Best AI Programming Languages You Can't Miss in 2024

The global surge of Artificial Intelligence (AI) and machine learning services has led to immaculate new tech opportunities for AI developers. In this rapidly evolving disruption, the tools utilized to make the systems more intelligent are being upgraded the minute we speak or read these lines. AI's entry into numerous sectors has considerably boosted the demand for strong and high-functioning programming languages designed specifically for it. Since the release of ChatGPT in 2021, developers all over the globe have inadvertently gravitated toward 'Machine Learning' and catapulted AI into the spotlight. Without a question, artificial intelligence is profoundly altering the ways we perform professionally and interact socially.

Giant global conglomerates like Apple and Google have participated in the trend of AI creativity by rendering personalized recommendation systems and digital assistants in IoT devices and other wearables. Even the most popular VAs i.e. Siri and Alexa strongly utilize artificial intelligence to generate prompt responses and also create miscellaneous applications. Nevertheless, it's vital to note the heavy influence of programming languages on AI development, and its advancements have shown incredible results. From introducing new techniques for novice programmers, AI has enabled them to master the foundations and achieve valuable insights by incorporating AI into their everyday work. Let's have a deeper understanding of the top 10 AI programming languages that you need to be aware of and experiment with in 2024, in addition to their benefits, and some insights on how AI development companies can help you achieve them.

Top 10 AI Programming Languages You Need to Know in 2024

Numerous benefits of AI programming languages have been loved by developers and incorporated into their daily operations. Most AI languages are intended to be succinct and have a simpler learning curve, which enables developers to comfortably browse extensive libraries and frameworks and learn all about fundamental AI tasks such as natural language processing, deep learning, and machine learning. Our list of best AI programming languages has been derived from the data rendered by large and active communities of developers, Let's check them out:

1) Python

Python is one of the most popular programming languages in AI development. Regardless of its existence before the existence of AI, it became one of the elementary programming languages for AI development. This language is used very generously for Machine Learning. Python has potentially established its position as a data analysis tool and is heavily leveraged in the big data space. Being one of the simplest programming languages to learn and implement, Python constantly ranks as the top AI-specific framework. It also has a humongous ecosystem of frameworks and libraries such as Matplotlib, Scikit-learn, and NumPy in addition to its Python-based frameworks PyTorch and TensorFlow. This versatile programming language is primarily used to develop neural networks and algorithms in machine learning. Python is also useful for analyzing data in order to uncover patterns, behaviors, anomalies, potential trends, and other relationships due to its open-source nature.

2) Java

Java is a versatile programming language for building dynamic online, desktop, mobile, and cloud-based applications. If you have pre-installed Java Virtual Machine (JVM), your Java programs can comfortably run on any computer, and also possesses portable and platform-independent capabilities. It has an object-oriented design that enables the development of high-performing and maintainable code. This also facilitates modularity, which enables software engineers to divide all their programs into trimmer, more effortless files. Among all the languages for artificial intelligence, Java is most compatible with large-scale, enterprise-level AI applications. Its robust libraries, including WEKA, H2O Deeplearning4j, and MOA, support major AI tasks effectively. Java is employed in AI systems that require interaction with current business systems and runtimes. Its expansive ecosystem contains dependable runtimes, an enormous developer community, and cross-platform portability, making it straightforward to create and deploy AI applications that smoothly function with diverse hardware and software. Java also hosts in-built modules and libraries such as Apache Commons and JUnit, along with extensions like RTSJ and multi-threading that greatly empower AI applications.

3) Lisp

Lisp has way longer been meshed with AI analysis and hence established itself as one of the best AI programming languages languages. Developed way back in the late 1950s, Lisp's primary focus lay on symbolic processing and still maintains being one of the oldest programming languages that still perform amazingly to date. The concept of its design is powerfully fused with the deficiencies of AI research, which periodically needs manipulating characters and processing indexes. Being incredibly flexible, enabling swift prototyping and dynamic development. Features such as diligent typing, conditionals, and recursion, are the top priority when it comes to AI tasks. Lisp can modify itself, quickly adepting to new data or issues during runtime, which is a strong ability for AI apps that grasp and grow. The code-as-data paradigm (homoiconicity) allows developers to swiftly render and manage code, which makes it better for writing complex AI algorithms, which heavily benefits from metaprogramming capabilities. Its vast ecosystem of AI libraries and immaculate AI community make it a developer's favorite.

4) Julia

Julia is a prominent framework among the AI community, and one of the programming languages for AI development due to its elevated performance and swift prototyping capabilities. Its faster processing abilities are crucial for AI development due to the involvement of huge amounts of data. Its user-friendly syntax is analogous to Python, making it more straightforward to understand. Julia is highly compatible with prominent programming languages like Python, R, and C. This enables developers to utilize powerful libraries and dependencies such as PyTorch and TensorFlow, that are typed in other languages, and further integrate them into their applications. Julia's parallelism and distributed computing is out of the box which allows AI platforms to comfortably manage grave computational workloads and huge data sets without compromising on performance. It's open-source nature lets developers profit from additional community-controlled libraries, and it's in-built tools Plots.jl and Gadfly.jl help with visualizing data while MLJ.jl for crafting modules for deep learning and machine learning services.

5) C++

C++ is used by AI development companies for performance-critical AI applications like resource-intensive computations and real-time systems. Its incredible execution speed makes it perfect for time-sensitive applications and also yields fine control over design resources. Just like Java, C++ generally needed to be coded at least five times longer than Python. Because of those elements, C++ excels in being a pre-compiled, general-purpose language that's immaculate for constructing AI infrastructure and operating in self-sustaining vehicles. Apart from rendering superior AI model management, it also helps in building flawless learning libraries for deep learning and machine learning. The Boost and Shark libraries provided in the language aid AI development. Although, its crucial to note that C++ is not a very developer-friendly AI programming language, due to its limitations in performance. One another library of C++ is OpenCV which is the Open Source Computer Vision Library, that's used for computer vision tasks. This library offers a myriad of tools for video & image analysis, that would be perfect for AI-based applications that need to be screened for visual data.

6) Prolog

Known for its symbolic reasoning and strength in logic programming, Prolog facilitates top-class development of AI applications. Its specialization is segregated into two arenas i.e. problem-solving and representation of knowledge. Prolog's declarative nature enables developers to define the computation logic without worrying about any underlying flow of control. Programmers can illustrate rules, relationships, and facts in a straightforward manner, which is perfect for assembling knowledge bases in AI systems. Prolog also boasts of pattern-matching capabilities depending on logical relationships, that's paramount for the NLP field, resulting in prompt answers to diverse inquiries. Its major focus is on formal logic, which is perfect for rule-based AI systems.

7) Scala

Scala is one of the most valuable AI programming languages, perfect for developing scalable AI applications that need concurrency support and reinforce functional programming. It boasts of specialty libraries that go beyond its core benefits of AI, rendering neural networks (ScalNet), distributed ML on Spark, numerics (Breeze), and potential interoperation elements with DeepLearning4J with the Java ecosystem. Scala seamlessly amalgamates object-oriented and functional programming techniques, which enable modular data abstraction through classes and methods, in addition to mathematical clarity via immutability and pattern matching. Its integration compatibility blends perfectly with major Java libraries and tools. Scala's robust type system leverages inference for flexibility while confirming scalability via static checking. Its high language features for better coherency, coupled with access to an eclectic tech stack, enable the spreading of AI across the parallel infrastructure.

8) JavaScript

JavaScript is one of the widely known programming languages for AI development utilized by top AI development companies. Utilized for generating interactive browser-based applications, it's primarily a client-side scripting language that directly perpetrates into user's browsers. JavaScript can assist with a variety of computing jobs and boost the enactment of AI applications. It's really useful for creating real-time AI applications like chatbots, virtual assistants, and recommendation engines. JavaScript lets developers blend AI services and features into your web applications by utilizing APIs. It's efficient in handling a multitude of tasks and also facilitates AI development in the browsers Brain.js, ConvNetJS, and TensorFlow.js. These frameworks facilitate the development and implementation of machine learning algorithms in the web browser, broadening the scope of AI apps.

9) Haskell

Haskell is a functional programming language that focuses on precise mathematical computation for AI algorithms. Minimizing adverse effects within operations eliminates bugs and improves authentication, which is beneficial for systems that require safety. The best thing about Haskell is its lazy code-analyzing capability, meaning it only performs calculations when required, hence, fostering performance. It also streamlines the abstraction and declaration of AI elements that can be reused. Haskell's primary libraries, LambdaNet and HLearn, concentrate particularly on neural networks and ML. In addition to this, BayesHaskell and Haxcel assist with crucial probability calculations and linear algebra.

10) R

R is one of the viable languages for artificial intelligence due to its statistical computations and data visualization capabilities. It comprises a humongous set of data visualization libraries, including Shiny, Lattice, and the most beloved Ggplot2, which enables programmers to instantly produce graphs, charts, or any other forms of visuals using just data. For developers looking to indulge in statistical modeling, its n-built functions, like glm and lm are pretty useful. R's Caret package opens avenues for forming, training, and sampling machine learning models. It also allows for hyperparameter tuning, which helps optimize their ML algorithm's performance. To assess the performance of their AI models and make timely modifications, developers can use R's performance assessment tools, such as cross-validation and resampling. R is interoperable with Java and Python, hence, it yields a robust ecosystem of libraries, creating a diverse portfolio of apps.

Conclusion

Selecting a valid programming language is paramount for leveraging the complete potential of AI, and learning its benefits can drastically enrich your projects. Programming languages are the future of AI development by empowering them to create more holistic and intelligent systems. Remaining updated on the trending breakthroughs and selecting the top-notch programming languages for AI development will empower your victory. If the whole AI programming language for the artificial intelligence process feels challenging, you should consider collaborating with reputed AI development companies like Hyperlink InfoSystem to accelerate your growth with their expertise and experience in the exciting field of AI.

Hyperlink InfoSystem is one of the premier AI development companies encompassing proficient AI developers excelling in a variety of languages and machine learning services. They ease the process of building complex AI solutions and deliver your desired solution much better. Boasting a team of experienced developers, they hold incredible expertise in the latest AI languages, algorithms, and other programming frameworks. Hyperlink InfoSystem helps you curate a definitive project scope, and help you select the appropriate tech stacks and tools, while also rendering impeccable ongoing support post-launch. Their expertise can help you accelerate the development process, open your avenues to accessing cutting-edge resources, and get your AI-powered up and running faster than ever. Connect with us today and discover what we can do for you.