The exponential rise of Artificial Intelligence (AI) has piqued the interest of developers looking for the best programming language for AI. As interest in AI increases so do AI companies and funding. Also, its popularity is high among developers, which has seen several AI programming languages emerge as favorites.
Breakthroughs in AI are enabling the development of unique and intelligent systems. However, given the huge selection of programming languages, choosing a programming language for AI can be daunting. Of course, the best AI programming language will vary depending on the objectives and use case of the system.
In this post, you will learn the top AI programming languages and how to pick the best language for your project. Continue reading to find out.
What is an AI programming language?
An AI programming language is a tool developers use to build solutions that mimic human intelligence. The language allows the creation of algorithms that use data to learn and make decisions. Different languages support different frameworks and libraries that enhance tasks like autonomous driving, data interpretation, and speech recognition.
Selecting a language that eases coding and provides libraries for tasks such as data analysis and machine learning is important. For instance, Python is popular for libraries such as PyTorch and TensorFlow but R is renowned for statistical analysis.
What programming language is used for AI?
Unfortunately, no one AI programming language fits all AI projects. The best AI programming language depends on the project's scope and requirements. In some cases, AI development projects use multiple languages. Regardless of whether you are building a machine-learning model or working on natural language processing, selecting the right language is paramount.
So, the best AI programming language should satisfy the different needs of the AI system you are designing and developing. It should support the desired function and features. Below is a look at the top programming languages in AI.
Python
Today, Python stands out as a general-purpose AI programming language because of its analytics and data visualization capabilities. Its user-friendly syntax makes it easier for analysts and data scientists to understand. The garbage collection feature in Python ensures automatic memory management.
One of Python’s strengths is the interpreted execution that enables quick iteration in development without recompilation. Also, Python, thanks to libraries like NumPy, has robust support for scientific computing and matrices. This offers a strong foundation for several AI algorithms in neural networks and statistical models.
What’s more, Python’s AI-specific frameworks and libraries like PyTorch, TensorFlow, and scikit-learn cover machine learning, high-level neural networks, and deep learning APIs.
Thus, this AI programming language is suitable for building machine learning systems, natural language processing, computer vision applications, and AI prototyping. Python excels in image recognition, deep learning, face detection, algorithm research, neural networks, chatbots, reinforcement, document analysis, and predictive models. That said, Python is not ideal for developing AI systems that require multi-thread performance.
Lisp
This robust functional programming language is ideal for building rule-based AI applications with logical reasoning. Lisp is unique in how it operates knowledge and data. For instance, it represents knowledge as code and is capable of modifying its code on the fly. Its nested lists code syntax is the bulk of modern machine learning systems since it is easy to process and analyze.
The latest versions retain Lisp’s foundations while adding helpful automation such as memory management. For key ecosystems and dialects, Clojure supports Lisp on Java virtual machines while CLIPS supports the development of expert systems. Lisp can interface with TensorFlow to expand its statistical capabilities in areas such as neural networks while maintaining its strengths.
Lisp is great for building AI systems around complex symbolic logic or knowledge. This includes logic programming, game-playing algorithms, natural language processing, and automated reasoning. The fact Lisp represents information as code, it intuitively encodes concepts and rules that help AI applications.
Although a pioneer in AI, Lisp trails the recently popular neural networks and statistical machine learning. Nevertheless, Lisp is uniquely suitable for building expert systems as well as decision-making logic that relies on symbolic reasons instead of data models. Lisp is particularly suitable for creating automated programming applications because of its ability to rewrite its code.
R
It is the language of choice in data science applications. This AI programming language shines when it comes to handling data such as probabilistic modeling, data analysis, and simulations. The available packages in R allow data manipulation and visualization, which are critical in AI development. For instance, the caret package improves machine learning abilities with its preprocessing and validation capabilities.
Plus, the language allows the creation of professional graphics and custom data visualizations through the flexible ggplot2 graphics concepts. As well, the TensorFlow R package promotes scalable deep learning by tapping into the capabilities of TensorFlow.
R supports various statistical machine learning use cases in AI. In data mining, R clusters data, creates association rules, and decreases dimensions for insights. The language is excellent for time series forecasting thanks to ARIMA and GARCH models.
Also, R is excellent for risk modeling from survival analysis to general linear models. That said, the language is ideal for research and early-stage experimentation. It lacks memory management and compilers which makes it bad for deploying real-world products. Besides, it has a steep learning curve for applications beyond data science.
Java
It is the best language for AI systems that integrate with existing systems. The JV supports AI deployment while maintaining codebase compatibility. Java’s extensive ecosystem offers extensive libraries, stable runtimes, and cross-platform portability. This eases the development and deployment of AI apps that work across platforms.
For example, DeepLearning4j allows neural network architecture on JVM. The Mallet provides natural language processing while the Weka machine learning library gathers classification, clustering, and regression algorithms for AI systems.
Java is perfect for standalone AI agents as well as analytics integrated into business software. Besides, capitalizing on Java’s monitoring and optimization use cases, developers can use this language for performance-tuning agents or predictive maintenance.
Also, Java allows the development of conversational interfaces like chatbots and voice assistants using its natural language processing. Java experts can use Java to empower recommendation engines for targeted advertising, suggest relevant products, or more. Java is ideal for large business AI systems because it is fast, reliable, and can run on many devices.
JavaScript
Arguably the best AI programming language for seamless end-to-end AI integration across web applications. JavaScript enables the development of AI applications via a popular web programming language. With a team of JavaScript experts, you can integrate AI features into websites and engage in full-stack development.
JavaScript uses events to handle user inputs and page updates in real time. Besides, JavaScript is a flexible language that can prototype code quickly.
The language has several libraries that support AI programming. For instance, the TensorFlow.js connects machine learning framework by Google to JavaScript for Node.js and browser deployment. Similarly, the brain.js neural network API offers deep learning capabilities while synaptic.js provides architecture-agnostic neural networks.
Javascript toolkits enable complex machine learning features like speech and image analysis on the browser without backend calls. Thanks to Node.js, it is easy to host and run machine learning models via serverless architectures.
In addition, frameworks like React Native help build AI-driven interfaces for iOS, Android, and web applications from one codebase. JavaScript’s versatility allows the integration of intelligent features into media-focused applications.
C++
This language is ideal for deploying AI in a low-latency production environment. It allows developers to control memory allowing quick execution of machine learning programs with little memory. Thus, C++ is great for AI systems that require plenty of processing power.
What’s more, compiling C++ code into a standalone executable program taps high performance across the major operating systems. The language lets complex AI software deploy reliably with hardware acceleration.
Libraries like Caffe2 and TensorFlow C++ interface support machine learning. However, others like ONNX define the exchange of neural networks while OpenCV offers building blocks for computer vision.
C++ is ideal for applications that require millisecond latency and scalability, such as autonomous robots, embedded appliances, and high-frequency trading algorithms. Also, production environments that require latency-sensitive inference can take advantage of C++’s speed. Combining C++ with Python developers can create scalable AI prototypes.
Haskell
This functional programming language’s AI algorithms rely on pure math functions. Avoiding side effects in functions helps decrease bugs and helps verification. This makes Haskell ideal for safety-critical systems.
Haskell’s lazy code evaluation means it only executes calculations when necessary, which boosts efficiency. In addition, it makes it easy to abstract as well as declare reusable AI components.
Haskell’s LambdaNet and HLearn libraries help with neural networks and machine learning. In addition, BayesHaskell and Haxcel offer support for the appropriate probability and linear algebra math.
Haskell fits naturally on AI systems built on symbolism and logic like probabilistic modeling, proving theorems, combinatorial search, and constraint programming. The language elegantly bridges mathematical specs into running code. It meshes well with how data scientists define AI algorithms.
Best of all, the strict foundations coupled with robust data types make Haskell ideal for math-heavy AI because of its flexibility and correctness. The language is best for quality-critical AI apps that need verifiable logical representation.
Scala
This AI programming language fuses functional and object-oriented programming. It supports modular data abstraction and mathematical clarity through immutability and pattern matching.
Scala’s advanced data type system relies on inference for flexibility maintaining scale robustness via static checking. Its asynchronous processes allow the distribution of AI workload to parallel infrastructure.
Special libraries extend the core advantages of Scala for AI applications. Libraries such as ScalNet supports neural networks while Breeze handles numerics. Also, Scala supports distributed machine learning on Spark as well as DeepLearning4J.
With Scala, it allows the deployment of high-performance machine learning in production environments. Moreover, Scala integrates with big data ecosystems which helps accelerate transformations underpinning most machine learning techniques.
Prolog
With Prolog, developers get a declarative logic programming language. It encodes knowledge into rules and facts similar to the way humans structure information. Prolog automatically deduces extra conclusions by connecting them to logical declarations.
The declarative, query-based technique makes it easy to focus on high-level AI development goals instead of stepwise procedures. This makes Prolog ideal for AI systems keen on reasoning and knowledge representation such as intelligent agents, expert systems, and formal verification.
The declarative approach intuitively models rich logical constraints while at the same time supporting automation via logic programming. Prolog is great for systems that capture intricate relational knowledge or require rule-based deduction to verify or validate models.
How to select the best programming language for AI
It is evident you can use several programming languages in your AI software workflows. Even though some AI programming languages share similarities, their unique features make them ideal for specific AI projects.
Consider the following when choosing the right programming language:
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Assess project needs - Focus on the app structure and features and establish if the project is web-based or robotics.
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Take into account your skill level and learning curve - Programming languages differ in terms of learning curve. So, consider your skill level. Fortunately, some languages like Python have a flatter learning curve compared to the steep learning curve of others like C++.
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Evaluate available libraries or frameworks - Consider an AI programming language that has a rich ecosystem of frameworks, libraries, and tools. These help accelerate the development process.
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Compare language performance and stability - Take into account the language’s memory management techniques as well as its speed since they affect performance and stability. Also, parallel processing and multithreading capabilities can affect performance.
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Available community and support - A programming language with a strong community helps with resource sharing, continuous learning, and troubleshooting. These also provide better support to new AI developers.
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Future-proofing - When selecting a programming language for AI, take into account its future relevance. Establish if the language's popularity is growing and if it regularly updates its tools, syntax, and frameworks.
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Assess integrations - If the project combines different tools, select a programming language that integrates well with other libraries and platforms. For instance, C++ accommodates libraries built in other languages.
The bottom line on AI programming languages
The best AI programming language boils down to the one that matches your project goals and uses cases. For example, Python is excellent for intelligent AI apps thanks to frameworks such as PyTorch and TensorFlow. However, when building a specialized system with intense computing needs, consider AI languages like C++ and Java.
