All the buzz at AI’s big shindig
Machine learning’s bigevent
“CORPORATE conferences still suck.”So read the T-shirt sported by Ben Recht, a professor at the University of California, Berkeley, as he collected an award at the Neural Information Processing Systems (NIPS) conference this week. Dr Recht, pictured above in lecture mode, was protesting against the flood of corporate money pouring into NIPS, aping the words Kurt Cobain wrote on a T-shirt when he appeared on the cover of Rolling Stone in1992.
“It’s not an academic conference anymore,” Dr Recht sayswistfully, perched in the Californian sun on the steps of the Long Beach Convention Centre. He complains that folk would rather go to corporate-sponsored parties these days (Intel’s featured Flo Rida, a rapper), than poster sessions.AI, it seems, is the new rock and roll.
( wistful: having or showing sad thoughts and feelings about something that you want to have or do and especially about something that made you happy in the past
这个教授虽然在AI的会议上拿了奖，但是他还是觉得现在同行的教授们喜欢参加这种商业组织举办的会议，而不是参加单纯的学术会议，说白了就是嫌大家铜臭味太重了...
AI 是现在最火热的话题，是明星，是万千宠爱)
NIPS began in 1987 as a humble little conference on an obscure branch of machine learning called neural networks. It spent the first 13 years of its life in Denver, then moved to Vancouver for a decade. It used to be a quiet affair, with a few hundred mathy computer scientists coming together to explain how they had solved some abstract problem in a new way.
Then, at the 2003 conference, Geoffrey Hinton, a British polymath, and acabalof AI researchers founded the Neural Computation & Adaptive Perception (NCAP) working group.As aproponentof neural networks, Dr Hinton and the group helped accelerate the pace of research into a form of machine learning known as deep learning,leading to huge advances in image recognition in 2012. Deep learning, whichstacksmany neural networks on top of one another to learn the features of giant databases, now powers the image-processing operations of firms like Facebook and Google. As machines, trained with heaps of data to develop clever algorithms, have become capable of carrying out more and more tasks, so interest has grown. Google was sponsoring NIPS by 2010, and this year all of the world’s largest tech firms could be found on the sponsor sheet.
（ cabal: a small group of people who work together secretly
machine learning 的迅速发展让图像识别有突破性进展，现在很多科技大头公司都开始赞助这个会议，google从2010年开始赞助，今年基本科技大头都在赞助墙上。教授的抱怨可以理解，吃人家的嘴短，就该听别人的话去研究，而不是单纯地研究自己的兴趣领域了）
For the 7,850 attendees, the big draw is the algorithms presented in halls heaving with mostly male bodies (90% of the authors of NIPS papers were male this year, a gender imbalance widely found in science). They hang on every word of AI wisdom imparted byluminariesfrom Google and Microsoft;pore over a dizzying number of advances (laid out in more than 670 published papers)from the likes of Facebook, DeepMind (a unit of Google) and Tencent; and devour stories of novel ways to train machines to perform useful tasks.
Those stories come not just from the big names of technology, but also from moreold-fangled companies, such as Target, abricks-and-mortarAmerican retailer. Bryan Copeland, one of the firm’s data scientists in Minneapolis, says he is trying to apply machine-vision algorithms to the video feeds from the cameras inTarget’s stores. Retailers employ behavioural experts to watch such videos so they can work out how people use their stores and where to place goods to the best advantage. With the right algorithms, Target could automate the process and run it in real time.
(luminary: a very famous or successful person: celebrity
pore over: to read or study something very carefully
老牌零售商Target运用算法分析店里的视频，然后分析出怎么摆放货品最好，这个真的是牛逼...)
Many firms were also putting on a show as part of the battle for AI talent. They include Mercedes-Benz, a first-time sponsor, which is trying to recruit data scientists to work on its autonomous cars. The German producer is already some way down the road, with Rigel Smiroldo, the firm’s machine-learning boss in North America, happy to recite how the E-class Mercedes he drove to NIPS handled 250miles of highway driving without him needing to intervene.
Mr Smiroldo does put his finger on one of the main trends at this year’s NIPS:the merging of Bayesian statistics with deep learning.Instead of algorithms presenting deterministic “yes” or “no” results to queries, new systems are able to offer up moreprobabilistic inferencesabout the world. This is particularly useful for Mercedes-Benz, which needs driverless cars that can handle tricky situations. Instead of an algorithm simply determining if an object in the road is a pedestrian or a plastic bag, a system using Bayesian learning offers a more nuanced view that will allow AI systems to handle uncertainty better.
(大家都在为争夺AI方面人才而上演一场大show，奔驰在招数据专家投入研究它的无人驾驶汽车
像无人驾驶汽车就需要更复杂的算法和辨识功能来应对处理路面上发生的各种情况）
Netflix already uses data science to recommend shows to its subscribers. Nirmal Govind, who develops algorithms at the firm, was on the lookout at NIPS for new, improved versions that can handle imagery and video.The firm is particularly interested in automating the generation of promotional material around its original shows and finding ways to make that material more engaging.
（Netflix已经在营使用数据来推荐节目给订阅者，而且对自动生成宣传推广材料，使这些材料和观众更加地互动这个领域特别感兴趣）
Besides fundamental algorithms which firms hope to apply to their own operations, NIPS is also home to applied research, particularly in healthcare and biology. Becks Simpson from Maxwell MRI, a startup from Brisbane in Australia, showed a way tocombine magnetic resonance imaging with deep learning to improve the diagnosis of prostate cancer. Elisabeth Rumetshofer from Johannes Kepler University Linz presented a system that could automatically recognise and track proteins in cells, helping toilluminatethe underlying biology. A team from Duke University in North Carolina had used machine learning todetect cervical cancer automatically using a pocket colposcope, to the same level of accuracy as a human expert.Some used AI to mine doctors’ notes to estimate the chances that a patient will be readmitted to hospital, to categorise and understand the allergic reactions of children and to model the geographic distribution of naloxone, which can help block the effects of opioids, in order to get a better grip on the use of such drugs.
（人工智能在医学和生物领域研究有极大的推进作用，本段列举了4-5个例子来说明）
Other applications range from researchers at the Federal University Lokoja in Nigeria trying to use machine learning to identify potential child suicide bombers to the Donders Institute in the Netherlands presenting a system that can reconstruct pictures of faces that a person sees simply by scanning their brains. Google researchers used machine learning to hide a complete image inside another picture of the same size. What they might do with that remains to be seen.
（还有用人工智能来寻找自杀式炸弹儿童的！）
New hardware for machine learning was on display, too. At its party Intel unveiled its latest chip dedicated to solving AI problems. NVIDIA, a chipmaking rival whose share price has increased ninefold in the past three years thanks to sales of its graphical-processing units for deep learning, displayed its latest wares.Graphcore, a British startup, caused particular waves. It presentedbenchmarksfor its chip’s performance on common machine-learning tasks that tripled speeds for image recognition and delivered a claimed 200 times improvement over NVIDIA for the kinds of machine learning required for speech-recognition and translation applications.
Among older hands at NIPS, especially those who can remember its origins, there is a sense that the corporate obsession with machine learning will not last. They should not be so sure. The systems being developed are just beginning to be a broadly useful technology, andnew algorithms presented at the conference are likely to be adopted rapidly.Powerful computers and large volumes of data lie waiting forexploitation. The world’s most valuable companies have grasped the power of machine learning, and they are unlikely to let go.
（NVIDIA 因为它家的芯片在识别图像上比其他快，销售大增股价大涨
在这个会议上的新算法很快会被广泛使用，而大公司们一旦抓住了这个机会就不可能放手的了）
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Results
Lexile®Measure: 1400L - 1500L
Mean Sentence Length: 24.45
Mean Log Word Frequency: 3.14
Word Count: 929 （最后三段没有放进去测，因为蓝思值网站测试不能超过1000，所以本文是1150多字）
这篇文章的蓝思值是在1400-1500L， 适合英语专业大三大四的水平学习，应该是经济学人里属于普通难度，读起来一点不难，可能句型比较复杂。
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