Last week, I was recognized and nominated as VentureBeat’s AI Rising Star, one of the AI Leadership Awards awarded annually:

上周，我被认可并提名为VentureBeat的AI Rising Star ，这是每年颁发的AI领导奖之一：

This award will honor someone in the beginning stages of her AI career who has demonstrated exemplary leadership traits.

该奖项将表彰表现出杰出领导才能的AI事业初期的某人。

Here’s what I learned as a newcomer, leader in AI.

这是我作为AI的新手，领导者所学到的。

As a newcomer to the industry — fresh out of university and a year in the industry, I’ve been able to shift and mold very rapidly. Not long ago, I was just a Data Science Researcher in my own little lab bench corner of a Particle Accelerator Research Lab while pursuing my undergraduate studies. I shouldn’t say “just”, because data science is a lot of hard work! Little did I know that a year later, I’ll be recognized in the industry as a Leader.

作为该行业的新手，刚从大学毕业并在该行业工作了一年，我已经能够非常Swift地进行转变和塑造。 不久前，我只是在粒子加速器研究实验室的实验室小角落里从事数据科学研究的，当时我正在攻读本科。 我不应该说“公正”，因为数据科学是很多艰苦的工作！ 我几乎不知道，一年后，我将在业界被公认为领导者。

I went from 0–60 mph in under two seconds!

我在不到两秒钟的时间内从0–60 mph加速了！

Most importantly, I’ve also been able to grasp and observe the industry with a newcomer perspective. Here’s what I learned in a year of going from a data science researcher to an industry leader.

最重要的是，我还能够以新手的视角来掌握和观察行业。 这是我从数据科学研究员到行业领导者一年的经验。

人工智能需要完全不同的观点 (AI requires a completely different perspective)

Most AI leaders today are “transcended data scientists”. They typically have pursued formal training in science, engineering or maths, and then woke up one day and decided they were more interested in leading people.

今天，大多数AI领导者都是“ 超越数据科学家 ”。 他们通常接受科学，工程或数学方面的正规培训，然后醒来一天，并决定对领导者更感兴趣。

In all too common situations they find themselves in, the double mastery of technical and leadership skills was earned in series not in conjunction with each other.

在所有常见的情况下，他们都陷入了对技术和领导技能的双重掌握，而不是彼此联系在一起。

They usually fall in one of two buckets:

它们通常属于以下两个桶之一：

1. A leader who starts to lose the technical side of things and can only talk about products at a high-level

   领导者开始失去技术层面的知识，只能在高层次上谈论产品

2. A leader who is so entrenched in the technology and usually gets too into the weeds of the development

   如此根深蒂固的领导者，通常会沉迷于开发的杂草

And typically, AI companies today fall in one of two buckets:

通常，当今的AI公司属于以下两个类别之一：

1. A company that pursues abstraction of technology and focuses on the real-world problem at hand (i.e., domain experts)

   一家追求技术抽象并专注于眼前的现实问题的公司(即领域专家)

2. A company that gets too in the weeds of technology and loses sight of the real-world problem they were trying to solve in the first place (i.e., technology providers)

   一家过于涉足技术领域，却忽视了他们最初试图解决的现实问题的公司(例如，技术提供商)

The industry requires all of you — the technical and visionary leader, and the abstracted and technology-focused organizations. But, there is a silo problem.

这个行业需要你们所有人-技术和远见卓识的领导者，以及抽象的和关注技术的组织。 但是，这是一个筒仓问题。

The industry is silo-ed. In the ecosystem, there are organizations that develop the underlying, necessary technologies, like frameworks, pre-trained models, and libraries. On the other hand, there are organizations that are specialized in certain domains — they know specific industries really well and understand the underlying problems the industry faces best. But these organizations don’t typically partner closely with each other. They build technologies independent of one another.

这个行业是孤岛。 在生态系统中，有一些组织开发基础的必要技术，例如框架，预先训练的模型和库。 另一方面，有些组织专门研究某些领域，他们非常了解特定行业，并且了解该行业最能解决的潜在问题。 但是，这些组织通常不会彼此紧密合作。 他们建立彼此独立的技术。

Within those organizations, there are AI leaders that are either too abstracted or too entrenched in the technology. There are leaders that only know how to spell AI, and leaders that understand the maths behind it all. They are typically not the same person.

在这些组织中，有些AI领导者要么对技术太抽象，要么太根深蒂固。 有些领导者只知道如何拼写AI，而有些领导者则了解这背后的数学原理。 他们通常不是同一个人。

What is needed in order to optimize AI technologies and deliver to its promise are leaders that can be both abstracted and entrenched in the technology, and organizations that work closely with each other and in harmony with one another.

为了优化AI技术并兑现其诺言，需要的是既可以抽象又根深蒂固于该技术中的领导者，以及彼此紧密合作，彼此和谐相处的组织。

But it is not an easy feat. If it was, then we would be doing it already, right?

但这不是一件容易的事。 如果是的话，那我们就已经做了，对吧？

There is an attitude problem. Do we really value these intersections and the inter-weaving of skills and organizations?

有一个态度问题 。 我们是否真的珍视这些交叉点以及技能和组织的交织？

人工智能中最有价值的领导特质 (The most valuable leadership trait required in AI)

In the age of information, ironically, leaders are typically shielded from new information, because of the sheer volume of information and the rate at which new information is generated every day.

具有讽刺意味的是，在信息时代，由于庞大的信息量和每天产生新信息的速度，领导者通常都无法获得新信息。

On July 16, 2020 alone, there were about 70 papers submitted to arXiv.org that have to do with machine learning with each paper ranging from 15–50 pages of information. That doesn’t even include other research publishers. That is a lot of new information and knowledge generated every day.

仅在2020年7月16日，大约有70篇论文提交给arXiv.org，这些论文与机器学习有关，每篇论文的信息覆盖15至50页。 甚至不包括其他研究出版商。 这就是每天产生的许多新信息和知识。

The most necessary leadership trait required of an AI leader today is humility.

今天，AI领导者最需要的领导特质是谦卑。

In the sea of rapid change and everyday shifts in the industry, it is impossible to purely rely on 20-year-old wisdom of how the industry works. AI leaders need to be willing to learn and be open to learning from someone who 20 years younger or multiple levels of the hierarchy down. Organizations need to empower more Rising Stars.

在行业日新月异的变化之海中，仅依靠20年的行业运作智慧是不可能的。 AI领导者需要愿意学习，并愿意向年轻20岁或多层以下层次的人学习。 组织需要授权更多的后起之秀 。

The most effective leaders I’ve come to follow are those that are unafraid to learn from interns and direct reports.

我要追随的最有效的领导者是那些不怕从实习生和直接报告中学习的领导者。

Companies typically hire newcomers to lead as product managers or project leads, because the situation warrants an outside-in perspective and a fountain of knowledge full of new and yet-to-be primed ideas. Organizations require things to be shaken up and rapidly adapt to new information every day.

公司通常会聘请新来的人担任产品经理或项目负责人，因为这种情况需要一种由内而外的观点，并且需要充满新的和尚待完善的想法的知识源泉。 组织要求事情要动摇并每天Swift适应新信息。

人工智能需要民主化 (AI needs to be democratized)

Gone are the days when AI was only accessible to PhD researchers and organizations that could afford a research residency program. For the industry to realize the full potential of AI, there needs to be democratization. Making the technology accessible expands the possibilities and the realm of what we can do with it.

只有能够负担研究驻留计划的博士研究人员和组织才能使用AI的日子已经一去不复返了。 为了使行业实现AI的全部潜力，需要实现民主化。 使技术变得可访问性扩展了我们可以使用该技术的可能性和领域。

More than any other industry, AI requires a large community to collaborate and datasets, insights, and models that can be shared among each other, which is why most industry leaders today open-source code. This creates and engenders trust.

人工智能比任何其他行业都需要更大的社区进行协作，并且可以相互共享数据集，见解和模型，这就是当今大多数行业领导者开放源代码的原因。 这创建并产生信任。

Not only do datasets and models need to be democratized, knowledge and know-how also need to be accessible, especially to industry experts who best understand the real-world problem.

不仅需要使数据集和模型民主化，而且还需要获取知识和专有技术，尤其是对于最了解现实问题的行业专家而言。

AI is not a black box nor is it magic. Simply put, it is a construct that allows us to generate instructions out of examples, as opposed to relying on programmers to outline step-by-step instructions.

AI不是黑匣子，也不是魔术。 简而言之，它是一种允许我们从示例中生成指令的结构，而不是依赖于程序员来概述逐步的指令。

Once more of the industry understands this, the more we could better understand the use cases where AI is needed and when it is not (and yes! Some use cases do not require AI — sometimes, it only requires data analytics and statistics). The more industry leaders understand this, the better we could decipher the possibilities where AI can enhance existing solutions we have today and create new solutions to unsolved issues, and the less we get frustrated at the data scientists in our teams who spend full-day business hours reading papers on arXiv.

一旦业界对此有了更多的了解，我们就可以更好地了解需要AI和何时不需要AI的用例(是的！有些用例不需要AI-有时只需要数据分析和统计) 。 行业领导者对此了解得越多，我们就越能更好地理解AI可以增强我们现有的现有解决方案并为未解决的问题创建新解决方案的可能性，而我们对全天投入业务的团队中的数据科学家的挫败感就越少小时在arXiv上阅读论文。

To summarize, leaders in AI need to be open-minded, have humility, and work to democratizing AI. AI needs to be an accessible technology that is available to everyone who sees and wants to solve problems.

总而言之，人工智能领域的领导者必须胸襟开阔，谦虚谦卑，并努力使人工智能民主化。 人工智能需要成为一种可访问的技术，所有看到并希望解决问题的人都可以使用。

Successful real-world implementation of AI needs a new breed of decision-makers that could adapt to the ever-so-changing industry.

成功地在现实世界中实现AI需要新的决策者，以适应日新月异的行业。

This is not to say we only need young, newcomer leaders in the industry. I have a minuscule wisdom dataset compared to someone who has been in the industry for more than 20 years! It is a “yes, and.” We need both leaders that have acquired a foundation of wisdom (after all, there is no compression framework to experience!) and new leaders who could shake up the industry.

这并不是说我们只需要业内年轻，新来的领导者。 与从事该行业超过20年的人相比，我拥有微不足道的智慧数据集！ 这是“是的，并且”。 我们既需要获得智慧基础的领导者(毕竟，没有压缩的经验框架！)，也需要能够撼动整个行业的新领导者。

It is no easy feat to change the world, there is more to be gained if we learn from each other.

改变世界绝非易事，如果我们彼此学习，就会有更多收获。