How can a student—or someone with a student mindset and opportunity—get started with AI? Here are three tips on how to turn a buzzword into knowledge, skills, or even a job.
At Superlinear, every single member of the team was once new to the field of Artificial Intelligence.
We know that there are many different paths to learn a new skill or enter a new field. These steps can help you get started:
Three steps:
Get to know a community, for example, by visiting a meetup
Sign up for an education program
Get an internship
1. Community
Meetup events are a good first step to enter just about any field. They don’t require any commitment or investment. They often feature experts in the field, both from industry and academia.
What to expect?
Meetups are events where researchers, students and industry professionals meet to share recent advances, projects, or ideas. They are open and welcoming to newcomers. Before the current pandemic, they usually happened physically, but they moved into the online space nowadays. This makes them even more accessible but takes out some of the natural interaction.
What does it take?
Look up a meetup. One of the most popular platforms for organizing events is meetup.com. We provide some links below.
AI meetups in Belgium
AI meetups in Europe - every larger city has at least one meetup going on!
What will you take away?
Recent research advances in AI
Real-world applications of AI and the science behind them
Connection to the community
2. Education
Yes, there is science behind AI, and most of it needs mathematics. You really don’t have to be a math whiz - but at least some knowledge in the area is necessary. Topics will include linear algebra, statistics and probability, multivariable calculus and optimization.
Broadly, there are two paths you could take to educate yourself:
Formal education in the form of a bachelor or masters degree
Online courses and other forms of informal education
What to expect
Formal education gives you a more robust understanding of the science behind AI. It involves several years of structured training that has been tested and verified on generations of students. You will be a part of a community of students with whom you can support each other, and (if the university is any good), you will have regular interaction and feedback with academic experts. If you are not sure what exactly you want to do, formal education also gives you a broader taste of what you can do in AI: research, applied work, business.
If you have an undergraduate degree in another domain, you can use your master’s degree to switch to AI. Think of it as a strength that distinguishes you. You can definitely benefit from a degree in formal sciences, but especially if you are interested in applied AI, a major from humanities like linguistics, or from natural sciences, like biology, can come in really handy.
Informal education gives you focused, typically more applied training in a field. Online courses may last for 5-12 weeks and require a workload of 5-10 hours per week. They are way more flexible: you can study at your own pace, and many courses are on-demand (you can start any time you want). Many are offered by some of the best universities in the world, and top experts give the lectures. At the same time, you might not get enough individual feedback, and you would do the work without the community that universities create.
It can be hard to get from zero to a full understanding of AI just through informal education, so you might want to combine the two. For example, you can get a formal education in a broader field and then re-focus on AI in online courses. Some universities also offer part-time or remote courses that take advantage of online tools. It really depends on your context: your ability to self-learn, your need for a community, your capacity to dedicate full-time to education. But as many online courses are free or offer a free trial, you can try it out and see how it fits you.
What will you take away?
A robust education might take time and effort to acquire, but the investment will pay off many times. There will always be a new framework, language, or model architecture to learn, but the fundamental concepts and tools will stay the same.
3. Internship
You’ve met the community, and you’re working on your education or completed it. If you’re interested in how AI is applied, consider applying for an internship. Don’t be pressured into thinking you can’t go for a regular position, but an internship will give you a taste of what you might want the future job to look like (or not look like). Also, a part-time contract might be a good bridge between jobs, domains, or between academia and business. A few more tips:
Check out both startups, which might have the advantage of a more personal approach, and larger companies, which typically hold regular internship programs for multiple interns at once.
Your applications don’t only have to target companies with AI as their core business. Many companies have a department working with data. If you have an interest or education in a different field, here is an opportunity to use it to your advantage!
How to get started
Sign up! It might take multiple attempts, so definitely don’t give up after a rejection. The reality is that from some companies or organizations you’ll never hear back. Others will reply with a delay of many weeks or months.
What will you learn?
In a good internship, you will get hands-on experience with AI-driven projects. You will get to understand the obstacles of real-life applications. If you’re interested, you might also get experience in other areas of the project process, like defining a problem, planning a project, or communicating with a client.
Check out the experience of Cyril, who completed an internship at Superlinear.
Conclusion
At Superlinear, each of us has a unique story of how we entered the AI field. We value the variety in paths that the team took to get where they are today.
We hope this article gave you some tips on how to find your path, wherever you stand.
