Top Programming Languages for AI
With demand for AI skills more than doubling in recent years, a career in AI has become a highly attractive option for people with interest in data science and software engineering.
According to PwC’s recent report “Sizing the Prize,” global GDP is forecasted to be 14 percent (or $15.7 trillion) higher in 2030 because of AI. This makes it the most significant commercial opportunity in today’s economy.
As AI boosts productivity, product quality, and consumption, the most dramatic sector gains will be in financial services, healthcare, and retail. AI opens up a whole new world of possibilities for both enterprises and software engineers, so if you’re eager to take advantage of this opportunity, you might be wondering where you should start.
However, to improve your chances of kick-starting a career in AI quickly, you’ll want to learn AI programming languages that are supported by several machine learning (ML) and deep learning (DL) libraries. You’ll be learning an AI language that benefits from a healthy ecosystem of tools, support packages, and a large community of programmers.
Best Programming Languages for AI
When it comes to AI programming languages, Python leads the pack with its unparalleled community support and pre-built libraries (like NumPy, Pandas, Pybrain, and SciPy) that help expedite AI development. For example, you can leverage proven libraries like scikit-learn for ML and use regularly updated libraries like Apache MXNet, PyTorch, and TensorFlow for DL projects.
For Natural Language Processing (NLP), you can go old school with NLTK or take advantage of lightening-fast SpaCy. Python is the leading coding language for NLP because of its simple syntax, structure, and rich text processing tool.
However, while it’s sometimes referred to as the best programming language for AI, you’ll have to look past its five different packaging systems that are all broken down in different ways, some white spacing issues, and the disconnect between Python 2 and Python 3.
But in the grand scheme of things, it makes perfect sense to learn Python, as it boasts the most comprehensive frameworks for both DL and ML. As this highly flexible AI language is platform agnostic, you’ll only have to make minor changes to the code to get it up and running in a new operating system.
We can’t discuss the best programming language for AI without talking about the object-oriented programming language, Java. Since it first emerged in 1995, Java has grown to become a highly portable, maintainable, and transparent language that’s supported by a wealth of libraries.
Like some of the programming languages on this list, Java is also highly user-friendly, easy to debug, and runs across platforms without the need to engage in any additional recompilation. This is because its Virtual Machine Technology allows the code to run on all Java-supported platforms.
When it comes to working with NLP, it’s easy to find enough support from the vibrant community that’s built around it. As Java enables seamless access to big data platforms like Apache Spark and Apache Hadoop, it has cemented its place within data analytics-related AI development.
If you need more reasons to learn Java, consider the fact that it works seamlessly with search engine algorithms, improves user interconnections, and its simplified framework supports large-scale projects efficiently.
Whenever a task demands high-performance numerical computing and analysis, Julia (developed by MIT) will be the best programming language for AI projects. Explicitly designed to focus on the numerical computing that’s required by AI, you can get results without the typical requirement of separate compilation. Its core programming paradigm includes a type system with parametric polymorphism and multiple dispatch capabilities.
Unlike the languages above, Julia isn’t exactly the go-to language right now. As a result, it’s not supported by a wealth of libraries or a rapidly growing community.
However, as an open-source language (under a liberal MIT license), its popularity is slowly increasing. Wrappers like TensorFlow.jl and Mocha provide excellent support for DL, so there is help out there—just not the same amount as Python.
One of the primary benefits of working with Julia is its ability to translate algorithms from research papers into code without any loss. This significantly reduces model risk and improves safety.