The idea of Artificial Intelligence (AI) has historically been a hugely inspiring and interesting subject, with ideas of what can be made possible by AI flourishing in movies, books and just about every other medium of creative work.
A lot of these popular ideas, like fully autonomous humanoid robots, or an intelligence capable of overseeing every aspect of our lives, still remain in the realm of science fiction.
In recent years however, we have made extremely fast progress in popular fields like Deep Learning (a subfield of Machine Learning), and big data, which has resulted in a huge leap in our AI capabilities of today.
AI is already impacting our lives on a large-scale, whether applied to the products we buy along with their automated manufacturing processes, how we are fed information online via search and personalisation, and even our smartphones, that now ship with an ever-expanding array of AI-based features.
The discipline has come a long way since being first introduced by John Mccarthy and co in the late 70s.
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Is there a Programming Language for AI?
There is not one specific programming language for AI. Instead, AI can be thought of as a series of mathematical models that can be applied to a range of programming languages.
Before discussing the best programming languages used for AI development, it is important to understand what we want to program when creating AI applications.
The AI applications we interact with today are mostly derived from 3 subfields:
Deep Learning (DL)
Utilising deep neural networks to generate or derive information from text, images, video, etc.
Reinforcement Learning (RL)
A set of algorithms that rely on an action-reward loop concept, where the same task is carried out a large number of times and incrementally improved upon.
This approach is heavily relied upon in robotics, with companies like Boston Dynamics effectively demonstrating what can be achieved with Reinforcement Learning.
Natural Language Processing (NLP)
Natural Language Processing comprises mathematical models for language that identify and learn patterns of and between different languages. E.g. article generation, text to speech, English to Chinese, etc.
These three fields are often combined to achieve a particular task, and collectively form the field of Machine Learning.
Machine Learning and the Neural Network
The most popular form of AI application today are derived from the Artificial Neural Network – often just referred to as neural nets – that are designed to mimic the neurons of the human brain.
You can think of neural nets as functions that learn their own blocks of code, rather than you having to write the code within the function block yourself.
Like traditional functions, neural nets accept inputs (akin to function arguments) and generate an output (akin to the return value of a function).
AI Programming Essential Skills
As a result of AI’s dependence on data and data analysis techniques, it is commonly reliant upon data science know-how in addition to a strong foundation in computer science.
To read more about programming on data science specifically, see Iglu’s accompanying article, Best Data Science Programming Languages.
The AI programming languages we’ll discuss in this article are all heavily used by both computer scientists and data scientists – and we’ll see some of the reasons why this is the case.
Statistics, probability and the field of calculus (especially differentiation) are all heavily relied upon in the field of AI and Machine Learning in-particular.
These requirements may already be giving you clues as to which AI programming languages are used today.
So with all that said, let’s now discuss those programming languages for artificial intelligence relevant for 2021.
AI Programming Languages
Considering which programming language is the best for AI development boils down to a few key components, the most important of which being:
- Great support for core maths functions, notably for efficient calculation of matrices.
- The ecosystem of tools specifically for artificial intelligence tasks, ranging from machine learning libraries, data analysis and data management tools, graph plotting and simulation tools.
- Community support, and popularity in industry.
There is a standout winner in 2021, and that is Python.
Python: The Dominant AI Programming Language
The object oriented programming language Python is the number one programming language for AI development in 2021, and therefore the best programming language to recommend for those interested in AI.
This popularity is corroborated by the PYPL rankings, where Python is number 1 with an over 10% gap from the number 2 spot, Java.
PYPL measures the popularity of programming languages based on google searches for tutorials of that language.
So not only is Python a well-established general purpose programming language, there are also more software engineers learning it today than any other programming language – and this is undoubtedly due to the adoption Python is getting from the artificial intelligence community.
Search results for artificial intelligence projects on GitHub are dominated by Python projects, accompanied by Java projects and Jupyter Notebooks projects – with Jupyter Notebook being a valuable tool for executing code in stages while visualising results.
Efficiency is extremely important in artificial intelligence programming; it is not uncommon to perform millions of calculations when training a neural network. Efficiency is key, and Python delivers in this respect.
Many of these packages’ core APIs are written in C or C++ to perform calculations as close to the hardware as possible, bypassing Python’s interpreter and putting them on-par with more languages like Julia or Java.
Python’s Machine Learning Libraries
The above mentioned packages have allowed Python developers to build libraries, targeting a range of artificial intelligence tasks, and this has become the primary reason why Python has experienced its growth amongst data scientists and the artificial intelligence community in general.
Which one should you choose to learn AI?:
- TensorFlow contains more complex APIs for creating Machine Learning models, and more granularity than PyTorch and Keras, but is a very capable and efficient framework for a range of Machine Learning tasks. It is the best-suited platform for production deployments, and supports an ecosystem of tools.
- PyTorch is more suited for research and study purposes being heavily used in academia, both for computer science courses and for research purposes. Common AI programming algorithms are implemented and wrapped in simple APIs, with a focus on neural networks and derivations of them.
- Keras is a library that runs on top of TensorFlow, providing a more abstract API and enabling a faster learning process for the developer.
At the time of writing Keras is still more popular than PyTorch, although PyTorch is growing much faster than the former.
On the Reinforcement Learning side, OpenAI’s Gym toolkit makes it quite simple to start experimenting with RL algorithms, further boosting Python’s AI proposition.
Gym and the surrounding tools are industry leading, easily being the go-to library for RL students and researchers.
So why has Python enjoyed its huge growth in Artificial Intelligence capabilities? There are a range of factors.
Python is easy to read. It’s read-like-English and expressive syntax aids in writing complex algorithms while maintaining readability.
Also being a general purpose, open source, and multi-platform language, Python has enabled the ecosystem to flourish, evident by the Pip package manager and huge archive of packages it offers.
Google Colab: Removing the Hardware Bottleneck of AI Programming
To further complement Python’s offering, Google’s Colab service offers free access to cloud-based GPUs to run AI applications.
This is achieved by offering a Jupyter Notebook interface in the web browser that connects to Google’s network of GPU resources – a valuable tool for any student, researcher, or ML engineer.
Of course, many of the libraries we rely on, ranging from NumPy to PyTorch or Tensorflow, are all supported with Colab that aid in the AI programming pipeline.
R: Statistical Computing
The R programming language was designed with many of the attributes that make artificial intelligence applications popular in mind – statistics, data storage, and graphical utilities for plotting and analysing data.
For this reason, R is amongst the strongest competitors to Python for AI applications.
Like Python, R is an open source, multi-platform project that comes with a range of packages that are well suited for AI based tasks.
In fact, R first appeared in 1993 with the above mentioned use cases in mind, and initially offered a stronger proposition for artificial intelligence before Python rose to prominence.
R still has a strong community of statisticians and analytics engineers, with its reliable tools available through its popular CRAN archive – with many of these tools required for machine learning tasks.
R can be more challenging to learn than Python, with its syntax being highly expressive and hard to read for less experienced R engineers.
Another concern with R is its efficiency, with similar offerings like the newer Julia programming language offering much faster execution of code.
Julia: An Efficient Data Science Programming Language
Julia is a newer programming language (first appearing in 2012) that is well suited for implementing artificial intelligence algorithms (with machine learning, data manipulation and visualization being primary use cases advertised on its homepage).
Julia offers very high performance with some tasks offering similar efficiency to C. This makes Julia very attractive for AI programming tasks that require heavy calculation on a lot of data.
MLJ.il is the flagship machine learning library for Julia, with many surrounding tools and utilities around it.
Although Julia is technically a promising programming language for the realm of artificial intelligence, it has a steep hill to climb to rival larger ecosystems such as those of Python and Java:
- Only 1.29% of developers use Julia in 2021.
- There are many packages, particularly in mathematics, that engineers can utilise with Julia, but the ecosystem of tools is still very small.
- Interest in Julia is questionable, with only a 0.1% increase in the PYPL rankings year-over-year, currently sitting in number 22 with 0.52% of tutorial searches relating to the programming language.
Java and Scala: Big Data for AI Programming
Large amounts of data are required to train AI applications, so the ability to store and process at scale is a key component of any AI based deployment.
For this reason, Java and Scala (that runs on the Java Virtual Machine [JVM]) have gained adoption among machine learning engineers, mostly because of its tight integration with Apache Spark, with its parallel and distributed computing capabilities.
Java Virtual Machine technology also allows these programming languages to be efficiently fast to carry out processor-heavy tasks.
Apache Spark is a powerful data analytics engine for large scale processing written in Scala, making Java arguably the best programming language for scaling large data applications.
Spark comes with its own machine learning library, namely MLlib, hosting a range of tools for common Java machine learning algorithms. Spark also supports Python and R.
C and C++: Low Level Implementations
Although C and C++ play a role in artificial intelligence programming, they are often found at low-level implementations of various functions and libraries across higher level programming languages.
C++ does have a maintained library for Machine Learning tasks in the form of MLPack, offering a fast and efficient framework with bindings to other languages (including Java, Julia and Go).
But for the most part, C and C++ are found where functions need to be efficient, especially in large number crunching algorithms.
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Clingo: Logic Programming Language for AI
Logic programs model a scenario and generate every possible solution to a problem. It is the job of a Clingo program to find an optimal solution to such a problem.
Common scenarios include the automated warehouse problem, where robots are assigned to collect and deliver items for orders to be assembled.
The addition of more variables for a given scenario exponentially increases the complexity of a problem however, so hardware limitations are quickly reached in ASP problems.
Swift: Core ML
As an iOS developer, AI programs will more than likely require access to the ML chip, via the Core ML library provided by Apple.
Use cases pertaining to computer vision and natural language are a focus of the library, and Swift is the best programming language to take advantage of Apple’s ML technologies.
Haskell is capable of compiling many of the use cases we have discussed, but they lack community support and an ecosystem large enough to carry AI based packages forward.
The awesome Haskell deep learning library contains a list of active AI projects for Haskell specifically, demonstrating Haskell’s capability as an AI programming language. But alas, Haskell is very rarely used in industry for the purpose of AI.
If the reader is interested in functional programming, Scala is a stronger option with access to Apache Spark, allowing Machine Learning to be done at scale.
Scala has enough support for statically typed, functional programming that it can be used in a similar fashion to Haskell, in addition to object oriented programming support.
This article has presented the programming languages for artificial intelligence and popular libraries used today for AI applications.
There is no one AI programming language, but rather a collection of programming languages that have their unique advantages, whether its scaling with Java, or the efficiency of Julia.
With Python currently being the dominant programming language for AI in all the metrics available to us, it is hard to see it being replaced by newcomers or established players in the short term.