Here’s one way to do it.
这是一种方法。
Many people want to work in AI, and most that start off their journey aspire to become a data scientist. Apparently the sexiest job of the century. The first thing they do is a Coursera course and then start working on Kaggle competitions, followed by trying to find a job as a data scientist. If this is you, you are one of many, and it will be hard to stand out of the crowd.
许多人都想在AI上工作，而大多数刚开始的人都渴望成为一名数据科学家。 显然是本世纪最性感的工作。 他们要做的第一件事是Coursera课程，然后开始从事Kaggle竞赛，然后尝试寻找数据科学家的工作。 如果是您，那么您就是许多人中的一员，很难在人群中脱颖而出。
Whilst understanding AI through a Coursera course is not a bad idea — you need to educate yourself and become AI Literate — data science is only 5% of the work and many other roles are available or will become available in the future.
尽管通过Coursera课程了解AI并不是一个坏主意-您需要进行自我教育并成为AI Literate-数据科学仅占工作的5％，并且许多其他角色都可以使用或将来将可用。
So what else could you do?
那你还能做什么？
One way to break into AI is to look at your own job. Perhaps all or parts of your job can be enhanced or replaced by AI. Are you working with data and is this data used for making decisions? Even small decisions such as categorizing a new customer can be enhanced with machine learning. Identify these decision points and kick off an AI project within your company. You will be the ideal business stakeholder for a data science team.
闯入AI的一种方法是看自己的工作。 也许您的全部或部分工作可以被AI增强或替代。 您是否正在使用数据，这些数据是否用于决策？ 机器学习甚至可以增强诸如对新客户进行分类之类的小决策。 确定这些决策点，并在公司内启动一个AI项目。 您将成为数据科学团队的理想业务涉众。
Let’s assume you work in marketing. You can start thinking of the data you collect on your different customers. How are you making decisions who you are reaching out to, and when? Are you already collecting data on who is responding to your marketing efforts? You can start building the basis for the data, and the business case.
假设您从事市场营销工作。 您可以开始考虑在不同客户上收集的数据。 您如何决定与谁联系以及何时联系？ 您是否已经在收集有关谁在响应您的营销工作的数据？ 您可以开始构建数据和业务案例的基础 。
Look at companies that already work in AI and need your skills or industry expertise. Industry and domain knowledge are key to the success of AI projects. There is a large AI literacy gap on the business side and when you have educated yourself in the basics and can bring the desired domain or industry skills to a company you will be a huge asset.
看一下已经在AI中工作并且需要您的技能或行业专业知识的公司。 行业和领域知识是AI项目成功的关键。 AI在业务方面存在很大的差距，如果您已经接受了基础知识的培训，并且可以将所需的领域或行业技能带给公司，那么您将成为一笔巨大的财富。
Let’s assume you work in the energy industry. Great, there’s a lot of potential for the application of AI in energy. And if not now, many companies will start looking into AI in the next few years. Collect different potential use cases in the energy sector, predictive maintenance, energy trading, etc. Work your way into understanding these use cases and you’ll be a great asset for your own or a competitor company!
假设您从事能源行业。 太好了，人工智能在能源中的应用潜力很大。 如果不是现在，那么很多公司将在未来几年内开始研究人工智能。 收集能源部门，预测性维护，能源交易等方面的不同潜在用例。用自己的方式了解这些用例，对于您自己或竞争对手的公司而言，您将成为宝贵的资产！
The key to this story is, stick to what you know and are good at, and grow your skills from where you are strong.
这个故事的关键是， 坚持自己的知识和擅长的领域 ，并从强者那里发展自己的技能。
Build the bridge from the business side, this is where you’ll be successful.
从业务方面搭建桥梁，这将是您成功的地方。
If you want to learn to understand machine learning on the side, please do so. But there is no need to push yourself to become a data scientist. In fact, I truly believe that in the longer-term future many roles in AI will be non-data science roles. And maybe it will just require some patience to move to the right role, but that doesn’t mean now is a good time to start preparing for it.
如果您想从侧面了解机器学习，请这样做。 但是没有必要强迫自己成为数据科学家。 实际上，我真的相信，从长远来看，人工智能中的许多角色将是非数据科学角色。 也许只是需要一些耐心才能担任正确的角色，但这并不意味着现在是开始为此做准备的好时机。
about me: I am an Analytics Consultant and Director of Studies for “AI Management” at a local business school. I am on a mission to help organizations generating business value with AI and creating an environment in which Data Scientists can thrive. Sign up for my newsletter here.