Top 5 Easy AI Programming Languages to learn in 2024
Without a doubt, AI is subtly changing how people live and work. Artificial intelligence is heavily utilized to produce a wide range of applications, from virtual assistants like Alexa and Siri to personalized recommendation systems. Building an AI system or model, however, is difficult and calls for a variety of resources, including programming languages, tools, and knowledge. We’ll talk about the best AI programming languages and their salient characteristics in this blog. Now let’s get going.
Top Programming Languages for AI Development
There are several AI programming languages used in AI model development. Let’s have a look at the most popular ones:
1. Python
Python is without a doubt one of the most potent machine learning & AI programming languages available. Python’s large library, ease of reading, and simplicity make it the ideal language for developing custom artificial intelligence. There are two well-known Python AI libraries:
TensorFlow: One open-source AI framework that streamlines the creation of AI models is TensorFlow, created by Google. This is especially well suited for a number of deep learning applications, including neural networks and natural language processing. Keras, the high-level TensorFlow API, provides an incredibly intuitive interface for creating and refining sophisticated AI models.
2. R language:
R programming language is specially developed for data analysis and statistics. This is a good option for AI-based applications that mainly involve statistical modeling and huge data manipulation. As one of the best AI programming languages, R offers a number of packages, from random. Forest to caret that effectively facilitates predictive analytics and machine learning.
While R language excels in visualization and data analysis, it can’t be the ideal choice for production-level applications. Its scalability & performance limitations become apparent when dealing with complex neural networks or large-scale datasets. However, for data-centric AI-related tasks, R language will remain an important tool for AI development.
3. Java
Java is one of the object-oriented and versatile programming languages which is known for its high scalability and portability. It comes with lots of benefits, especially for making enterprise-level AI solutions. The strong typing system, robust performance, and extensive libraries of Java make this language one of the reliable options for professional AI developers who prioritize scalability and stability. Some of the important frameworks and libraries for AI in Java include:
4. C++:
C++ is one of the popular low-level programming languages widely used in game development and systems programming. Though C++ is not a very developer-friendly AI programming language, it offers a number of performance benefits. As compared to other languages, C++ isn’t the first choice for AI application development because of its verbosity and steep learning curve, but it provides some significant advantages in major scenarios:
5. JavaScript
JavaScript is used where seamless end-to-end AI integration on web platforms is needed. The goal is to enable AI applications through familiar web programming. It is popular for full-stack development and AI features integration into website interactions.
Plus, JavaScript uses an event-driven model to update pages and handle user inputs in real-time without lag. The language is flexible since it can prototype code fast, and types are dynamic instead of strict.
As for its libraries, TensorFlow.js ports Google’s ML framework to JavaScript for browser and Node.js deployment. The brain.js neural network API provides flexible deep learning. Synaptic.js offers architecture-agnostic neural networks. And Node-RED’s visual workflow simplifies model integration.
Comparative analysis of best AI programming languages
| Language | Strengths | Weakness | Usability | Common AI apps | Examples of AI apps |
| Python | Simple syntax, vast libraries, productive | Performance issues, GIL for threading | High, great for beginners & experts | Machine learning, computer vision, NLP | Siri, Alexa, Tesla Autopilot |
| R | Statistical modeling and analysis built-in | Not general purpose, steep learning curve | Moderate, for data scientists | Statistical analysis, data mining | IBM Watson Analytics, Google BigQuery ML |
| Java | Cross-platform, enterprise-ready, static typing | Verbose, not AI specialized | Moderate, good for production | Search algorithms, NLP, enterprise AI | Google Search, Cortana, Banking APIs |
| Julia | High performance, math capabilities | Newer language, smaller ecosystem | Moderate, similar to Python | Data science, numerical analysis | A central bank in the US, the NY Fed, uses Julia to do ML modeling to understand the US economy. |
| JavaScript | Ubiquitous, full stack web dev | Not optimized for math/ML | Moderate, front-end focused | Browser-based ML, Node.js backends | Chrome ML image search, Angular recommendation engine |
| C++ | Speed and efficiency, hardware control | Complex, systems programming | Hard, for advanced developers | Optimized ML apps, robotics | Stock trading systems, self-driving cars |
| Lisp | Symbolic AI, code as data constructs | Not widely used today, limited libraries | Moderate, niche usage | Early AI research, pattern recognition | SPIKE, the planning and scheduling application for the Hubble Space Telescope. |
| Haskell | Functional purity, type safety | Abstract, not widely used for AI | Hard, niche research usage | Theoretical ML research | Meta has worked on several Haskell projects including Sigma, an anti-abuse platform that processes more than a million requests per second. |
| Prolog | Logic programming, pattern matching | Limited adoption and libraries | Moderate, declarative style | Expert systems, natural language | The network interface configuration code of Microsoft Windows NT involved a Small Prolog interpreter. |
| Scala | Leverages Java, functional capabilities | Steep learning curve | Moderate, big data focused | ML on Spark, distributed systems | eSentire uses Scala for real-time cybersecurity solutions. |
