【考研抢手重磅双语新闻】分析我国正在AI领域快速追逐(考研双姐是谁)

china on track to cash in on the rise of machine learning分析：我国正在ai领域快速追逐some types of technology seem perfectly designed for fast followers. these competitors may not be on the leading edge of a new idea but can react fast enough to stake out a large part of the new market for themselves. the rise of machine learning looks like one such innovation — and china has positioned itself to become the fast follower to beat in this defining technology of the ai revolution.某些类型的技能如同是专为快速跟从者方案的。这些竞赛者或许并不置身于一个新构思的前沿，但他们可以做出满足快的反应，为自个拓荒一大块新商场。机器学习的鼓起看起来像是这样一类立异：我国已结束自我定位，要在人工智能(ai)改造的这个标志性技能领域变成头号快速跟从者。just two or three years ago, this still looked like the narrowest of fields. the most advanced form of the art, called deep learning, emanated from work at just three north american universities. the people behind those breakthroughs decamped to jobs at places such as 谷歌 and facebook. upstarts such as deepmind in london (now part of 谷歌) and openai in san francisco became centres for some of the most advanced research.只是两、三年前，这仍然形似最狭隘的领域。其最早进方法——被称为深度学习——源于三所北美大学的研讨作业。那些打破不和的我们，后来换岗到谷歌(谷歌)和facebook这样的当地。草创公司，如伦敦的deepmind（如今是谷歌的一有些）和旧金山的openai，则变成某些最早进研讨的中心。but now the basic techniques of machine learning — algorithms that become smarter as they are trained on large amounts of data — are well understood. and it turns out that this is a general purpose technology that can be applied to almost any problem.可是，如今机器学习的根柢办法——用海量数据进行练习后变得更聪明的算法——现已被极好地了解。实际证明，这是一种通用技能，可以使用于几乎任何疑问。thanks to open source software, many of the tools to build advanced ai systems are generally available. last year, for instance, two engineers with no previous knowledge of deep learning won a public competition to devise an algorithm that diagnoses heart disease. they did it by turning to github, an online repository of open source code that has become the toolbox for developers looking to extend their personal repertoire.得益于开源软件，构建 ai体系所需的许多东西可以揭露获得。例如，上一年，两名在深度学习领域并不具有先有常识的工程师赢得了一场揭露竞赛，他们方案出一种确诊心脏疾病的算法。他们的诀窍是求助于github，这个开源代码的在线材料库已变成开发者的东西箱，让他们得以拓宽自个的自个材料库。谷歌’s tensor flow — along with similar machine learning frameworks developed by other tech companies — has also been made freely available, opening up tools that were developed to help the search company’s own engineers apply the technology.谷歌的tensor flow——以及其他科技公司开发的类似的机器学习规划——也都现已免费供给，使这些正本为了协助这家查找公司的工程师使用该技能而开发的东西对一切人翻开。the open nature of much ai research is another factor that has made it easier for fast followers. deepmind’s publication a year ago of a research paper about alphago, the system that last month beat china’s world champion go player, is said to have sparked a flurry of copycats in china, where alibaba, tencent and baidu are leading a commercial race to catch up.许多ai研讨的翻开性，是让快速跟从者的日子愈加好过的另一个要素。deepmind在一年前宣告的一篇关于alphago（该体系上月打败我国的围棋世界冠军）的研谈论文，据悉在我国引发了一大堆仿照活动。在我国，阿里巴巴(alibaba)、腾讯(tencent)和baidu(baidu)正在领导一场追逐的商业竞赛。this ease of emulation raises a particular worry for the guardians of us national security. if robotic systems represent the future of warfare, and ai provides the brains, then the free flow of code and key research breakthroughs suggest it will be hard to maintain national superiority.这种仿照上的垂手可得致使了美国国家平安保卫者的特别忧虑。假定说机器人体系代表着战争的将来，而ai供给大脑，那么代码和要害研讨打破的安适活动如同标明，很难坚持国家优势。in applying machine learning, china is also rushing to mobilise a large new workforce. 谷歌 may be putting its engineers through some degree of machine learning training and universities such as stanford, in silicon valley, have seen a jump

in demand for courses on the technique.在使用机器学习方面，我国还在忙于培育一支大规划的新职工部队。谷歌或许在让其工程师承受必定程度的机器学习培训，一起地处硅谷的斯坦福(stanford)等大学看到了机器学习有关课程的需要有所添加。but those efforts pale in comparison with the millions of machine learning experts that china could soon produce as it turns its attention to this key technology, says kai-fu lee, an ai expert who once headed microsoft and 谷歌’s operations in the country.可是，ai专家、早年掌控微软(microsoft)和谷歌在华事务的李开复(kai-fu lee)标明，比较重视这项要害技能的我国有望很快培育出来的数百万机器学习专家，美国的这些尽力相形见绌。as mr lee puts it: “the days when america had undisputable, uncopyable, inimitable leadership in technologies are gone, at least with respect to computer science.”正如李开复所说：“美国在技能方面具有无可争议、无法仿制、无法仿效的领导方位的日子现已曩昔了，至少在核算机科学领域是这样。”there are other reasons why china looks well-placed to capitalise on machine learning. application of the technology depends on the availability of large data sets — indeed, many in the field argue that the ultimate competitive advantage will come not from having the best algorithms, but from access to the best data to train the ai systems.我国在机器学习方面处于有利方位还有其他一些缘由。该技能的使用有赖于巨大数据集的可获得性——的确，该领域的许多人认为，终极的竞赛优势将不在于具有最佳算法，而在于可以获得最佳数据以练习ai体系。if so, then china’s vast markets, along with the emergence of a group of internet leaders with a wide range of digital activity, should provide plenty of raw material to fuel the rise of intelligent systems.假定是这样，那么我国巨大的商场，加上一群从事广泛数字活动的互联网领军者，大约会供给许多的原材料来推进智能体系的鼓起。外刊共享群：4881899412021翻译硕士考研群：546804575