最近，吴恩达在其创办的人工智能周讯《The Batch》撰文，谈了关于从事AI职业的看法，现编译如下：

人工智能的迅速崛起导致很多相关工作的迅速崛起，许多人正在这个领域建立令人兴奋的职业生涯。职业生涯是一段长达数十年的旅程，而道路并不总是一帆风顺。多年来，我有幸看到成千上万的学生以及大大小小的公司的工程师在人工智能领域的职业生涯。在这封信和接下来的几封信中，我想分享一些想法，这些想法可能有助于您制定自己的路线。

职业发展的三个关键步骤是学习（获得技术和其他技能）、从事项目（加深技能、建立投资组合和创造影响）和寻找工作。这些步骤相互叠加：

最初，您专注于获得基础技术技能。
在获得基础技能后，您将投入到项目工作中。在此期间，您可能会继续学习。
稍后，您可能会偶尔进行求职。在整个过程中，您可能会继续学习和从事有意义的项目。

这些阶段适用于广泛的职业，但人工智能涉及独特的要素。例如：

  • 人工智能还处于萌芽阶段，许多技术仍在不断发展。虽然机器学习和深度学习的基础正在成熟——课程作业是掌握它们的有效方法——但除了这些基础之外，在人工智能中，跟上不断变化的技术比更成熟的领域更重要。
  • 项目工作通常意味着与缺乏人工智能专业知识的利益相关者合作。这使得找到合适的项目，估计项目的时间表和投资回报率以及设定期望变得具有挑战性。此外，人工智能项目的高度迭代性也给项目管理带来了特殊的挑战：当你事先不知道达到目标精度需要多长时间时，你怎么能想出一个构建系统的计划呢？即使在系统达到目标之后，也可能需要进一步迭代来解决部署后漂移问题。
  • 虽然在人工智能中寻找工作可能类似于在其他领域寻找工作，但存在一些差异。许多公司仍在试图弄清楚他们需要哪些人工智能技能，以及如何雇用拥有这些技能的人。你做过的事情可能与面试官所看到的任何事情都有很大不同，你更有可能不得不教育潜在的雇主关于你工作的某些元素。

在这些步骤中，一个支持性的社区是一个很大的帮助。有一群朋友和盟友可以帮助你 - 以及你努力帮助的人 - 使道路更容易。无论您是迈出第一步，还是已经踏上了多年的旅程，都是如此。

我很高兴能与你们所有人一起发展全球人工智能社区，这包括帮助我们社区中的每个人发展自己的职业生涯。在接下来的几周内，我将更深入地探讨这些主题。

 

The rapid rise of AI has led to a rapid rise in AI jobs, and many people are building exciting careers in this field. A career is a decades-long journey, and the path is not always straightforward. Over many years, I’ve been privileged to see thousands of students as well as engineers in companies large and small navigate careers in AI. In this and the next few letters, I’d like to share a few thoughts that might be useful in charting your own course.

Three key steps of career growth are learning (to gain technical and other skills), working on projects (to deepen skills, build a portfolio, and create impact) and searching for a job. These steps stack on top of each other:

  • Initially, you focus on gaining foundational technical skills.
  • After having gained foundational skills, you lean into project work. During this period, you’ll probably keep learning.
  • Later, you might occasionally carry out a job search. Throughout this process, you’ll probably continue to learn and work on meaningful projects.

These phases apply in a wide range of professions, but AI involves unique elements. For example:

  • AI is nascent, and many technologies are still evolving. While the foundations of machine learning and deep learning are maturing — and coursework is an efficient way to master them — beyond these foundations, keeping up-to-date with changing technology is more important in AI than fields that are more mature.
  • Project work often means working with stakeholders who lack expertise in AI. This can make it challenging to find a suitable project, estimate the project’s timeline and return on investment, and set expectations. In addition, the highly iterative nature of AI projects leads to special challenges in project management: How can you come up with a plan for building a system when you don’t know in advance how long it will take to achieve the target accuracy? Even after the system has hit the target, further iteration may be necessary to address post-deployment drift.
  • While searching for a job in AI can be similar to searching for a job in other sectors, there are some differences. Many companies are still trying to figure out which AI skills they need and how to hire people who have them. Things you’ve worked on may be significantly different than anything your interviewer has seen, and you’re more likely to have to educate potential employers about some elements of your work.

Throughout these steps, a supportive community is a big help. Having a group of friends and allies who can help you — and whom you strive to help — makes the path easier. This is true whether you’re taking your first steps or you’ve been on the journey for years.

I’m excited to work with all of you to grow the global AI community, and that includes helping everyone in our community develop their careers. I’ll dive more deeply into these topics in the next few weeks.

 

原文地址：https://read.deeplearning.ai/the-batch/issue-151/

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