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2019年02月23日 23:51:20来源:飞度新闻健康管家

学生过度崇拜明星影响学业An unhealthy obsession with celebrity culture is damaging the academic success of British students, a survey of teachers found on Friday, with celebrity couple the Beckhams the favorite inspiration.Many students are ignoring career aspirations to pursue the chance of fame instead, the Association of Teachers and Lecturers (ATL) survey found.Almost two-thirds of teachers said sports stars were the type of celebrity most pupils wanted to emulate while more than half of students wanted to be pop stars.The celebrities students aspired to be most like, the survey said, were Los Angeles-based David and Victoria Beckham, arguably Britain's most famous couple.Soccer player "Becks" topped the poll, with more than half the teachers saying their students modeled themselves on the 32 year-old. In second place, with almost a third of the poll's vote, was his 33 year-old wife and pop star "Posh."In an era of reality television "stars" and a media fixation with celebrities, a majority of teachers said celebrity culture negatively impacted the aspirations of their pupils.Almost half of the 300 teachers polled said pupils tried to look like and/or behave like celebrities they most admired, fuelling fears that girls particularly dressed in "unsuitable," or provocative styles."We are not surprised about infiltration of celebrity culture in schools -- it reflects the current media obsession with celebrity and the effect of celebrity culture on society as a whole," ATL general secretary Mary Bousted said in a statement."Celebrities can have a positive effect on pupils. They can raise pupils' aspirations and ambitions for the future."However ... celebrity culture can perpetuate the notion that celebrity status is the greatest achievement and reinforces the belief that other career options are not valuable."Chelsea midfielder Frank Lampard ranked third on the favorites list with 26 percent, actress Keira Knightley finished fourth (25 percent).Other celebrities on the list included U.S. heiress and socialite Paris Hilton (sixth) and Leona Lewis, a winner of Britain's "The X Factor" television talent show (ninth). 英国上周五公布的一项针对教师的调查发现,英国学生对明星的过度“痴迷”影响到了他们的学业。调查发现,“小贝”夫妇是英国学生最崇拜的明星。这项由英国教师与讲师协会开展的调查发现,很多学生由于追逐“明星梦”而忽视了正常的成才之路。近三分之二的教师称,大多数学生最想成为体育明星,一半以上的学生梦想成为流行歌星。该调查称,学生们最渴望自己能成为贝克汉姆夫妇那样的名人。已移居美国洛杉矶的小贝和维多利亚堪称英国最著名的夫妇。民调结果显示,足球巨星贝克汉姆最受学生崇拜,一半以上的受访教师称他们的学生以这位32岁的足球明星为榜样。小贝的爱妻、33岁的流行歌星“高贵辣” 以三分之一的得票率名列第二。多数受访教师称,在电视选秀明星大行其道、媒体大肆宣传明星的今天,名人文化对学生的志向产生了不良影响。在300名受访教师中,近一半的人称一些学生在外表或举止上效仿自己崇拜的明星,一些女孩着装“不当”或装扮过火,惹人担忧。英国教师与讲师协会秘书长玛丽#8226;鲍斯蒂德在一份声明中说:“明星文化深入校园其实并不奇怪——这体现了媒体过于关注名人的现状及名人文化对整个社会的影响。”“名人可以给学生带来积极影响。他们能激励学生们梦想未来。”“然而,名人文化也会助长一种错误观念,即‘成为明星才是最大成功,从事其它行业都没有价值’。”切尔西中场球员弗兰克#8226;兰帕德以26%的得票率成为第三大最受学生欢迎的明星,女星凯拉#8226;奈特莉名列第四(25%)。上榜的其他名人包括美国豪门女星、社交名媛帕里斯#8226;希尔顿(第六位)及英国选秀节目“The X Factor”冠军莉欧娜#8226;刘易斯(第九位)。 /200803/30962。

  • Beijing’s plans to create national champions in 10 high-tech manufacturing sectors by 2025 has come under fire from European businesses, which fear Chinese producers could swamp whole industries with overcapacity and spark a protectionist backlash.中国政府计划在2025年以前,在10个高科技制造业领域打造国家冠军企业。这一计划受到欧洲商界的抨击,后者担心中国制造商会以过剩产能淹没整个产业、并引发保护主义反弹。The EU Chamber of Commerce in Beijing on Tuesday released a 70-page critique of China’s industrial policy, known as China Manufacturing 2025.位于北京的中国欧盟商会(European Chamber of Commerce in China)周二发布了一份长达70页、题为《中国制造2025:产业政策对弈市场力量》(China Manufacturing 2025)的报告,批评中国的产业政策。“The broad set of policy tools that are being employed to facilitate CM2025’s development are highly problematic,” said the chamber, which represents European businesses in China. The report was timed to coincide with the National People’s Congress, the annual meeting of China’s rubber-stamp legislature.这家代表欧洲在华企业的商会表示:“用来推动落实《中国制造2025》的诸多政策手段是有很大问题的。”该报告的发布恰逢中国的“橡皮图章”立法机关全国人大召开年会之际。The chamber said, for instance, that foreign carmakers with electric models were being pressed to turn over their battery technology to Chinese partners in exchange for being able to produce and sell in China.该商会举例称,拥有电动车型的外国车企正面临压力,要它们把自己的电池技术交给中方合作伙伴,以换取能够在中国生产和销售。“European business is facing intense pressure to turn over advanced technology in exchange for near-term market access,” according to the report.报告称:“例如,近来通过的一项新能源汽车领域的政策令欧洲企业需以先进技术换取近期市场准入而面临巨大压力。”Other measures that have been aly implemented, such as subsiding local producers of battery-powered cars, are possibly in violation of China’s commitments to the World Trade Organisation, the EU Chamber said. However, foreign businesses fear WTO resolution may not be an option given the length of time the process takes and the potential for retaliation by China.中国欧盟商会表示,其他已经实施的措施——比如补贴本土电池动力汽车生产商——可能违反了中国对世贸组织(WTO)的承诺。不过,外国企业担心,将问题诉诸WTO来解决也许不是一个合适的选项,因为整个流程耗时太久,而且中国有可能会报复。On Sunday, at the opening of the NPC, China’s government appeared to react to criticism of its manufacturing plans, saying in a government work report: “Foreign firms will be treated the same as domestic firms when it comes to licence applications, standard-setting and government procurement, and will enjoy the same preferential policies under the Made in China 2025 initiative.”上周日,在全国人大年会开幕时,中国政府似乎针对外界对其制造业计划的批评作出了回应,它在一份政府工作报告中称:“在资质许可、标准制定、政府采购、享受《中国制造2025》政策等方面,对内外资企业一视同仁。” /201703/496370。
  • Isaac Newton may have been one of the finest minds of all time, but he turned out to be a miserable investor. “I can calculate the motions of the heavenly bodies, but not the madness of people,” he lamented after losing a fortune in the South Sea bubble. 艾萨克#8226;牛顿(Isaac Newton)可能是有史以来最聪明的人,然而事实明他是个糟糕的投资者。“我能计算出天体的运动,却不能计算出人的疯狂,”他在南海股票泡沫中损失了一大笔钱以后哀叹道。 Increasingly, however, technology-savvy investors think they can harness mathematics and bleeding edge computer science to predict the ebb and flow of financial markets. Some of the most advanced asset managers are turning to artificial intelligence techniques, with investment algorithms that can autonomously learn, adapt and scour vast data sets for tradable patterns. 然而,越来越多精通技术的投资者认为他们可以利用数学和计算机尖端科技,来预测金融市场的起起伏伏。一些最先进的资产管理公司现在正求助于人工智能(AI)技术,其中包括能够自动学习、适应和搜索大量数据组以研究出可交易的模式的投资算法。 But some “quantitative” financiers (quants) are sceptical that these tools are any more than a somewhat better mousetrap, and argue that areas such as “machine learning” are overhyped and AI used as a marketing gimmick. 但有些“量化”金融家(quant,即量化分析师)怀疑这类工具可能不过是一种高明一点的陷阱。他们认为“机器学习”这类领域被过度炒作,AI则是一种营销噱头。 “Everyone wants the Holy Grail, something they can invest in and it will make 1 per cent a month forever,” says Ewan Kirk, head of Cantab Capital, a Cambridge-based quantitative hedge fund. “I don’t want to be cynical, but I am sceptical.” “每个人都想要得到‘圣杯’,某种能够投资并且实现1%恒定月回报率的东西,”位于剑桥(Cambridge)的量化对冲基金Cantab Capital的负责人尤安#8226;柯克(Ewan Kirk)表示,“我不想表现得悲观,但我很怀疑。” David Harding, head of Winton Capital, one of the biggest quantitative hedge funds in the world, is also doubtful that AI represents a quantum leap for the investment industry. “I’m not a Luddite, we’re always interested in new ways to make money. But I have to be very sceptical because I constantly have world-class people showing me miracle cures that don’t actually work,” he says. 全球最大量化对冲基金之一温顿资本(Winton Capital)负责人戴维#8226;哈丁(David Harding)也怀疑,AI并不能给投资业带来重大飞跃。“我不是卢德分子(Luddite),我们总是对赚钱的新方式感兴趣。因为总有世界级的人物向我展示实际上并没有效果的灵丹妙药,我不得不对此深表怀疑,”他说。 Dramatic improvements in computing power have revolutionised the investment world, with algorithmic traders and investors increasingly influential across markets. Money is pouring into computer-driven hedge funds that have consistently managed to parse signals amid market noise. As a result many money managers are scrambling to hire computer scientists, often pitting them in direct competition for talent with Silicon Valley’s tech giants and hot start-ups. 计算能力的显著提升彻底改变了投资界,依据算法的交易商和投资者在市场上的影响力越来越大。大量资金涌入持续从市场杂音中分析出风向的计算机驱动对冲基金。这导致许多资金管理公司竞相雇佣计算机专家,直接与硅谷技术巨头和热门初创企业争夺人才。 AI is at the forefront of this. The field has also enjoyed several leaps forward in recent years. Most notably, Google’s DeepMind AI arm has created a programme that recently thrashed a legendary player of Go, an ancient Chinese game that is so complex that most experts previously reckoned it would take at least a decade before a computer could beat a human champion. AI处于领域的最前沿。近年来AI领域也经历了几次飞跃。最引人注目的是,谷歌(Google)旗下DeepMind的AI部门研发的程序,最近打败了一位著名围棋选手。围棋是一种古老的中国游戏,因为过于复杂,大多数专家此前都认为,计算机至少还需要10年才能打败人类围棋冠军。 The potentially wider applications of techniques used by the likes of DeepMind’s AlphaGo algorithm has fuelled optimism that investment management could be on the cusp of another technological revolution, possibly similar in scale to the electronification of markets in the 1970s and 1980s. DeepMind的AlphaGo这类算法所运用的技术或许还能得到更广泛的应用,这引发了有关投资管理可能即将迎来另一场技术革命的乐观情绪。在规模上,这场革命可能和上世纪七八十年代的市场电子化革命相仿。 “Machine learning and artificial intelligence is going to play a very large role in quant managers, but also with traditional asset managers that are aggressively expanding in this space,” says Osman Ali, a fund manager at Goldman Sachs Asset Management. “机器学习和人工智能将在量化资产管理中起到极大作用,但传统资产管理公司也会在这个领域大举扩张,”高盛(Goldman Sachs)资产管理部门的基金经理奥斯曼#8226;阿里(Osman Ali)表示。 Popular AI approaches such as machine learning can be used by computers to learn and develop autonomously. For example, a machine learning algorithm can learn to play and master a computer game such as Super Mario independently, at first playing the arcade classic randomly but quickly figuring out how the controls work and how to get to the end of the level. 计算机可以利用机器学习等流行的AI策略自主学习和发展。比如,一种机器学习算法可以独立上手和掌握如何玩《超级马里奥》(Super Mario)这样的游戏。一开始算法会随机地玩这款经典街机游戏,但很快算法就能摸清如何操作和通关。 There is therefore widesp enthusiasm over the potential of unleashing machine learning algos to find fleeting but profitable patterns in the vast sea of data. 因此,自由的机器学习算法在海量数据中寻找稍纵即逝的可盈利模式的潜能,引起人们的广泛兴趣。 “I think of algos as little children that can scale tremendously. And you can teach them to millions of books at the same time,” says Brad Betts, a former Nasa computer scientist working in BlackRock’s San Francisco-based Scientific Active Equity arm. “我认为算法就相当于拥有巨大潜力的幼童。你可以教它们同时阅读数百万本书,”美国国家航空航天局(NASA)前计算机科学家、现在供职于贝莱德(BlackRock)位于旧金山的“科学主动股票投资”部门的布拉德#8226;贝茨(Brad Betts)表示。 Yet scepticism, even among many quants, is still pervasive. They see areas such as machine learning and deep learning — the latter underpinned DeepMind’s Go exploits — merely as extensions or enhancements of techniques that have for long been in use. 然而,甚至是在很多量化分析师中,怀疑情绪依然普遍。在他们看来,机器学习和深度学习——后者撑了DeepMind的AlphaGo引人注目的成功——只不过是对已经投入使用很长时间的技术的扩展或加强。 “Lots of people use techniques that could be called machine learning for decades,” argues Robert Hillman, head of Neuron Capital. “There’s a huge difference between image recognition and using AI in markets. Will this be a paradigm change for investing? I don’t think so … It’s not a fundamental change, it’s an efficiency improvement.” “很多人使用了数十年的一些技术,都可以被称为机器学习技术,”Neuron Capital负责人罗伯特#8226;希尔曼(Robert Hillman)表示,“图片识别和把AI运用到市场之中存在巨大差异。这是否将带来投资的范式转变?我不这么认为……这不是根本性的变化,这是一种效率的提升。” Mr Kirk points out that most common AI approaches are focused on pattern recognition, such as telling the difference between a cat and a dog in an image. But markets are dominated by noise and chaos, the patterns are harder to find. 柯克指出,最常见的AI策略着重于模式识别,比如区分出图片中的一只猫和一只。但市场上充斥着杂音和乱流,要找到模式更为困难。 “As a geek I’m super-excited about AlphaGo, but it’s a big leap from beating a game with clearly defined rules and objectives and investing,” he says. “作为一名极客,AlphaGo让我超级兴奋,但从打赢一个有清晰规则和目标的游戏、到进行投资,中间还有巨大的跨度,”他说。 Even quants that are cautiously optimistic on the future of AI in investing warn of many pitfalls. Algorithms that may look ingenious and backrest superbly against historical data have a nasty habit of unravelling when confronted with unforgivingly fickle financial markets. 即使是对AI在投资界的应用前景抱谨慎乐观态度的量化分析师,也警告这个领域存在许多陷阱。一些看起来可能很巧妙、与历史数据完美契合的算法,在面对金融市场的反复无常时却常常出毛病。 “Playing Super Mario might not necessarily work for markets. If you hit the button you always know what will happen, but you don’t in markets,” says another quant at a large hedge fund. “It can take time for it to find the good trades and to optimise them. It can go through a lot of bad trades.” “能玩《超级马里奥》未必能驾驭市场。当你按下按键的时候,你总是知道会发生什么,但在市场上就不是这样了。”一家大型对冲基金中的另一名量化分析师表示,“算法可能需要时间才能找到好的交易机会并进行优化,可能先要经历很多糟糕的交易。” /201604/435174。
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