Artificial Intelligence Programming for Beginners

May 9, 2022 • Devin Partida


Humans have been dreaming of artificial human-like intelligence, what we know now as AI, for centuries. The first mentions of AI date back to philosophers in the 1700s, though we didn’t have any technology that could even create a computerized intelligence until the late 1940s and 1950s. 

Today, the AI industry is exploding. Experts estimate the global AI market will be worth more than $267 billion by 2027. By 2025, this technology will likely eliminate 85 million jobs — and create 97 million new ones. As the industry grows, so will the demand for people skilled in AI programming. 

If you’re curious about what it takes to program one of these AI systems or are considering changing your career to take a step into AI programming, it’s helpful to start at the beginning. Let’s take a look at artificial intelligence programming for beginners. 

A Staple of Science Fiction 

It feels like we’ve spent a very long time imagining what it would be like if we could create an AI with human-like intelligence. Artificial intelligence has been a staple in science fiction for decades. Ray Bradbury imagined a smart house that could do everything from cooking to cleaning in “There Will Come Soft Rains” back in 1950. In 1977, Doctor Who introduced us to K-9, an AI-powered robotic dog smart enough to beat The Doctor at chess. 

It hasn’t all been sunshine and roses, though. In 1984, James Cameron brought us the beginnings of The Terminator franchise. These movies featured Skynet, an ultra-intelligent AI that triggered a nuclear strike on humanity. The AI decided humanity’s destruction was in the species’ best interest in its infinite wisdom. We’ve used AI as both the hero and the villain in so many stories it’s impossible to list them here. Thankfully, the technology isn’t quite there yet.

We haven’t created an AI capable of human thought and emotion yet, but the technology is beginning to evolve to the point we can use it as part of our daily lives. It’s not perfect and is prone to corruption. Microsoft learned this the hard way when they introduced the world to Tay, an AI-powered Twitter chatbot programmed to learn from its interactions with other Twitter users. Microsoft had to take the bot offline after less than a day because the internet managed to turn it into a racist monster.

Predicting the Future by Looking at the Past

Current AI programs may not be able to end the world, but that doesn’t mean they are without uses. These systems feed on information and the more data they have access to, the more intelligent they become. Current AI programs rely on machine learning — absorbing and cataloging data — to understand their tasks and their world. A well-programmed AI system could even potentially predict the future with enough historical data. 

It’s important to note there’s no magic here. These aren’t the side-show fortune tellers you expect to see pouring over crystal balls. Instead, AI prediction is based on pattern recognition. By looking at what happened in the past, these systems can figure out what might happen in the future and when these things are likely to occur. Human analysts can do the same thing, but it takes a lot longer and by the time they come up with a reasonable prediction, its period of usefulness has already passed. 

Foundations of Artificial Intelligence Programming

What does it take to start programming AI? 

Start with a basis in mathematics. So much of this programming relies on linear algebra and calculus. You may also want to brush up on your probability and statistics lesions and classes in algorithm creation. It might not seem like math would apply here, but having a solid mathematical foundation can make the rest of the steps easier. 

Next, take some time to figure out what type of AI you’re trying to create. Are you trying to solve a specific problem? Creating a system with a narrow focus is a lot easier, both in terms of programming and preventing it from going totally off the rails, as Tay did back in 2016. 

Choosing the Right Programming Language

The next step is to choose the correct programming language for the task. There are as many languages to choose from as there are tasks to complete, but for now, we’re just going to focus on the top five programming languages you might want to study. 


The various incarnations of the C programming language have served software engineers well for decades. C++ has been a mainstay in the industry for more than 30 years. It is a very general-purpose programming language and one you can easily shape and manipulate for just about any task. One of the most significant benefits of C++ is commands written in this language speak directly to the CPU, removing any middlemen and eliminating potential failure points. 

The only major downside of C++ is, unlike other languages on this list, it can be challenging to read and it has a very high learning curve. It can be a fantastic tool, but it might not be the best place to start for amateur programmers.

Java and Scala

Java dates back to 1995 and is famous for its “write once, run anywhere” toolset. It doesn’t have a lot of extra dependencies, making it easy to run on most platforms. It generates a virtual machine between the code and the device, making it infinitely portable. 

Scala is the next step in the Java ecosystem. Scala is short for scalable language, using the same basic syntax as Java. Both languages are compatible with each other, so you can write in Scala and run it in a Java environment and vice-versa. 


R is a newer programming language, but it’s developed almost exclusively for use in the data science industry. This language specializes in statistical data processing, linear and non-linear modeling and other essential tasks for creating a functional AI system. It’s a complex language to learn, but it will likely gain traction as the AI industry grows.


Python is probably one of the easiest modern programming languages to learn. It’s easy to read, functional and offers support for AI libraries. Various tools and AI libraries, like TensorFlow, SciKit-Learn and Pybrain, help programmers and software engineers learn how to utilize Python for their AI systems.e

Narrow, General and Super Artificial Intelligence

While you’re deciding what type of AI to create, we can break it down into three categories: narrow, general and superintelligence. 

Narrow intelligence, also called weak AI, is perfect for simple low-level tasks. Program them to complete a single task or solve a single problem. They won’t offer much functionality beyond basic programming. 

General or strong AI is where most people will find these programs become useful. It still needs a lot of work, but this is where you’ll find things like speech and facial recognition and hypothesis testing. This category is also where you will likely find AI chatbots as they become more common in customer service and support.

Superintelligence is still a theoretical concept. These systems are the ones we think of when we see self-aware or human-like AI. A super-intelligent AI will, in theory, be able to set its parameters and may even be able to mimic human thought and emotion. 

Looking Toward the Future

If you’re looking to explore artificial intelligence programming for beginners, starting with your math skills is the best place to start. From there, choose a programming language and start developing your skills. There is a growing demand for programmers skilled in creating AI and machine learning systems and we’ll likely continue to see these positions become even more critical in the coming years.