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对话谷歌首席数据官沙克特——机器学习将如何发展

大数据

人工智能当下的发展势头可谓是“炙手可热”,在科技领域也掀起了一场激烈的角逐。亚马逊、微软、谷歌和IBM纷纷开始投资人工智能项目,研发出的人工智能应用可以说是五花八门,从无人驾驶车到新型癌症治疗法,逐渐在各个领域渗透发展。

虽然大部分科技巨头纷纷加入这场人工智能竞赛,但仍有不少人认为,目前阶段处于领先地位的还是谷歌。

谷歌当前的战略是兼并人工智能领域的创业公司,将人才和资源收入麾下,以实现谷歌人工智能方面的发展。

不久前,谷歌的首席执行官桑德尔·皮查伊(Sundar Pichai)对外表示谷歌正在向“以人工智能为首位的公司”转型。

去年三月份,谷歌子公司Deepmind的产品AlphaGo在与围棋冠军李世石的对弈中,以4:1完胜李世石,获得了世界媒体的广泛关注,也让人们开始注意到谷歌人工智能方面的发展。

沙克特·库尔玛(Saket Kumar)是谷歌现任的首席数据官。他在创新分析领域有着超过15年的工作经验,在将数据转化成洞察力来进行科学决策方面造诣颇深,被奉为思想领袖。

在加入谷歌之前,沙克特就已经在广告业、石油天然气行业、医疗保健和制造业等多个领域的分析项目中取得了成功。加入谷歌之后,他带领的数据学家团队专注于为谷歌的顶级客户提供更加高效的营销策略。

沙克特·库尔玛博士将出席2017年6月5日至6日在美国旧金山马里奥特联合广场举办的“机器学习创新峰会”,记者有幸在他演讲之前对他进行了采访。

记者:您认为在近期,最重要的机器学习应用是什么?

沙克特:这个问题很难回答啊。现在很多行业和消费活动都进行了电子化,由此产生的数据量也在不断增加。这对机器学习来说是一件好事,因为现在有了更多的数据集和案例能够用来分析和学习。

比如说图像识别、音频转写、不同语言间翻译等等。我认为近期最重要的机器学习应用可能当属消费者行为分析应用了。

现在谷歌、脸书、亚马逊和其他科技巨头都已经拥有了足够多的数据,并且已经打造了各自的知识库,使他们能够够产出自己的机器学习解决方案。

记者:日前,KDnuggets网站发起一项投票,有51%的投票者认为在未来十年内,人类数据学家的大部分预测分析和数据科学方面的工作能够交给机器。

您认为数据学家这个角色正面临威胁吗?您认为科技的发展会对人才市场造成什么样的影响呢?政府是否准备好迎接这个新时代了呢?

沙克特:不可否认的趋势是会有越来越多的自动化工具和改进工具帮助人们完成预测分析。但是,我并不认为这会威胁到人类数据学家的工作机会。

机器将代替他们去做那些枯燥的数据处理和清除工作,基础的数据分析和建模可能也将由机器自动完成。

尽管如此,对于那些懂得数据学、算法等深度领域知识的人来说,他们根本不用愁工作的问题,因为他们还有将这些基于数学分析的信息传达给商业决策者的重要任务。

记者:在机器学习领域,有哪些新技术或者新想法是让您觉得特别感兴趣,或者特别看好的呢?

沙克特:现在视频和多媒体消费领域也采用了机器分析,这是让我特别激动的。图像和视频识别技术还在持续改进中。在这个方面机器学习能够提供很大帮助,因为机器学习能够分析视频,并给出真实的消费者互动反馈。

记者:在未来几年,您预计机器学习行业会遇到哪些挑战?您认为该如何克服这些困难呢?

沙克特:目前机器学习行业出现了很多好的趋势,如计算和储存的成本都在降低。但是很多公司依然存在缺陷。比如说,有不少公司招不到优秀的数据学家。

很多大公司,特别是位于硅谷的那些公司,都面临着招不到合适人才的困难,因为现在这方面的人才实在是不多,供他们选择的人才范围很小。

英文原文

Interview With Saket Kumar, Chief DataScientist At Google

‘The most important applications willlikely be analyzing consumer behavior’

The race to lead the way in AI is hottingup. Amazon, Microsoft, Google, and IBM are among those to have invested heavilyin research of the technology, with applications ranging from driverless carsto improved cancer treatment.

Arguably leading the way is Google, whosemain focus has been on acquiring innovative startups in the field and bringingthem under their umbrella.

Sundar Pichai, Google’s chief executive officer,recently said that the company was ‘really transitioning to becoming anAI-first company.’

Perhaps its most eye catching demonstration was the victorylast year of Google Deepmind’s AlphaGo over Go star Lee Sedol, but even moreexciting things are happening behind the scenes.

Dr. Saket Kumar is Chief Data Scientist atGoogle. He has more than 15 years experience as an innovative analyticspractitioner and thought leader, with a focus on translating data into insightsfor decision makers.

He has led successful analytics assignments in multipleindustries, including advertising, oil & gas, healthcare, andmanufacturing. At Google, he leads a team of data scientists focused onimproving marketing effectiveness for top tier clients.

We sat down with him ahead of hispresentation at the Machine Learning Innovation Summit, taking place this June5-6 at the Marriott Union Square in San Francisco.

Where do you think machine learning’s mostimportant applications will be in the near future?

This is a hard question. We see tons ofbusiness and consumer activities being digitized. The amount of data that getsdigitized continues to grow.

Machine learning is great for situations wherethere are large data sets and cases to learn from. Examples of this includeImage identification, voice transcription, translation etc.

The most importantapplications will likely be analyzing consumer behavior as companies likeGoogle, Facebook, Amazon, and others have tons of such data and have developedlarge knowledge base that they can leverage to build ML solutions.

In a recent KDnuggets poll, 51% ofrespondents said that they expect most expert-level Predictive Analytics/DataScience tasks currently done by human Data Scientists to be automated to happenwithin the next decade.

Do you think the data scientist’s role is really underthreat? What kind of impact do you think it will have on the job market ingeneral, and are governments prepared?

There is going to be automation andimprovement in the tools that help with predictive analytics. However, I do notsee any threat to the work done by human data scientists.

A lot of drudgery ofprocessing and cleaning data will hopefully go away. The base analysis andmodeling is likely going to be commoditized.

Despite this, there will still bea role for people who know data, algorithms, deep domain knowledge, and caneffectively communicate math based insights to business leaders.

Are there any new technologies or ideas inthe machine learning space that you find particularly exciting or believe willbe especially important in the next few years?

I am excited about the intersection ofvideo/multi-media consumption and analytics. Image and video recognition isstill work in progress.

There is a lot of exciting stuff that can be done withrespect to what ML sees in videos and actual consumption/interaction responseof the consumers.

What challenges do you foresee holding backmachine learning from achieving its potential? How do you think these could beovercome?

There are a lot of positive trends(computing and storage costs going down). There are still data silos with andacross organizations.

One obvious one is the lack of qualified data scientists.Most companies – with the exception of large silicon valley companies -struggle to get right talent as the pool to draw from is not large.

翻译:灯塔大数据

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