As you think about your talent and your workforce for the age of AI and where we’re moving to, there’s a few things that are very important to think about.

想想人工智能时代以及未来你的人才和员工 有几件很重要的事需要考虑

I think about two broad categories of skills that you need to prepare for.

你大约需要准备两大类技能

One is what I’d call the Talent or the skills – the people that “do” AI.

一种是我所谓的人才或技能——“研究”AI的人

Who’s going to develop the AI?

谁来发展AI？

Those are the machine learning experts, the data scientists, the people with the STEM skills and coding skills that are going to build the technology of the future.

是学习专家 数据科学家 拥有STEM和编码技能的人 由他们来打造未来的技术

And that is an important area that every organization needs to be preparing for in developing and building those types of skills.

在发展和培养这些技能时 所有机构都需要做好准备 这些领域很重要

And that’s one important thing.

这是件很重要的事情

The bigger set of skills that I think every organization needs to think about are the people who use AI.

我觉得每家机构都需要考虑的更大的技能是使用AI的那些人

Not the ones who do and build it, but the people who use AI.

不是研究和构建AI的人 而是使用它的那些人

And that’s going to be basically everybody in the workforce, or almost everybody in the workforce, in your workforces or organization.

那基本就是指机构中的所有员工了 或者几乎是所有员工

And I think that’s an area where organizations haven’t spent enough time.

我觉得所有机构在这个领域花的时间还不够

Everybody knows they have to develop and hire the AI experts and the coding experts;

所有人都知道他们得开发和雇佣AI专家和编码专家

I think how the rest of your organization is going to adapt and use AI is the big question that we’re really trying to address in Human + Machine.

我觉得你的机构如何适应和使用AI是我们在人类+机器方面真正要解决的大问题

There’s a few things that I’d say that are really important there.

有几件很重要的事情 我想说一下

One is you need to think about the learning platforms that you’re developing for your organization.

一个是你得考虑你正在为你的机构开发的学习平台

One thing that we found in the survey in the research work we did is two-thirds of organizations, roughly,

我们在研究调查中发现 大约有三分之二的机构

believe that their workforce isn’t ready for AI, broadly for using AI – a big number.

相信他们的员工还没有为AI做好准备 仅仅是使用AI——很大的数字

Only three percent of organizations plan to increase their training spending to account for that, which isn’t appropriate.

只有3%的机构打算增加AI知识的培训开销 这并不正常

That means that generally, people think it’s somebody else’s problem to prepare the workforce.

这意味着 总体上来说 人们都觉得总有别人去让员工做好准备

And we believe that that’s not the right answer.

然而并不是这样的

At Accenture, we’re investing about a billion dollars a year in training and retraining our workforce, in developing talent platforms that continually retrain people.

在埃森哲 我们每年会投资十亿美元反复培训员工 开发不断再教育人们的人才平台

And we think that that’s the approach you need to take because we’re in an age of continuous innovation.

我觉得你也应该采用这种方法 因为我们处在一个不断创新的时代

The roles of your workforce are going to continue to change, and you can’t flush and replace the workforce, and that’s not the right way to view it.

你的员工的角色会不断变化 而你又不能大批量替换员工 这样不对

How do you look at your employees as an investable resource,

你如何把员工视作可投资的资源

where you’re investing in the talent and developing the right learning platforms that they can learn how to use AI in the initial applications you’re rolling out now,

对人才进行投资 开发合适的学习平台 让他们学习如何在你推出的初步软件中使用AI

and continue to learn so that their skills are relevant and they’re productive contributors to your organization as you continue to progress?

并继续学习 让自己的技能跟上时代的步伐 并成为你公司的高效贡献者？

Another area that we really overlooked and where there’s huge potential is using AI itself to help prepare the workforce.

另一个被我们忽略又有着巨大潜力的领域就是利用AI去帮助员工做好准备

And I think there’s huge opportunities for innovation here.

我觉得这方面有很大的创新机会

We’re starting to see some real interesting possibilities coming.

我们看到一些真正有趣的可能性正在出现

One experiment we’ve done as an organization, and this is still in the research and development stage,

作为机构 我们所做的一项实验是 它还处于研究和发展阶段

we’ve looked at all of our employees in our Accenture organization (and we have over 430,000 people, so it’s a large workforce).

我们研究了埃森哲公司的所有员工（我们有43万多名员工 很庞大的数量）

We’ve developed a machine learning model using artificial intelligence that can take the resumes and experience of any one of our employees –

我们利用人工智能打造了一个机器学习模式 可以吸收任何员工的简历和经验——

and this is something our employees can use to understand how their job will be impacted by AI.

员工可以通过这种方式理解AI会对他们的工作有何影响

So it might say that as you feed in all of your information, it’ll compare it to external job postings and trends in the marketplace.

所以当你输入你的各种信息时 它会把这些信息跟外部的工作岗位和市场趋势进行比较

And it might say “Well, your skills are at risk in about one to three years.”

它可能会说“嗯 你的技能大约一到三年后就没有市场了”

And it doesn’t stop there, but it says “and based on what you know here’s the adjacent types of jobs that you should start looking to train yourself for.”

这还没完 它还会说“根据你已经掌握的知识 这是一些相似的工作 你可以看看 开始培养自己的能力”

And again this is at the research and development stage, so I wouldn’t say it’s a product out there yet

再说一遍 这还只是在研究和发展阶段 所以我不会说它已经是种产品了

but it shows the kind of innovation and creativity and way that we can use technology itself to help prepare workforces for the changes that are coming down the road.

但它彰显出了革新 创造力和我们可以利用技术本身让员工为即将到来的改变做好准备的方法

There’s a lot of voices out there that are very well-regarded voices –

有很多呼声得到了重视——

Elon Musk, the late Stephen Hawking, who talked very eloquently about the perils and dangers that we face with AI.

伊隆·马斯克 去世的斯蒂芬·霍金 他们都痛斥了我们在AI领域所面临的风险和危险

I do think we need to consider those and we need to think about the longer term implications of AI, like we do of any technology.

我们确实需要考虑这些方面 还要考虑AI的长期影响 任何技术都应如此

Every technology that’s ever existed from, the first stone wedge that the cavemen carved or the first fire that was lit, was used for good, and it could be used for bad as well.

所有存在于世的技术 从洞穴人刻出的第一个石头楔子到人类燃起的第一把火都是用来做好事的 但它们也可以被用来干坏事

AI is no different, and the thing that I think concerns people sometimes is the pace of AI and the ability of AI to make decisions that are not in the interest of us as human beings.

AI也不例外 我觉得人们所担心的事情是AI的发展速度以及AI可能做出不顾人类利益的决定

The reality is that the risk of that happening is far away.

事实是 这些风险还遥不可及

We should be thinking about it.

我们应该想想

There are organizations that are set up to think about those implications, and we support and are involved in some of those organizations.

有些公司的成立就是为了专门考虑这些风险 我们支持这类公司 也参与了进去

But that’s for the distant future, and not something that we need to think about in our generation right now in terms of real, serious consequences.

但这些都在遥远的未来 我们这一代还不需要去考虑真实 严重的后果

The opportunity for us now is to think about how do we apply this to live more effectively on the planet,

我们现在应该考虑的是如何利用AI在这颗星球上更高效地生活

to better use our resources and to operate businesses and educational institutions and our governments more effectively?

如何用它去优化我们的资源使用 去更高效地经营企业和教育机构以及政府？

There’s massive opportunity for this, so rather than be consumed and stuck by what might happen with the technology in the future

我们有很大可能实现这些 所以与其终日担忧未来的技术会引发什么后果

we need to think about that and prepare for it,

我们确实需要考虑这一点并做好准备

but let’s apply the technology in a responsible way, which is what we talk about in Human + Machine: to solve these problems today.

还不如负责地应用技术 也就是我们所说的人类+机器：去解决当今面临的问题