If you’re aiming for AI and don’t know which coding language to choose for your next big project, you’ve come to the right place. Below, we’ve laid out which programming language is best for developing AI software.
Julia
Julia is designed for high-performance numerical analysis and computational science without the usual requirement for a separate compilation step. It includes a type system with parametric polymorphism and multiple dispatch as its core programming paradigm.
Julia’s deep mathematical roots and extensive customizability make it very convenient for data analysts to work with. With Julia, it’s fairly easy to translate algorithms from research papers into code without losing anything in translation, reducing model risk and improving safety.
But the most important factor is the community—it’s an open-source language released under the permissive MIT license. Julia runs on virtually all types of hardware, including IBM, Intel, NVIDIA, and ARM.
Because Julia combines ease of use and familiar syntax from Matlab, Python, and R with the speed of C++, developers no longer need to evaluate models in one language and reproduce them in a faster production language. This reduces errors and saves time and money.
This language is now being used by some of the world’s biggest companies. In 2016, IBM and Juliacomputing analyzed retinal images and developed deep learning solutions that provide better diagnostics and eye care to thousands of rural residents of India.
Haskell
Haskell is a strongly statically typed, lazy programming language developed in 1990. Since there aren’t many Haskell developers, private companies are reluctant to try Haskell.
One thing Haskell excels at is abstraction (abstract mathematics, not Java OOP). It lets expressive and efficient libraries describe AI algorithms. For example, HLearn uses well-known algebraic structures (modules, monoids, etc.) to express simple machine learning algorithms and make them faster.
Although you can write these algorithms in any language, Haskell makes them more expressive than other languages while still delivering decent performance. For example, faster cover trees are written in Haskell.
Haskell supports embedded domain-specific languages, a well-known area of programming-language research that applies to a wide range of fields, including artificial intelligence. In particular, it’s a good host for probabilistic programming, which helps developers catch errors at compile time. If you’re interested, you can check out Hakura, a research project creating embedded probabilistic programming.
The language has CUDA bindings and compiles to bytecode. Since it’s functional and stateless, code can easily be run across different processors in the cloud. As for industry adoption, Facebook uses Haskell to fight spam.
jаvascript
A high-level, interpreted, event-driven programming language mainly used to create interactive web pages and online programs, including games.
With jаvascript, you don’t have to learn the dialogue model. Study the data on the server side, then call the learner with Ajax to make predictions. There are plenty of libraries for developing your lean production system.
Let’s sum up three of them:
- ConventJS: a library for implementing deep learning—training a convolutional neural network in the browser. It supports fully connected layers as well as nonlinear neural network modules, classification and regression functions.
- Synaptic: a neural network library for node.js. Its generalized algorithm is architecture-independent, allowing you to develop and train almost every type of first- and second-order neural network architecture.
- Mind: it uses a matrix implementation to process training data. You can fully customize the network topology and load or unload minds that have already been learned.
In short, you don’t need to reinvent the wheel—just define what kind of “learning” the AI will do.
Prolog
Prolog is a logic programming language and semantic inference engine associated with computational linguistics and artificial intelligence. It’s a flexible and powerful platform that is widely used for theorem proving, non-numeric programming, natural language processing, and AI in general.
It’s a declarative language based on formal logic. AI developers value it for its built-in search mechanism, nondeterminism, backtracking mechanism, recursive nature, high-level abstraction, and pattern matching.
Prolog is well suited to problems involving structured objects and the relationships between them. For example, in Prolog it’s easier to express spatial relationships between objects, such as a green triangle being behind a blue one. It’s also easy to state a general rule: if object A is closer to a person than object B, and B is closer than C, then A must be closer than C.
Prolog’s nature makes implementing facts and rules simple and straightforward. In fact, everything in Prolog is a fact or a rule. This lets you query a database even if it contains thousands of facts and rules.
Prolog supports the development of graphical user interfaces, administrative applications, and network applications. It’s well suited to projects such as voice-control systems and template filling.
Java
The best thing about Java is its Java Virtual Machine technology, which lets you build a single version of an application that will run on all platforms supporting Java. Its strengths are transparency, maintainability, and portability.
What are the advantages of programming AI in Java? Well-supported large-scale projects, better user interaction, easy debugging, simplified visualization, and the inclusion of Swing and the Standard Widget Toolkit.
Its main advantage is its versatility: if you’re a beginner, thousands of useful tutorials are available online (for free) to make learning easier and more effective.
Some well-known applications developed in Java include:
- The WEKA machine learning package, designed for machine learning and data mining.
- JOONE, a neural engine for designing, training, and testing neural networks.
- ALICE (short for Artificial Linguistic Internet Computer Entity), chatbots that process natural language.
- Robocode, an open-source game for learning the principles of Java programming.
Lisp
Lisp is one of the oldest (developed in 1958) and most prominent languages, created by Dr. John McCarthy, who coined the term “artificial intelligence.” Although it isn’t used much these days, the language is both flexible and extensible.
It was originally designed for lambda calculus computations, and has changed considerably since its creation. The language introduced many ideas in computer science, such as recursion, dynamic typing, higher-order functions, automatic memory management, a self-hosting compiler, and a tree data structure.
Lisp is used to develop artificial intelligence software because it’s very good at supporting the implementation of programs that compute with symbols. Symbolic expressions and computations on them are what Lisp is good at.
In addition, Lisp has a macro system, a well-designed compiler capable of producing efficient code, and a library of collection types, including hash tables and dynamically sized lists.
Thousands of AI applications have been developed in Lisp; some of them are:
- American Express’s Authorization Assistant, which checks transactions (credit cards)
- METAL, a natural-language translation system
- Macsyma, the first major computer algebra system
- ACL2, a theorem prover used by AMD
С++
C++ is faster than other languages—its ability to interact at the hardware level can reduce code execution time. This is extremely useful for time-sensitive AI projects. It can be used for a statistical approach to AI, like the one used in neural networks.
With its faster execution time and OOP principles, C++ makes itself a good candidate for AI programs. In fact, most machine learning and deep learning libraries are written in C/C++ and offer APIs for those languages, plus wrappers for other programming languages.
If you want control over execution time and performance, C++ is obviously a good choice. Templates are safer to use (type-safe) and provide a better way to generalize APIs. Although templates are a powerful technique that can simplify most things, they take more time and experience to figure out when their use is appropriate.
The language handles the complexities of 3D games by optimizing resource management and making multiplayer gaming easier over a network. A real-world example is the science-fiction game Doom 3, which uses C++ and Unreal Engine, a game development toolkit (written in C++). Microsoft Windows, Mac OS, Adobe Photoshop, Maya 3D software, CAD, and Mozilla Firefox are a few well-known applications that use C++.
Python
Python is focused on DRY (don’t repeat yourself) and RAD (rapid application development). Developed in the early 1990s, Python has become one of the fastest-growing programming languages because of its scalability, adaptability, and ease of learning.
Python has hundreds of libraries that make any kind of project possible, whether it’s a mobile app, a web app, data science, or artificial intelligence. For example, “Numpy” for scientific computing, “Pybrain” for machine learning, “Scipy” for advanced computing, and “AIMA” for artificial intelligence.
Python’s holistic language design, its balance of low-level and high-level programming, modular programming, and testing environments set it apart from other languages. Another advantage is rapid prototyping. AI makes up about 80% of research. In Python, almost any idea can be tested quickly with 30-40 lines of code.
