In fact, PwC predicts that by 2030, AI alone will contribute $15.7 trillion to the global economy.

And with household names like ChatGPT only making up a fraction of the AI ecosystem, the career opportunities in the space also seem endless. AI and machine learning specialist roles are predicted to be the fastest-growing jobs in the world, according to the World Economic Forum’s 2023 Future of Jobs Report.

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Even beyond namesake AI experts, the technology is being utilized more and more across the text world. In fact, 70% of professional developers either use or are planning to use AI tools in their workflows, according to Stack Overflow’s 2023 Developer Survey.

So, for those especially outside the world of tech, how does AI even work and get created? Programming is at the core.

What are the top AI programming languages?

By and large, Python is the programming language most relevant when it comes to AI—in part thanks to the language’s dynamism and ease.

“Python dominates the landscape because of its simplicity, readability, and extensive library ecosystem, especially for generative AI projects,” says Ratinder Paul Singh Ahuja, CTO and VP at Pure Storage.

Rakesh Anigundi, Ryzen AI product lead at AMD, goes even further and calls Python a “table stakes” language—meaning it is a baseline skill all those working in AI need to know.

LinkedIn even ranks Python as the second-most in-demand hard skills for engineering in the U.S., second only to “engineering” itself.

“In particular, skills in key programming languages commonly used in the development of AI—Python, Java, and SQL—rank among the top five most sought-after skills on the technical side in the U.S.,” writes LinkedIn’s head of data and AI, Ya Xu.

How can you learn AI programming languages?

The programming languages that are most relevant to the world of AI today may not be the most important tomorrow. And, even more crucially, they may not be most utilized by your company.

Regardless, having foundation skills in a language like Python can only help you in the long run. Enrolling in a Python bootcamp or taking a free online Python course is one of many ways to learn the skills to succeed. Students may also be exposed to Python in an undergraduate or graduate level coursework in data science or computer science. 

Anigundi also notes it is important for students to be able to know how to efficiently set up programming work environments and know what packages are needed to work on a particular AI model. Being an expert at mathematics like statistics and regressions is also useful. 

“We have been through these tech trends always—it’s just that the pace at which some of these changes are happening is mind boggling to me, at least in my lifetime,” he says. “But that still doesn’t take away some of the institutional knowledge that these different educational institutes impart in you.”

It can be worth considering specializing in a sub-field aligning with personal interests like natural language processing, computer vision, or robotics, Singh Ahuja says. Prioritizing ethics and understanding the true implications of AI are also critical. 

But since AI technology is changing so rapidly, soft skills can be argued to be even more important than technical capabilities. Some of the critical skills Singh Ahuja identifies include:

• Problem-solving and critical thinking
• Creativity and innovation
• Effective communication
• Adaptability and commitment
• Ethical judgment

Above all, demonstrating your passion and desire to learn through real-world experience can help you distinguish yourself among the competitive field.

“If you’re in a very early part of your career—picking a project, doing a project demonstrating value, sharing it, writing blocks, that’s how you create an impact,” Anigundi says.

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