人工智能英文版论文

人工智能英文版论文

人工智能(AI)系统被认为是神经网络，可以识别图片，翻译，甚至精通古老的游戏。以下是精心整理的人工智能英文版论文的相关资料，希望对你有帮助!

人工智能英文版论文篇一Google's AI Reasons Its Way around the London

Underground

谷歌人工智能推导出环绕

伦敦地铁系统的路线

DeepMind‟s latest technique uses external memory to solve tasks that require logic and reasoning;a step toward more humanlike AI

维和推理能力的任务

By Elizabeth Gibney, Nature magazine on October 14, 2016

伊丽莎白.吉布尼2016年10月14日发表于《自然》杂志

深度思维最新技术使用了外部存储来解决需要逻辑思

Artificial-intelligence (AI) systems known as neural networks can recognize images, translate languages and even master the ancient game of Go. But their limited ability to

represent complex relationships between data or variables has prevented them from conquering tasks that require logic and reasoning.

人工智能(AI)系统被认为是神经网络，可以识别图片，翻译，甚至精通古老的游戏。但他们描绘数据或变量之间的复杂关系的能力有限，这妨碍了他们克服需要逻辑思维和推理能力的任务。

In a paper published in Nature on October 12, the

Google-owned company DeepMind in London reveals that it has taken a step towards overcoming this hurdle by creating a neural

network with an external memory. The combination allows the neural network not only to learn, but to use memory to store and recall facts to make inferences like a conventional algorithm. This in turn enables it to tackle problems such as navigating the London Underground without any prior knowledge and solving logic puzzles. Though solving these problems would not be impressive for an algorithm programmed to do so, the hybrid system manages to accomplish this without any predefined rules.

在10月12日《自然》杂志中发表的一篇论文中，谷歌在伦敦的子公司深度思维展示了他们通过结合外部存储创造了一个神经网络，来进一步克服这些障碍。这种和外部存储的结合不仅允许神经网

络学习，还可以通过存储器来存储和回忆事件，并以此来像正常情况那样做推断。这反过来能够让它解决难题，比如在没有任何经验的情况下操控伦敦地铁，比如解决逻辑谜题。尽管对于一个算法程序来说做到这点并不会令人印象深刻，但这个混合系统在没有任何先决条件的情况下做到了这点。

Although the approach is not entirely new;DeepMind itself reported attempting a similar feat in a preprint in 2014;“the progress made in this paper is remarkable”, says Yoshua Bengio, a computer scientist at the University of Montreal in Canada.

虽然这个方法不是一个全新的技术;;深度思维自己就在2014年报告过他们尝试了一种相似的技术;;但“在论文中的这个进步是非凡的”，加拿大蒙特利尔的计算机学家本吉奥.本希奥赞叹道。

MEMORY MAGIC

记忆魔法

A neural network learns by strengthening connections between virtual neuron-like units. Without a memory, such a network might need to see a specific London Undeground map thousands of times to learn the best way to navigate the tube.

神经网络通过加强虚拟神经元之间的联系来学习。如果没有存储器，这样一个网络可能需要看一副特定的伦敦地铁地图数千次来学习

最佳路线。

DeepMind's new system;which they call a 'differentiable

neural computer';can make sense of a map it has never seen before. It first trains its neural network on randomly generated map-like structures (which could represent stations connected by lines, or

other relationships), in the process learning how to store descriptions of these relationships in its external memory as well as answer questions about them. Confronted with a new map, the DeepMind system can write these new relationships;connections between Underground stations, in one example from the paper;to memory, and recall it to plan a route.

深度思维的新系统;;他们称它为微分神经计算机;;可以理解它从未见过的地图。第一次训练神经网络是在随机生成的类似结构的地图上(被铁路线链接的车站，或者其他关系)，在这个过程中学习如何将这些关系的描述存储在它的外部存储器并且回答问题。面对一个新的地图，深度思维的系统可以把这些新关系;;按照一个图纸上例子来连接各地铁站之间的关系;;写到存储器，并能够回忆这些关系然后计划路线。

DeepMind‟s AI system used the same technique