Snakes, letters and coffee: the best languages ​​for AI

AI is now used in healthcare, banking, manufacturing and more. AI solutions require advanced knowledge and technologies. This is why it is so important that you choose the right programming language. While all languages ​​can potentially be used to create advanced machine learning models, computational requirements tend to favor some programming languages ​​over others.

Let's start with a caveat: this article is not about picking a clear winner, nor is it about convincing people that their language of choice is terrible. Each language has its own strengths and weaknesses, and a good software developer will be able to navigate any issues that arise based on their skills 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 your skills and finding solutions within that framework. Only look for new solutions when it becomes clear that your current ecosystem cannot solve the problem at hand.

Today we want to talk about Python, C++, Java and Julia. They are all famous, they are all used consistently, and they are all amazing AI programming languages. Which should you choose? Let's see if we can help you make that choice.

Python: the language of AI

While it's really difficult to define the popularity of a programming framework or 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 for). This high-level, dynamic language is a data science industry standard due to its large support libraries and rapid prototyping without sacrificing performance due to its simple syntax.

Python's open source packages such as 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 user-friendly that I can safely show my code to a client and know that I can get a good idea of ​​what I'm doing.

Python's advantages over popular languages

• Due to its text processing capabilities, Python can handle large amounts of unstructured data, which machine learning models use.
• Python's simplicity speeds up design thinking iterations and reduces errors during development.
• The active Python community provides abundant documentation at all levels, making learning easy for beginners and efficient for experts in tackling complex problems.
• Python is easier to use than other popular languages. Python is more accessible than C++ due to its simple syntax.
• Python's interactive shell allows developers to test code without compiling.
• Python 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 quickly even if you don't have any programming knowledge!

I cannot emphasize how simple Python is compared to other alternatives on the market. By sacrificing some maneuverability in terms of memory management, it provides a concise syntax that allows developers to focus on problem solving rather than language details. Even non-programmers can learn.

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 required to build sophisticated applications.

Large 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 solving bugs and creating new libraries and frameworks.

This in itself is fantastic, but when you take into account the enormous effort that technology companies like Google and Microsoft have put into contributing to Python machine learning libraries like TensorFlow, it's hard not to recommend it as a solution. .

Interoperability

Python's cross-platform compatibility allows programmers to write code on one operating system and run it on another without modification. This simplifies global and cross-environment collaboration for developers. C++ and other languages ​​require recompilation when switching platforms, which slows down agile teams.

Flexible use

Python can 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 allows you to easily switch between skill sets once you learn them.

Overall, Python is an ideal choice for developing AI applications due to its simplicity, ease of use, wide usage in the industry, and wide support through its online communities. It can handle enterprise-wide applications and research projects without losing functionality, making it an ideal choice for many companies entering the AI ​​space. It's definitely worth considering adopting this language.

C++

Ask even the most ardent Python fan what the biggest drawback of our favorite language is, and they will have to agree, regrettably, that Python is extremely slow. That's the downside to its ease of use, and while for many use cases the difference isn't significant enough, if performance is an issue you might want to look elsewhere, like C++, the powerhouse of programming languages.

C++ is as close to machine code as possible without working with assembler. This low-level compiled language is, in one way or another, underlying every operating system on the market, along with C and assembler. Notoriously complex, C++ fans will cite high-performance algorithms with low-level memory management as the language's most frequent use case, as long as you know what you're doing!

The fundamental characteristic of C++ is its ability to optimize code execution through low-level memory management techniques, such as direct access via pointers, which provide great control over resource consumption – essential when working with streams of data in real time. real. Its fast calculations make it ideal for latency-sensitive applications such as computer vision and robotics.

Advantages of C++ over popular languages

• System-level optimizations: SLO allows C++ to create highly optimized software that fully utilizes the hardware, resulting in faster results.
• Interoperability : C++ is one of the few programming languages ​​that can operate across all platforms, making it adaptable because you can develop your code once and migrate it between operating systems. Of course, keep in mind that the code must be recompiled for each platform.
• Multithreading: C++ supports multithreaded processes for parallel computing.
• Faster than 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 allows developers to tailor program performance to the needs of the application.
• Easy hardware integration: C++ can be easily integrated with hardware components like microcontrollers 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 make it easy to develop complicated algorithms in the language.
• Large community of users and libraries: Many users support and contribute to open source libraries that simplify the development of AI applications with machine learning.

C++ is an object-oriented artisanal tool; It is similar to the legendary Stradivarius violins because, 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 countless landmines of potential problems 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: business-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 detailed 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 across the world.

The Java SDKs are updated virtually annually, enabling continuous improvements in optimization metrics based on the latest industry standards/benchmarks, setting new standards over time, and making Java suitable for production-grade big data analytics software solutions . I can say with the utmost confidence that Java has one of the best development pipelines on the market.

Other benefits include:

• Java supports large-scale enterprise applications. Its success stems from its platform-independent software development architecture, which makes it easy for developers to create programs that operate on Windows, Mac OS, and Linux.
• Java's multithreaded programming support allows developers to 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 applications with its strong security infrastructure, which includes bytecode verification and encryption.

Java's adaptability makes it perfect for high-precision machine learning models. The open-source Deep Learning 4 Java (DL4J) framework creates popular deep learning models such as artificial neural networks (ANNs). The enterprise-grade features mentioned above are ideal for building complicated AI systems in large enterprises. Java can be combined with scalable data processing frameworks such as Apache Hadoop or Apache Spark to build large-scale, real-time AI systems.

DL4J uses Hadoop distributed computing to scale well. This library is faster than TensorFlow and PyTorch as it has better support for GPU processing (not much). In addition to ANNs, the Weka and Massive Online Analysis libraries provide implementations of pre-built machine learning algorithms.

Python remains more popular than Java among researchers working on cutting-edge deep learning models or rapid prototyping of new ideas. Still, given companies' need for scalable solutions, there will be use cases where companies prefer language preferences over production, in other words, deployment solutions built with JDK-based technologies.

In summary, Java's powerful libraries/tools/frameworks for effectively processing large data sets and providing time-tested scalability and security make it perfect for building enterprise-grade AI systems.

Enterprise applications have used Java for decades. Its potential for development and research in AI is growing. AI uses Java in the following ways:

• ML Frameworks : Machine learning (ML) frameworks help you 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 part-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 perform various jobs with minimal human intervention. The Robot Operating System's Java client library supports Java programming languages, which has helped in the development of robotics.
• Big Data Processing : To learn from large amounts of data from diverse sources and formats, AI algorithms need a strong pipeline to support them. Java Big Data frameworks can handle large amounts of data 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 Labs and is currently contributed by countless people around the world under GPLv2.
• Data analysis : Data analysis uses statistical approaches to find patterns in collected data. Due to its versatility and ease of use, the Weka Library, a collection of ML algorithms, is widely used by researchers around the world for data analysis in Java.

Julia: A Promising Newcomer in Scientific Computing and Machine Learning

Julia is an emerging high-level dynamic scripting language with design elements similar to MATLAB, R, and Python, but with enhanced computational power like that of lower-level system programming languages ​​such as C++. Research teams are interested in Julia because it enables rapid prototyping and robust coding foundations for later design optimizations. Julia is becoming more popular in all environments, especially in academia.

Why Julia adapts to AI development? Julia works like low-level compiled programming languages ​​like C/C++, enabling faster execution time 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 few alternatives exist.

Julia's performance makes it ideal for high-performance computing tasks like ML. Julia supports distributed computing, multithreading, shared memory, and GPU acceleration, making it ideal for large-scale parallel computations. It handles large 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 more than 1,900 packages to the official package repository to develop these libraries.

Julia's Read-Evaluate-Print loop allows researchers to 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 main benefits include:

• Speed : Julia's just-in-time (JIT) compilation runs programs almost as fast as C or Fortran. It is ideal for resource-intensive applications such as 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 calculations across multiple 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. Therefore, 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 main 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 codes faster, making it perfect for data analysis and ML. Its shallow learning curve makes it a good language for beginners.

However, C++ excels at computationally expensive work like image processing and computer vision. Direct memory access allows developers to create code faster than Python or Java. It offers low-level hardware and system resources.

Java supports enterprise applications well. It provides developers with tools and libraries to create secure, scalable, and large-scale applications. Java's exception handling is ideal for data-oriented applications.

Julia is an emerging language for scientific computing and ML. MATLAB users can get started quickly because their syntax is similar. Julia's JIT compiler allows users to write code quickly without performance bottlenecks like Python.

When designing an AI application, your programming language must meet your goals. Python is a popular and easy-to-learn language. C++ is best for speed, Java for enterprise-level solutions, and Julia for performance, ease of use, and scientific computing.

Before choosing, consider the following:

1. Project goals : Need a big data language? Building a high-performance real-time application? Knowing your project goals helps you choose a language.
2. Language support resources : Does your language have suitable tutorials, libraries and frameworks? Can enough developers code or debug?
3. Time constraints : If you have little time to develop an AI solution, choose a language with high-level libraries that are easy to learn.
4. Platform compatibility : Languages ​​work best across systems.
5. 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 applications. When choosing a programming language, consider all these criteria.

If you liked this, be sure to check out our other articles on AI.

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