Start with these steps if you aspire to a career in artificial intelligence.
Start with these steps if you aspire to a career in artificial intelligence.
AI is a growing industry that can use your abilities. What to know and do before pivoting.
Readers have been asking me how to get into the AI boom since I covered it. Many of them are like Rick’s letter:
I read your articles on IBM, OpenAI, and Deep Learning’s free AI courses and wanted your advise.
I want to go from life science to large tech. I want to learn more about AI and its applications to become a product manager who can demonstrate its use cases.
Do you have any advice for an experienced product manager with limited machine learning knowledge beginning out in AI/ML to become marketable? I’ll start with IBM’s free classes as you suggested. I want to collaborate with engineering and development teams on products using these technologies.
This letter jumps out because Rick is a product manager in biological sciences, not IT. He has transferable talents, thus this experience is crucial.
Also: 10 hours? IBM offers free AI basics training.
I get messages from people who don’t mention experience or talents but see prompt engineers making large money and want in on the action. Many less experienced people read tales of app developers earning millions or prompt engineers getting six-figure salaries and assume one course or working hard would get them the job.
Half of my entry-level programming pupils wanted to program. The other half desired high-paying programming positions. That second group of pupils wasn’t really craft-oriented. They assumed that taking a programming course would earn them a job. It may have. That work wouldn’t have lasted more than about weeks without expertise.
You must be willing to work and add something to the job. Rick seems eager to work and has relevant talents. I propose Rick and anybody interested in switching to AI job follow these five steps.
1. Assess your abilities
This is crucial for career changes. You have what skills?
Rick, a product manager, can handle people. Product managers are sometimes called CEOs without power or remuneration. They must manage and cajole personnel from many fields and departments.
His writing talents are likely impressive. Product requirements specs are difficult to write.
Product managers may have marketing communications abilities, depending on their kind. This means writing product advertising content for prospects, not only implementation teams.
As an experienced product manager, he likely has great project management, organization, and product expertise (for life science products).
2. Find transferable talents
Rick may not realize it, but he has AI-friendly talents. creating instructions for generative AI tools in prompt engineering is more about phrasing requests in plain language than creating code.
See also: 6 AI prompt engineer talents.
If product managers can accomplish anything, it’s write explicit specs that account for restrictions. That’s almost prompt engineering. He’ll need to grasp prompt engineering’s complexities and how to overcome limits, but he’s ready for the task.
He knows development teams, projects, and product management, which tech and life science organizations need.
How about non-product managers? What transferable talents do you have?
Classic AI expert systems were constructed by modeling subject matter experts’ skills. Large language models currently gather information from massive amounts of data, typically from the internet. Medical knowledge, petroleum modeling, or even home construction knowledge may be useful to AI businesses aiming to penetrate into those areas.
Don’t think knowledge must be high-tech or expensive. Teacher competence includes teaching and sharing information, as well as your specialty. Parents know the ins and outs of parenting kids. Warehouse expertise gets you to the front of the supply chain.
To clarify, knowing something doesn’t guarantee you an AI job in that field. Be conscious of your strengths and convey them in your transfer quest.
Return to the instructor example. Teaching requires lesson planning, breaking down knowledge into manageable parts, and validating student learning. That’s useful in AI too.
