 . 机器学习数据驱动与机理模型融合及可解释性问题 [J]. 石油物探 , 2022 , 61 2 : 205 ‒ 212 .
Xiao L Z . The fusion of data-driven machine learning with mechanism models and interpretability issues [J]. Geophysical Prospecting for Petroleum , 2022 , 61 2 : 205 ‒ 212 .
[20] Tan C Q, Sun F C, Kong T, al et. A survey on deep transfer learning [EB/OL]. (2018-08-06)[2022-04-15]. https://arxiv.org/abs/1808.01974. 链接1
[21] Yuan X Y, He P, Zhu Q L, al et. Adversarial examples: Attacks and defenses for deep learning [J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(9): 2805‒2824.
[22] Zhou B L, Khosla A, Lapedriza A, al et. Learning deep features for discriminative localization [EB/OL]. (2015-11-14)[2022-04-15]. https://arxiv.org/abs/1512.04150v1. 链接1
[23] Shen D G, Wu G R, Suk H. Deep learning in medical image analysis [J]. Annual Review of Biomedical Engineering, 2017, 19: 221‒248.
[24] Hamet P, Tremblay J. Artificial intelligence in medicine [J]. Metabolism, 2017, 69S: 36‒40.
[25] Jumper J, Evans R, Pritzel A, al et. Highly accurate protein structure prediction with AlphaFold [J]. Nature, 2021, 596(7873): 583‒589.
[26] Raissi M, Perdikaris P, E Karniadakis G. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations [J]. Journal of Computational Physics, 2019, 378: 686‒707.
[27] Wang Q, Mao Z D, Wang B, al et. Knowledge graph embedding: A survey of approaches and applications [J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12): 2724‒2743.
[28] He X, Zhao K Y, Chu X W. AutoML: A survey of the state-of-the-art [J]. Knowledge-Based Systems, 2021, 212: 1‒17.
[29]
柴天佑 . 工业人工智能发展方向 [J]. 自动化学报 , 2020 , 46 10 : 2003 ‒ 2012 .
Chai T Y . Development direction of industrial artificial intelligence [J]. Acta Automatica Sinica , 2020 , 46 10 : 2003 ‒ 2012 .
[30] Sculley D, Holt G, Golovin D, al et. Hidden technical debt in machine learning systems [J]. Advances in Neural Information Processing Systems, 2015, 2: 2503‒2511.
[31] Oliveira R. Combining first principles modelling and artificial neural networks: A general framework [J]. Computers & Chemical Engineering, 2004, 28(5): 755‒766.
[32]
埃森哲公司 . 人工智能应用之道 [EBOL]. 2019-08-14 [ 2022-04-15 ]. https:cloud.tencent.comdeveloperarticle1487158 .
Co. Accenture , Ltd . Application of artificial intelligence [EBOL]. 2019-08-14 [ 2022-04-15 ]. https:cloud.tencent.comdeveloperarticle1487158 .
链接1
[33]
赵邦六 , 雍学善 , 高建虎 , 等 . 中国石油智能地震处理解释技术进展与发展方向思考 [J]. 中国石油勘探 , 2021 , 26 5 : 12 ‒ 23 .
Zhao B L , Yong X X , Gao J H , al e t . Progress and development direction of PetroChina intelligent seismic processing and interpretation technology [J]. China Petroleum Exploration , 2021 , 26 5 : 12 ‒ 23 .
[34]
埃森哲公司 . 2021中国企业数字转型指数 [R]. 北京 : 埃森哲公司 , 2021 .
Co. Accenture , Ltd . 2021 China enterprise digital transformation index [R]. Beijing : Accenture Co., Ltd. , 2021 .
[35]
匡立春 , 刘合 , 任义丽 , 等 . 人工智能在石油勘探开发领域的应用现状与发展趋势 [J]. 石油勘探与开发 , 2021 , 48 1 : 1 ‒ 11 .
Kuang L C , Liu H , Ren Y L , al e t . Application and development trend of artificial intelligence in petroleum exploration and development [J]. Petroleum Exploration and Development , 2021 , 48 1 : 1 ‒ 11 .
[36]
董幼鸿 . 上海城市运行"一网统管"的创新和探索 [M]. 上海 : 上海人民出版社 , 2021 .
Dong Y H . Innovation and exploration of "one-network unified management" in Shanghai´s city operation [M]. Shanghai : Shanghai People´s Press , 2021 .
[37] M Monarch R. Human-in-the-loop machine learning: Active learning and annotation for human-centered AI [M]. New York: Simon & Schuster, 2021.
[38]
肖立志 . 数字化转型推动石油工业绿色低碳可持续发展 [J]. 世界石油工业 , 2022 , 29 4 : 12 ‒ 20 .
Xiao L Z . Digital transformation promotes the petroleum industry green, low-carbon and sustainable development [J]. World Petroleum Industry , 2022 , 29 4 : 12 ‒ 20 .