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Thinking about getting into AI? Great choice. Here’s a list of the best AI programming languages. Not only is the AI field growing at a dizzying pace and AI-related jobs popping up virtually everywhere but many non-programming jobs are now requesting AI knowledge as well as it can be a great tool in automating monotonous and repetitive tasks. Also, AI is a great skill to develop if you are a startup founder or entrepreneur. If you don’t believe me, you can just take a look yourself at the amount of money that has gone into AI and Machine Learning startups in the past 2 years.

The first step (not surprisingly) is to learn a programming language. But which programming language? That is the million dollar (or billion if you play your cards right) question. Answering it can be a bit tricky and some say it’s a matter of opinion (We call those people wrong) but I’m gonna take a stab at it anyway. There are many programming languages with some of the most popular ones being Python, Java, Julia, Haskell and JavaScript and choosing one should not be taken lightly as it will determine:

  • How easily you can develop AI applications.
  • The tools, libraries and frameworks you have at your disposal.
  • The documentation and community you have for support.
  • How quickly you can develop and deploy your applications
  • What kind of applications you can develop.

So getting this right is almost like choosing a partner for marriage (or even more important depending on who you ask). But fear not because there’s a neat little framework for thinking about this which entails looking at 5 key factors which are:

  1. Code length – How long is the code?
  2. Performance – How long does it take for the code to run?
  3. Typing – Is it dynamically or statically typed?
  4. Difficulty – How difficult is it to Learn?
  5. AI codebases – How many codebases are there for you to draw on?


If you look at it from this perspective, the clear winner is Python. But you don’t have to take my word for it. You can just see for yourself that Python is:

  • Easy to learn. Python’s English-like syntax is extremely flexible and the language has many quality-of-life and ease-of-use features. This is extremely useful as even programming noobs will find Python intuitive. This low barrier to entry is especially important because many data scientists and machine learning engineers working with AI come from a mathematical and statistical rather than a programming background.
  • Well-Integrated. Python is a batteries-included kind of language which means programmers don’t need to reinvent the wheel every time they work on a new project. Tons of AI frameworks, libraries, and platforms have already been developed in Python and are available as open-source projects (which is great because most of us hate to pay for software tools)
  • Well-Documented. It’s even easier to learn Python because there are so many tutorials, projects, bootcamps and even books online. Less popular languages will probably not have as many resources available.
  • Simple and easy to read. Because of its simple syntax, Python is extremely readable. What I mean by this is that it’s relatively easy for other programmers to understand your code and this is vital for collaboration.
  • Platform-Independent. Python can run on virtually any platform, from Windows to Unix. It doesn’t have to be compiled because it’s an interpreted language.

However, Python is not without its faults. The most notable drawback of Python is its speed. Because Python is an interpreted language, it’s execution time is long and this can be problematic in applications where speed is important such as in stock trading. But for AI and machine learning applications, rapid development is more important than raw performance and this tradeoff is seen as a good deal.

Other Programming Languages

Of course, one can use other programming languages for Artificial Intelligence. In fact, in theory all programming languages are interchangeable and what you can accomplish in one language, you can accomplish in all others but as any experienced developer will tell you, that is hardly the case since programming languages are often developed with some task in mind. Anyway, let’s take a look at some of the other not so best languages for AI along with their pros and cons. (One of the reasons I am doing this is so as to not get lynched by the other languages’ zealots)

1. Java


Java is a popular general-purpose language with a large community of developers. It is statically typed, meaning that you can catch bugs early and run programs faster.


Java is incredibly verbose (sometimes requiring 5 lines of code to do what you can accomplish using one line of a language like Python and JavaScript) and has a steep learning curve. It has very few quality-of-life features; programmers will need to do quite a lot manually.

2. Julia


Julia was designed for high-performance numerical computing and has solid support for machine learning.


Julia is a young language and consequently doesn’t have a lot of community support. This means learning the language may be challenging

3. Haskell


Haskell is a functional programming language that emphasizes code correctness. It can be used for AI development, although it’s more commonly used teaching and research.


Haskell is challenging to learn. It can also be extremely confusing as the language is very niche.

4. Lisp


Lisp has been used for AI for many years (since the Bill Gates era). It’s known for its flexibility and symbolic, logic-oriented approach.


Lisp can be difficult to read and write. It also has a small community of developers (which is kinda weird given how long it has been around).

5. R


R is a popular statistical programming language among data scientists. It integrates well with other languages and has many packages available. It is excellent for AI with robust data processing needs.


R can be slow and has a steep learning curve. It is also not well-supported.

6. JavaScript


JavaScript is a popular language for web development which means it has a large community. Developers use JavaScript in machine learning libraries like Tensorflow.js.


JavaScript is more complex and challenging to learn than Pythons. It’s robust and has so many options that it may confuse non-developers

7. C++


C++ is a fast, powerful language among game developers. It’s well-supported and well-documented and can be used for many different things. It’s also highly efficient.


C++ can be difficult to learn and doesn’t have a lot of quality-of-life features which means a lot of things have to be handled manually by the programmer.

8. Scala


Scala is a general-purpose language with many features suitable for AI development. It integrates well with Java and has a large community of developers


Scala can be complex and difficult to learn. Scala is primarily used for advanced development such as data processing and distributed computing

Programming Languages You Shouldn’t Use for AI

I’ve said that any programming language can be used for AI but there are some I wouldn’t recommend unless you are a masochist and you like making life difficult. These are usually languages that are too niche and consequently don’t have enough community support or languages that are too low-level and make the development process long. They include:

  1. PHP
  2. Ruby
  3. C
  4. Fortran


Python is the best programming language for Artificial Intelligence. This is regardless of whether you are a programming veteran or a total newbie. The only time I wouldn’t recommend python is if you are already familiar with another language such as JavaScript and you don’t plan on working on AI for long. Java is also a good choice, but it’s more challenging to learn. Other popular programming languages include Julia, Haskell, Lisp and R.

If you are completely new to programming AI now, there are many advantages to beginning with Python. You can hit the ground running and start developing immediately and get that valuable hands-on experience.

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