Are you tired of hearing about the latest and greatest programming languages for artificial intelligence? .. or maybe not. But hey, at least it’s better than scrolling through endless lists of “Top 10” articles on LinkedIn.

So without further ado, Let’s kick this off with the top six AI programming languages for 2023 (or whatever year you’re reading this in).

6) Julia The Language That Wants to Be Everything

Julia is a high-performance computing language that has gained popularity among data scientists and machine learning engineers. It offers lightning-fast execution times, making it ideal for large-scale AI projects. But here’s the catch: Julia wants to be everything. Seriously, this language can do anything from web development to financial modeling. And while that might sound impressive at first, it also means that there are fewer resources and support available compared to more specialized languages like Python or R.

5) Swift The Language That’s Not Quite There Yet

Swift is a relatively new programming language developed by Apple for iOS and macOS development. It has gained some traction in the AI community due to its ease of use and performance capabilities, but it still lacks the extensive libraries and frameworks available in other languages like Python or R. Plus, let’s be real: if you want to work on AI projects that involve Apple products, you might as well just learn Objective-C instead.

4) Kotlin The Language That Wants to Be Java’s Better Half

Kotlin is a statically typed programming language developed by JetBrains for the JVM and Android platforms. It has gained popularity among developers due to its concise syntax, null safety features, and interoperability with Java. In terms of AI applications, Kotlin can be used for machine learning projects that involve data processing or model training on large datasets. However, it still lacks some of the advanced libraries and frameworks available in other languages like Python or R.

3) Scala The Language That’s Too Smart For Its Own Good

Scala is a statically typed programming language developed by Martin Odersky for the JVM and Spark platforms. It has gained popularity among data scientists due to its concise syntax, type safety features, and support for functional programming concepts like immutability and higher-order functions. However, Scala can be quite complex and difficult to learn compared to other languages on this list. Plus, it still lacks some of the advanced libraries and frameworks available in Python or R.

2) TensorFlow The Language That’s Not Really a Programming Language

TensorFlow is an open-source machine learning library developed by Google for use with Python, C++, and JavaScript. While not technically a programming language, it has become one of the most popular tools in the AI community due to its ease of use and extensive support libraries. However, TensorFlow can be quite resource-intensive compared to other languages on this list, making it less ideal for smaller or more complex projects.

1) Python The Language That’s Already Here (And Will Probably Always Be)

Python is a high-level programming language developed by Guido van Rossum in the late 1980s. It has become one of the most popular languages for data science and machine learning due to its ease of use, extensive support libraries, and active community. In terms of AI applications, Python can be used for everything from data preprocessing and model training to deployment and monitoring. And while there are certainly other programming languages that offer similar capabilities (like R or Julia), none have been able to dethrone Python as the go-to language for AI projects in 2023… or any year, really.

But let’s be real: if you want to work on cutting-edge AI projects that involve the latest and greatest technologies, you might as well just learn Python. Or R. Or Julia. Or… you get the idea.

Later!