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.
Here’s what I learned as a newcomer, leader in AI.
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!
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.
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.
They usually fall in one of two buckets:
A leader who starts to lose the technical side of things and can only talk about products at a high-level
A leader who is so entrenched in the technology and usually gets too into the weeds of the development
And typically, AI companies today fall in one of two buckets:
A company that pursues abstraction of technology and focuses on the real-world problem at hand (i.e., domain experts)
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)
The industry requires all of you — the technical and visionary leader, and the abstracted and technology-focused organizations. But, there is a silo problem.
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.
But it is not an easy feat. If it was, then we would be doing it already, right?
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.
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.
The most necessary leadership trait required of an AI leader today is humility.
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.
The most effective leaders I’ve come to follow are those that are unafraid to learn from interns and direct reports.
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.
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.
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.
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.
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.
Successful real-world implementation of AI needs a new breed of decision-makers that could adapt to the ever-so-changing industry.
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.