AI is now used in healthcare, banking, manufacturing, and more. AI solutions require advanced expertise and technologies. That’s why it’s so important that you choose the right programming language. While all languages could potentially be used to create advanced machine learning models, the computational requirements tend to favor some programming languages over others.
Let’s begin with a caveat, this article isn’t meant to pick a clear winner, nor is it aimed at convincing people that your language of choice is terrible. Each language has its own strengths and weaknesses, and a good software developer will be able to navigate whatever problems arise based on their skill and experience.
In other words, if your team is already an expert in a specific language, don’t chase new trends. Instead, focus on nurturing their abilities and finding solutions within that framework. Only look for new solutions once it has become clear that your current ecosystem can’t solve the problem at hand.
Today we want to talk about Python, C++, Java, and Julia. All of them are famous, all of them are used consistently, and all of them are amazing AI programming languages. Which one should you pick? Let’s see if we can help you make that choice.
Python: AI’s Language
While it’s really hard to pin down the popularity of a framework or programming language, I can comfortably say that Python is probably the most used language in machine learning and AI (and as a Pythonista, that’s a hill I’m willing to die on). This dynamic high-level language is a data science industry standard due to its large support libraries and its rapid prototyping without sacrificing performance due to its simple syntax.
Python’s open-source packages, like TensorFlow, Keras, PyTorch, and Scikit-learn allow developers to design complicated algorithms with fewer lines of code than other languages. It supports sophisticated applications and is easy to use for non-experts. In fact, the syntax is so friendly that I can confidently show my code to a client and know that I can have a pretty good idea of what I’m doing.
Python’s Advantages Over Popular Languages
- Due to its text processing capabilities, Python can handle vast amounts of unstructured data, which machine learning models use.
- Python’s simplicity speeds up design thinking iterations and reduces errors throughout development.
- Python’s active community provides copious documentation at all levels, making it easy for beginners to learn and efficient for experts to handle complex problems.
- Python is easier to use than other popular languages. Python is more approachable than C++ because of its simple syntax.
- Python’s interactive shell lets developers test code without compiling.
- Python’s library support is another advantage for AI and machine learning applications.
- Developers can reuse code from many libraries. TensorFlow, PyTorch, Keras, Scikit-learn, and OpenCV are popular.
- Finally, Python is widely used in web development and data science. Therefore, many free tutorials help you learn it rapidly, even if you have no programming expertise!
I cannot stress just how simple Python is compared to other market alternatives. By sacrificing some maneuverability in terms of memory management, it provides a concise syntax that lets developers focus on problem-solving rather than language details. Even non-programmers can learn it.
Python’s many libraries and frameworks accelerate AI development. These technologies provide ready-made solutions for typical tasks like data analysis, machine learning, natural language processing, computer vision, robotics, etc., decreasing the time needed to construct sophisticated applications.
Big Developer Community
Python’s popularity has produced a significant community of skilled developers who provide code snippets, tutorials, and other tools for AI beginners and problem solvers. This community is constantly resolving bugs and creating new libraries and frameworks.
This by itself is fantastic, but when you take into account the huge amount of effort tech companies like Google and Microsoft have put toward contributions to Python’s machine learning libraries like TensorFlow, it’s hard not to recommend it as a solution.
Interoperability
Python’s cross-platform compatibility lets programmers write code on one OS and run it on another without modification. This simplifies cross-environment and global collaboration for developers. C++ and other languages require recompiling when switching platforms, which slows down agile teams.
Flexible Use
Python may be used to build data analysis projects that analyze massive databases and real-time computer vision applications that analyze images or videos. This broad applicability lets you easily switch between skill sets once learned.
Overall, Python is an ideal choice for developing AI applications due to its simplicity, ease of use, widespread use in industry, and extensive support through its online communities. It can handle both enterprise-wide applications and research projects without losing functionality, making it a go-to choice for many businesses entering the AI space. It’s definitely worth considering adopting this language.
C++
Ask even the most ardent Python fan what the biggest downside of our favorite language is, and they’ll have to regretfully agree that Python is extremely slow. That’s the tradeoff for its ease of use, and while for many use cases, the difference isn’t significant enough, if performance is an issue then you might do well to look elsewhere, like to C++, the powerhouse of programming languages.
C++ is about as close as you can get to machine code without working with assembler. This low-level compiled language is, to one degree or another, under the hood of every operating system on the market alongside C and assembler. Notoriously complex, C++ fans will quote high-performance algorithms with low-level memory management as the most frequent use case for the language, as long as you know what you are doing!
C++’s fundamental feature is its ability to optimize code execution via low-level memory management techniques like direct access via pointers, which give great resource consumption control—essential when working with real-time data streams. Its fast computations make it ideal for latency-sensitive applications like computer vision and robotics.
C++’s Advantages Over Popular Languages
- System Level Optimizations: SLO allows C++ to build highly optimized software that fully utilizes hardware, resulting in faster outputs.
- Interoperability: C++ is one of the few programming languages that can operate on every platform, making it adaptable because you can develop your code once and migrate it between operating systems. Of course, keep in mind that the code has to be recompiled for each platform.
- Multi-threading: C++ supports multi-threaded processes for parallel computing.
- Faster than the alternatives: C++ contains compiled code. C++ is ideal for real-time applications with low latency, such as self-driving cars or robotics.
- Efficient memory management: C++ allows the programmer to allocate and manage memory. This optimizes memory usage and lets developers adapt program performance to application needs.
- Easy hardware integration: C++ can easily integrate with hardware components like micro-controllers or sensors, making it a good choice for embedded systems that use machine learning algorithms.
- Advanced algorithm development features: Templates, operator overloading, and parallel processing facilitate complicated algorithm development in the language.
- Large user community and libraries: Many users support and contribute to open-source libraries that simplify AI application development with machine learning.
C++ is an object-oriented craftsman tool; it’s akin to the legendary Stradivarius violins in that in the right hands, it can produce awe inspiring results. In the end, it’s not the instrument but the artist. For the inexperienced, C++ has myriad landmines of potential issues because it doesn’t hold your hand, but for a person who truly understands the underlying architecture of a computer system, it gives them almost absolute control over every part of the process.
Java: Enterprise-Friendly Language
Java has advantages over Python and C++ for AI. Java, a general-purpose, object-oriented programming language, is efficient in both data science and machine learning. It’s a highly verbose language that gained traction in the 90s based on its motto: Write once, run anywhere.
The Java Virtual Machine was a powerful solution to the problem of having to rewrite and recompile code depending on the platform. With its massive adoption and rich ecosystem, Java has become an industry standard worldwide.
Java SDKs update virtually annually, allowing continuous improvements on optimization metrics based on the latest industry standards/benchmarks, setting new standards over time, and making Java suitable for production-grade big data analytical software solutions. I can say with the utmost confidence that Java has one of the best development pipelines in the business.
Other benefits include:
- Java supports large-scale enterprise applications. Its success stems from its platform-independent software development architecture, which makes it easier for developers to build programs that operate on Windows, Mac OS, and Linux.
- Java’s multithreaded programming support lets developers use modern hardware architectures.
- Another point in its favor is that it has faster processing and better memory management with less complexity involved.
- Regarding cybersecurity, Java is ideal. Developers can protect their apps with its strong security infrastructure, which includes bytecode verification and encryption.
Java’s adaptability makes it perfect for high-accuracy machine learning models. Deep Learning 4 Java’s (DL4J’s) open-source framework builds popular deep learning models like artificial neural networks (ANNs). The aforementioned enterprise-grade features are ideal for building complicated AI systems in large enterprises. Java can be combined with scalable data processing frameworks like Apache Hadoop or Apache Spark to build real-time, large-scale AI systems.
DL4J uses Hadoop’s distributed computing to scale well. This library is quicker than TensorFlow and PyTorch since it has better support for GPU processing (not by much). In addition to ANNs, Weka and Massive Online Analysis libraries provide pre-built machine learning algorithm implementations.
Python remains more popular than Java among researchers working on cutting-edge deep learning models or quickly prototyping new ideas. Still, with enterprises’ need for scalable solutions, there will be use cases where enterprises prefer language preferences towards production, in other words, rollout solutions built with JDK-based technologies.
In summary, Java’s powerful libraries/tools/frameworks for effectively processing big datasets and providing time-tested scalability and security make it perfect for constructing enterprise-grade AI systems.
Enterprise apps have used Java for decades. Its AI development and research potential is growing. AI uses Java in the following ways:
- ML Frameworks: Machine learning (ML) frameworks help build and train models. Java-based DL4J is an example. It can process massive datasets with Hadoop and other big data tools.
- Language Processing: Natural language processing (NLP) helps machines understand human language. OpenNLP has pre-built models for sentence segmentation, tokenization, named entity recognition, and more. Stanford NLP provides parts-of-speech tagging, named entity recognition, coreference resolution, and sentiment analysis.
- Robotics: Robotics combines mechanical engineering, electrical engineering, and computer science to build robots that can do various jobs with minimal human intervention. The Robot Operating System Java client library supports Java programming languages, which has helped robotics development.
- Big Data Processing: To learn from large amounts of data from multiple sources and formats, AI algorithms need a strong pipeline to support them. Java’s Big Data frameworks can handle massive data quantities without crashing or slowing down.
- Computer Vision: Computer vision (CV) allows machines to interpret images and videos like humans do. Java’s Open Source Computer Vision Library (OpenCV) was founded by Intel laboratories and currently has contributions from numerous people worldwide under GPLv2.
- Data Analytics: Data analytics uses statistical approaches to find patterns in collected data. Due to its versatility and user-friendliness, the Weka Library, a collection of ML algorithms, is widely used by researchers worldwide for data analytics in Java.
Julia: A Promising Scientific Computing and Machine Learning Newcomer
Julia is an emerging high-level dynamic scripting language with design elements similar to MATLAB, R, and Python but with enhanced computational capability like that of lower-level system programming languages like C++. Research teams are interested in Julia because it allows speedy prototyping and robust coding foundations for later project optimizations. Julia is growing more popular in all settings, especially academics.
Why does Julia suit AI development? Julia performs like compiled low-level programming languages like C/C++, enabling faster runtime and requiring fewer manual memory allocations, making code “cleaner” than expected from traditional scripting styles. Many engineering disciplines, such as fluid dynamics, strongly support Julia because there are few alternatives.
Julia’s performance makes it ideal for high-performance computing tasks like ML. Julia supports distributed computing, multi-threading, shared memory, and GPU acceleration, making it ideal for large-scale parallel computations. It handles big data sets without slowing down.
Julia has many high-quality scientific computing and ML libraries, such as Flux.jl for deep learning, JuMP.jl for mathematical optimization, and DataFrames.jl for data tables. Communities have contributed over 1900 packages to the official package repository to develop these libraries.
Julia’s Read-Evaluate-Print Loop lets researchers prototype their code and get instant feedback. Julia’s concise, easy-to-read syntax reduces development time compared to C++ or Java.
Some of its core benefits include:
- Speed: Julia’s just-in-time (JIT) compilation runs programs nearly as fast as C or Fortran. It is ideal for resource-intensive applications like ML techniques.
- Flexibility: Julia supports object-oriented, functional, and procedural programming paradigms. Developers can solve challenges in the same language using different methods.
- Built-in support: Julia’s built-in support for calling C and Fortran libraries makes it easy to integrate with existing code.
- Parallel processing: Julia has built-in distributed computing functionality, allowing developers to conduct computations across numerous processors or machines without extra software or libraries.
- Rich ecosystem: Julia offers a growing ecosystem of packages for ML frameworks, optimization libraries, data visualization tools, and more.
- Community: Julia also has a dedicated developer community. So, several online resources teach Julia for use in AI and data science.
Conclusion: Which Language Should You Start Your Next Big Project In?
Selecting a programming language for your AI project is difficult. Python, C++, Java, and Julia are the top AI programming languages. Each language has pros and cons.
Due to its simplicity and large library, Python is the most popular AI development language. Its high-level syntax makes writing complex code fast, making it perfect for data analysis and ML. Its shallow learning curve makes it a good language for beginners.
Nonetheless, C++ excels at computationally expensive jobs like image processing and computer vision. Direct memory access lets developers create faster code than Python or Java. It offers low-level hardware and system resources.
Java supports enterprise applications well. It gives developers tools and libraries to build secure, scalable, large-scale applications. Java’s exception-handling is ideal for data-driven applications.
Julia is an emerging language for scientific computing and ML. MATLAB users can start quickly because its syntax is similar. Julia’s JIT compiler lets users write code quickly without performance bottlenecks like Python’s.
While designing an AI application, your programming language should fit your objectives. Python is a popular, easy-to-learn language. C++ is best for speed, Java for enterprise-grade solutions, and Julia for performance, ease of use, and scientific computing.
Before choosing, consider these:
- Project goals: Need a big data language? Building a high-performance real-time application? Knowing your project goals helps you choose a language.
- Language-supporting resources: Does your language have adequate tutorials, libraries, and frameworks? Can enough developers code or debug?
- Time constraints: If you have little time to develop an AI solution, choose a language with high-level libraries and easy learning.
- Platform compatibility: Languages operate better on various systems.
- Budget: Programming language development costs may influence your choice.
In conclusion, the right programming language depends on your skill level, project requirements, available resources (like libraries), and personal preferences. Depending on the purpose, each programming language has pros and cons for designing AI apps. When choosing a programming language, consider all these criteria.
If you enjoyed this, be sure to check out our other AI articles.
