New libraries, programming languages, and frameworks appear on the market monthly. But how many of them are designed for AI development? What does a developer need to know to start creating real working solutions on AI? The artificial intelligence industry is thriving today but it still has an immense number of questions to answer.

We have also taken a detailed look at the technologies best suited for machine learning. You can read it here.

Top Languages for AI Development

For full-fledged AI development, you need to be proficient in at least one of the languages mentioned below. If you are not familiar with any of them, this is not the reason to give up. There are tons of material on the Internet about AI programming and most of it is available for free. Besides, there are tutorials and videos online that will make the learning process both enjoyable and efficient.

And if you don’t know which language you should start with, we are here to help. Below we name the most popular ones and describe their key benefits. So let’s start!

Python

Python is the most popular programming language ​​used in ML today. It was created by the Python Foundation in the early 1990s and is a powerful data analysis tool widely used in big data technology. It is a high-level programming language that has many different uses, including data science and internal web development. It earned a special status due to a large community of ML developers, mainly focused on the rapidly growing AI industry.

Key features of Python that make it good for AI development are:

• uses simple syntax
• the active Python community created many ready-made libraries
• can be adapted to almost any operating system
• uses object-oriented paradigm
• it is open-sourced, so developers can access any language stack.

Python frameworks for AI development: TensorFlow, Keras, Theano, Scikit-learn.

Lisp

Lisp language was developed by a group of researchers led by John McCarthy of the Massachusetts Institute of Technology (MIT) in 1960. This language was widely used for scientific research in the field of natural languages, theorem proving, and artificial intelligence problems.  For half a century, a large number of versions of the LISP language have been created.  

The popularity of Lisp is explained by the fact that it allows for writing complex and elegant systems for processing symbolic information using fairly simple constructions. 

An essential feature of the Lisp language is that "data" and "programs" do not differ from each other outwardly within its programming environment. This makes it possible to write programs in Lisp that manipulate not only "data", but also "programs". It is exactly that property that made Lisp one of the best languages for AI programming.

The most salient features of Lisp:

• Lisp is the equivalence of program form and data form, which allows data structures to be executed as programs and programs to be modified as data.
• Recursion is used as the control structure, not iteration (loop) as in imperative languages.
• It is an ideal option for processing symbolic information.
• It can be used to develop superior prototypes and create new objects dynamically.

Common Lisp Object-System design patterns:

Lisp is the best language for AI systems that generated several “dialects” over time: InterLisp, QLisp, CommonLisp.

Frameworks: SabraOnTheHill, Weblocks, Clack.

R

The R programming language appeared in the early 1990s and is a part of the GNU Project. It is widely used in data analysis and, as a rule, is targeted for solving general ML problems such as regression, classification, and decision tree formation. Like Python, it is open-sourced, easy to install, configure, and use as packages.

The favorite language of statisticians, R, is widely known for analyzing and processing data. The programmer can use R to create AI projects based on mathematical symbols, graphs, and formulas when needed. 

The creation of a model for making predictions in R looks like this:

Key benefits:

• R effectively handles huge numbers of inputs (much better than Python).
• It allows working on functional programming/vectorial/computation, and object-oriented programming.
• R is independent and integrates well with other programming languages.
• Along with data analysis, R is geared towards data visualization.

The R frameworks for AI development: MXNet, Keras, TensorFlow.

Struggling to choose between Python and R? Read the detailed Python vs R for data science comparison on our blog.

Prolog

Prolog is the most famous example of a logic programming language. Its development began in 1970 by Alain Colmeroe and Philippe Roussell. They set out to create a language that could provide logical conclusions from a given text.

Prolog is based on first-order predicate theory. As the program is executed, the interpreter constantly executes the output based on logical specifications. The idea of ​​using these capabilities is one of the main advantages of using the Prolog language for computer science in general and AI in particular. Applying first-order predicate theory to a programming language provides transparent, elegant syntax, and well-defined semantics.

This is the figure of predicate implementation in Prolog:

Being a pure implementation of logical programming principles, the Prolog makes an interesting and significant contribution to solving the problems of artificial intelligence. The most important one is declarative semantics, a means of expressing relationships directly in AI tasks, as well as built-in unification tools, matching, and search techniques.

Key benefits of Prolog:

• Prolog uses simple syntax and simple semantics.
• It is much more powerful than algorithmic languages like Pascal or C.
• Prolog databases (facts and rules) can be dynamically changed by the program itself or by the user at runtime.

Frameworks:  SWI-Prolog, ProFL, Eclipse.

JavaScript

JavaScript (Flanagan, 2002) was originally developed by Brendan Eich at the Netscare Corporation. It was originally called Mocha, later renamed LiveScript. The standard for the JavaScript language was developed in the late ‘90s by the European Computer Manufacturers Association (ECMA) as ECMA-262. This standard has also been approved by the International Organization for Standardization as ISO-16262. In late 1995, LiveScript was co-developed by Netscape and Sun Microsystems to become JavaScript. JavaScript evolved significantly from version 1.0 to version 1.5. Many new features and capabilities have been added.  

JavaScript is a high-leveled and dynamically typed language that is flexible and multi-paradigm. A full-blown library for ML development - Google’s Tensorflow.js -is based on JavaScript. The figure below is an example of code written in Tensorflow:

Key benefits:

• It has high computing performance accelerated with WebGL and can be further enhanced by a GP.
• JavaScript is known for being a secure development environment because it cannot access your files.
• Development/performance/debugging processes within a JavaScript environment happen with high speed.

As for the benefits of JavaScript in ML, it opens up opportunities to more easily embark on an uncharted path for web and application developers who are largely familiar with it. If you’re chasing after security, hiring JavaScript developers should be your number one priority.

Frameworks: Apache, PowerLoom, Eye, Tweety.

C ++

C++ is the oldest programming language in use today. It was created in the origin of Bell Labs in the early 1980s as a research project aimed at empowering the C language. With the capabilities of both a low-level and a high-level programming language at the same time, in the context of AI and ML, C ++ provides a higher level of control and efficiency than other programming languages.

The flexibility of the language is well-suited for resource-intensive applications and subsets of ML programs. Considering that C ++ is a statically typed language, it can perform tasks at a relatively high speed.

The use for AI: building AI solutions in C++ is fast and easy due to its supreme programming capacities. In particular, programming library Boost provides robust mathematical processing capacity to C++ so that it easily processes complex numerical/statistical math. 

Let’s see a linear regression model built with C++:

Key benefits: 

• Allows solving complicated AI problems.
• Has a large set of library functions/programming tools.
• Follows object-oriented principles that help achieve organized data.

Frameworks: TensorFlow, Boost, Microsoft Cognitive Toolkit, Caffe, Shark.

Java

Introduced in the mid-1990s, Java was originally conceived as a high-level and object-oriented programming language that closely resembles C ++ in structure. With its immense popularity, Java boasts a wide range of algorithms that are very useful for the Machine Learning software development community. In many ways, Java is considered one of the safest programming languages ​​due to its use of bytecode and sandboxing.

It can be described as a successful manifestation of C ++,  devoid of the latter's drawbacks - problems with the security of the codebase and the complexity of compilation.

The most important benefits that Java provides are:

• simple debugging
• simple-to-use
• simplifies work on scalable projects
• graphical data presentation
• user-friendly

The use for AI: Java incorporates both WORA and object-oriented patterns, so it is ideal for creating neural-network projects, developing search algorithms, and GP. Building AI solutions with Java is the easiest path you can take.

Frameworks: Spring, Hibernate, JSF, GWT, Blade.

Scala

Scala provides developers with powerful tools, robust security, and great convenience. As a result, they feel more manageable and more productive. Scala supports object-oriented programming (a widely-used technique) and functional programming, so it can boost performance and help solve the scaling issue. Scala combines these two techniques into a coherent whole. 

One of the best Scala tools is its compiler: this tool can figure out the types for you. It also added several new functions on top of Java to provide more features for developers. Examples of the functionality provided in Scala:

• improvements in string comparison
• pattern matching
• mixins that include functions in class definitions.

In Scala, developers can utilize futures and promises to process data asynchronously, which makes it easy to parallelize/distribute the app. In the figure below, you can see how Future{} estimates its argument asynchronously and reverses a handle to the asynchronous event as a Future[Int]. 

Scala’s key benefits include:

• supports permanent data structure
• can be combined with Java stack
• provides a concise and secure set of common programming patterns.

The use for AI: Scala provides great concurrency support, which helps in real-time parallel analytics projects. It has a large open-source for statistical learning, information theory, and AI in general. It is perfect for large-scale projects. 

Frameworks: Play, Scalatra, Pinky.

Julia

The recently created Julia language is an open, dynamic compiled language primarily focused on performance computing in scientific and technical fields. Before its appearance, it seemed natural that interactivity was inherent only in interpreted languages ​​(for example, Python, R, MATLAB). However, the combination of JIT compilation in Julia, made it possible to achieve almost the same effect: it turned out to be interactive and fast. Indeed, the speed of execution of its programs is comparable to the speed of programs written in C.  JIT compilation in Julia is performed only when the code needs to be executed, with multiple dispatching (meaning that functions are called dynamically at runtime, depending on the type of parameters passed to them).

Julia inherits almost all features from modern popular languages: 

• common numeric data types
• arbitrary precision values
• a rich set of mathematical functions
• tuples, dictionaries, code introspection 
• built-in package manager 
• the ability to interact with other languages ​​and libraries.

It also generated a lot of novelties like high-level tools for parallel and distributed computing and support for metaprogramming. 

Julia provides ready-to-use pre-trained models for general machine learning tasks. Take a look at one of them:

source: julialang.org

Although Julia is still a relatively young language, sometimes it just amazes with its precocious mature ecosystem. This is especially true in the field of machine learning. And while other languages, such as Python, boast a large number of well-developed packages for this area, their counterparts in Julia definitely deserve attention because:

1. These packages are written in a fast, high-level language.
2. They are solid software components that are easy to use, no matter what language they are transmitted from.

Key benefits:

• simple and easy-to-use syntax
• JIT-compilation
• A huge number of specialized packages
• fast service
• high-performance.

At this point, Julia can be used for many purposes in AI, such as analysis and processing of images, classification of text and thematic modeling, analysis of statistical models.

Frameworks: Genie, Bukdu, Merly.

Programming Languages Comparison for AI Development 

Best Frameworks for AI

Now that you're aware of the best programming languages for AI implementation, let’s make a short overview of AI-focused frameworks. A framework is a set of tools and standard implementations that enables faster development of a software product. Let's see what frameworks for building solutions based on artificial intelligence are the most popular on the market.

TensorFlow

TensorFlow (Python, C++) is an open-source, comprehensive machine learning platform. It was developed by the Google Brain team as an extension of DistBelief - a software environment that can use computing clusters with thousands of machines to train large models. Due to its multi-platform nature, TensorFlow allows you to train and deploy models in the cloud and locally, regardless of the language used by the user. TensorFlow is perfect for advanced projects like building multilayer neural networks, but it can also be used for speech, face, object, and image recognition, and for working with text. For instance, see how the code for image predictions looks like:

TensorFlow is constantly evolving due to an open-source code and a huge community of enthusiasts. Due to its popularity, there are many already solved problems, which greatly simplifies the life of newbie developers.

PyTorch 

The next popular framework is PyTorch (Python, C++, CUDA), an open-source machine learning framework written in Python. It was developed by Facebook, introduced in 2016, and six months later was opened to third-party developers. PyTorch is suitable for training models quickly and efficiently. It is convenient for rapid prototyping in research for newbies, pros, and small projects. 

This is a figure of the training-to-mobile deployment model in PyTorch:

Keras

Keras is an open-source deep learning framework also written in Python. It was developed by François Chollet, an engineer at Google, and introduced to the public in 2015. Keras is focused on neural network operations and is compact, modular, and extensible. It is best suitable for small projects since it is difficult to create large-scale solutions on it and not lose performance speed. Still, it is highly efficient for translation, image, and speech recognition.

This is a glimpse of Keras code:

The next widely used framework is an open-source library Darknet (C, CUDA) which is written in C. It is fast, lightweight, easy-to-use, supports CPU and GPU computation. Darknet is suitable for small projects and is a powerful tool for object detection tasks.

This is how their code for detection using a pre-trained model looks like:

XGBoost 

The last one on our list is an optimized, flexible, portable library for creating solutions based on AI - XGBoost. It is an open-source framework that offers a gradient boosting system for C++, Java, Python, R, Julia, Perl, Scala. It was originally a Distributed Machine Learning Community research project but was later expanded and presented to the public and became a hit. XGBoost can be used to solve regression, classification, ordering, and custom predictive analytics problems.

The Python code within XGBoost looks as follows:

In addition to frameworks and programming languages, there are manipulation tools, data analysis tools, sets of standard implementations, functions, and data structures that facilitate the solution of any task in AI programming. The most prominent are:

• pandas (Python), a library for data processing and analysis;
• Matplotlib (Python), a library for plotting 2D graphs;
• scikit-learn (Python), which posits itself as a simple library tool for predictive data analysis;
• Eclipse Deeplearning4j (Java, Scala, C, C++, CUDA), an open-source deep-learning library for the Java virtual machine.

There are also ready-to-use chatbot frameworks on the market, like Microsoft Bot Framework, Chatfuel, Botkit, machine-vision machine learning tools like Google's Cloud AutoML Vision, Facebook's Caffe2. One of the greatest AI computer systems - Watson, was created by IBM and is packed with tools for both developers and business users.

Final Word

The world's interest in AI development is constantly growing. We see it taking root everywhere: in our homes, in our cars, at work. Soon, any company, regardless of the size, will be able to implement AI solutions into its processes using ready-made platforms. Despite the popularity of programming languages ​​described above, new languages ​​are constantly appearing on the market. Let’s hope that those are going to be even better and be able to build truly futuristic programs.

If you want to use artificial intelligence in practice, but you don’t know which platform to choose, you can always rely on us in this regard. Our AI consultants can help you identify your business needs and select the right technology, while our experienced AI developers can help you create the AI solutions you need.