How do I get started with AI?
Over the past few months, I’ve seen a noticeable increase in people asking how to get started with AI.
Modern AI tools like ChatGPT, DALL-E 2, and Stable Diffusion have made major headlines.
Software developers and IT professionals now understand the power of AI to solve day-to-day problems.
They also realize that 3rd-party pre-trained AI/ML services are easy to use — if you know the basics.
However, most people are still confused about how to start a career in AI.
So, in this 9-part series of articles, we’ll learn everything you need to know to get started with AI.
[Note: As each new article becomes available, click on the headlines below to learn more about each topic.]
First, you need to decide why you want to start a career in AI. Do you want to use AI to be more productive at your current job? Do you want to build new AI systems to solve complex problems? Or do you want to advance the state of the art with cutting-edge AI research?
Knowing why you’re interested in AI will help you choose a career path that is right for you. Choosing a career path helps you choose a learning path — which will help you decide what to practice — and so on. Every step on your AI journey starts with this first question: “why?”
Next, you need to choose a career path in an AI-related field. You can learn how to be more productive in your current job as an AI tool user. Or, you might want to build new AI systems with a career as an AI Developer, ML Specialist, Robotics Engineer, or a Data Scientist.
There are also supporting roles on AI teams, such as AI Project Manager, AI Consultant, or AI Ethicist. Or, you can help to advance the state of the art in cutting-edge AI research by becoming a Computer Scientist, Data Scientist, or AI Research Scientist.
Many career paths that involve building AI systems require (at minimum) a 4-year degree. These include roles like AI Developer, ML Specialist, and Robotics Engineer. However, not all career paths require a 4-year degree. For example, AI tool users and AI project managers.
If you’re just getting started or plan to return to college, then you need to choose a degree that’s right for you. These degrees include Computer Science, Data Science, Software Engineering, Robotics Engineering, Computational Neuroscience, and Artificial Intelligence.
Regardless of your degree or learning path, you will need to learn basic AI skills. All career paths require learning basic AI literacy. Many career paths will require learning how to program in Python, R, and SQL. Others will require knowledge of various AI tools and techniques.
More advanced roles will require deep foundational knowledge in computer science, data science, machine learning, and artificial intelligence. Finally, other roles will require skills in AI problem-solving, AI business strategy, and AI ethics.
Once you have basic AI skills, you will need to learn practical AI skills. There are four main levels of practical AI skills. Each level is more difficult than the last. How far you go depends on the types of problems you want to solve with AI and how deep you want to go.
You can be more productive with AI tools. You can use pre-built AI services to solve common problems. You can customize pre-trained ML models to solve more specific problems. Or, you can create new ML models and AI algorithms to solve completely new problems.
Learning concepts is only a small part of your education. Practicing your skills is a much bigger and more important part. Expect to spend roughly 20% of your time learning concepts and 80% of your time practicing your skills with hands-on exercises.
Start by solving simple “toy” problems through practice exercises. Then, gradually work your way up to larger and more difficult problems with programming assignments. Finish by completing a few complex, multi-faceted projects that solve real-world problems with AI.
One of the best ways to quickly communicate your proficiency with AI skills is to obtain certification in AI tools and practices. Certifications from respectable organizations and institutions provide potential employers with evidence that you are proficient in your AI skills.
This can mean a certificate of completion for an online course, an industry certification from a respected vendor, or a degree from an accredited university. They each have their pros and cons, so it’s important that you get the right certification for the work you plan to do.
After you’ve completed all of the previous steps, you’re ready to get your first job in AI. To do so, you need to find a potential employer within the AI value stream and demonstrate that you have the knowledge, skills, and credentials to provide them with real business value.
This involves steps like building your professional network, performing a job search, and creating a resume. It also involves interviewing with potential employers, following up with them, and using feedback to improve your job search each and every time.
I believe we are just beginning to see the true potential of Artificial Intelligence. AI will have a fundamental and profound impact on our world for many years to come. There are tremendous opportunities ahead for those who are willing to invest in the future of AI today.
The time to get started is now. Don’t get left behind!
To learn more, please read the next article in this series: Why Should I Learn AI Skills?