AI Programming: The Best Programming Language for Artificial Intelligence
Artificial Intelligence is massively being introduced into the business and into everyday life. After reading the post, you’ll get more clear concerns about what 8 most popular programming languages you can add to your developer’s toolkit while integrating AI.
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“What is the best programming language for Artificial Intelligence (AI)” is that very question you face before AI development for your business. To approach it well-prepared, in the article we’ll explore the eight most popular programming languages for creating Artificial Intelligence systems, their peculiarities, and use cases.
To dive deeper, read also “Interview with FreySoft Data Scientist and Machine Learning engineer”.
- FAQ: Top Programming Languages for AI
Created by the Python Foundation in the early 1990s, now Python is by far the best coding language for AI. It obtains the following features:
- has an easy-to-master syntax and logical, strict grammatical structure of the program;
- is platform-independent, so engineer can adapt it to almost any operating system;
- is built on the basis of open source technologies, so developer can access any language stack;
- supports a range of programming styles, such as functional, object-oriented, and procedural.
Python has many libraries for artificial intelligence, among which are PyTorch – for speech processing and computer vision; Numpy – designed to perform complex calculations; TensorFlow – for machine learning and dataset work; scikit – for training machine learning models. This is a tangible advantage that can help speed development and save resources by allowing businesses to use off-the-shelf solutions instead of building them from scratch.
Java, an extremely popular programming language, can also be considered a good choice for AI development since it provides neural networks and search algorithms. The major benefits include:
- easy in use tool and simple for debugging;
- high-quality interaction between the client and server systems of the resource;
- batch services;
- the tools Swing and SWT (Standard Widget Toolkit) so that the graphics and interface are both attractive and complex;
- adaptability to work with any platform;
- ease of handling large-scale projects.
Java is supported by many libraries, including the Java Machine Learning Library. Further, it is suitable both for machine learning (ML) algorithms and for projects that leverage robotic systems, sensors, Natural Language Processing (NLP), and neural networks. An example of an ML project in Java is GROBID – a program for parsing and extracting data even from unstructured documents.
The other AI programming language and free open-source software environment is R. Kaggle’s recent survey has shown that the R often acts as a first choice for the software, which uses a lot of statistical data. And that is not a surprise since the language covers data analysis, large data modeling, and data visualization. An example of an ML project in R is Healthcare.ai, a platform for creating AI for medical-specific purposes.
Using R, engineers can easily create well-designed, quality publication drawings, including the necessary mathematical symbols and formulas. Besides being a general-purpose language, R also has many software packages such as Class, Gmodels, and Tm that make easy to implement ML algorithms, thereby solving business-related problems. Moreover, it combines well with other coding languages such as SQL, C++, and Java. As a result, many financial institutions and large computing companies prefer R in their research and development.
Prolog (which stands for “programming in logic”) refers to interactive languages that operate on a symbolic system. The original use concerns natural speech processing. For example, the first chatbot, ELIZA, was created using Prolog. Now, it is widely popular for non-numerical programming, theorem proving, and expert systems. Thus, it is the perfect choice for the development of AI systems and projects that require high logic ability, in particular, medical-oriented.
Among developers, AI has gained an excellent reputation for optimal obstructive types of work, built-in analysis algorithms, non-determinism, etc. As a declarative type, it uses formal or figurative “thinking”. To continue, other major features of the language include efficient pattern matching, tree-structured data, and automatic return. They provide an amazingly powerful and flexible programming environment.
The ancestor of the programming languages for AI is LISP that still takes one of the leading positions because of its unique features. It is flexible in use, has easy-to-expand functionality, and an advanced object-orientation system. What’s more, thanks to the ability to quickly prototype and set up macros, developers save a lot of time.
LISP can handle relatively heavy and light tasks equally well. Its development cycle allows engineers to interactively evaluate expressions and re-compile functions or files while the program is still running. Over the years, progress has ported many of these features to many other languages, making LISP unique.
Developed in 1990, Haskell is a strongly typed and standardized programming language designed with loose semantics. Its key advantages for AI development include:
- focus on mathematical functions and logic;
- high efficiency and easy maintenance due to less code and more consistency;
- creating expressive algorithms while maintaining excellent performance;
- Repa native processing of parallel numeric arrays;
- web frameworks Happstack, Snap and Yesod.
Thus, the Haskell will fit projects involving the creation of advanced neural networks and best suitable for ML, image processing, creating a user interface, writing servers. Besides, Haskell has a huge community and a large number of libraries.
Considering the best programming language for Artificial Intelligence, we should also pay attention to young representatives of the market. One of them is Julia, created in 2009. It is designed with high-performed numerical analysis and computational science, so it does not include the need of separate compilation for speed. Julia also has particular powerful libraries for ML such as Flux, TensorFlow.jl, ScikitLearn.jl.
Other features of Julia include:
- a highly developed general and meta-programming syntax;
- powerful macros;
- a very flexible object system;
- multiple dispatching.
Julia can be used for low-level systems programming as a specification language. It supports dynamic typing and this makes it more flexible, but also increases the likelihood of error.
An example of an ML-project on Julia is SALSA, a software for advanced work with stochastic algorithms.
C++ was created as a research project by Bell Labs in the early 1980s. Now it is called “the fastest programming language” due to its higher execution and lower latency than in other competitors. For this peculiarity, it is often used for computer games, desktop applications, and search engines. For example, Google Chrome is written using C++. To observe ML-project in C++, check AirSim – a simulator of drones and unmanned vehicles.
When choosing a programming language for AI, C ++ is definitely worth considering because of its chief advantage in terms of speed. In particular, C ++ has libraries available for ML and neural networks that facilitate the faster deployment of complex algorithms. In general, many developers prefer this language for creating high-performance applications. However, when it comes to the choice of developers, they must be well-experienced in C++ as it is difficult to master tool, and novice programmers very often make unforced errors when creating new projects in it.
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Now, you have more clear concerns about what most popular programming languages you can add to your developer’s toolkit while integrating AI. However, keep in mind that the choice of the best coding language should depend on many other important factors, such as:
- the specifics of your business;
- product features – be it a new product or an existing one;
- market situation;
- the problem you need to solve with AI;
- your customers’ requirements;
- the expected load, etc.
We, at FreySoft, know that each case is unique. Therefore, before making any recommendations, we ask our clients all these questions, delving deeper into research and planning, before starting the project. And already in the development process, we gain invaluable experience in AI programming, which we gladly share with everyone who is trying to figure out code examples and complex technical solutions.
FreySoft Data Scientist and ML experts have over 11 years of experience in delivering the following solutions:
- Chatbots, online support, and virtual assistance agents
- Voice and facial recognition
- Service personalization
- Detection of fraud
- Sentiment analysis
- Predictive analytics and customer service
- Email spam filters and categorization
- Image tagging
- Natural language processing.
Depending on whether your business needs an AI model for a specific activity or for the day-to-day processes of a company, the FreySoft team offers a customized product, a standardized solution, or a temporary service. Contact us today for more information.
FAQ: Top Programming Languages for AI
AI is developing and massively being introduced into the business, into everyday life. Therefore, the developers who are aware and specialize in AI programming are highly in demand today.
To achieve a smooth transition and proper integration of AI/ML models into internal systems, companies apply for a technical team or data scientist who is qualified to integrate the models to be implemented in the company’s systems. To illustrate, with their help, businesses automate routine tasks, advance analytics, and increase efficiency by better handling large amounts of data.
Yes, coding is required when integrating Artificial Intelligence solutions. AI, like any program, is primarily a code. That is, a text formatted in a certain way. This code needs development, maintenance and improvement.
Unfortunately, the code does not happen by itself, it cannot “come to life” without a programmer. Therefore, if your business needs AI development, you need to select the right vendor for this task.
Artificial Intelligence is a wide and growing technological field, which means that AI can be implemented in different programming languages. In our above-mentioned article, you can discover the eight best programming languages for AI. However, remember, language is not the most important thing here, because first it is a tool, and it depends on the engineering team how to use it. Also, when approaching AI, it is useful for developers to have experience with various databases and distributed systems.