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原創譯文|2017年大數據年中盤點——預測的趨勢現況如何

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本文為燈塔大數據原創內容,歡迎個人轉載至朋友圈,其他機構轉載請在文章開頭標註:

2016年底,我們對大數據在新一年的發展趨勢做出了預測。當時與我們的「大數據書獃子網路」進行了充分討論,最後得到了一份預測報告。現在2017年已經過去一半了,我們想回顧一下我們去年年底時發布的預測,看看當時水晶球告訴我們的結果到底準不準確。

2017年已經過半,是時候回顧一下前半年大數據行業的發展情況

預測一:機器學習、人工智慧和物聯網將普及

現實:2017年以來,機器學習,人工智慧,以及物聯網行業的發展速度一直居高不下,不論是從消費者角度還是從商業角度來看,這三者的應用領域得到了迅速拓展。

IBM的Watson已經家喻戶曉,人工智慧威脅論時不時就霸佔世界各地新聞頭條,也有不少人吹捧人工智慧是我們抵禦日益猖獗的網路攻擊的重要防線。

同樣地,物聯網的發展也令人矚目,越來越多的家庭和企業採用互聯的設備,促進了物聯網的快速發展。

物聯網的一大成功案例是信息技術服務供應商「EPAM系統」,2017年上半年該公司股票已經升值36%,以及「Skyworks」公司,在上半年其股價增長了47%,在股市的成功表明物聯網市場正在蓬勃發展,這些物聯網領域的大公司運營情況良好,也足以證明物聯網正走進大眾視線,成為人們日常生活的一部分。

預測準確性:準確

預測二:人工智慧準確度加強

現實:人工智慧絕對是2017年的一個焦點話題,已經從早期的模糊概念發展成現實中的應用,在此發展過程中,人工智慧的準確度在不斷增強。

最好的證明是人工智慧在醫療領域的應用,如乳腺X光檢測、心臟病和中風治療方面,人工智慧的應用提高了治療的精準性。

在疾病診斷測試中,人類醫生得分平均為0.728,而人工智慧的分數為0.745到0.764之間(1表示100%準確)。人工智慧還能用於預測兒童自閉症的發生概率,在癥狀出現之前就發現疾病,這在以前是不可想象的。

但是人工智慧準確度加強也帶來了消極影響,比如在大選中人工智慧就扮演了一次不折不扣的黑暗元素。

「Cambridge Analytica」公司在英國脫歐公投期間,就利用精準人工智慧和心理戰策略,利用下流手段干預投票,該公司在美國大選期間,還發表了「唐納德·川普,我們的人工智慧總統」文章來迷惑大眾。

預測準確性:準確

預測三:公司需準備迎接快速運營(operate at speed)時代

現實:這點我們現在很難去評定它到底準不準確,因為我們當時預測的是說,相較於投入人力財力發展內存技術和量子計算技術,越來越多的公司會選擇集中精力擴大市場份額。

因此現在很難說這些公司是否在積極準備中,但是可以肯定的一點是不少數據平台都在積極發展快速運營方面的業務,而很多大公司,像Hortonworks、IBM和 SAP現在也都在提供內存服務,未來很可能有越來越多的公司會採用他們的服務。

預測四:行業專業化減弱

現實:這項預測現在我們也不好說到底準確不準確,因為2017年才過去一半,我們尚未找到客觀的、可量化的方式來衡量這個預測的準確性。

可以肯定的一點是,我們已經看到了一些零零星星的證據來為這一趨勢作證,還有我們熟悉的來自不同領域的專家也表達了類似的觀點。

這些專家擁有數據應用相關經驗,而不僅僅局限於某個領域,這和他們過去「各自為戰」沒有團隊合作的情況完全不同。

越來越多的數據學家開始與外界合作,這樣他們就不需要再去分散精力了解其他行業,只需要專註於提高數據應用的效率就夠了。

預測準確性:尚待確認

預測五:政府將加大對數據的審查力度

現實:毋庸置疑,數據已經成為各國政府關注的焦點領域之一,尤其是美國。

2016年,我們就已經看到有針對數據的政治討論,從希拉里郵件門,到俄羅斯黑客竊取美國民主黨自由委員會(DNC)數據,再到近期「深根分析」公司(Deep Root Analytics)兩千萬客戶數據泄露,人們對數據安全問題的討論一直沒有平息過。

儘管對世界各國政府來說,數據安全確實已經成為十分重要的議題,但是在過去一年間,還沒有任何國家在這方面加強立法。

唯一具有影響力的法案是歐洲《通用數據保護條例》(General Data Protection Regulation),該條例出台後,歐盟國家的政府和公司都根據要求加強了數據安全保護工作。

2018年是該條例的截止期限,在最後期限到來之前,各國政府和企業還會在數據安全方面投入更多。

預測準確性:準確

英文原文

Big Data Top Trends 2017 - How Are WeDoing?

Half way through the year, we check back tosee how we're doing

In November 2016 we made our predictionsabout what the market would look like in the coming 12 months. We put ourcollective heads together, spoke to our network of data wonks, and put ourideas down on paper.

As we are now halfway through 2017 we wanted to revisitour predictions and see how clear our glance into the crystal ball was.

Prediction: Machine Learning, AI, And IoTTo Become Common

Reality: The pace of change within machinelearning, AI, and the IoT has been high in 2017, with huge leaps forward intheir use across the board, both from a consumer and business perspective.

IBM』s Watson has become practically ahousehold name, the 『threat』 of AI has seen the term become common on frontpages across the world, and it is being touted as a way of protecting the worldagainst the increasing number of cyber attacks.

Equally we have seen the IoT make hugestrides, with an increasing number of people bringing connected devices intotheir homes and companies making significant gains in IoT.

Some of the successstories so far include EPAM Systems, who have seen their shares increase by 36%in the first six months of 2017 and Skyworks who have seen theirs increase by47% in the same time.

The success of these stocks signifies that the market isbuoyant and some of the key companies in the sector are performing well, whichis a clear indication that the IoT is becoming increasingly common, both inpeople』s homes and perception.

Accuracy: Correct

Prediction: Increased AI Accuracy

Reality: AI has taken centre stage in 2017,moving from a relatively indistinct concept to reality, and alongside this,there has been an increased accuracy.

One of the key manifestations of this hasbeen within the medical field, with treatments like Mammograms, heart attacks,and strokes predicted considerably more accurately thanks to AI usage.

In testsconducted against doctor-led diagnosis, AI scored between 0.745 and 0.764 (with1 being 100% accuracy) compared to 0.728 from doctors. It can also predictautism in young children, before symptoms manifest, something that wasimpossible before.

It has also had more negative consequences,with AI at heart of the darker elements of recent elections. Its accuracy usehas been credited with suppressing the votes of certain parts of thepopulation.

This stems from companies like Cambridge Analytica who have beenaccused of underhand methods using precise AI combined with psychologicalwarfare techniques (https://www.theguardian.com/technology/2017/may/07/the-great-british-brexit-robbery-hijacked-democracy)and stemmed headlines like 『Donald Trump, Our A.I.

President』(https://www.nytimes.com/2017/05/22/opinion/donald-trump-our-ai-president.html).

Accuracy: Correct

Prediction: Companies Will Need To PrepareTo Operate At Speed

Reality: This is a prediction that isdifficult to gauge given that we predicted that rather than actively adoptingin-memory and quantum computing techniques, companies would instead bepreparing for their increased presence.

It is therefore hard to say whether ornot companies are actively preparing, but there have certainly been moves fromcompanies who provide many of the data platforms used that suggests they areincreasingly moving towards this kind of service.

Large players likeHortonworks, IBM, and SAP all now offer some kind of in-memory service that islikely to be adopted by companies in the future.

Accuracy: This is probably wrong, ifplatforms are preparing to offer it as part of their package, there will belittle need for companies to do the same.

Prediction: Less Industry Specialization

Reality: This is again a prediction that ishard to get an accurate read on half way through the prediction period becausethere aren』t really any objective and quantifiable ways to find solid numberson the subject.

What is certain is that we have seen anecdotal evidence ofthis, with several of our speakers from the past 6-12 months working acrossmany different industries throughout their career to date.

It means that they have experience relatedto the use of data rather than the use of data within a specific industry,which has typically been the case in the past, especially when they wereworking as individuals rather than in teams.

As more data scientists have begunto take a collaborative approach, there has been less of an onus onunderstanding of the business and more on actually using the data effectively.

Accuracy: TBC

Prediction: Government Scrutiny On Data

Reality: There is little doubt that datahas taken center stage in terms of government attention, especially in the US.

We have seen it become the biggest talking point in politics over the lastyear, from Hillary Clinton not having good enough data security, Russianhackers stealing data from the DNC, and recently a leak from Deep RootAnalytics which released details from 200 million users.

However, despite it becoming a huge issuefor governments across the world, there is little additional legislation thathas been passed this year that is likely to have an impact on companies.

Considerable work being done in the area though, primarily as a result of theGDPR (General Data Protection Regulation), an EU legislation that requirescompanies and governments who hold data on EU nationals to protect that data inrobust ways.

The approaching 2018 deadline for this has meant that despite alack of original legislation in the area, it is clear that a considerableamount is being done in terms of preparation for it.

Accuracy: Correct

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