Have you ever wondered how ChatGPT is programmed? Or which programming language would be useful for creating or testing new AI application features? Let’s explore the most popular programming languages for AI and the scenarios in which they excel.
When you have a question, you typically turn to Google. But nowadays, you might also seek a response from ChatGPT. Depending on the knowledge cutoff of the large language model (LLM), you will receive different answers. Here is the latest response from ChatGPT-4 with the knowledge cutoff in December 2023:
ChatGPT has done a great job highlighting useful programming languages for specific AI developments. However, the choice of programming language depends on the context, methods, and use-case. “It depends” is a common answer in IT projects like this one.
First and foremost, ask yourself: What do you want to achieve, and what is the desired outcome? Based on your answers, choose your tools such as programming languages, libraries, and software stack.
Consider if you or your team already have knowledge in the programming language or software needed. Are there stable libraries available that you can leverage? While learning new things is valuable, think carefully before diving into a new programming language or software tool.
Always remember: Applying AI involves utilizing techniques and methods from statistics and mathematics, which are initially separate from any tech stack.
How you apply your chosen algorithms to your data is up to you. Programming languages and specific libraries are tools that help you achieve your desired outcome.
Here’s a breakdown of useful programming languages for AI, categorized by their main advantages. Each programming language is accompanied by a sample code demonstrating how it intelligently reads and outputs a CSV file.
Simplicity: Python
Python is the undisputed champion in AI due to its simplicity. It’s easy to learn, has a large community, and is highly extensible. Many AI frameworks, databases, Cloud APIs, etc., support an SDK in Python. When using Python, there’s usually a library available for any task.
Many data scientists, data engineers, and data analysts have increasingly turned to Python in recent years. Python is well-supported on major data platforms, alongside SQL.
Notable libraries: PyTorch, TensorFlow, Keras, Sci-kit learn, Numpy, Pandas, Polars, Jupyter, Matplotlib, openCV, spaCy
Performance: C++
C++ excels in computational speed, making it ideal for computationally intensive AI tasks. Many AI Python libraries utilize C++ code for better performance. While Python provides the input parameters, the actual computation is done by C++ code. This hybrid approach allows users to achieve tasks in Python without needing to learn C++.
If speed, latency, and performance are critical, C++ is a top choice, although it has a steeper learning curve compared to Python.
Notable libraries: PyTorch, TensorFlow, mlpack, Caffe, OpenNN
Full-Stack: JavaScript
JavaScript is widely used for both client-side and server-side programming, making it a versatile “full-stack” language. While JavaScript allows for adding AI features to applications, it may not be suited for intensive AI workloads and lacks extensive data science libraries.
Notable libraries: TensorFlow.js, Brain.js, D3.js, Synaptic.js
Data Analysis: R
R is a popular choice for data analysis, offering many extension packages for machine learning and statistical analysis. While favored by statisticians, R can be challenging for beginners and may not be suitable for production environments.
Notable libraries: tidyverse (dplyr, ggplot2), XGBoost, Gmodels, H2O
Enterprise-level applications: Java
Java is suitable for enterprise-level applications and offers libraries for machine learning and data analysis. While it may not replace Python in AI, Java is valuable for scalability and performance in large-scale applications.
Notable libraries: Deeplearning4j, Deep Java Library, Apache OpenNLP
The emerging star?: Julia
Julia, a newer programming language, offers speed and performance advantages over Python. While Julia has its benefits, Python’s extensive libraries may suffice for many AI tasks. Julia’s future success remains to be seen, but it’s worth exploring.
Notable libraries: Flux, Pluto, Makie, MLJ, Tensorflow
This list provides a starting point for programming with AI workloads and is not exhaustive. Other languages like Lisp, C#, Golang, Scala, Prolog, and others also have their place in AI projects, depending on the specific requirements and team skillset.
Feel free to share additional languages or AI libraries you’ve used in your projects and how they performed!
Whether you’re starting or looking to enhance your AI programming skills, check out the learning resources below:
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