译文丨大数据技术PK人脑的这些方面,谁更胜一筹?

译文丨大数据技术PK人脑的这些方面,谁更胜一筹?优质

挪威卑尔根Uni Research公司的科学家Eirik Thorsnes表示:“计算机的高级图像识别是一项复杂繁琐的过程,你必须让计算机模仿人类大脑,从大量无效信息中提取出有效信息。” Uni Research公司大数据分析中心致力于研发大数据在研究和商业领域的应用战略。该大数据中心还在开发高级计算机算力,模仿人脑进行复杂信息处理。 在很多方面,人脑的信息处理能力和工作方法都比电脑更加强大,但是在有些方面,电脑的表现比人脑更好。 “近几年来我们取得了很大的发展,在图像识别和图像分析方面,我们的技术已经超过了人脑。电脑在观看大量极尽相似的图片时不会感到疲惫,而且还能够发现人眼发现不出的细微差别。随着我们的技术日益成熟,处理大量图片和视频将更加方便快捷,很多人类社会中常见的流程都能得到改进和优化,”大数据分析中心负责人Thorsnes解释道。 识别重要目标 Thorsnes和大数据分析中心的工作伙伴预测,图像识别和图像分析在医疗卫生、环境监测、海底调查和卫星图像分析等领域的重要性将日益凸显。 大数据在图像分析和图像识别方面的应用,对硬件、算法和软件的都提出了很高的要求,同时,还要求管理者拥有卓越的能力,能够找到最佳监测途径。 Thorsnes 说:“在未来几年,对这项技术的需求只会不断增加,但是它并不是‘即插即用’、能快速上手的技术。我们的研究员在处理大规模数据方面已经积累的足够的专业知识和经验,才能抓住最核心的应用技术。” Uni Research计算部门的研究员开发出一套计算机系统,能够在图像中准确识别目标,并在图像中发现具有重要性的对象。 人工智能、图像识别和机器学习方面的专家Alla Sapronova说: “我们训练电脑的方式和教孩子是一样的。我向电脑输入信号模式,并告诉它我们想要什么样的输出信息。我就一直重复这个过程,直到系统开始自动识别信号模式。之后,我再给电脑展示一个新的输入信号,比如一张电脑没有识别过的图片,看它是否能够看懂。”这种机器学习技术有很大应用空间,比如,用手机相机识别笑脸。 自闭症儿童的音乐疗法 这项技术的高级应用还包括医药领域,它可以分析身体疾病的外部信号,与临床医生保持沟通,检查并报告身体状况。 “我们已经与GAMUT合作开展了一个试点项目,分析自闭症儿童接受音乐治疗的视频录像。通常,医生必须花费几个小时观看视频录像,才能找到最能揭示患者精神状态或者最能展示治疗效果的镜头。 如果我们教电脑去识别这些画面,那么电脑就能去自动寻找和发现医生想要的镜头,尽管到目前为止电脑还不能够对它们进行排序。我们相信在这个领域,我们的技术拥有很大潜力。”Thorsnes说道。 在另一个项目中,研究人员将挪威卑尔根Danmarksplass十字路口的网络摄像头作为实验对象,教电脑识别经过该路口的车辆类型和数量。 由此可见,这项技术可应用在交通领域,帮助人们进行交通布局规划和相关决策。此外,冬天某些时间段,Danmarksplass的空气状况非常差,Thorsnes认为,引入此项技术帮助优化交通布局之后,环境质量也能得到很好地改善。 Thorsnes认为图像分析技术在改善交通安全方面具有巨大潜力,特别是在监测公路和隧道方面。计算机可以监测到不同的交通状况,包括车辆逆行、火灾、乱停的废弃汽车、隧道里的行人等。 Thorsnes 说:“我们还能让电脑监测主要公路边已发生滑坡的山体,让电脑识别什么样的山体变化是即将发生滑坡的征兆。” 监测渔场的“漏网之鱼” 由Klaus Johannsen 率领的Uni Research计算与大数据分析中心的团队与Uni Research的环境部开展合作,共同监测绘制鲑鱼和鳟鱼在河口的运动情况。 “我们在河口的位置安装了摄像头,让电脑记录这些鱼的游动轨迹,并识别鱼是野生的还是养殖的。这样,我们就能监测是否有鱼从渔场逃出来。”Thorsnes解释道。 监测技术近几年来取得了重大进展,其中一部分原因就是“人工智能算法的再发现”。 我们将行业的需求和人工智能理念结合起来,同时,博彩业强大的计算机处理能力和复杂图形处理系统也被我们拿来进行数据分析。 “以前,这些过程都是由人来完成的,你必须要找一个人坐在那里盯着屏幕,看好几个小时的医疗分析录像或者交通路况录像,” Thorsnes说。 这种算法来自“深度学习”,是具有“文艺复兴式”重大意义的突破。我们的处理器已经非常先进,供我们分析的材料也越来越丰富,而且我们的计算机也已经拥有足够的计算能力来处理更加复杂的问题,学习更“深度”的算法。 英文原文 Using big data to analyze images, videobetter than the human brain Improving traffic safety, better healthservices and environmental benefits -- Big Data experts see a wide range ofpossibilities for advanced image analysis and recognition technology. "Advanced image recognition bycomputers is the result of a great deal of very demanding work. You have tomimic the way the human brain distinguishes significant from unimportantinformation," says Eirik Thorsnes at Uni Research in Bergen, Norway. Thorsnes heads a group in the company'sCentre for Big Data Analysis focus area, which develops strategies for use ofbig data for research and commercial purposes. The Centre also works ondeveloping advanced computing power that works in the same complex way as thehuman brain. In many areas, the human brain's fantasticcapacity and working methods will continue to outperform computers, but thereare some areas where computers can do things better. "There has been a tremendousdevelopment in recent years, and we are now surpassing the human level in termsof image recognition and analysis. After all, computers never get tired oflooking at near-identical images and may be capable of noticing even thetiniest nuances that we humans cannot see. In addition, as it gets easier toanalyse large volumes of images and video, many processes in society can beimproved and optimised," Thorsnes explains. Recognise which objects are importantThorsnes and his colleagues at the Centre for Big Data Analysis predict thatimage recognition and analysis will become increasingly important in areas suchas health care, environmental monitoring, seabed surveys and satellite images. Using big data in image analysis andrecognition requires a combination of good hardware, algorithms (formulae) andsoftware, as well as people who manage to recognise the best approaches. "The need for this kind of technologywill only increase in coming years, but it is not 'plug and play'. Ourresearchers have developed specialised knowledge about handling huge amounts ofdata, and thus how essential knowledge can be identified," says Thorsnes. Researchers in the department Uni ResearchComputing develop computer systems that learn to recognise objects andrecognise which objects are important in the image. Alla Sapronova is an expert in artificialintelligence, image recognition and machine learning: "I train computers in the same way weteach children. I show the computer patterns of input signals and tell it whatI expect the output signal to be. I repeat this process until the system beginsto recognise the patterns. Then I show the computer an input signal, such as animage, that it has not seen before and test whether the system understands whatit is," Sapronova explains. For example, on a relatively simple level,this kind of machine learning has resulted in smile recognition technology formobile phone cameras. Autistic children undergoing music therapyMore advanced areas of application include medicine, with analysis of externalbodily signs of illness, or the detection of positive / negative situations inconsultation with a therapist. "We have run a pilot project withGAMUT, with analysis of video footage of autistic children undergoing musictherapy. Normally, the therapist would have to spend hours reviewing thefootage to identify the exact moment that best reveals the status or progressof the patient. However, if we teach a computer what constitutes an interestingmoment, it will be able to find and select them, although to date computerscannot rank them. There is great potential for further development in asubsequent project," says Thorsnes. In another project, the researchers used apublicly available webcam at Danmarksplass, Bergen's busiest road intersection,as a starting point to teach computers to register how many and what types ofvehicles passed through the junction during the course of the day. This allows identification of trafficpatterns, which can then be used in planning and decision-making. In addition,at times the air quality at Danmarksplass is very poor in winter, and Thorsnesenvisages that better mapping of the traffic could also provide a basis forenvironmental improvements. However, he believes that at the currenttime image analysis has the greatest potential in improving traffic safety,which is basically a matter of monitoring selected stretches of roads ortunnels. Computers could detect a range of different situations, including carstravelling in the wrong direction, fire, abandoned cars, people inside tunnels,etc. "It will also be possible to getcomputers to monitor slopes susceptible to landslides along major roads, andteach the computers to recognise which changes in the landscape might imply anincreased risk of a landslide," says Thorsnes. Monitor the incidence of escapees from fishfarms Uni Research Computing and the Centre for Big Data Analysis, headed byresearch director Klaus Johannsen, have also worked on a project mapping themovements of salmon and trout at the mouth of a river. This work was done incollaboration with another department in the company, Uni Research Environment. "A camera was installed at the mouthof the river, and the computer was trained to record what kind of fish passed,and whether it was a wild fish or a farmed fish. In this way, we can monitorthe incidence of escapees from fish farms, among other things," saysThorsnes. Part of the reason that detectiontechnology has made such good headway in recent years is what Thorsnes calls arediscovery of algorithms for artificial intelligence. The industry's needs and some good oldartificial intelligence ideas found one another at the same time as massivecomputing power and sophisticated graphic processors from the gaming industrybecame available for use in analyses. "Traditionally, these kinds ofanalyses have been carried out by people who have to sit and watch hours ofvideo footage, for example medical analysis or traffic in tunnels," saysThorsnes. The algorithms that have had something of arenaissance come from what is now called 'deep learning', because we now haveenough computing power thanks to advanced processors and access to interestingmaterial to be able to teach more advanced and 'deeper' algorithms. 注:本文摘自数据观入驻自媒体—灯塔大数据,转载请注明来源,微信搜索“数据观”获取更多大数据资讯。 

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