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The Best Programming Language for AI: Your Ultimate Guide

best programming language for AI
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    People have finally arrived at a point where artificial intelligence and machine learning are no longer viewed as scary as Hollywood often depicts them. On the contrary, they have turned out to be smart and friendly. More and more industries are adopting AI that allows them to solve urgent problems and set ambitious goals. 

    It's true that AI programming is intricate and powerful. It has become evident that keeping up with the latest tech today is not a luxury but rather a necessity if one wants to stay afloat and prosper. 

    We know what you may think now: "Well, OK, I'm going for AI, but I don't speak technical language. How do I know what's best for my particular business? Which technology is right for my needs and my pocket? Which programming language is best for AI?"


    We Provide AI-Powered Solutions from Concept to Launch.

    We hear you. Choosing between AI programming languages is a big deal, and it can be overwhelming. That's, as an AI development company, why we have come up with a guide to help you get a picture of the most common AI programming languages and what they are suitable for. 

    C ++, Java, Lisp, Prolog, Python and R are the main AI programming languages that are best suited for development and design for different kind of software.

    There is no best language for AI; each is good where it fits best. We won't go too far into technical details, just enough for you to feel confident and be on the same page when choosing your AI development partner. 



    The indisputable leader among AI programming languages is Python. It's widely used in all industries and is loved for its simplicity, flexibility, and scalability. 

    First of all, Python is open source. It means that it is available for any modifications developers see necessary. This AI programming language keeps continually evolving, making its syntax easier, which only contributes to its efficiency. 

    Secondly, there is a large number of ready-to-use libraries that help accelerate AI coding. For instance, TensorFlow is widely applied for machine learning and working with datasets; scikit - for training machine learning models; PyTorch - for natural language processing and computer vision. This is a tangible benefit that can help accelerate development and save some funds since it assumes applying some ready-made solutions instead of producing them from scratch.

    Finally, Python is platform-independent and, on top of that, combines well with other AI programming languages. 

    Regardless of the industry or project size, Python is a sound technology choice: it can easily cope with small scripts as well as easily support enterprise applications. 

    It's difficult to name one best programming language for AI, but Python is pretty close to it. 



    Lisp is one of the oldest programming languages and is actually an ancestor of a few AI programming languages. In fact, the term Artificial Intelligence emerged and was officially introduced after Lisp had been invented by John McCarthy in the 1950th. 

    It was fundamental at the time and boosted AI programming being a functional tool for machine learning. Yet, today it's not an often-used AI language but rather a fine vintage for sophisticated lovers who enjoy studying programming history.

    The thing is that its many core features that made Lisp attractive for AI programming have migrated to other languages, making them a far better option for machine learning and AI coding. 

    Lisp currently lacks libraries and has quite a weird syntax. In addition, it requires special configurations of software and hardware to work with it.

    The community gives Lisp proper credit since it represents the origins of artificial intelligence programming languages, but the reality is that it is outranked by other AI programming languages.



    C++ holds the title of "the fastest programming language" that's why it is often used for computer games, desktop apps, and search engine development. For example, Google Chrome is built on C++. 

    This AI programming language is primarily a tool for building complex products such as banking software, cloud systems, e-learning software (including AI in education), and enterprise software.

    As to AI programming, in particular, C++ has libraries available for machine learning and neural networks that facilitate faster execution of complex algorithms. On the whole, many developers prefer this AI language for building apps that require high productivity, not necessarily code for AI only. 

    When deciding on the best AI programming language, C++ should definitely be reviewed as an option since its fast execution is an enormous benefit for AI coding. 



    Java is one of the most popular programming languages, and when one thinks of languages for artificial intelligence, Java is certainly on the list. 

    Whether you've just become a programmer or you are experienced with AI development, you'll likely need this language.

    Its fans mainly appeal to Java Virtual Machine technology, which makes the AI language easy to implement on various platforms. Its significant advantages refer to user-friendliness, fast debugging, and ability to produce nice-looking graphics. 

    Java is open source and is supported by numerous libraries, which among others, include Java Machine Learning Library. It's suitable not only for machine learning algorithms, as the name suggests, but also for projects that imply robot systems, sensors, nlp, and neural networks. 

    Java solutions can be implemented in any domain, not just data science. 

    With Java being one of the leaders as the best language for machine learning and AI programming in general, it is most common for large-scale projects, since its simplified network is handy for supporting big software infrastructures. 



    R is one of the emerging AI programming languages that has gained its popularity thanks to the very specific tasks it can perform well. 

    R is especially good at crunching large numbers, even better than Python. Kaggle's recent survey says that R often comes as the first choice for software that uses a lot of statistical data. 

    Open-source, just like some other AI programming languages we have highlighted above, R's packages allow the application of machine learning, data mining, and other advanced data analysis tools. 

    R's capabilities aren't exactly meant for general purposes. What's R is best suited for is statistics-related software. Probably no other AI language can be as efficient as R in this regard. 



    Prolog stands for "Programming in logic." This programming language for AI was created back in the 1960th and varies from other languages common for AI programming nowadays. 

    As its description implies, Prolog is a logic language, which is unlike the "classical" AI languages. It operates with a few basic mechanisms, like pattern matching and automatic backtracking. From a developer's standpoint, it assumes specifying rules, facts, and end goals. Not every AI programmer opts for Prolog when deciding to learn AI programming languages.

    Prolog's original field of use is natural language processing. For example, the first chatbot ever created, ELIZA, was developed using Prolog. Later on, Prolog has been used in research and education areas for expert systems, theorem proving as well as for some machine learning cases. Prolog is also a part of academic teaching for many artificial intelligence courses. 


    If you are reading this, you must have already realized AI's potential and the opportunities it can bring. 

    In this article, we often referred to the phrase "best programming language for AI." Yet, the truth is that there hardly is one. 

    The choice of programming language for AI always depends on many crucial factors: your business specifics, whether it's a brand new product or an existing one, what is the market like, who are your customers or users, what is the expected load, what problems you are trying to resolve or what are your goals etc. Moreover, many solutions don't rely on a single technology only, comprising a few to implement the desired functionality and achieve high efficiency. 

    We are convinced that each case is unique. That's why before giving any recommendations, we ask our clients all these questions diving deep into research and planning before starting off the development. 

    Spare yourself hours of searching the net, trying to make sense of code examples and complicated technical terms, and leave it to our trusted top-notch expertise!


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