富阳中山新地址
时间:2019年07月19日 16:35:12

Microsoft has hit out at the US government’s “stockpiling” of cyber weapons for facilitating attacks such as the WannaCry ransomware that has run rampant in recent days.微软(Microsoft)向美国政府发出了抨击,指责其“囤积”网络武器、为“想哭”(WannaCry)这类勒索软件发起的攻击提供了方便。最近几天,“想哭”病毒在全球肆虐。“The governments of the world should treat this attack as a wake-up call,” Brad Smith, Microsoft’s general counsel, wrote in a strongly worded blog post on Sunday afternoon.周日下午,微软法律总顾问布拉德?史密斯(Brad Smith)在一篇措辞激烈的客文章中写道:“全球各国政府应该把这次攻击视为一记警钟。”In its statement, Microsoft for the first time confirmed publicly what security analysts and intelligence officials would only say in private: that the technique hackers used to distribute WannaCry was originally developed by, and later stolen from, the US National Security Agency.微软在其声明中首次公开实了安全分析师和情报官员只会私下吐露的说法:黑客用来散播“想哭”病毒的技术,是最初由美国国家安全局(NSA)开发的,后来又被人从该局窃走。“This attack provides yet another example of why the stockpiling of vulnerabilities by governments is such a problem,” Mr Smith wrote, referencing the disclosure of apparent exploits used by the CIA by WikiLeaks.史密斯写道:“这次攻击是又一个例子,明了为何政府囤积软件漏洞是如此重大的问题。”这句话指向了维基解密(WikiLeaks)披露的、美国中央情报局(CIA)对明显的漏洞加以利用的行为。“Repeatedly, exploits in the hands of governments have leaked into the public domain and caused widesp damage,” he said. “An equivalent scenario with conventional weapons would be the US military having some of its Tomahawk missiles stolen.”他说:“政府手中掌握的漏洞泄露至公共领域、并造成大范围破坏,这样的情况已多次发生。若以常规武器来类比,这就好像是美国军方部分战斧导弹(Tomahawk)被窃。”While the leaked NSA tools were not used to create the ransomware itself, they did help hackers to accelerate its distribution, creating havoc for organisations around the world.尽管从NSA泄露的工具未被用来创造“想哭”病毒本身,但这些工具确实帮助黑客加速了该病毒的传播,对世界各地机构造成了严重破坏。Edward Snowden, the NSA whistleblower who is wanted in the US for leaking thousands of classified documents, called Microsoft’s comments “extraordinary”.NSA泄密者爱德华?斯诺登(Edward Snowden)称,微软方面的这些话“非同寻常”。斯诺登因泄露了成千上万份机密文件而被美国通缉。“Until this weekend#39;s attack, Microsoft declined to officially confirm this, as US Gov refused to confirm or deny this was their exploit,” he said in a tweet.他在Twitter上发帖称:“直到周末的攻击发生前,微软一直拒绝公开实这一点,美国政府则既不愿实也不否认这个漏洞出自他们之手。” /201705/509398

A new app which monitors facial expressions to assess mood and then suggests the perfect food to lift the spirits, or quell anxiety, has been developed by scientists at Oxford University.牛津大学的科学家们近日研发出一款新应用,它能通过检测面部表情判断你的心情,进而为你推荐提振精神或平息焦虑的最佳食物。The new app scans the face for signs of emotions, such as downturned lips and eyes and frown lines, and can often pick up on hidden feelings that a person may be ignoring.这款新应用通过扫描面部表情获取情绪特征,比如下垂的嘴角、眼睛、眉间皱纹等,捕捉人们可能忽略的隐藏情绪。;Face mapping can provide a more accurate and objective assessment of a person#39;s mood or emotional state than they can,; said Prof Spence.史彭斯称:“相比于你自己,表情图谱能对你的心情或情绪状态做出更加准确、客观的评估。”;Often people are not able to say how they are feeling or just don#39;t feel they want to. After all, we might know that we are in a bad mood, but not know why.“人们往往无法表达自己的感受,或者只是不想表达。因为我们可能知道自己心情很差,但是却没有察觉到原因。”;There is a growing body of evidence that demonstrates that your mood has a significant impact on your taste and smell - it can deaden or liven the effect of both - a reverse of this is also believed to be true, that food can have a number of affects on your mood.;“越来越多的据表明,情绪对人的味觉和嗅觉有很大影响,它可能抑制或刺激味觉和嗅觉反应。反之,食物也能对人的情绪产生一定影响。”He said that mood and emotion can affect the #39;sensory discriminatory aspects of tasting#39; which is why people often stop eating following a relationship break-up or when they are grieving because food simply does not taste as good as during happier times.史彭斯表示,情绪可以让人们“品尝到的感觉产生区别”,人在分手或感到悲伤时往往不吃东西,就是因为这时候食物尝起来不像心情好时那么好吃。;This is at the very cutting edge of what technology and science can do but in the future it is likely to become much more the norm,; he added.史彭斯称,“情绪图谱现在还是尖端科技,但是未来它将变得非常普及。”The app can detect anger, disgust, fear, surprise, sadness and joy and makes suggestions according to what it finds. For example, an angry face suggests that a person is stressed and so would benefit from calming foods, such as dark chocolate and nuts which contain magnesium.该应用程序可以检测到愤怒、厌恶、恐惧、惊喜、悲伤和喜悦等情绪,并根据检测结果推荐菜谱。比如,表情愤怒说明一个人有压力,因此适合使人镇静的食物,比如含有镁元素的黑巧克力、坚果。In contrast, people who are excited may benefit from blood sugar regulating foods such as whole grains and legumes.相反,心情激动的人可能适宜吃调节血糖的食物,比如全麦食品和豆类食品。Nutritionist Ruth Tongue, said: ;Not only do our moods affect the foods we choose to eat, but the foods we eat can in turn help us to feel happier, energised, relaxed, focused or fired up and y for the day.营养学家鲁斯.唐称:“情绪能影响我们的饮食选择,我们吃的食物也能反过来让自己更快乐、精力充沛、心情放松、精神集中、四射,让我们准备好迎接每一天。;It#39;s important to recognise the relationship between the foods we eat and our moods so that we can ensure that we#39;re looking after not only our physical, but also our emotional wellbeing.;“关键要认识到我们吃的食物和情绪之间的关系,这样便可以确保自己的生理和心理健康。”Graham Corfield, UK Managing Director of Just Eat added: ;We know that mood plays a part in what we choose to eat, so innovations like Emotion-Analysis-Technology, while fun, also serve a real purpose. ;Just Eat英国区总经理格雷厄姆.科菲尔德说:“情绪影响我们对食物的选择,所以像情绪分析这样的科技创新不仅有趣,而且有实用价值。”;Ultimately we want people be thinking about food and the impact it can have on their daily life.;“我们的根本目的是让人们关心食物及其对日常生活的影响。” /201612/481868

TThere are many ways of being smart that aren’t smart like us.” These are the words of Patrick Winston, a leading voice in the field of artificial intelligence. Although his idea is simple, its significance has been lost on most people thinking about the future of work. Yet this is the feature of AI that ought to preoccupy us the most. “(人工智能)有很多与人类不同的智能方式。”这是人工智能领域的领军人物帕特里克#8226;温斯顿(Patrick Winston)说过的话。尽管他的观点很简单,但多数思考工作未来的人没有领悟到它的含义。然而,他所说的是我们应该最为关注的人工智能的一个特征。 From the 1950s to the 1980s, during the “first wave” of AI research, it was generally thought that the best way to build systems capable of performing tasks to the level of human experts or higher was to copy the way that experts worked. But there was a problem: human experts often struggled to articulate how they performed many tasks. 从上世纪50年代到80年代,在人工智能研究“第一次浪潮”时期,人们一般认为,创建能够将任务执行到达到人类专家水平或更高水平的系统的最佳方法,是复制专家们的工作方式。但问题是:对于很多任务,人类专家都常常难以说出他们是如何执行的。 Chess-playing was a good example. When researchers sat down with grandmasters and asked them to explain how they played such fine chess, the answers were useless. Some players appealed to “intuition”, others to “experience”. Many said they did not really know at all. How could researchers build a chess-playing system to beat a grandmaster if the best players themselves could not explain how they were so good? 下棋是一个很好的例子。当研究人员与大师们坐下来,请他们解释如何把棋下得这么好时,都是毫无用处的。一些大师认为是“直觉”,还有一些人则归因于“经验”。很多人表示,他们根本不知道原因。如果最优秀的棋手自己都不能解释他们为何如此出色,那么研究人员如何能够创建一个可以打败大师的下棋系统? A turning point came in 1997. Garry Kasparov, the then world chess champion, was beaten by IBM’s supercomputer, Deep Blue. What was most remarkable was how the system did it. Deep Blue did not share Mr Kasparov’s “intuition” or “experience”. It won by dint of sheer processing power and massive data storage capability. 1997年,一个转折点出现了。当时的国际象棋世界冠军加里#8226;卡斯帕罗夫(Garry Kasparov)被IBM的超级计算机“深蓝”(Deep Blue)击败。最引人瞩目的是这个电脑系统击败人类的方法。“深蓝”没有卡斯帕罗夫的“直觉”或“经验”。它是凭借强大的处理能力和大规模数据存储能力获胜的。 There then followed AI’s “second wave”, which we are in today. Google’s AI, AlphaGo, has just finished a five-game series of Go against Lee Se-dol, perhaps the best player of the game alive. Until recently, most researchers thought we were at least ten years away from a machine victory. Yet AlphaGo beat Mr Lee in four of the five games. It did not have his genius or strategic insight; it relied on what are known as “deep neural networks”, driven, once again, by processing power and data storage. Like Deep Blue, AlphaGo was in a sense playing a different game. 接着出现了人工智能的“第二次浪潮”,就是现在。谷歌(Google)的人工智能程序AlphaGo刚刚在5局的围棋对弈中击败或许称得上目前最优秀的棋手李世石(Lee Se-dol)。不久以前,多数研究人员还认为,我们距离机器获胜至少还有10年的时间。然而,AlphaGo在与李世石的5局交锋中,有4局获胜。它没有李世石的天赋或战略眼光;它凭借的是被称为“深度神经网络”的系统,同样的,该系统是由处理能力和数据存储能力驱动。与“深蓝”一样,从某种程度上来说,AlphaGo玩的是不同的游戏。 In retrospect, we can see that early researchers made the mistake we now call the “AI fallacy”: they assumed that the only way to perform a task to the standard of a human expert is to replicate the approach of human specialists. Today, many commentators are repeating the same mistake in thinking about the future of work. They fail to realise that in the future systems will out-perform human beings not by copying the best human experts, but by performing tasks in very different ways. 回过头来看,我们能够看出早期的研究人员犯下了我们现在称之为“人工智能谬论”的错误:他们认为,要把一项任务执行到达到人类专家的标准,唯一途径是复制人类专家的方法。如今,很多人士在思考工作的未来时也在重复同样的错误。他们未能意识到,将来系统战胜人类不是通过模仿最优秀的人类专家,而是通过以截然不同的方式执行任务。 Consider the legal world. Daniel Martin Katz, a law professor, has designed a system to predict the voting behaviour of the US Supreme Court. It can perform as well as most specialists, but it does not mirror the judgement of a human being. Instead it draws on data that captures six decades of Court behaviour. 以法律界为例。法学教授丹尼尔#8226;马丁#8226;卡茨(Daniel Martin Katz)设计了一个预测美国最高法院投票行为的系统。它可以预测得与多数专家一样好,但它并不是模仿人类的判断。它利用的是记录美国最高法院60年行为的数据。 We see similar developments in other parts of the economy. Millions of people in the US use online tax preparation software, not a personal interaction with an accountant, to file their returns. Autodesk’s “Project Dreamcatcher” generates computerised designs, not by mimicking the creativity of an architect, but by sifting through a vast number of possible designs and selecting the best option. And IBM’s Watson helps to diagnose cancer, not by copying the reasoning of a doctor, but by trawling enormous bodies of medical data. 我们还在其他经济领域看到了类似的事情。在美国,数百万人利用在线报税软件,而不是亲自与会计师会面,来提交纳税申报表。Autodesk的“Project Dreamcatcher”会通过筛选大量可能的设计以及选择最佳方案(而不是模仿建筑师的创意)来生成电脑化设计。IBM的超级计算机“沃森”(Watson)通过查阅海量医疗数据(而非复制医生的推理方法)帮助诊断癌症。 All this does not herald the “end of work”. Rather, it points to a future that is very different from the one most experts are predicting. It is often said that because machines cannot “think” like human beings, they can never be creative; that because they cannot “reason” like human beings, they can never exercise judgement; or that because they cannot “feel” like human beings they can never be empathetic. For these reasons, it is claimed, there are a great many tasks that will always require human beings to perform them. 所有这一切都没有预示“工作的终结”。它只是表明未来与多数专家的预测截然不同。人们经常说,因为机器无法像人类那样“思考”,所以它们永远无法变得有创意;因为它们无法像人类那样“推理”,所以它们永远无法做出判断;因为它们无法像人类那样“感受”,所以它们永远无法变得有同情心。出于这些原因,有人声称,很多任务永远需要人类去执行。 But this is to fail to grasp that tomorrow’s systems will handle many tasks that today require creativity, judgement or empathy, not by copying us, but by working in entirely different, unhuman ways. The set of tasks reserved exclusively for human beings is likely to be much smaller than many expect. 但这未能意识到,未来的系统将处理很多现在需要创意、判断或同情的任务,不是通过模仿我们,而是通过用一种完全不同的非人类的方式工作。人类专属的任务可能会比很多人预测的少得多。 /201603/433922


文章编辑: 赶集报
>>图片新闻