Top 8 Programming Languages For Artificial Intelligence

Did you know that no single programming language is perfect for every AI application? Before you kick off your first or next AI project, you should consider your language choice (or choices) carefully.

A good-fit language can boost development speed and flexibility. But a bad fit? It’s more likely to stall progress and make implementing advanced models or new features much harder, wasting both your team’s time and the department’s budget.

Do you want to avoid future setbacks and select the best option(s) from the outset? In this article, we’ll explore the strengths, limitations, and best use cases for some of the top programming languages in AI development.

#1 Python

Python powers some of the world’s most innovative AI solutions, from Google’s TensorFlow-driven applications to Netflix’s recommendation algorithms. Its simplicity and readability make it a top choice for AI and machine learning development. With libraries and frameworks like TensorFlow, PyTorch, and scikit-learn, this programming language comes with pre-built modules that significantly speed up the development of machine learning models and AI systems.

Python’s Strengths:

Extensive Libraries and Frameworks: Python comes with a wide range of libraries like TensorFlow, PyTorch, and scikit-learn that simplify AI and machine learning tasks. These tools help developers build complex models without starting from scratch.
Ease of Use and Readability: Python’s clean and straightforward syntax makes it easy to learn and work with. So developers can focus more on algorithm development and problem-solving rather than battling complex syntax.
Integration Capabilities. Python easily integrates with other languages (like C++) for performance-critical parts of an AI system. It also works well with big data frameworks like Apache Spark and can be paired with cloud services (AWS, GCP) for scalable AI solutions.
Cross-Platform Flexibility. Python runs on multiple platforms, including Windows, macOS, and Linux. With it, developers can develop AI applications and deploy them across diverse environments with minimal compatibility issues.

Python’s Limitations:

Slower Execution Speed: Python is an interpreted language, meaning it runs slower than compiled languages like C++ or Java. This can become an issue in AI applications that require high-performance computing or real-time processing, such as autonomous systems or time-sensitive tasks.
Memory Consumption: Python’s ease of use and flexibility come at the cost of higher memory usage. In AI tasks that involve large datasets or complex models, this can lead to inefficient memory management and slow down the system’s performance.

Python’s AI Applications:

Thanks to its AI-focused libraries and tools, Python is an excellent choice for a wide range of AI applications. This includes machine learning, natural language processing (NLP), and data analysis. If you’re building recommendation engines, chatbots, or predictive analytics systems, this programming language could be your top choice.

That said, this language isn’t always the best fit for real-time AI systems or embedded environments. Python can become a bottleneck for applications that require high-performance computing or low-latency processing, like autonomous vehicles or robotic controls.

#2 Java

Many large-scale enterprise applications rely on Java, particularly for backend and data processing tasks. For instance, LinkedIn uses Java for its messaging service, and Goldman Sachs and Citigroup use it in their trading platforms. Thanks to the Java Virtual Machine (JVM) and its object-oriented design, it’s especially relevant in projects where scalability, performance, and cross-platform compatibility are key requirements.

Java’s Strengths:

Cross-Platform Compatibility: Java can run on any system with the Java Virtual Machine (JVM), so it’s easy to deploy AI applications across different environments. This flexibility is especially helpful for large-scale or distributed AI projects.
AI Libraries: Java comes with libraries like Deeplearning4j for deep learning, Weka for machine learning, and Apache Spark’s MLlib for big data processing.
Good Performance: Java is a compiled language, which means it generally runs faster than interpreted languages like Python. It’s a strong fit for AI applications that need fast, efficient computation, like real-time processing.
Scalability in Enterprise: Java is commonly used in enterprise environments, so it’s easier to integrate AI into existing systems.

Java’s Limitations:

Verbose Syntax: Java’s code can be more complex and verbose than languages like Python. This makes it harder to learn and slower to work with, especially for those new to AI development.
Fewer AI Libraries: While Java has solid AI libraries, it doesn’t have as many as Python. So this language may be less appealing for cutting-edge AI projects.
Higher Memory Usage: Java’s memory management system can result in higher memory usage, which can slow down AI computations.

Java’s AI Applications:

Given its strengths, it’s no surprise that Java excels in enterprise AI solutions and big data processing. You might choose Java when building scalable, distributed AI systems or real-time applications that need to process large datasets—like fraud detection systems, customer recommendation engines, and predictive analytics tools, for example.

However, Java is not always the best choice for rapid AI prototyping. Its complex syntax can make it slower to write and iterate on AI models. Mobile or embedded systems may not be a good fit either. Java’s higher memory consumption and garbage collection system may not perform as efficiently in environments with constrained resources.

#3 C++

Does your AI application need to make split-second decisions? If so, consider C++. This programming language is built for real-time processing, and it gives developers the granular control they need to fine-tune performance. Plus, it integrates with popular AI frameworks like TensorFlow and Caffe, which makes development easier and more efficient.

C++’s Strengths:

High Performance and Speed: C++ is a compiled language, so it’s much faster than interpreted languages like Python. This speed is an asset in AI tasks like real-time processing, robotics, and autonomous systems, where decisions need to happen in milliseconds.
Memory Control: With C++, developers have fine control over memory management, which helps when working with large datasets or complex algorithms. It’s especially useful for deep learning models or high-performance computing, where efficiency matters.
Concurrency and Multithreading: C++ supports multithreading and parallel processing, which allows AI applications to handle multiple tasks at once. This is essential for training neural networks and processing big data quickly and efficiently.
Hardware Integration: C++ works well with low-level hardware, so it’s ideal for AI systems that need to interact with devices, like robotics or IoT applications. It also allows developers to optimize performance on resource-limited hardware.

C++’s Limitations:

Steep Learning Curve: C++ is harder to learn compared to languages like Python. It requires a deep understanding of memory management and low-level operations, making it less beginner-friendly.
Fewer AI Libraries: C++ doesn’t have the same AI libraries as Python, so developers often need to build more solutions from scratch.
Complex Syntax: Writing in C++ is more time-consuming due to its complex syntax, which slows down projects that require frequent iterations—like building and testing machine learning models.

C++’s AI Applications:

With its high execution speed and fine-grained memory control, C++ is a strong fit for performance-critical AI applications. For instance, it excels in scenarios that require split-second decision-making, like in autonomous vehicles and robotics. C++ is also commonly used in gaming AI to manage intelligent behaviors for non-player characters (NPCs) in resource-heavy games, and in financial systems that require fast data processing for algorithmic trading.

However, C++ is not ideal for rapid prototyping. Its complex syntax and lack of AI libraries make it slower and more challenging to build and iterate on machine learning models.

#4 R

R stands out as a top language for statistical computing and data analysis. It gives data scientists powerful tools to extract insights from vast datasets and present them in visually compelling ways—customizable charts, interactive dashboards, and advanced visualizations like heatmaps. Even better, R comes with AI and machine learning packages (caret and mlr) that provide pre-built functions for data preprocessing, model training, and evaluation.

R’s Strengths:

Statistical Computing and Data Analysis: R is famous for its strengths in statistical analysis and data visualization. It provides powerful tools for manipulating and analyzing large datasets. It’s easily a top choice for AI applications where deep statistical insights are required.
Library Support for AI: R offers packages, including caret, randomForest, and nnet, which simplify the development of machine learning models. It’s particularly strong in applications like data mining, predictive modeling, and time series forecasting.
Visualization Capabilities: R excels at data visualization, thanks to libraries like ggplot2 and plotly. These tools allow developers to create highly detailed, customizable graphs and charts.

R’s Limitations:

Slower Execution Speed: R is an interpreted language, which often leads to slower execution compared to languages like C++ or Python. This can be a limitation for AI applications that require real-time processing or handle large-scale data at high speeds.
Limited for General AI: Though R is excellent for statistical analysis, it lacks the deep learning frameworks that Python offers. This limits its use for complex AI tasks, like image recognition or natural language processing, which rely on advanced neural networks.
Memory Usage and Scalability: R tends to be less efficient in terms of memory management, which can cause issues when working with large datasets or building scalable AI solutions. This makes it less suitable for AI applications that require extensive computational resources or enterprise-level scalability.

R’s AI Applications:

Given these capabilities, R is best suited for data-centric AI applications involving data analysis and statistical modeling when real-time processing isn’t required. For example, you might choose R if you were building an Investment Strategy and Risk Assessment Tool. With its libraries, you could analyze historical data on asset returns and model different investment scenarios. Then, you could generate reports and interactive dashboards summarizing portfolio performance over time, with charts illustrating potential risk and reward trade-offs.

However, you likely wouldn’t use R to build a stock trading platform. Though it could handle the statistical analysis and data modeling, its execution speed is too slow to support real-time processing tasks like trade execution or high-frequency trading.

#5 Julia

Julia is a high-performance programming language designed for numerical and scientific computing. What’s special about this language? It blends the ease of use found in languages like Python with the execution speed of C. This combination plus AI libraries like Flux.jl make it a natural fit for performance-critical AI applications. In fact, Julia’s so efficient that top organizations like NASA rely on it for mission-critical simulations.

Julia’s Strengths:

Speed and Performance: Julia’s Just-In-Time (JIT) compilation allows execution speeds that rival C. So it’s a good fit for demanding computational tasks like numerical analysis and large-scale simulations.
Native Support for Parallelism and Distributed Computing: Its built-in capabilities for parallel and distributed computing allow developers to efficiently run large-scale calculations across multiple processors or machines.
AI Libraries: Julia features libraries like Flux.jl, designed for machine learning and optimization, which provides high-level abstractions for quick model development.
Easy to Use with High-Level Syntax: Despite its focus on performance, Julia maintains a high-level syntax similar to Python, making it relatively easy to learn and use.
Compatibility with Other Languages: Julia is designed to work well with other programming languages like Python and C. This means developers can use Julia for the performance-critical parts of an AI system while integrating with existing codebases in other languages.

Julia’s Limitations:

Fewer Libraries: While Julia is growing in popularity, its ecosystem is still relatively small. It has fewer pre-built tools and integrations, which can slow development for some tasks.
Longer Compilation Times: As a compiled language, Julia can have slower first-time compilation due to its Just-In-Time (JIT) compiler. This delay can be an issue in AI workflows that require frequent prototyping and experimentation.
Steeper Learning Curve: Newcomers to scientific computing may find Julia’s syntax and concepts challenging to master, especially without prior programming experience.

Julia’s AI Applications:

Julia is a strong option for AI applications that require high-performance computing, particularly in scientific computing, large-scale simulations, and optimization problems. Let’s say you’re developing a climate change prediction model. With Julia’s capabilities, the application can quickly make complex calculations from massive sets of atmospheric data, simulate long-term climate patterns, and create accurate models of ocean currents, temperature fluctuations, and greenhouse gas concentrations.

On the contrary, Julia is less suited for rapid prototyping and natural language processing (NLP). Its ecosystem is smaller with fewer libraries for tasks like deep learning or web-based AI applications.

#6 Haskell

Admittedly, Haskell is not the most appealing programming language for mainstream AI development for reasons we’re about to explore. It’s sometimes chosen for mathematically intensive niche applications. You’ll see it in fields like finance and blockchain, where accuracy and reliability are essential for tasks like formal verification and safety-critical systems.

Haskell’s Strengths:

Mathematical Precision and Accuracy: Its focus on mathematical correctness and strong static typing makes it a top choice for fields that require high accuracy, like finance and aerospace.
Concurrency and Parallelism: Haskell excels at concurrent and parallel processing. So it’s ideal for AI tasks that involve large-scale data processing, such as distributed computing. Its concurrency model allows developers to build systems that can efficiently manage multiple tasks at once.
Immutability and Pure Functions: With Haskell, data cannot be changed once created. This reduces unintended side effects and ensures consistency across computations. This also minimizes bugs and makes the code easier to reason about, particularly in complex applications with multiple states
High-Level Abstractions: Haskell allows developers to work at a high level of abstraction, which can be useful for mathematically complex AI algorithms. This makes it easier to represent AI models, optimization problems, and other complex computations succinctly and clearly.
Lazy Evaluation: Haskell uses lazy evaluation. This means computations are deferred until their results are needed. This can lead to performance optimizations in AI algorithms when only a part of the data needs to be processed or when you’re working with large datasets.

Haskell’s Limitations:

Steep Learning Curve and Complexity: Many developers find Haskell’s functional programming paradigm challenging, especially those more familiar with imperative programming. Mastering advanced concepts like monads, lazy evaluation, and type systems may require more time and effort.
Limited AI Library Support: Haskell lacks specialized AI libraries. This means developers may need to spend more time building tools from scratch, which can delay prototyping.
Slower Development Cycle: Haskell’s focus on mathematical rigor and pure functions can lead to longer development times compared to more agile languages like Python.

Haskell’s AI Applications:

Haskell’s high-level abstractions and support for concurrency make it a strong candidate for mathematically intensive applications. For instance, Cardano, a blockchain platform, uses Haskell extensively because of its focus on security, mathematical rigor, and formal verification. In blockchain development, correctness and safety are paramount to prevent vulnerabilities, and Haskell’s strong type system and functional programming paradigm help make sure the system behaves as expected.

#7 Prolog

Prolog is a logic programming language designed for tasks like symbolic reasoning and knowledge representation. It helps computers understand and work with concepts that are often abstract, like relationships and rules. Perhaps its most unique capability is its intelligent backtracking mechanism. When searching for an answer, Prolog can explore different possibilities. If it tries one path and doesn’t find a solution, it can “backtrack” and try another approach.

Prolog’s Strengths:

Pattern Matching: Prolog recognizes patterns and relationships. It’s a solid choice for AI tasks that involve logical reasoning
Rule-Based Logic: Its foundation in logical rules allows developers to simulate human decision-making.
Built-In Backtracking: When its intelligent backtracking mechanism encounters a dead end, it can backtrack to find alternative paths. This increases the odds of arriving at the correct answer.
Declarative Programming: Prolog is a declarative programming language, meaning developers focus on what needs to be solved, not how to solve it. This is especially helpful in AI, where the complexity of defining logical relationships and rules is higher.

Prolog’s Limitations:

Performance Limitations: Prolog isn’t designed for high-performance tasks like machine learning or big data analysis. It lacks the processing speed and numerical power that languages like Python or C++ offer.
Limited Ecosystem and Libraries: Prolog has a smaller ecosystem and fewer specialized AI libraries. This limits its ability to handle more modern AI techniques like deep learning or computer vision, where a strong set of pre-built tools is needed.
Steeper Learning Curve for Non-Logicians. Prolog’s declarative, logic-based style can be tough for developers used to more common languages like Python or Java. Its syntax and approach to solving problems require a different mindset, which can slow down development.

Prolog’s AI Applications:

Prolog is great for AI applications that rely on symbolic reasoning and complex rule-based systems. It’s particularly useful in industries like law or medicine, where decisions are based on logical rules and relationships.

Consider, for instance, a medical diagnosis system. With Prolog, the system can analyze symptoms, match them against a database of known conditions, and apply logical rules to suggest potential diagnoses. It can also handle complex reasoning, backtrack through multiple diagnostic paths, and refine its conclusions based on patient data and medical knowledge.

However, given its slower execution speeds, Prolog is not suitable for AI applications that require high-performance computing or large-scale data processing—tasks like training neural networks or building image recognition systems.

#8 Scala

Scala stands out as a versatile hybrid language that blends the best of object-oriented and functional programming. It’s compatible with the Java Virtual Machine (JVM), which means developers can leverage Java libraries and frameworks while writing clean, concise code. This makes Scala a practical choice for teams already invested in the Java ecosystem.

Scala’s Strengths:

Powers Apache Spark: Scala is the native language for Apache Spark, a powerful platform for big data processing and machine learning. This gives Scala an advantage in building large-scale, distributed AI systems that need to handle vast datasets efficiently.
Functional and Object-Oriented Programming: By combining object-oriented programming with functional programming, Scala allows developers to write clear, concise code. It’s useful in AI where complex algorithms need to be easy to manage and maintain.
Concurrency and High Performance: Scala can handle many tasks at once, like for real-time data streaming or automated trading platforms. This means high-performance systems built in Scala can scale without hitting performance bottlenecks.
Integration with Java: Scala runs on the JVM, which means it’s easy to integrate with existing Java systems. Developers can access Java libraries while benefiting from Scala’s more expressive, higher-level syntax.

Scala’s Limitations:

Smaller AI Ecosystem: Scala’s AI libraries are limited. Tools like TensorFlow and PyTorch aren’t as natively integrated, which can make it harder to implement machine learning models.
Complexity and Learning Curve: Scala’s hybrid of functional and object-oriented programming can be tough for beginners, particularly those without experience in functional languages.
Slower Compilation: It takes more time for the Scala compiler to convert the written code into machine code that can be executed by the computer, which slows down the development process.

Scala’s AI Applications:

Based on its capabilities, Scala is best suited for large-scale AI applications that involve significant data processing, such as distributed AI systems or real-time analytics platforms. For example, companies might use Scala to build an automated risk assessment tool for insurance policies, a predictive maintenance system for manufacturing equipment, or a real-time fraud detection system for banking transactions.

On the contrary, Scala is not a good fit for projects that need rapid prototyping, like those involving natural language processing or quick machine learning model development. That’s because Scala lacks AI-focused libraries and frameworks, and its slower compilation times make it harder to test and modify models quickly.

What programming languages aren’t suitable for AI?

We’ve covered a handful of programming languages suitable for different types of AI projects. Yet, the question remains: Are there languages you should avoid? The answer is: Yes. Some programming languages do, in fact, lack the capabilities needed for effective AI development. Here are a few popular programming languages that fall short when it comes to AI tasks:

PHP and Ruby don’t have the specialized libraries and computational power necessary for AI applications. Nor do they offer strong data manipulation features or support for machine learning algorithms.
Perl and Visual Basic are useful for text manipulation and GUI development, but they’re held back by outdated ecosystems and limited AI support. They also don’t have the modern libraries needed for tasks like data analysis or training AI models.
Objective-C and Swift are great for iOS and macOS development, but they don’t yet have the broad range of AI libraries available to easily implement machine learning features. Though Swift is gaining more AI support through frameworks like Core ML, it still lags far behind languages like Python.

It’s important to remember that as the demand for AI solutions evolves, so will the programming languages we use. Those that currently lack AI capabilities may include more support and specialized libraries in the future.

How to choose the best programming language for your AI development project

Understanding the strengths and limitations of top programming languages is the first step. The next is evaluating them against your AI development needs. How do you make an informed decision? To start, consider these factors:

Project Priorities: Are you looking for rapid prototyping (Python), or do you need high-speed performance (C++)? Define your project’s core needs first to narrow down your language options.
Libraries and Frameworks: Review the available AI libraries and frameworks. For tasks like machine learning or natural language processing, some languages offer stronger support. For example, Python has TensorFlow, Keras, and PyTorch, while Java has Deeplearning4j.
Integration with Existing Systems: Make sure the language you choose integrates with your current infrastructure. This is especially important for enterprise systems that rely on specific ecosystems like Java or .NET.
Performance Needs: Does your AI project require real-time processing or large-scale data handling? In these cases, languages like Scala (with Apache Spark) or C++ may make your shortlist.
Scalability: Consider your project’s long-term needs. Choose languages that can scale (like Python or Java) with your needs over time.
Team Expertise: Don’t overlook your team’s experience. To avoid long learning curves and inefficiencies, choose a language that fits your team’s skillset or hire AI developers to fill the skill gaps.

By taking the time to carefully evaluate these factors, rather than rushing in, you’ll set a solid foundation for your AI project. Thoughtful planning now can save you from costly roadblocks later. Plus, it increases the likelihood of choosing a programming language that fits both your immediate needs and your long-term goals.

Conclusion

It’s easy to assume that as long as the algorithms are sound, any programming language will do. That’s simply not true. As you’ve seen in this article, every programming language comes with unique strengths and limitations, which influence the AI applications it’s best suited for.

Whether you’re rapidly prototyping AI models (Python), processing real-time data (C++), or managing large-scale datasets in distributed environments (Scala), you’re now equipped to select a programming language that aligns with your AI development needs.

FAQ

Can you use JavaScript for machine learning and artificial intelligence?

JavaScript is one of the best languages for web development but isn’t particularly well known for machine learning and AI. There is increasing interest in using JavaScript for Data Science, but many believe that this is due to the popularity of the language rather than its suitability.

Is Python enough to learn AI?

Python is one of the most important languages for starting out in machine learning and AI, but if you want to specialize, you’ll often need to supplement your Python skills with those of one of the other key programming languages.

What is the best language for AI?

There is no singular “best” language for AI. It depends on what kind of AI model you plan to implement. For example, in the case of machine learning, you might choose Python, which is a great choice for this AI subset.

Which is better for AI, Python or Java?

Python and Java are both languages that are widely used for AI. The choice between the programming languages depends on how you plan to implement AI. For example, in the case of data analysis, you would probably go with Python. However, given how popular AI is for mobile apps, Java, which is frequently used in this case, may well be the best language for this type of program.

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