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==Legal considerations==
 
==Legal considerations==
 
==法律问题==
 
==法律问题==
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Skeptics have argued that there is no way to apply the electronic personhood, the concept of personhood that would apply to neuromorphic technology, legally. In a letter signed by 285 experts in law, robotics, medicine, and ethics opposing a European Commission proposal to recognize “smart robots” as legal persons, the authors write, “A legal status for a robot can’t derive from the [[Natural person|Natural Person]] model, since the robot would then hold [[human rights]], such as the right to dignity, the right to its integrity, the right to remuneration or the right to citizenship, thus directly confronting the Human rights. This would be in contradiction with the [[Charter of Fundamental Rights of the European Union]] and the [[Convention for the Protection of Human Rights and Fundamental Freedoms]].”<ref>{{Cite web|url=http://www.robotics-openletter.eu/|title=Robotics Openletter {{!}} Open letter to the European Commission|language=fr-FR|access-date=2019-05-10}}</ref>
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Skeptics have argued that there is no way to apply the electronic personhood, the concept of personhood that would apply to neuromorphic technology, legally. In a letter signed by 285 experts in law, robotics, medicine, and ethics opposing a European Commission proposal to recognize “smart robots” as legal persons, the authors write, “A legal status for a robot can’t derive from the [[Natural person|Natural Person]] model, since the robot would then hold [[human rights]], such as the right to dignity, the right to its integrity, the right to remuneration or the right to citizenship, thus directly confronting the Human rights. This would be in contradiction with the [[Charter of Fundamental Rights of the European Union]] and the [[Convention for the Protection of Human Rights and Fundamental Freedoms]].”<ref name=":30">{{Cite web|url=http://www.robotics-openletter.eu/|title=Robotics Openletter {{!}} Open letter to the European Commission|language=fr-FR|access-date=2019-05-10}}</ref>
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Skeptics have argued that there is no way to apply the electronic personhood, the concept of personhood that would apply to neuromorphic technology, legally. In a letter signed by 285 experts in law, robotics, medicine, and ethics opposing a European Commission proposal to recognize “smart robots” as legal persons, the authors write, “A legal status for a robot can’t derive from the Natural Person model, since the robot would then hold human rights, such as the right to dignity, the right to its integrity, the right to remuneration or the right to citizenship, thus directly confronting the Human rights. This would be in contradiction with the Charter of Fundamental Rights of the European Union and the Convention for the Protection of Human Rights and Fundamental Freedoms.”
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质疑者者认为，在法律上没有办法应用能够适用于神经形态技术的电子人格。在一封由285名法律、机器人技术、医学和伦理学专家的联名信中，作者们反对欧盟委员会提出的承认“智能机器人”为法人的提议。他们写道，“机器人的法律地位无法从自然人模型中推导出来，因为机器人将被赋予人权，如尊严权、完整权、报酬权或公民权，从而直接面临人权问题。这将有悖于《欧联基本权利宪章》和《欧洲保障人权和根本自由公约》”。<ref name=":30" />
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怀疑论者认为，在法律上没有办法应用电子人格，这个人格概念将适用于神经形态技术。在一封由285名法律、机器人、医学和伦理学专家签名的信中，作者们反对欧盟委员会承认“智能机器人”为法人的提议，他们写道，“机器人的法律地位不能从自然人模型中推导出来，因为机器人将拥有人权，如尊严权、完整权、报酬权或公民权，从而直接面对人权。这将有悖于《欧洲联盟基本权利宪章和《保护人权和基本自由公约》。”
      
===Ownership and property rights===
 
===Ownership and property rights===
 
===所有权及财产权问题===
 
===所有权及财产权问题===
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There is significant legal debate around property rights and artificial intelligence. In ''Acohs Pty Ltd v. Ucorp Pty Ltd'', Justice Christopher Jessup of the [[Federal Court of Australia]] found that the [[source code]] for [[Material safety data sheets|Material Safety Data Sheets]] could not be [[Copyright law of Australia|copyrighted]] as it was generated by a [[software interface]] rather than a human author.<ref>{{Cite web|url=http://www.lavan.com.au/advice/intellectual_property_technology/copyright_in_source_code_and_digital_products|title=Copyright in source code and digital products|last=Lavan|website=Lavan|language=en|access-date=2019-05-10}}</ref> The same question may apply to neuromorphic systems: if a neuromorphic system successfully mimics a human brain and produces a piece of original work, who, if anyone, should be able to claim ownership of the work?<ref>{{cite journal |last1=Eshraghian|first1=Jason K. |title=Human Ownership of Artificial Creativity |journal=Nature Machine Intelligence |date=9 March 2020 |volume=2 |pages=157–160  |doi=10.1038/s42256-020-0161-x}}</ref>
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There is significant legal debate around property rights and artificial intelligence. In ''Acohs Pty Ltd v. Ucorp Pty Ltd'', Justice Christopher Jessup of the [[Federal Court of Australia]] found that the [[source code]] for [[Material safety data sheets|Material Safety Data Sheets]] could not be [[Copyright law of Australia|copyrighted]] as it was generated by a [[software interface]] rather than a human author.<ref name=":31">{{Cite web|url=http://www.lavan.com.au/advice/intellectual_property_technology/copyright_in_source_code_and_digital_products|title=Copyright in source code and digital products|last=Lavan|website=Lavan|language=en|access-date=2019-05-10}}</ref> The same question may apply to neuromorphic systems: if a neuromorphic system successfully mimics a human brain and produces a piece of original work, who, if anyone, should be able to claim ownership of the work?<ref name=":32">{{cite journal |last1=Eshraghian|first1=Jason K. |title=Human Ownership of Artificial Creativity |journal=Nature Machine Intelligence |date=9 March 2020 |volume=2 |pages=157–160  |doi=10.1038/s42256-020-0161-x}}</ref>
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There is significant legal debate around property rights and artificial intelligence. In Acohs Pty Ltd v. Ucorp Pty Ltd, Justice Christopher Jessup of the Federal Court of Australia found that the source code for Material Safety Data Sheets could not be copyrighted as it was generated by a software interface rather than a human author. The same question may apply to neuromorphic systems: if a neuromorphic system successfully mimics a human brain and produces a piece of original work, who, if anyone, should be able to claim ownership of the work?
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法律界围绕财产权和人工智能有着重大争论。在Acohs Pty有限公司诉Ucorp Pty有限公司一案中，澳大利亚联邦法院的克里斯托弗·杰瑟普法官发现，版权保护不适用于材料安全数据表的源代码，因为它是由软件生成而非人类工作者生成的。<ref name=":31" />同样的问题可能也适用于神经形态系统: 如果一个神经形态系统成功地模仿了人类的大脑，并产生了一部原创作品，那么该如何确认这部作品的所有权归属？<ref name=":32" />
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围绕财产权和人工智能有着重大的法律争论。在 Acohs Pty Ltd 诉 Ucorp Pty Ltd 一案中，澳大利亚联邦法院的克里斯托弗 · 杰瑟普法官发现，材料安全数据表的源代码不能受版权保护，因为它是由软件界面而不是人工作者生成的。同样的问题可能也适用于神经形态系统: 如果一个神经形态系统成功地模仿了人类的大脑并产生了一部原创作品，那么谁，如果有人，应该声称拥有这部作品的所有权？
      
==Neuromemristive systems==
 
==Neuromemristive systems==
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:\frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S
 
:\frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S
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:\frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S
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: \frac{d}{dt} \vec{W} = \alpha \vec{W}-\frac{1}{\beta} (I+\xi \Omega W)^{-1} \Omega \vec S
    
as a function of the properties of the physical memristive network and the external sources. In the equation above, <math>\alpha</math> is the "forgetting" time scale constant, <math> \xi=r-1</math> and <math>r=\frac{R_\text{off}}{R_\text{on}}</math> is the ratio of ''off'' and ''on'' values of the limit resistances of the memristors, <math> \vec S </math> is the vector of the sources of the circuit and <math>\Omega</math> is a projector on the fundamental loops of the circuit. The constant <math>\beta</math> has the dimension of a voltage and is associated to the properties of the [[memristor]]; its physical origin is the charge mobility in the conductor. The diagonal matrix and vector <math>W=\operatorname{diag}(\vec W)</math> and <math>\vec W</math> respectively, are instead the internal value of the memristors, with values between 0 and 1. This equation thus requires adding extra constraints on the memory values in order to be reliable.
 
as a function of the properties of the physical memristive network and the external sources. In the equation above, <math>\alpha</math> is the "forgetting" time scale constant, <math> \xi=r-1</math> and <math>r=\frac{R_\text{off}}{R_\text{on}}</math> is the ratio of ''off'' and ''on'' values of the limit resistances of the memristors, <math> \vec S </math> is the vector of the sources of the circuit and <math>\Omega</math> is a projector on the fundamental loops of the circuit. The constant <math>\beta</math> has the dimension of a voltage and is associated to the properties of the [[memristor]]; its physical origin is the charge mobility in the conductor. The diagonal matrix and vector <math>W=\operatorname{diag}(\vec W)</math> and <math>\vec W</math> respectively, are instead the internal value of the memristors, with values between 0 and 1. This equation thus requires adding extra constraints on the memory values in order to be reliable.
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<nowiki>作为物理记忆网络和外部源的性质的函数。在上述方程中，α 是“遗忘”时间尺度常数，xi = r-1，r = frac { r _ text { off }{ on }{ r _ text { on }}是记忆电阻器极限电阻的开关和开关值之比，vec s 是电路源的矢量，Omega 是电路基本环路的投影仪。常数 β 具有电压的尺寸，与记忆电阻器的特性有关; 它的物理起源是导体中的电荷迁移率。对角矩阵和向量 w = 操作者名{ diag }(vec w)和 vec w 分别是记忆电阻器的内值，值在0到1之间。因此，这个等式需要在内存值上添加额外的约束，以保证可靠性。</nowiki>
 
<nowiki>作为物理记忆网络和外部源的性质的函数。在上述方程中，α 是“遗忘”时间尺度常数，xi = r-1，r = frac { r _ text { off }{ on }{ r _ text { on }}是记忆电阻器极限电阻的开关和开关值之比，vec s 是电路源的矢量，Omega 是电路基本环路的投影仪。常数 β 具有电压的尺寸，与记忆电阻器的特性有关; 它的物理起源是导体中的电荷迁移率。对角矩阵和向量 w = 操作者名{ diag }(vec w)和 vec w 分别是记忆电阻器的内值，值在0到1之间。因此，这个等式需要在内存值上添加额外的约束，以保证可靠性。</nowiki>
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==See also==
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==See also ==
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==相关词条==
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== 相关词条==
 
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{{Columns-list|colwidth=18em|
 
* [[AI accelerator (computer hardware)|AI accelerator]]
 
* [[AI accelerator (computer hardware)|AI accelerator]]
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{{Portal bar|Electronics}}
 
{{Portal bar|Electronics}}
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== References==
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==References==
 
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*Computation and Neural Systems department at the California Institute of Technology.
 
*Computation and Neural Systems department at the California Institute of Technology.
 
*Human Brain Project official site
 
*Human Brain Project official site
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*Building a Silicon Brain: Computer chips based on biological neurons may help simulate larger and more-complex brain models. May 1, 2019. SANDEEP RAVINDRAN
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* Building a Silicon Brain: Computer chips based on biological neurons may help simulate larger and more-complex brain models. May 1, 2019. SANDEEP RAVINDRAN
    
=<nowiki>外部链接</nowiki>=  
 
=<nowiki>外部链接</nowiki>=  
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