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著作权视野下人工智能生成内容的研究

Analysis of the Content Generated by Artificial Intelligence from the View of Copyright
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摘要 人工智能生成的内容纷杂多样,要想在著作权视野下展开有效的研究,首先就要确定合适的研究范围。然而,由于人工智能的特殊属性,在现行著作权理论下,暂不考虑主体因素的情况下对其进行有效的研究。文章探讨的是那些在形式上符合著作权中作品独创性要求的生成内容。然后,再结合目前人工智能的发展概况分析出该独创性的内容实际上是算法模板运行的结果,并非源于人工智能自身的创作。而对于此类具有独创性的内容,我们也无需再把它纳为新的著作权法保护客体,因为它在著作权法的实际运作中已经得到了无形的保护。 Content generated by artificial intelligence are complex and diverse. With the current copyright law principle and the special attributes of artificial intelligence, effective research can be conducted without considering the subjective factors. Therefore, the following mainly focus on those contents which comply with the copyright law in the form and have their own originality. Then, according to the development of artificial intelligence, the original content is the result of the algorithm template operation, and the original content is not by artificial intelligence itself,but for these original generated content, the new object of protection in copyright law is not needed, because it has been protected actually in the laws operation.
作者 董梦晴 Dong Mengqing(School of Law ,Anhui University, Hefei Anhui,230601)
机构地区 安徽大学法学院
出处 《池州学院学报》 2018年第2期34-37,共4页 Journal of Chizhou University
关键词 人工智能 独创性 算法 Artificial Intelligence Originality Algorithm
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  • 1倪菲,付庄,曹其新,赵言正.肖像漫画绘制机器人技术研究[J].自然杂志,2007,29(4):212-216. 被引量:4
  • 2BENGIO Y, DELALLEAU O. On the expressive power of deep archi- tectures[ C ]//Proc of the 14th International Conference on Discovery Science. Berlin : Springer-Verlag, 2011 : 18 - 36.
  • 3BENGIO Y. Leaming deep architectures for AI[ J]. Foundations and Trends in Machine Learning ,2009,2 ( 1 ) : 1-127.
  • 4HINTON G,OSINDERO S,TEH Y. A fast learning algorithm for deep belief nets [ J ]. Neural Computation ,2006,18 (7) : 1527-1554.
  • 5BENGIO Y, LAMBLIN P, POPOVICI D, et al. Greedy layer-wise training of deep networks [ C ]//Proc of the 12th Annual Conference on Neural Information Processing System. 2006:153-160.
  • 6LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning ap- plied to document recognition[ J]. Proceedings of the iEEE, 1998, 86( 11 ) :2278-2324.
  • 7VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[ C ]//Proc of the 25th International Conference on Machine Learning. New York: ACM Press ,2008 : 1096-1103.
  • 8VINCENT P, LAROCHELLE H, LAJOIE I, et aL Stacked denoising autoencoders:learning useftd representations in a deep network with a local denoising criterion [ J ]. Journal of Machine Learning Re- search ,2010,11 ( 12 ) :3371-3408.
  • 9YU Dong, DENG Li. Deep convex net: a scalable architecture for speech pattern classification [ C]//Proc of the 12th Annual Confe-rence of International Speech Comunication Association. 2011 : 2285- 2288.
  • 10POON H, DOMINGOS P. Sum-product networks:a new deep architec- ture[ C ]//Proc of IEEE Intemational Conference on Computer Vi- sion. 2011:689-690.

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