谷歌人工智能文章中英文对照.doc

1 5 非专业人士翻译 如有错误请谅解 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 深度思维最新技术使用了外部存储来解决需要逻辑思 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 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 2 5 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 记记忆忆 魔魔法法 A neural network learns by strengthening connections between virtual neuron like units Without a memory such a network might 3 5 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 to tackle puzzles that require reasoning After training on 20 different types of question and answer problems it learnt to make accurate deductions For example the system deduced correctly that a ball is in a playground having been informed that John picked up the football and John is in the playground It got such problems right more than 96 of the time The system performed better than recurrent neural networks which also have a memory but one that is in the fabric of the network itself and so is less flexible than an external memory 4 5 深度思维的人工智能系统使用同样的方法来处理需要推理能力的 智力游戏 在通过 20 种不同类型的问答训练之后 它学会了做出准确 的推论 例如 系统通过被告之 约翰抓着足球 和 约翰在操场上 准确的推断出一个球在操场上 答对问题的概率超过了 96 这个系统 的效率比拥有一个内部存储器的周期神经网络更高 也更灵活 Although the DeepMind technique has proven itself on only artificial problems it could be applied to real world tasks that involve making inferences from huge amounts of data This could solve questions whose answers are not explicitly stated in the data set says Alex Graves a computer scientist at DeepMind and a co author on the paper For example to determine whether two people lived in the same country at the same time the system might collate facts from their respective Wikipedia pages 虽然深度思维的技术已被证明只针对人工问题 但它能够被应用 到需要通过海量数据来进行推断的真实世界的工作 这能够解决那些 在数据中没有明确答案的问题 来自深度思维的计算机科学家 研究 报告的合著者 亚历克斯 格雷夫斯介绍说 例如 对于判断两人是 否在同一时间住在同一个国家 系统可能会核对他们各自在维基百科 上的事项 Although the puzzles tackled by DeepMind s AI are simple Bengio sees the paper as a signal that neural networks are advancing beyond mere pattern recognition to human like tasks such as reasoning This extension is very important if we want to ap