The 5 best Programming Languages for AI Engineers

As AI boosts productivity, product quality, and consumption, the most dramatic sector gains will be in financial services, healthcare, and retail. With demand for AI skills more than doubling in recent years, a career in AI has become a highly attractive option for people with interest in data science and software engineering.

AI opens up a whole new world of possibilities for both enterprises and software engineers, so if you’re eager to take advantage of this opportunity, you might be wondering where you should start. The answer to these questions will depend on your knowledge and experience, the type of AI project you’re interested in, and current industry trends.

After remaining somewhat stagnant for decades, artificial intelligence (AI) is going through a period of powerful acceleration. You’ll be learning an AI language that benefits from a healthy ecosystem of tools, support packages, and a large community of programmers.

1. Python

Python is the leading coding language for NLP because of its simple syntax, structure, and rich text processing tool. When it comes to AI programming languages, Python leads the pack with its unparalleled community support and pre-built libraries (like NumPy, Pandas, Pybrain, and SciPy) that help expedite AI development. For Natural Language Processing (NLP), you can go old school with NLTK or take advantage of lightening-fast SpaCy.

As this highly flexible AI language is platform agnostic, you’ll only have to make minor changes to the code to get it up and running in a new operating system. But in the grand scheme of things, it makes perfect sense to learn Python, as it boasts the most comprehensive frameworks for both DL and ML.

However, while it’s sometimes referred to as the best programming language for AI, you’ll have to look past its five different packaging systems that are all broken down in different ways, some white spacing issues, and the disconnect between Python 2 and Python 3.

2. Java

Like some of the programming languages on this list, Java is also highly user-friendly, easy to debug, and runs across platforms without the need to engage in any additional recompilation. We can’t discuss the best programming language for AI without talking about the object-oriented programming language, Java.

When it comes to working with NLP, it’s easy to find enough support from the vibrant community that’s built around it. Since it first emerged in 1995, Java has grown to become a highly portable, maintainable, and transparent language that’s supported by a wealth of libraries. As Java enables seamless access to big data platforms like Apache Spark and Apache Hadoop, it has cemented its place within data analytics-related AI development.

3. Julia

Engaging in AI programming with Julia reduces errors and cuts costs because it combines the familiar syntax and ease of use of languages like C++, Python, and R. This negates the need to estimate a model in one language and reproduce it in a faster production language. Unlike the languages above, Julia isn’t exactly the go-to language right now.

Its core programming paradigm includes a type system with parametric polymorphism and multiple dispatch capabilities. Explicitly designed to focus on the numerical computing that’s required by AI, you can get results without the typical requirement of separate compilation. Whenever a task demands high-performance numerical computing and analysis, Julia (developed by MIT) will be the best programming language for AI projects.

This significantly reduces model risk and improves safety. One of the primary benefits of working with Julia is its ability to translate algorithms from research papers into code without any loss.

4. Haskell

Haskell is used in research projects because it supports embedded domain-specific languages that play a significant role in programming language research and AI. Its popularity is primarily concentrated in academic circles, but tech giants such as Facebook and Google have also been known to use it.

Unlike Java, Haskell is perfect for engaging in abstract mathematics, as it allows expressive and efficient libraries to create AI algorithms. Haskell is a standardized strong static typing (general) language developed in the 1990s with non-strict semantics (based on the Miranda programming language).

It’s also an excellent host for probabilistic programming and helps developers quickly identify errors during the compile phase of the iteration. While you can code these algorithms in any AI language, Haskell makes them far more expressive than others while maintaining an acceptable level of performance.

5. Lisp

Lisp can be described as a practical mathematical notation for computer programs. While AI has only started making a significant consumer impact in recent years, research and development within this field goes back as far as the 1950s.

AI developers often turn to Lisp for AI projects that are heavy on ML because it offers rapid prototyping capabilities, support for symbolic expressions, a library of collection types, and is highly flexible and adaptable to their problem-solving needs.

While the program is still running, you can also enable interactive evaluation of expressions and recompilation of functions or files concurrently. It’s also popular among AI programming languages because it allows the easy dynamic creation of new objects, with automatic garbage collection.

SHARE THIS