What does "machine learning" really mean? Step inside for a closer look.
Computers that can work faster and better than humans are no longer the stuff of science fiction. Artificial intelligence (AI) powers some of the tools we use in our everyday life like ride sharing apps, smartphone assistants, and social media, and that’s just the beginning.
According to a recent study by Adobe, just 15 percent of organizations are currently using AI, but 31 percent said it’s on the agenda for the next 12 months. BrainStation’s 2019 Digital Skills Survey also found that AI is top of mind with 78 percent of respondents saying that machine learning and AI are the trends that will have the most impact on development in the next five to 10 years.
It’s clear the industry is booming, and so is the market to hire AI talent. That’s because AI experts have a specialized set of skills that’s not easy to find. The good news is that anyone with a background in programming, data science, math, or statistics has a head start when it comes to building a career in AI.
To break down the most important skills you need to work in AI, we spoke to Adam Thorsteinson, a Data Scientist and Educator at BrainStation.
Computer Programming and Coding
A background in coding, computer science, or programming is a major benefit for anyone who wants to work in AI. The most important coding language you need to know? That depends on the problem or project you’re working on, but SQL and either R or Python are a good place to start.
“These coding languages are the backbone of extracting, manipulating and finding insights in data,” says Thorsteinson.
Understanding other common coding languages like C, C++, and Java is helpful, too.
Linear Algebra and Calculus
Math — whether you love it or hate it – it’s an important part of AI. But thanks to coding, you don’t need to be a mathematician to work in AI. Being able to understand the math that’s going on behind the scenes, however, is important.
“Luckily, the coding languages take care of the actual computation, but understanding the intuition behind algebra and calculus and how they become part of the AI process is helpful,” says Thorsteinson.
For algebra, it’s important to understand the concept of a scalar versus a vector, matrix multiplication, changes of basis operations, and projection into higher/lower dimensions. For calculus, understanding the concept of a derivative and finding minimums and maximums of functions will be helpful.
Statistics, a type of math that collects and analyzes data, is the foundation of AI and machine learning tools.
In fact, some people even argue that machine learning is just glorified statistics. What’s certain is that people who want to work in AI should have some knowledge of statistics and statistical models.
“Understanding basic statistics like hypothesis testing, p-values and the fundamentals of regression models is helpful,” says Thorsteinson.
In addition to technical AI skills, the importance of soft skills like communications shouldn’t be underestimated.
“Communications skills separate the good technical workers from the great ones,” says Thorsteinson.
Business leaders need to be able to understand and communicate the value of AI to potential customers and investors. That’s why someone who can do the technical work and talk about it in plain language is a valuable asset.
“Yes, you may have built some really impressive machine learning models, but can you explain what you’ve done to someone else? Can you sell someone on it? Can you frame your work in terms of a business problem and the solutions it’ll enable?” says Thorsteinson.
Support Roles in AI
The future is bright for AI experts, and that means businesses will be in the market to hire non-technical roles (like marketing, communications, sales, project management and recruiting) to support AI teams and projects.
“Working in AI doesn’t necessarily mean being a Machine Learning Engineer or Data Scientist. For anyone non-technical, working in AI is still highly achievable,” says Thorsteinson.
For anyone wanting to work in a non-technical AI role, Thorsteinson recommends learning basic terminology from the field, what a predictive model is, what they can and can’t do, and the difference between business intelligence, business analytics, and machine learning.
“This will help any non-technical people collaborate with any technical staff in an AI organization,” says Thorsteinson.
Find out how BrainStation’s online and on campus courses can help you build a career in AI. Talk to a BrainStation learning advisor today.