麦肯锡《信息技术》15 期Word格式文档下载.docx

1、New-Tech（科技前沿） 3Big Data Analysis Is Changing the Nature of Sports Science 3大数据分析正在改变体育科学本质 52016 is turning out to be an amazing year for augmented reality 72016年增强现实技术蓬发展 8AMD doubles down on VR with Sulon Q, LiquidVR 10超微半导体公司携Sulon Q, LiquidVR双重来袭 12Essay: Will the First Amendment survive the in

2、formation age? 13第一修正案在信息时代能存活下去吗？ 15Unlocking the secrets of the brains intelligence to develop smarter technologies 17解锁大脑的智力秘密以开发更加智能的技术 18Notes from SXSW Interactive: Moores Law, software, and disruption 20“南偏西南”互动媒体大会关键词：摩尔定律、软件和介入 22Computer Software & Application（计算机应用与软件） 23Lawrence Livermor

3、e and IBM collaborate to build new brain-inspired supercomputer 23劳伦斯利弗莫尔实验室和IBM合作创建新型脑启发超级计算机 24Tool chain for real-time programming 25实时编程工具链 26Seagate unveils PCIe x16 SSD with 10GB/s bandwidth at Open Compute Summit 26希捷科技于开源计算峰会首次发布秒速10GB的PCIe x16固态驱动器 275 best add-ons for the Raspberry Pi 3 28

4、树莓派迷你小电脑的5个顶级附加组件 29Microsoft Says Maverick Chatbot Tay Foreshadows the Future of Computing 30微软表示特立独行的聊天机器人Tay预示着计算机技术的发展前景 30New machine unlearning technique wipes out unwanted data quickly and completely 31新型“机器免学”技术可快速彻底清除无用数据 33Instrument error: AMD, FCAT, and Ashes of the Singularity benchmark

5、s 35仪器误差：AND、FCAT以及奇点灰烬的基准 39Communication（通信） 42Proposed bill would block anonymous sale of burner phones in US 42美国提案将阻止匿名销售一次性手机 43Its time to hold manufacturers responsible for Android vulnerabilities 43是时候让制造商为Android漏洞负责了 44As smartphones stall, PC and mobile manufacturers slug it out over 2-i

6、n-1 PCs 45随着智能手机暂停，PC和手机厂商在2合1电脑方面决一雌雄 46Apple Goes All Out On Rival Microsoft 46苹果全线紧逼微软 47Intenet（网络） 48Faced with FCC regulations on router capabilities, TP-Link blocks open-source updates 48在联邦通信委员会的严令限制下，TP-Link屏蔽了路由器的开源升级功能 50Researchers say new generation of ransomware emerging 51研究人员宣告新一代勒索软

7、件出现 52Comcast rolls out gigabit in Atlanta, uses data caps to force customers into contracts 52康卡斯特公司在亚特兰大推出千兆速，并利用数据限额强迫顾客接受合同 53The cybersecurity threat are we protected yet? 54网络安全的威胁我们还在被保护着吗？ 55Turkey passes long-awaited data protection law 56土耳其通过了期待已久的数据保护法 57New regulations could further clo

8、se Chinas Internet 57新法规的颁布使中国网络更为封闭 58New-Tech（科技前沿）Big Data Analysis Is Changing the Nature of Sports ScienceThe best-selling book Moneyball by Michael Lewis changed the way people thought about sport, particularly for those owners, managers, and players with the biggest vested interests. Lewiss b

9、ook helped bring about a revolution in which player performance was measured and assessed using an evidence-based approach rather than a tradition dominated by anecdote and intuition.Since then, sports scientists have attempted to replicate the success of this approach in sports such as basketball,

10、soccer, American football, and so on. This science is driven by the relatively new ability to gather vast amounts of data about the players and the play while the game is in progress.However, in many of these sports, the capacity to gather data has not been matched by an ability to process it in mea

11、ningful ways. So an interesting question is what challenges sports sciences face in crunching this data effectively. What are the open questions in this rapidly evolving field?Today we get an answer thanks to the work of Joachim Gudmundsson and Michael Horton at the University of Sydney in Australia

12、, who have reviewed this field and listed the outstanding challenges that researchers face in making analytics meaningful.The sports these guys consider are together known as invasion games. They all consist of two teams that compete for possession of a ball in a constrained playing area. Each team

13、has the simultaneous objective of scoring by putting the ball into the oppositions goal and also of defending its own goal. The team that scores the greatest number of goals by the end of the game is the winner.Invasion sports that share this structure include soccer, basketball, ice hockey, field h

14、ockey, rugby, Australian rules football, American football, lacrosse and so on. However, most of the data comes from games such as professional soccer and basketball, which have the resources to gather it.This data generally consists of player and ball trajectories throughout the game, and event log

15、s that describe events such as passes, shots, tackles, and so on at specific times. “State of the art object tracking systems now produce spatio-temporal traces of player trajectories with high definition and high frequency, and this, in turn, has facilitated a variety of research efforts, across ma

16、ny disciplines, to extract insight from the trajectories,” say Gudmundsson and Horton.The big challenge in sports science is to use this data to gain a competitive advantage, whether in real time during the game or to help in training, preparation, or recruitment. But while researchers have made sig

17、nificant progress, there are also important hurdles barring the way.One of the most significant involves understanding how players can dominate parts of the pitch near them. In sports science, a players dominant region is the region he or she can reach before any other player. A simple way to calcul

18、ate this is to draw a Voronoi diagram, which divides the pitch into the regions closest to each player （see diagram）.Such a diagram can be modified with the help of other information, such as the observation that dominant regions tend to be larger for the attacking team than the defending team.Howev

19、er, calculating the Voronoi diagram for each player on the pitch is computationally expensive. Nobody has successfully done it in real time, even for RoboCup football.Instead, researchers calculate a different propertythe region each player can reach in a given timeand then look for overlaps, which

20、are then resolved. This increases speed by a factor of 1,000 at a cost of a 10 percent loss in accuracy.But even then, this approach ignores a number of crucial factors. Perhaps the most significant is that it takes no account of the players momentum. Clearly, a player in motion can dominate a great

21、er region ahead than a stationary player.This can lead to complex subdivisions of the pitch. When player A runs at an opposing player B who is stationary, each may have more than one dominant region, and these may not be connected to each other. For example, player As momentum gives better access to

22、 some, but not all, of the region behind B.So an important open problem in sports science is how to calculate realistic dominant regions in real time.Another related challenge is to work out whether a player is open to receive a pass. That means determining if there is a certain speed and direction

23、that the ball can be passed so that a given player can intercept it before any other.This is obviously linked to the players dominant region. Given an accurate idea of what that region is, its straightforward to work out a straight-line pass that falls within it. Indeed, thats how the current tools

24、that do this work.The problem is that only certain trajectories meet the criterion of being straight-line passes. An aerial trajectory, for example. is not a straight-line pass. No tool yet exists that can handle these （or more complex motions involving the spin of the ball）, and this is another ope

25、n problem in sports scienceThen there is the way that one player can put pressure on other players by closing down the space around them. How can this be measured and incorporated into models?An increasingly important area of sporting analysis involves network science. This treats each player as a n

26、ode and draws a line between them when the ball travels from one to the other. This has been a fruitful area of research because a wide range of mathematical tools have already been developed for analyzing networks.For example, it is straightforward to work out the most important nodes in the networ

27、k using a measure known as centrality. In soccer, goalkeepers and forwards have the lowest centrality, while defenders and midfielders have the highest.The same kind of science also allows the network to be divided into clusters. So some team members might only pass to each other or work more effect

28、ively together.However, the problem with network science is that there are numerous different ways of measuring centrality and determining clusters, and it is not always clear why one method should be preferred over another. So another open problem is to systematically evaluate and compare these dif

29、ferent methods to determine their utility and value.Another class of problems come from analyzing game-play data. For example, given the list of player trajectories and event logs for a period during the game, is it possible to determine the team formation for example, 4-4-2 in soccer or the type of

30、 marking used by the defensive team, such as a full-court press or a zone defense in basketball?There is some evidence that this can be done some of the time in certain sports. But matching or beating human performance in this is still the goal.Gudmundsson and Horton describe various other open prob

31、lems and how ideas developed in sports such as football and basketball could usefully be applied in other invasion sports, such as hockey and handball.But perfecting algorithms that can solve these problems is only half the battle. The next stage will be to ask how these tools can help improve perfo

32、rmance both on and off the field. Can they be used as a metric of player performance and value? Can they determine whether a player who is successful on one team will be also be successful on another? And can they work in real time during a game to help coaches and fans alike?There are likely to be significant developments in the coming years. Clea