Programming is at the core of developing AI systems that help businesses reach their goals. Among dozens of programming languages, each has its advantages and shortcomings. The language you use depends on the requirements and components of your project.
Gartner showed that the global AI software market would reach $62 billion by the end of 2022. This has led to high demand for programmers and software developers working in artificial intelligence. Learning a programming language for AI is a great career move.
By factoring in popularity, job demands, and projects across industries, AI engineers should add these languages to their skill set for a rewarding future. Let’s get started.
Selecting a programming language
– AI codebases
AI codebases refer to the complete body of different project source codes available. The codebases must be plentiful and of good quality. More shared codebases for different intelligence projects also provide consistency and mentoring opportunities, which is valued in modern-day programming.
– Code Length
The primary goal of programming an enterprise application is to simplify and solve business-oriented problems. But, It is also equally important to focus on the code writing process. A short code is usually preferable to simplify complexities. This could also affect its readability.
– Learning curve
Code Academy estimates that learning the fundamentals of different programming languages takes approximately the same amount of time (3-6 months). Make sure your AI programming language matches your proficiency and capability levels.
– Typing (Dynamic/Static)
Dynamic and statically typed languages function differently and have their own pros and cons. Knowing these features in your scripting languages will help you understand them better. Typing also affects code length and debugging.
– Performance
The most important parameter is performance. Among the 700+ programming languages currently used, Geeks for Geeks only places about 5% in the high-performing category. This involves the compilers, platform compatibility, security, speed, and more.
Apart from these, efficiency, extendability, flexibility, and ease of use are also relevant. We consider these below and compile the most in-demand languages.
Popular programming languages for AI
1. Python
Python is a high-level programming language known for its simple syntax. It is well equipped to handle big and complex AI projects. Pandas, SciPy, nltk, PyTorch, and Keras are some of its most popular pre-existing libraries and frameworks used in machine learning, data science, and natural language processing.
Pros of Using Python for AI Projects
– Easy to learn
– Platform independent
– Well-integrated and documented
– Excellent visualization tools
Cons
– Limited speed
– Design limitations
– Not intuitive to mobile environment
2. Java
Java is widely used across different sectors of IT. It is also popular for developing enterprise AI projects. Java is a preferred programming language for many developers for machine learning, neural networks, genetic programming, search algorithms, and more. Weka, MOA, Encog, Arbiter, Drools, and d3web are Java’s most popular AI libraries.
Pros of Using Java for AI Projects
– Speed
– Ease of debugging
– Active community of developers
Cons
– Long Code Length
– Can be difficult to learn
3. R
R is a statistical programming language used widely among data scientists. It is perfect for AI projects with robust data processing needs. It supports popular libraries like MXNet, TensorFlow, Keras, etc., and also integrates with other AI programming languages in this list.
Pros of Using R
– Highly compatible
– Facilitates quality plotting and graphing.
– Platform independent
Cons
– Steep learning curve
– Can be slow for big processing
4. C++
C++ is another highly reliable language. It has a vast application across different development fields, but AI engineers use it, particularly for its efficiency. It supports capable libraries like SHARK, MLPACK, and other notable frameworks.
Pros of Using C++ for AI Projects
– Incredibly fast
– Effective memory management
Cons
– Complex syntax
– Limited libraries
5. Lisp
Lisp is one of the oldest AI programming languages developed by John McCarthy, the father of artificial intelligence. It is known for its flexibility and handy features like quick prototyping, automatic garbage collection, and more. It has a small but dedicated developer community updating LISP libraries.
Pros of Using Lisp for AI Projects
– Flexible
– Logic-oriented approach.
Cons
– Poor readability
– Language bloat
6. JavaScript
According to an Analytics Insight study, Javascript is the most used programming language on GitHub. It has a wide range of applications across fields of IT. Engineers can build models without R or Python thanks to new libraries like Tensorflow.js and BrainJS. Although not as complete as other languages in this list, professionals with existing knowledge of JS prefer it more.
Pros of Using JavaScript for AI Projects
– High computational performance
– Secure with better range
Cons
– Data Limitations
– Single-threaded
7. Haskell
Haskell is another popular statically-typed language with lazy evaluation and type interface. Haskell’s unique features for type classes, type polymorphism, pattern matching, and list comprehension are valued while building AI programs.
Pros of Using Haskell for AI Projects
– Fast and secure
– Emphasizes code correction
– Great for teaching & research
Cons
– Niche language with steep learning curve
Bottom line
The above are the most in-demand programming languages in 2022. AI will always have new languages, skills, and technologies. AI engineers should be aware and ready to upgrade. Joining Squadery helps you to stay in the loop with the latest updates and work with like-minded AI professionals for the top US companies.
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