Introduction
Universally, Artificial intelligence (AI) is becoming the buzzword. With OpenAI’s DALL-E creating images from text prompts and DeepMind predicting protein structures, these advancements are changing the way how we think about technology.
The potential of Artificial Intelligence is huge and navigating the landscape of programming languages and frameworks can feel overwhelming. If you are thinking about which programming language is the best fit for your AI application needs, then this blog is for you.
Read on to learn more about the top programming languages used in AI applications and the key features that make them ideal for different types of AI projects.
1) Python:
Python is synonymous with AI development, due to its clean and straightforward syntax. The language is beginner-friendly and a favourite among data scientists and AI developers. With libraries like NumPy, sci-kit-learn, TensorFlow, and PyTorch, Python has a vast ecosystem, providing the necessary tools for basic data manipulation, cutting-edge neural networks APIs, and more.
Why Python is suitable for AI applications?
- Python is a versatile language used for machine learning, natural language processing (NLP), computer vision applications and more.
- It provides complete frameworks for developing, deploying and maintaining AI systems.
- It provides exceptional results when used in predictive models, neural networks, deep learning, image recognition, face detection, chatbots, etc.
2) R:
R is the go-to language if your AI project is about statistical analysis or data visualization. It is best suited for handling complex data and data visualization critical for AI applications.
Why R is suitable for AI applications:
- R is the best fit for your AI application when you require statistical approaches for creating AI algorithms for probabilistic modelling, simulations, and data analysis.
- Its ggplot2 package allows you to create stunning custom graphics.
- The caret package simplifies preprocessing and model training.
- It’s highly scalable and delivers exceptional results when used for time series forecasting and multivariate regression analysis.
3) Java:
Java is highly reliable and scalable and is deal for integrating AI into business systems. The Java Virtual Machine (JVM) framework is compatible with cross-platform applications. It has extensive libraries, like DeepLearning4j and Mallet that can support AI applications like natural language processing, predictive analytics, etc.
Why Choose Java?
- Java is perfect for embedding AI into large-scale business applications.
- Code can be effortlessly executed across different environments.
- It has frameworks like Weka and libraries like OpenNLP simplifies AI application development.
4) Julia:
Julia is a perfect choice for your AI project if you seek speed and numerical precision. It has high-performance computing capabilities and is compatible with Python and R code, which makes it suitable for data science and AI research.
Key Features of Julia
- Julia’s execution is comparable to C and Fortran and is suitable for scientific simulations and models, time series analysis, signal processing workflows, bioinformatics, etc.
- It allows you to easily distribute tasks across different cores and platforms.
- With libraries like Flux.jl and DataFrames.jl it simplifies AI and data science functions.
5) JavaScript
JavaScript is used in developing web and Artificial Intelligence applications. It’s used for full-stack development applications and allows developers to embed AI features directly into web applications. It’s frameworks like TensorFlow.js and brain.js help browsers and Node.js environments support machine learning frameworks.
Key features of JavaScript
- It allows you to perform AI tasks directly in the browser, like image or speech recognition.
- It allows you to easily integrate AI features into existing web platforms.
- You can use frameworks like React Native for cross-platform AI-driven apps.
6) C++:
C++ is the go-to language if you seek low latency and high-performance capabilities in your AI applications. C++ code can be easily compiled providing unparalleled control over memory and processing, which is critical for robotics, gaming, and embedded systems.
Key features of C++
- The C++ language is suitable for your AI application If it involves computationally intensive tasks.
- You can use TensorFlow’s C++ API, OpenCV for computer vision, and Caffe2 for deep learning applications.
- It allows you to compile your C++ code into independent executable programs to ensure reliable performance across different platforms.
7) Lisp:
Lisp is one of the oldest programming languages used in AI applications. It’s widely known for symbolic reasoning and meta programming features, making it suitable for rule-based AI systems and logical reasoning.
Why Lisp is suitable for AI applications?
- It is widely used in AI systems to deliver the best outcomes in automated reasoning, natural language processing, game-playing algorithms, and logic programming.
- It allows you to modify its code dynamically through meta programming.
- It’s ideal for building applications that require intricate decision-making logic.
8) Haskell:
The Haskell programming language uses a functional approach, making it a natural fit for AI applications based on logic and math. It’s highly useful in safety-critical systems as it avoids side effects in its functions to ensure bug-free, reliable and correct code.
Why Choose Haskell?
- It’s ideal for AI systems that are required for proving theorems, probabilistic modelling, constraint programming and combinatorial search.
- It has lazy code evaluation features that ensure calculations are only performed when necessary.
- It has built-in libraries like HLearn for machine learning and LambdaNet for neural networks.
9) Prolog:
Prolog encodes knowledge as facts and rules for logical inferences as it is a declarative programming language. It has the ability to deduce new facts from existing rules, which makes it a natural fit for expert systems and formal reasoning.
Why Use Prolog?
- It delivers the best outcomes in AI systems that deal with knowledge representation and reasoning Encodes complex relationships and rules naturally.
- It focuses on what to achieve rather than how to achieve it.
- It has built-in tools like SWI-Prolog and ProbLog to improve the functionality of your AI systems.
Conclusion
Karpagam Institute of Technology is one of the top engineering colleges in Coimbatore that equips you with the right tools and knowledge to become a competent AI developer.
Before deciding on which programming language used in artificial intelligence it’s essential to understand your AI project requirements and objectives. This allows you to choose the best coding language for your AI application.