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Are you thinking of venturing into artificial intelligence as a developer? Wondering what the best AI programming language is? Adding AI to your skillset makes you a one-of-one professional in tech, an industry known for its wealth and high demand. That means you can avoid competitive salaries and name your rate.
Before you start daydreaming, though, you must first learn an artificial intelligence programming language.
Keep reading to find out the best programming languages for designing AI tools. We will objectively review a handful of programming languages, looking at each one’s abilities, pros, and cons.
Let’s get to it!
What Is Artificial Intelligence Development?
Artificial intelligence development involves the design of software that can dynamically solve complex problems or perform tasks by fetching and analyzing data streams. In plain English, an AI development is a computer program that uses feedback to learn how to approach new problems.
Humans learn new things and make decisions based on the information received through our senses. Artificial intelligence is essentially what it sounds like: computers with human-like reasoning. They can tear apart new information, use them to form new connections, and take the best possible action.
At one time, people widely accepted to think of AI technology in that way—machines with human cognizance. But as time passed, this view became obsolete.
A new wave of major researchers disagreed and developed a more holistic definition of artificial intelligence as “rational” machines.
Modern AI Development
One of the most relatable examples of artificial intelligence is the TikTok “For You” page. Over time, the app somehow learns to float up the kind of content you find the most entertaining.
You could say the same for other social networking platforms and streaming services—NetFlix, YouTube, Instagram, etc.
Mysterious as it may seem, such applications use machine learning to study your recent activity and predict your next move.
The Tesla self-driving cars and computer-controlled game characters make good examples of AI technology at its finest. Other fascinating applications include personalized virtual assistance (Siri and Alexa), fraud prevention systems, and so on.
Traditionally, computers can only solve static problems. Usually, developers leverage a high-level programming language to instruct computers on how to solve specific problems.
As long as the nature of the problem remains the same, the computer performs well. But if you introduce a new puzzle to the same computer, it becomes clueless and runs into an error.
AI Development Progress
Before artificial intelligence found its way into the real world, it was a fictional concept often depicted in sci-fi movies.
Artificial intelligence development began in 1958 as an academic discipline. Since then, several attempts at achieving a successful AI model have failed. AI research explored different approaches, such as simulating the human brain and mimicking animal behavior, logic, and big data.
After decades of misses and despair, the first breakthrough occurred with a mathematically-statistical machine learning method. Machine learning led to many jaw-dropping innovations and astronomical advances in academic research.
Artificial intelligence has since experienced colossal growth and expanded into a field with multiple branches and applications throughout the industry.
The classical branches of AI are knowledge, natural language processing, planning, perception, reasoning, and learning. AI is also applied to initiate an intuitive movement of objects.
The long-term goal of this technology is to imitate the human ability to solve arbitrary problems—general intelligence. To this end, researchers and computer engineers have whipped up several methods and programming languages to give computers near-human problem-solving abilities.
Artificial neural networks are one of the most sophisticated techniques of the bunch. The other approaches are statistics and probability, formal logic, search optimization, and so on.
What Are the Different Types of Programming Languages for AI?
There are loads of programming languages that support AI development in the market. The mainstream alternatives are Python, Java, Javascript, C++, R, Julia, etc.
Before choosing a particular programming language, consider these factors: your fluency with the language, how easy it is to learn and code using that language, and the availability of resources, e.g., packages, tools, documentation, and libraries.
Let’s explore the best options!
1. Python
Python is the best programming language for AI development. Though other popular programming languages may outperform Python speed-wise, it has the best overall qualities.
Whatever Python may lack in speed, it makes up for with ease, support, and utilities. Python developers can write code in fewer words than other programming languages.
Let’s see why Python has become the go-to programming language for AI programmers.
Pros:
- Simple Syntax: Python is the AI programming language with the closest syntax to spoken English. In effect, it is the easiest language to learn and use.
- It’s a good thing Python is so flexible since many professionals from fields with zero programming knowledge take an interest in artificial intelligence.
- Readability: While working on big projects, it is essential that team members can also read the code. Programs written in Python have the shortest code length and are easy to read.
- Excellent Visualization Tools: AI Programmers need to visualize data and understand how everything holds together, especially for complex applications with different moving parts. Python provides a wide selection of AI-focused graphic tools that allows users to plot data.
- Fully-packed Library: Python comes with an extensive list of machine-learning libraries, frameworks, and tools. Without having to build anything from scratch, developers can leverage these facilities to save hours of coding time.
- Well-documented: As a popular programming language, Python has more than enough learning resources for beginners, including boot camps, online tutorials, and sample projects.
- Cross-platform Compatible: Python works on any standard operating system—Windows, Mac, or Linux. It has an in-built interpreter for communicating with the computer.
Cons:
- The execution speed of Python codes is slower than other competing artificial intelligence programming languages.
2. Java
Java is a mainstream programming language created by Oracle. It is one programming language AI developers cherish for its flexibility—developers can use Java to develop a diverse range of applications.
This language remains relevant today due to its ability to adapt to changing technology. Being true to its nature, Java offers a programming language for developers to create scalable applications using AI technology.
Pros:
- Debugging: Java has a code base that spots errors on the go and optimizes the speed of running programs. This early bug detection mechanism and fast processing speed help projects move along faster.
- Visualization Tools: Java has professional-grade graphic tools for visualizing during data analytics. With this, developers can have a clearer picture of how everything holds together.
- Java Virtual Machine: This feature allows programmers to develop applications that strictly run on Java-enabled platforms.
Cons:
- Long Code Length: Java is quite wordy and difficult to read.
- Hard to Learn: The language isn’t beginner-friendly. Java’s syntax includes a lot of symbols and characters, which new developers may find confusing.
- Inadequate packages: Java has few building blocks that programmers can leverage to make coding easier. They have to build everything from the ground up.
3. Javascript
Javascript and AI development are a good pair. Though it is more popularly known as a programming language for building dynamic and secure web applications, Javascript is also suitable for AI needs.
Javascript has a similar syntax style to Java: cryptic and verbose. AI programmers who tolerate Javascript’s intense syntax enjoy its easy integration with other web-based source codes (HTML and CSS).
Pros:
- Dense Library: This language has a large, supportive community that is constantly creating new packages to make things easier for developers. Javascript has powerful libraries that make developing AI applications a breeze, like React, Vanilla, and jQuery.
- Flexibility: Programmers can access both the backend and front end of their applications through Javascript.
- High-performance: Javascript programs are honed for speed, security, and efficiency.
Cons:
- Javascript’s complex syntax is one of its major entry barriers.
- Its lengthy, abstract style scares off beginners.
4. C++
C++ is a general-purpose programming language with a limited but powerful toolbox of deep learning libraries for AI programming languages. Programmers also gravitate towards C++ because of its high performance.
Pros:
- Performance: C++ is built for speed.
- Well-documented: Learners can access abundant tutorial materials: guides, documentation, and boot camps.
Cons:
- Steep-learning curve: Beginners often find learning this programming language difficult.
5. R
R is a good programming language for artificial intelligence tasks due to its statistical computing skills. Data scientists use R to perform various data analysis tasks, such as cleaning, manipulating, analyzing, and visualizing data.
Pros:
- Friendly Languages: R interacts well with other programming languages.
- Several Data processing Toolkits: R is a good match for AI developers with a broad catalog of packages for handling big data.
Cons:
- Steep Learning Curve: Beginners may need more tutorial materials to help them learn the language quickly.
6. Julia
Julia is a modern programming language designed by MIT engineers in 2012. It was developed to improve other AI programming languages that came before it.
Julia is skillful at computing large-scale data and performing advanced numerical analysis. It is also stacked with multiple packages for probabilistic programming.
Pros:
- Computational Power: Julia runs commands at incredible speed and can handle large data arrays.
- Simple Syntax: Julia has an easy-to-understand syntax, making it convenient for beginners to pick up.
- Extensive Library: This programming language has many AI-focused packages for AI development, graphics, and a dynamic interface. Some popular examples are Flux, Mocha.jl, and TensorFlow.jl.
Cons:
- Small Community: As a relatively new programming language, Julia is yet to develop the community support seen in other well-established programming languages.
7. Haskell
Haskell is a relatively new functional programming language. Haskell has several remarkable features that benefit AI developers: type classes, list comprehension, pattern matching, lambda expression, and much more.
Pros:
- Prioritizes Security & Speed: It is a powerful AI programming language for building AI-powered tools with top-notch security. It also has a fast execution speed and debugging tools for helping developers detect errors quickly.
- Resourceful: It has applications in various fields: education, research, and industry.
Cons:
- Hard to learn: Haskell has a tough syntax, making learning the language challenging for beginners.
8. Prolog
Prolog is a logic-based programming language dedicated to artificial intelligence and natural language processing. It was first developed 50 years ago by a scholar at the University of Aix-Marseille, France.
Despite being decades old, Prolog has continued to evolve with the times and is regarded as a well-aged language among senior developers.
Pros:
- Logic-based: Prolog forms logical relations between datasets, making it a good fit for AI development.
Cons:
- Prolog has challenging syntax and inadequate training material.
9. Lisp
Lisp is the second oldest programming language still relevant today, only behind FORTRAN. Lisp is a dynamically-typed programming language, which means its compiler ignores potential errors in a code until runtime.
Pros:
- Flexibility: Lisp is a flexible, logical programming language packed with functions that aid artificial intelligence programming, e.g., dynamic object creation, garbage collection, symbolic expressions, etc.
Cons:
- The language suffers from some design flaws.
10. Scala
Scala is an object-oriented programming language commonly used to build complex AI projects. This language suits developers who like to create AI applications with data and objects. One advantage of Scala is that it is a statistically-typed programming language that allows developers to spot bugs faster.
Pros:
- Easy to use: Scala uses an immutable data structure, making coding more accessible.
- Fast Bug Detection: The language doesn’t wait till you run a code before pointing out errors.
Cons:
- Small Community: Developers have limited options for community-provided libraries, frameworks, or forums to ask questions.
11. Rust
Developers want a secure programming language that allows them to write scripts with ease—and let’s not forget speed and efficiency. Rust checks all the boxes. Programmers use Rust in various software development fields, including artificial intelligence.
Pros:
- High-performance: Rust is optimized to run programs efficiently.
- Memory Safety: Rust provides secure memory management for AI development.
Cons:
- Challenging Syntax: This AI language has a similar syntax to C++; it is abstract and challenging to learn.
Conclusion
Python is the all-around best AI programming language option. But, if you prefer any of the other languages for AI development, learning another language is always beneficial. If you’re interested in learning AI, check out our list of the best online artificial intelligence courses.
