Programming languages are notoriously versatile, each capable of great feats in the right hands. AI (artificial intelligence) technology also relies on them to function properly when monitoring a system, triggering commands, displaying content, and so on.
As a programmer, you should get to know the best languages for developing AI. Below are 10 options to consider and how they can benefit your smart projects.
1. Python
It’s Python’s user-friendliness more than anything else that makes it the most popular choice among AI developers. That said, it’s also a high-performing and widely used programming language, capable of complicated processes for all kinds of tasks and platforms.
Artificial intelligence is difficult enough, so a tool that makes your coding life easier is invaluable, saving you time, money, and patience.
Another advantage to consider is the boundless support from libraries and forums alike. If you can create desktop apps in Python with the Tkinter GUI library, imagine what you can build with the help of machine learning libraries like NumPy and SciPy.
2. Java
Here’s another programming language winning over AI programmers with its flexibility, ease of use, and ample support. Java isn’t as fast as other coding tools, but it’s powerful and works well with AI applications.
One key feature is its compatibility across platforms, so you don’t have to rewrite code every time you use a different system. Java also produces good visuals. You don’t need to worry so much about the quality of your AI graphics.
Related: Best Ways to Learn Java Online
There’s more coding involved than Python, but Java’s overall results when dealing with artificial intelligence clearly make it one of the best programming languages for this technology.
3. JavaScript
The pros and cons are similar to Java's, except that JavaScript is used more for dynamic and secure websites. Programmers who don’t mind coding will choose it over Python.
Its AI capabilities mainly involve interactivity that works smoothly with other source codes, like CSS and HTML. It can manage front and backend functions, from buttons and multimedia to data storage.
JavaScript is also blessed with loads of support from programmers and whole communities. Check out libraries like React.js, jQuery, and Underscore.js for ideas.
4. Scala
Scala took the Java Virtual Machine (JVM) environment and developed a better solution for programming intelligent software. It’s compatible with Java and JavaScript, while making the coding process easier, faster, and more productive.
Thanks to Scala’s powerful features, like high-performing functions, flexible interfaces, pattern matching, and browser tools, its efforts to impress programmers are paying off. It’s now one of the best languages to use for AI development.
Another perk to keep in mind is the Scaladex, an index containing any available Scala libraries and their resources.
5. Lisp
Developed in the 1960s, Lisp is the oldest programming language for AI development. It’s very smart and adaptable, especially good for solving problems, writing code that modifies itself, creating dynamic objects, and rapid prototyping.
There are downsides, though. Lisp’s syntax is unusual compared to modern computer languages, making it harder to interpret. Relevant libraries are also limited, not to mention programmers to advise you.
Despite its flaws, Lisp is still in use and worth looking into for what it can offer your AI projects. Grammarly, DART, and Routinic are some of its success stories.
6. R
If you’re working with AI that involves analyzing and representing data, R is your go-to programming language. It’s an open-source tool that can process data, automatically apply it however you want, report patterns and changes, help with predictions, and more.
Apart from mainly serving statistical functions, R is a tricky language to learn and should be paired with other reliable tools to produce well-rounded software and a productive workflow for your business.
Related: Data Analytics Software Tools You Can Learn Quickly
But, what R lacks in range it makes up for with high compatibility and great style when creating visuals like graphs and charts, no matter how detailed.
7. Prolog
For a more logical way of programming your AI system, take a look at Prolog. Software using it follow a basic set of facts, rules, goals, and queries instead of sequences of coded instructions.
Prolog can understand and match patterns, find and structure data logically, and automatically backtrack a process to find a better path. All-in-all, the best way to use this language in AI is for problem-solving, where Prolog searches for a solution—or several.
Hence its use in chatbots and virtual assistants like IBM’s Watson. Think of how simple but helpful these forms of smart communication are. Prolog might not be as versatile or easy to use as Python or Java, but it can provide an invaluable service.
8. Julia
Julia is another high-end product that just hasn’t achieved the status or community support it deserves. Even so, its features don’t disappoint. This programming language is useful for general tasks but works best with numbers and data analysis.
It offers several tools for creating a dynamic interface and impressive graphics to visualize your data, for example. There’s also memory management, metaprogramming, and debugging for efficiency.
In terms of AI capabilities, Julia is great for any machine learning project. Whether you want premade models, help with algorithms, or to play with probabilistic programming, a range of packages await, including MLJ.jl, Flux.jl, Turing.jl, and Metalhead.
9. Haskell
If you want pure functionality above all else, Haskell is a good programming language to learn. Getting the hang of it for AI development can take a while, due in part to limited support.
But, its abstraction capabilities make it very flexible, especially when dealing with errors. Haskell’s efficient memory management and type system are major advantages, as is your ability to reuse code.
Related: A Beginners Guide To Abstraction in Object-Oriented Programming
It’s also a lazy programming language, meaning it only evaluates pieces of code when necessary. On the one hand, it can help produce a very smart process. On the other, it might end up lagging. Even so, the right setup can make Haskell a decent tool for AI developers.
10. C++
This may be one of the most popular languages around, but it’s not as effective for AI development as the previous options. The main problem in relation to AI is the syntax of C++. It’s too complicated to quickly create useful coding for machine or deep learning applications.
At the same time, we wouldn’t have TensorFlow, a key machine learning framework, without C++. It remains a powerful and flexible tool for designing software, so you should keep it in mind for certain tasks and alongside other tools.
For example, search engines like Google make use of its memory capabilities and fast functions to ensure low response times and an efficient ranking system. In other words, feel free to use C++, but do so carefully.
Explore What You Can Create With AI Programming
You have several programming languages for AI development to choose from, depending on how easy or technical you want your process to be. Another factor to consider is what system works best for the software you’re designing.
One way to tackle the question is by looking at the popular apps already around. What do they do? How do their basic or interactive functions work? What languages do they use? The more you find out, the more ideas you'll get.
