Nat.: 科学家 在地球上 观察 到 重力异常 诸平 据物理学家组织网（ Phys.org ） 2017 年 7 月 21 日 转载来自美国 国际商用机器公司 （ IBM ）的消息，科学家在地球上观察到重力异常现象，有关研究结果于 2017 年 7 月 19 日已经在《 自然 》（ Nature ）杂志网站发表 —— Johannes Gooth , Anna C. Niemann , Tobias Meng , Adolfo G. Grushin , Karl Landsteiner , Bernd Gotsmann , Fabian Menges , Marcus Schmidt , Chandra Shekhar , Vicky Süß , Ruben Hühne , Bernd Rellinghaus , Claudia Felser , Binghai Yan , Kornelius Nielsch . Experimental signatures of the mixed axial–gravitational anomaly in the Weyl semimetal NbP . Nature , 2017 , 547 ： 324–327 . DOI: 10.1038/nature23005 . 图 1 就是 IBM 研究院（ IBM Research ）提供的 UAM/CSIC 理论物理研究所（ Instituto de Fisica Teorica ）的 弦理论 教授卡尔 · 兰德斯泰纳博士（ Prof. Dr. Karl Landsteiner ）、同时也是上述《自然》杂志论文的合作者为了解释重力异常所勾画的图示。 Fig.1 Prof. Dr. Karl Landsteiner, a string theorist at the Instituto de Fisica Teorica UAM/CSIC and co-author of the paper made this graphic to explain the gravitational anomaly. Credit: IBM Research 参加此项研究的科学家包括来自德国汉堡大学（ Universität Hamburg ）纳米结构与固态物理研究所、瑞士苏黎世 IBM 研究院（ IBM Research -Zurich ）、德国金属材料研究院德累斯顿 的 莱布尼兹固态和材料研究所（ Leibniz Institute for Solid State and Materials Research Dresden, Institute for Metallic Materials ）、德国 德累斯顿 工业 大学 （ Technical University Dresden ）理论物理研究所、美国加利福尼亚大学（ University of California ）物理系、西班牙 马德里自治大学 （ Universidad Autónoma de Madrid ）理论物理研究所（ Instituto de Física Teórica UAM/CSIC ）、德国马克斯 · 普朗克固态化学物理研究所（ Max Planck Institute for Chemical Physics of Solids ） 以及 以色列 魏茨曼科学研究学院 ( Weizmann Institute of Science ) 凝聚态物理系 的研究人员 。 现代物理学 特别是 量子物理学 ， 已经 使 我们习惯 于现实的 奇怪的和违反直觉的概念 ， 量子物理学以离开物理对象 的 奇怪叠加态 而著名 。例如 , 薛定谔猫 （ Schrödinger's cat ） , 他发现自己无法判断 其 是死是活。然而有时量子力学 是 更果断的 、 甚至是破坏性的。 对于 物理学家 来说， 对称性 就 是圣杯。对称意味着一个人可以某种方式改变一个对象 并 让 其 不变的。例如 , 一个圆形的球可以被任意角度旋转 , 但看起来总是相同的。物理学家说这是在旋转 操作之下是 对称 的 。一旦一个物理系统的对称性 被 识别 ， 预测其 动力学 通常 是 可以 的 。 不过有时 量子 力学的 规律 破坏对称性 , 在 没有 量子力学 的世界也就是 经典系统 中对称性将会恰当 存在。 即使 物理学家 对此 看起来很奇怪 , 但 他们 将 这种 奇怪 现象 称之为 “ 异常 （ anomaly ）。 对其 历史的 绝 大部分 而言 , 这些量子异常 被 局限于基本粒子物理学探索 的范畴， 巨大加速器实验室如瑞士欧洲核子研究中心的大型强子对撞机 （ Large Hadron Collider at CERN in Switzerland ） 。然而 , 现在一种新型的材料 , 即 所谓的 威尔 半金属 （ Weyl semimetals，NbP ） 新形式 , 类似于三维石墨烯 , 允许我们把对称破坏量子异常 表达成 日常工作现象 , 如电流的创建。 在 这些特殊材料 之中， 电子有效的 表现为完全相同的方式，如同在 高能加速器 中 研究的基本粒子一样。这些粒子有奇怪的属性 , 它们不能在 处于静止状态，它 们必须 在任何时候 以一 种 恒定的速度移动。 它 们也有另一个 被称为 自旋 的 特性 ， 就像在 微粒上 附着 了 一个微小的磁铁 , 它 们 以 两种 状态运动 。自旋 既 可以在运动的方向 ，也可以是 相反的方向。 Fig. 2 An international team of scientists have verified a fundamental effect in a crystal that had been previously only thought to be observable in the deep universe. The experiments have verified a quantum anomaly that had been experimentally elusive before. The results are appearing in the journal Nature. Credit: Robert Strasser, Kees Scherer; collage: Michael Büker 图 2 是罗伯特 · 施特拉塞尔 （ Robert Strasser ）、凯斯 ·舍雷尔（ Kees Scherer ）以及 迈克尔 · 巴克（ Michael Büker ）提供的照片。 一个国际 性的 科学家小组已经验证 了晶体中一种 基本效应 ，此 晶体先前只认为是 在 宇宙深处可观测的。实验 已经 验证 了一种之前一直在实验上 难以捉摸量子异常。 相关研究结果已经于 2017 年 7 月 19 日在《 自然 》（ Nature ）杂志网站发表 。 当人 们谈及右手粒子或者 左 手粒子时，这些 粒子 就是具有某种属性即 手性 的 。通常情况下 , 两个不同物种 的 粒子 , 除了 它 们的手性 差异之外其余都是相同的 , 伴随两个不同对称性而又粘附在一起情况的发生， 它们的数量分别是守恒的。然而 , 量子异常可以摧毁 它 们的和平共处 ，使手性粒子发生转变，即将 一 种 左 手性 粒子 转化 成一 种 右 手性粒子 , 或者将一种右手性粒子转化为一种左手性粒子 。 2017 年 7 月 19 日 在 《 自然 》杂志网站 发表的 研究 论文中 , 一个 由 国际 性的 物理学家 、 材料科学家和弦理论家 组成的 研究小组 , 已经 观察到这样一 种具有 最奇异的量子异常 效应的 材料 ，这种效应 迄今为止被认为只有爱因斯坦的相对论 所 描述 过 时空曲率 才会引起的 。但 此研究 团队的惊喜 就在于 他们 竟然 发现 ， 在地球上的 固体物理 的 属性 之中也有其存在，很多都是 计算机行业的基础 , 从微小的晶体管到云数据中心 无不涉及 。 IBM 研究 院的 科学家 、同时也是上述 论文的主要作者约翰 尼斯 ·古奇 博士 （ Dr. Johannes Gooth ） 说 ： “对于 第一次 而言 , 我们 在 实验 上已经 观察到这个地球上基本量子异常 ， 对我们 更好地了解 宇宙是非常重要的。我们现在可以基于此异常 来 构建 一种新的 固态设备 , 而这种异常 以前从未被认为是在经典电子设备如晶体管 内 潜在的规避固有的一些问题。 ” 使用弦理论部分方法的新计算表明 ， 这种重力异常 也与如果此材料同时受热和磁场作用 生产 的 电流 有关 。 UAM/CSIC 理论物理研究所（ Instituto de Fisica Teorica UAM/CSIC ）的弦理论家、 教授 、也是上述研究论文的合作者 卡尔 · 兰德斯泰纳博士 （ Dr. Karl Landsteiner ） 说 ： “ 这是一个令人难以置信 、而又 激动人心的发现。我们可以清楚地得出结论 , 在任何物理系统 中都 可以观察到相同的对称性破坏 , 无论是发生在宇宙的开始或正在今天发生 , 恰恰就是在 地球上 。 ” IBM 的科学家预测这一发现将 会围绕 传感器 、 开关 、 热电冷却器或能量采集 等设备的开发，改进 能耗 打开新局面 , 开辟 新 方向 。 更多信息请注意浏览原文( https://export.arxiv.org/ftp/arxiv/papers/1703/1703.10682.pdf )或者相关报道： Scientists observe gravitational anomaly on Earth ; New breakthrough discovery—every quantum particle travels backwards Abstract The conservation laws, such as those of charge, energy and momentum, have a central role in physics. In some special cases, classical conservation laws are broken at the quantum level by quantum fluctuations, in which case the theory is said to have quantum anomalies 1 . One of the most prominent examples is the chiral anomaly 2 , 3 , which involves massless chiral fermions. These particles have their spin, or internal angular momentum, aligned either parallel or antiparallel with their linear momentum, labelled as left and right chirality, respectively. In three spatial dimensions, the chiral anomaly is the breakdown (as a result of externally applied parallel electric and magnetic fields 4 ) of the classical conservation law that dictates that the number of massless fermions of each chirality are separately conserved. The current that measures the difference between left- and right-handed particles is called the axial current and is not conserved at the quantum level. In addition, an underlying curved space-time provides a distinct contribution to a chiral imbalance, an effect known as the mixed axial–gravitational anomaly 1 , but this anomaly has yet to be confirmed experimentally. However, the presence of a mixed gauge–gravitational anomaly has recently been tied to thermoelectrical transport in a magnetic field 5 , 6 , even in flat space-time, suggesting that such types of mixed anomaly could be experimentally probed in condensed matter systems known as Weyl semimetals 7 . Here, using a temperature gradient, we observe experimentally a positive magneto-thermoelectric conductance in the Weyl semimetal niobium phosphide (NbP) for collinear temperature gradients and magnetic fields that vanishes in the ultra-quantum limit, when only a single Landau level is occupied. This observation is consistent with the presence of a mixed axial–gravitational anomaly, providing clear evidence for a theoretical concept that has so far eluded experimental detection.
Latest Headline News: Samsung acquires Viv, a next-gen AI assistant built by the creators of Apple's Siri . Wei: Some people are just smart, or shrewd, more than we can imagine. I am talking about Fathers of Siri, who have been so successful with their technology that they managed to sell the same type of technology twice, both at astronomical prices, and both to the giants in the mobile and IT industry. What is more amazing is, the companies they sold their tech-assets to are direct competitors. How did that happen? How nice this world is, to a really really smart technologist with sharp business in mind. What is more stunning is the fact that, Siri and the like so far are regarded more as toys than must-carry tools, intended at least for now to satisfy more curiosity than to meet the rigid demand of the market. The most surprising is that the technology behind Siri is not unreachable rocket science by nature, similar technology and a similar level of performance are starting to surface from numerous teams or companies, big or small. I am a tech guy myself, loving gadgets, always watching for new technology breakthrough. To my mind, something in the world is sheer amazing, taking us in awe, for example, the wonder of smartphones when the iPhone first came out. But some other things in the tech world do not make us admire or wonder that much, although they may have left a deep footprint in history. For example, the question answering machine made by IBM Watson Lab in winning Jeopardy. They made it into the computer history exhibition as a major AI milestone. More recently, the iPhone Siri, which Apple managed to put into hands of millions of people first time for seemingly live man-machine interaction. Beyond that accomplishment, there is no magic or miracle that surprises me. I have the feel of seeing through these tools, both the IBM answering robot type depending on big data and Apple's intelligent agent Siri depending on domain apps (plus a flavor of AI chatbot tricks). Chek: @ Wei I bet the experts in rocket technology will not be impressed that much by SpaceX either, Wei: Right, this is because we are in the same field, what appears magical to the outside world can hardly win an insider's heart, who might think that given a chance, they could do the same trick or better. The Watson answering system can well be regarded as a milestone in engineering for massive, parallel big data processing, not striking us as an AI breakthrough. what shines in terms of engineering accomplishment is that all this happened before the big data age when all the infrastructures for indexing, storing and retrieving big data in the cloud are widely adopted. In this regard, IBM is indeed the first to run ahead of the trend, with the ability to put a farm of servers in working for the QA engine to be deployed onto massive data. But from true AI perspective, neither the Watson robot nor the Siri assistant can be compared with the more-recent launch of the new Google Translate based on neural networks. So far I have tested using this monster to help translate three Chinese blogs of mine (including this one in making), I have to say that I have been thrown away by what I see . As a seasoned NLP practitioner who started MT training 30 years ago, I am still in disbelief before this wonder of the technology showcase. Chen: wow, how so? Wei: What can I say? It has exceeded my imagination limit for all my dreams of what MT can be and should be since I entered this field many years ago. While testing, I only needed to do limited post-editing to make the following Chinese blogs of mine presentable and readable in English, a language with no kinship whatsoever with the source language Chinese. Question answering of the past and present Introduction to NLP Architecture Hong: Wei seemed frightened by his own shadow.Chen: Chen: The effect is that impressive? Wei: Yes. Before the deep neural-nerve age, I also tested and tried to use SMT for the same job, having tried both Google Translate and Baidu MT, there is just no comparison with this new launch based on technology breakthrough. If you hit their sweet spot, if your data to translate are close to the data they have trained the system on, Google Translate can save you at least 80% of the manual work. 80% of the time, it comes so smooth that there is hardly a need for post-editing. There are errors or crazy things going on less than 20% of the translated crap, but who cares? I can focus on that part and get my work done way more efficiently than before. The most important thing is, SMT before deep learning rendered a text hardly readable no matter how good a temper I have. It was unbearable to work with. Now with this breakthrough in training the model based on sentence instead of words and phrase, the translation magically sounds fairly fluent now. It is said that they are good a news genre, IT and technology articles, which they have abundant training data. The legal domain is said to be good too. Other domains, spoken language, online chats, literary works, etc., remain a challenge to them as there does not seem to have sufficient data available yet. Chen: Yes, it all depends on how large and good the bilingual corpora are. Wei: That is true. SMT stands on the shoulder of thousands of professional translators and their works. An ordinary individual's head simply has no way in digesting this much linguistic and translation knowledge to compete with a machine in efficiency and consistency, eventually in quality as well. Chen: Google's major contribution is to explore and exploit the existence of huge human knowledge, including search, anchor text is the core. Ma: I very much admire IBM's Watson, and I would not dare to think it possible to make such an answering robot back in 2007. Wei: But the underlying algorithm does not strike as a breakthrough. They were lucky in targeting the mass media Jeopardy TV show to hit the world. The Jeopardy quiz is, in essence, to push human brain's memory to its extreme, it is largely a memorization test, not a true intelligence test by nature. For memorization, a human has no way in competing with a machine, not even close. The vast majority of quiz questions are so-called factoid questions in the QA area, asking about things like who did what when and where , a very tractable task. Factoid QA depends mainly on Named Entity technology which was mature long ago, coupled with the tractable task of question parsing for identifying its asking point, and the backend support from IR, a well studied and practised area for over 2 decades now. Another benefit in this task is that most knowledge questions asked in the test involve standard answers with huge redundancy in the text archive expressed in various ways of expressions, some of which are bound to correspond to the way question is asked closely. All these factors contribute to IBM's huge success in its almost mesmerizing performance in the historical event. The bottom line is, shortly after the 1999 open domain QA was officially born with the first TREC QA track, the technology from the core engine has been researched well and verified for factoid questions given a large corpus as a knowledge source. The rest is just how to operate such a project in a big engineering platform and how to fine-tune it to adapt to the Jeopardy-style scenario for best effects in the competition. Really no magic whatsoever. Google Translated from 【泥沙龙笔记：从三星购买Siri之父的二次创业技术谈起】 , with post-editing by the author himself. 【Related】 Question answering of the past and present Introduction to NLP Architecture Newest GNMT: time to witness the miracle of Google Translate Dr Li’s NLP Blog in English
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