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Alan Turing’s unorganized machines and artificial neural networks: his remarkable early work and future possibilities

机译:艾伦·图灵(Alan Turing)的无组织机器和人工神经网络:他杰出的早期工作和未来的可能性

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In a technical report submitted in 1948, Alan Turing presented a far-sighted survey of the prospect of constructing machines capable of intelligent behaviour. This report was all the more remarkable for having been written at a time when the first programmable digital computers were just beginning to be built, leaving Turing with only paper and pencil with which to explore his modern computational ideas. Turing may have been the first to suggest using randomly connected networks of neuron-like nodes to perform computation, and proposed the construction of large, brain-like networks of such neurons capable of being trained as one would teach a child. Some modern work has been performed on Turing’s neural networks, but all of it involves the invention of new structures outside the scope of Turing’s original specifications in order to solve a technical error on Turing’s part. I propose an alternative solution to Turing’s technical error, one which does not require the invention of new structures, and outline an approach which may allow the better exploration of the particular properties of Turing’s networks in their own right. I also give examples of ways in which to avoid viewing Turing’s early and strikingly original work through the lens of the conventions of modern neural network theory. Using a “genetical” search to configure such networks, as Turing suggested, is likely to yield non-intuitive or algorithmically opaque results, but this is likely to be a property of brain-like networks themselves, and a sign we are approaching Turing’s initial goal.
机译:在1948年提交的技术报告中,艾伦·图灵(Alan Turing)对构建具有智能行为的机器的前景进行了有远见的调查。该报告是在第一台可编程数字计算机刚刚开始建造时就撰写的,因此更加引人注目,而图灵只剩下纸和铅笔来探索他的现代计算思想。图灵可能是第一个建议使用类似神经元节点的随机连接的网络来执行计算的人,并提出了构建这种神经元的大型,类似于大脑的网络的结构,这种结构能够像教孩子一样受到训练。图灵的神经网络已经完成了一些现代工作,但所有这些工作都涉及发明图灵原始规范范围之外的新结构,以解决图灵方面的技术错误。我为Turing的技术错误提出了一种替代解决方案,该解决方案不需要发明新的结构,并概述了一种方法,该方法可以允许自己更好地探索Turing网络的特定属性。我还将通过现代神经网络理论的惯例来举例说明如何避免查看图灵的早期和惊人的原创作品。正如图灵所建议的那样,使用“遗传”搜索来配置这样的网络可能会产生非直觉或算法上不透明的结果,但这很可能是类似大脑的网络本身的特性,这是我们接近图灵最初研究的标志目标。

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