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删除6字节 、 2022年4月29日 (五) 10:44
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While the interdisciplinary concept of neuromorphic engineering is relatively new, many of the same ethical considerations apply to neuromorphic systems as apply to [[human-like machines]] and [[artificial intelligence]] in general. However, the fact that neuromorphic systems are designed to mimic a [[human brain]] gives rise to unique ethical questions surrounding their usage.
 
While the interdisciplinary concept of neuromorphic engineering is relatively new, many of the same ethical considerations apply to neuromorphic systems as apply to [[human-like machines]] and [[artificial intelligence]] in general. However, the fact that neuromorphic systems are designed to mimic a [[human brain]] gives rise to unique ethical questions surrounding their usage.
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虽然神经形态工程这个跨学科概念相对较新,但许多适用于'''<font color="#ff8000">类人机器Human-like machines</font>'''和人工智能的伦理考虑大体上也适用于神经形态系统。另一方面,神经形态系统是为了模仿人类大脑而设计,这一事实引起了一些针对性的独特伦理问题。
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虽然神经形态工程这个跨学科概念相对较新,但许多适用于'''<font color="#ff8000">类人机器Human-like machines</font>'''和人工智能的伦理讨论在神经形态系统领域也无法避免。另外,神经形态系统是为了模仿人类大脑而设计,这一底层逻辑也导致了一些新的伦理问题。
    
However, the practical debate is that neuromorphic hardware as well as artificial "neural networks" are immensely simplified models of how the brain operates or processes information at a much lower [[complex system|complexity in terms of size and functional technology]] and a much more regular structure in terms of [[brain connectivity|connectivity]]. Comparing [[neuromorphic chip]]s to the brain is a very crude comparison similar to comparing a plane to a bird just because they both have wings and a tail. The fact is that neural cognitive systems are many orders of magnitude more [[Energy efficiency (physics)|energy-]] and compute-efficient than current state-of-the-art AI and neuromorphic engineering is an attempt to narrow this gap by inspiring from the brain's mechanism just like many engineering designs have [[bioengineering|bio-inspired features]].
 
However, the practical debate is that neuromorphic hardware as well as artificial "neural networks" are immensely simplified models of how the brain operates or processes information at a much lower [[complex system|complexity in terms of size and functional technology]] and a much more regular structure in terms of [[brain connectivity|connectivity]]. Comparing [[neuromorphic chip]]s to the brain is a very crude comparison similar to comparing a plane to a bird just because they both have wings and a tail. The fact is that neural cognitive systems are many orders of magnitude more [[Energy efficiency (physics)|energy-]] and compute-efficient than current state-of-the-art AI and neuromorphic engineering is an attempt to narrow this gap by inspiring from the brain's mechanism just like many engineering designs have [[bioengineering|bio-inspired features]].
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