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[title]Artificial intelligence is being used in almost every industry to improve productivity and decision-making and to create new opportunities. As a result, AI skills are becoming increasingly needed and expected, no matter who you are or what you do. Employment for computer & information research scientists—many with AI duties—is projected to grow 26% from 2023‑33, per the Bureau of Labor Statistics. In addition, these are relatively high-paying jobs, earning an average of $140,000 annually for AI specialists.
If you’re thinking about learning AI, you’ll probably want to know how long it’s going to take. A realistic expectation can help you understand what to expect from the learning process and also make it easier to budget your time and money so that you’ll be able to complete your training successfully.
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How long will it take to learn AI skills?
Learning AI can take anywhere from a few months to a number of years. The learning process will depend in part on where you start from. It's important to have a strong foundation in math, programming, and data. Many beginner classes will help you learn these skills; however, if you already have a background in any of them, you’re likely to learn much more quickly. The length of time it takes to learn AI also depends on where you want to go with your skills. You can learn some basics, like how to use a particular generative AI tool, fairly quickly. However, advanced skills take more than a few months to develop. For instance, if you want a professional career in a technical role like an AI Engineer or Research Scientist, the learning process might take years. If you’re trying to earn a specific AI certification, the learning process can also take longer.
Skills you’ll learn in an AI class
When you learn AI, you’ll cover a long list of skills. This is a very technical field, and you’ll need to learn many technical skills, like math and programming so that you can work with this technology. However, to be successful in AI, you’ll also need soft skills like problem-solving and adaptability, which will help you successfully utilize your technical skills.
Technical skills:
- Mathematics
- Programming
- Data analysis
- Data modeling
- Machine learning
Soft skills:
- Communication
- Problem-solving
- Collaboration
- Critical thinking
- Adaptability
Ways to speed up the AI learning process
One of the best ways to speed up the AI learning process is to enroll in an AI class. One benefit to a class is that you won’t have to waste time trying to figure out what you need to learn and how to learn it. Instead, you’ll be guided through the process by an expert and will follow a comprehensive curriculum that covers all the important knowledge and skills in this field. This can save you a lot of time. A class will also allow you to ask questions and get feedback, which can help you progress much more quickly and avoid wasting time on incorrect techniques.
With a class, you’ll learn with a group of peers, which can also help you learn more quickly. When you learn with a group, you’ll often participate in discussions and group activities, which can enhance your understanding and improve your retention. You’ll also get the benefit of being exposed to diverse viewpoints. Finally, many people find the group learning environment more engaging.
A class can also be helpful when it comes to finding a job. Many classes come with a certificate or other type of evidence of completion of the class, which you can put on your resume. Some professional training centers like Noble Desktop or General Assembly also offer career assistance, such as help building your portfolio and advice about interviewing or improving your resume.
Practice is also essential to learning AI and can help you learn more quickly. When you practice, you learn how to apply your knowledge to the real world, which enhances your understanding of new concepts. Practice also helps you retain your skills and perform them more quickly and easily in the future since they will become more automatic over time. Finally, it reveals your weaknesses, allowing you to improve in those areas.
Beyond the basics
The learning process doesn’t end with the introductory lessons and the basic parts of your training. There are other factors to consider and more complex and detailed topics to address as you move through your AI training.
Don’t forget about ethics
While it's easy to focus on technical skills when you’re learning AI, ethics are also important. AI technology has significant potential consequences for society, and students should spend some time educating themselves about the responsible use of this technology. In an IBM article titled “What Is AI Ethics?” the author explains the importance of thinking about ethics if you want to work in AI. They stress, “Artificial intelligence performs according to how it is designed, developed, trained, tuned, and used.” That means that the ethical use of this technology depends in part on AI professionals working according to ethical standards.
Keeping a balance
When you’re learning AI, you’ll probably want to learn as quickly as you can. But, as a LinkedIn article titled “You're considering a career in AI. How can you make sure you're ready for the challenges ahead?” explains, focusing on learning above all else can be problematic. The author says, “AI is a demanding and rewarding field that can consume a lot of your time and energy. You need to balance your work and life to avoid burnout, stress, and isolation.” They advise making sure you also have other interests outside of learning about AI, such as hobbies and friends and family.
The AI learning process is never over
In the field of AI, as with many tech fields, it's important for students to realize that the learning process will never be over. Both technology and data patterns are evolving rapidly, meaning that to be competitive in AI, you’ll need to be a lifelong learner. The LinkedIn article stresses, “AI is a fast-moving and evolving field that requires constant learning and adaptation.” The authors offer some tips on how to keep yourself informed on updates in the field of AI, explaining that there are many options. They state, “You can do this by reading blogs, newsletters, podcasts, or journals that cover AI topics. You can also attend webinars, workshops, conferences, or meetups that showcase AI innovations and best practices. By staying updated, you can keep up with the pace of change and find new opportunities and challenges in AI.”