譯文丨大數(shù)據(jù)技術(shù)PK人腦的這些方面，誰更勝一籌？

來源：數(shù)據(jù)觀 時間：2017-03-29 16:43:17 作者：燈塔大數(shù)據(jù)

?挪威卑爾根Uni Research公司的科學(xué)家Eirik Thorsnes表示：“計算機的高級圖像識別是一項復(fù)雜繁瑣的過程，你必須讓計算機模仿人類大腦，從大量無效信息中提取出有效信息?！?/p>

?Uni Research公司大數(shù)據(jù)分析中心致力于研發(fā)大數(shù)據(jù)在研究和商業(yè)領(lǐng)域的應(yīng)用戰(zhàn)略。該大數(shù)據(jù)中心還在開發(fā)高級計算機算力，模仿人腦進行復(fù)雜信息處理。

?在很多方面，人腦的信息處理能力和工作方法都比電腦更加強大，但是在有些方面，電腦的表現(xiàn)比人腦更好。

?“近幾年來我們?nèi)〉昧撕艽蟮陌l(fā)展，在圖像識別和圖像分析方面，我們的技術(shù)已經(jīng)超過了人腦。電腦在觀看大量極盡相似的圖片時不會感到疲憊，而且還能夠發(fā)現(xiàn)人眼發(fā)現(xiàn)不出的細微差別。隨著我們的技術(shù)日益成熟，處理大量圖片和視頻將更加方便快捷，很多人類社會中常見的流程都能得到改進和優(yōu)化，”大數(shù)據(jù)分析中心負責(zé)人Thorsnes解釋道。

?識別重要目標

?Thorsnes和大數(shù)據(jù)分析中心的工作伙伴預(yù)測，圖像識別和圖像分析在醫(yī)療衛(wèi)生、環(huán)境監(jiān)測、海底調(diào)查和衛(wèi)星圖像分析等領(lǐng)域的重要性將日益凸顯。

?大數(shù)據(jù)在圖像分析和圖像識別方面的應(yīng)用，對硬件、算法和軟件的都提出了很高的要求，同時，還要求管理者擁有卓越的能力，能夠找到最佳監(jiān)測途徑。

?Thorsnes 說：“在未來幾年，對這項技術(shù)的需求只會不斷增加，但是它并不是‘即插即用’、能快速上手的技術(shù)。我們的研究員在處理大規(guī)模數(shù)據(jù)方面已經(jīng)積累的足夠的專業(yè)知識和經(jīng)驗，才能抓住最核心的應(yīng)用技術(shù)。”

?Uni Research計算部門的研究員開發(fā)出一套計算機系統(tǒng)，能夠在圖像中準確識別目標，并在圖像中發(fā)現(xiàn)具有重要性的對象。

?人工智能、圖像識別和機器學(xué)習(xí)方面的專家Alla Sapronova說：

?“我們訓(xùn)練電腦的方式和教孩子是一樣的。我向電腦輸入信號模式，并告訴它我們想要什么樣的輸出信息。我就一直重復(fù)這個過程，直到系統(tǒng)開始自動識別信號模式。之后，我再給電腦展示一個新的輸入信號，比如一張電腦沒有識別過的圖片，看它是否能夠看懂?！边@種機器學(xué)習(xí)技術(shù)有很大應(yīng)用空間，比如，用手機相機識別笑臉。

?自閉癥兒童的音樂療法

?這項技術(shù)的高級應(yīng)用還包括醫(yī)藥領(lǐng)域，它可以分析身體疾病的外部信號，與臨床醫(yī)生保持溝通，檢查并報告身體狀況。

?“我們已經(jīng)與GAMUT合作開展了一個試點項目，分析自閉癥兒童接受音樂治療的視頻錄像。通常，醫(yī)生必須花費幾個小時觀看視頻錄像，才能找到最能揭示患者精神狀態(tài)或者最能展示治療效果的鏡頭。

?如果我們教電腦去識別這些畫面，那么電腦就能去自動尋找和發(fā)現(xiàn)醫(yī)生想要的鏡頭，盡管到目前為止電腦還不能夠?qū)λ鼈冞M行排序。我們相信在這個領(lǐng)域，我們的技術(shù)擁有很大潛力?！盩horsnes說道。

?在另一個項目中，研究人員將挪威卑爾根Danmarksplass十字路口的網(wǎng)絡(luò)攝像頭作為實驗對象，教電腦識別經(jīng)過該路口的車輛類型和數(shù)量。

?由此可見，這項技術(shù)可應(yīng)用在交通領(lǐng)域，幫助人們進行交通布局規(guī)劃和相關(guān)決策。此外，冬天某些時間段，Danmarksplass的空氣狀況非常差，Thorsnes認為，引入此項技術(shù)幫助優(yōu)化交通布局之后，環(huán)境質(zhì)量也能得到很好地改善。

?Thorsnes認為圖像分析技術(shù)在改善交通安全方面具有巨大潛力，特別是在監(jiān)測公路和隧道方面。計算機可以監(jiān)測到不同的交通狀況，包括車輛逆行、火災(zāi)、亂停的廢棄汽車、隧道里的行人等。

?Thorsnes 說：“我們還能讓電腦監(jiān)測主要公路邊已發(fā)生滑坡的山體，讓電腦識別什么樣的山體變化是即將發(fā)生滑坡的征兆?！?/p>

?監(jiān)測漁場的“漏網(wǎng)之魚”

?由Klaus Johannsen 率領(lǐng)的Uni Research計算與大數(shù)據(jù)分析中心的團隊與Uni Research的環(huán)境部開展合作，共同監(jiān)測繪制鮭魚和鱒魚在河口的運動情況。

?“我們在河口的位置安裝了攝像頭，讓電腦記錄這些魚的游動軌跡，并識別魚是野生的還是養(yǎng)殖的。這樣，我們就能監(jiān)測是否有魚從漁場逃出來?！盩horsnes解釋道。

?監(jiān)測技術(shù)近幾年來取得了重大進展，其中一部分原因就是“人工智能算法的再發(fā)現(xiàn)”。

?我們將行業(yè)的需求和人工智能理念結(jié)合起來，同時，博彩業(yè)強大的計算機處理能力和復(fù)雜圖形處理系統(tǒng)也被我們拿來進行數(shù)據(jù)分析。

?“以前，這些過程都是由人來完成的，你必須要找一個人坐在那里盯著屏幕，看好幾個小時的醫(yī)療分析錄像或者交通路況錄像，” Thorsnes說。

?這種算法來自“深度學(xué)習(xí)”，是具有“文藝復(fù)興式”重大意義的突破。我們的處理器已經(jīng)非常先進，供我們分析的材料也越來越豐富，而且我們的計算機也已經(jīng)擁有足夠的計算能力來處理更加復(fù)雜的問題，學(xué)習(xí)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：本文摘自數(shù)據(jù)觀入駐自媒體—燈塔大數(shù)據(jù)，轉(zhuǎn)載請注明來源，微信搜索“數(shù)據(jù)觀”獲取更多大數(shù)據(jù)資訊。

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責(zé)任編輯：陳近梅