The best coding languages for AI
You do not know which programming language to use for your AI project? We will show you the best coding options in Artificial Intelligence.
When it comes to programming artificial intelligence, you have several options to choose from. We show you the best AI programming languages. Artificial intelligence (AI) opens up completely new possibilities for software developers: With the help of machine and deep learning, better user profiles and recommendations, a higher degree of personalization, smarter search options or more intelligent interfaces can be realized. The question inevitably arises as to which programming language should be used for this. The requirements that an AI coding language must meet are manifold: a large number of machine and deep learning libraries should be available, as well as a high-performance runtime environment, extensive tool support, a large developer community and a healthy ecosystem.
Although this list of requirements is comprehensive, there are some good options available when it comes to programming artificial intelligence. We show you a selection of the best AI programming languages.
Python
If you are a developer working with artificial intelligence, there is almost certainly no way around Python. By now (April 2020) almost all current libraries support Python 3.x – the times when the switch from Python 2.x to 3.x caused compatibility problems are almost over. In other words, you can now finally benefit from the many new features of Python 3.x. This is not to say that the packaging hurdles in Python are no longer an issue at all, but most of the problems can be solved with the help of Anaconda. Nevertheless, it would be nice if the Python community would finally be freed from these hurdles.
Apart from that, the available mathematical and statistical libraries of Python are far ahead of those of other programming languages: NumPy is now so ubiquitous that it can almost be called the standard API for tensor operations, while Pandas carries the flexible dataframes of R into the Python world. When it comes to Natural Language Processing (NLP), you have the choice between the time-honored NLTK and the super-fast SpaCy, while for machine learning purposes the proven scikit-learn is recommended. If, on the other hand, it is about deep learning, all current libraries (TensowrFlow, PyTorch, Chainer, Apache MXNet, Theano, etc.) are basically “Python-first” projects.
If you are a regular visitor of arXiv, you will have noticed that the majority of the deep learning research projects there, which provide source code, rely on Python for this purpose. When it comes to deployment models, microservice architectures and technologies such as SeldonCore have made it much easier to deliver Python models in production environments.
Python is undoubtedly the programming language of choice when it comes to AI research: it offers the widest range of machine and deep learning frameworks and is the coding language that sets the tone within the AI world.
C++
C++ is most likely not the first choice for your AI project. However, deep learning is becoming an increasingly common scenario in the edge domain. In this case, you need to run your models on systems that have very limited resources. To squeeze the last bit of performance out of the system, it may be necessary to dive into the shallows of the pointer world once again.
Fortunately, modern C++ code can actually be pleasant to write. There are several approaches to this: Either you use libraries like Nvidia’s CUDA to write your own code that flows directly into the GPU – or you use either TensorFlow or PyTorch to gain access to flexible high-level APIs. Both PyTorch and TensorFlow allow you to integrate models written in Python into a C++ runtime environment. This brings you much closer to productive use, while remaining flexible in development.
Because AI applications are increasingly spreading across all devices – from embedded systems to huge clusters – C++ is an important part of the AI coding toolkit. In order to realize artificial intelligence in the edge area, it is not only necessary to program accurately, but also qualitatively well and fast.
Java and other JVM languages
The family of JVM programming languages (Java, Scala, Kotlin, Clojure, etc.) continues to be a good choice when it comes to developing AI applications. A rich selection of libraries is available for almost all aspects – be it Natural Language Processing (CoreNLP), Tensor Operations (ND4J) or GPU-accelerated Deep Learning (DL4J). In addition, these coding languages also ensure easy access to big-data platforms such as Apache Spark and Apache Hadoop.
For most companies, Java is the lingua franca – and with Java 8 and newer versions, the creation of Java code also loses its horror. Programming an AI application in Java may feel a little boring, but it usually produces satisfactory results and allows you to use all existing components of a Java infrastructure for development, deployment and monitoring.
JavaScript
Learning JavaScript exclusively for the development of AI applications is a highly unlikely scenario. However, Google’s TensorFlow.js still offers a good way to deliver your Keras and TensorFlow models via browser or Node.js.
Nevertheless, the big rush of JavaScript developers in the field of Artificial Intelligence has failed to materialize so far. This could be because the JavaScript ecosystem lacks the necessary depth in terms of available libraries – at least compared to programming languages like Python. Furthermore, there are no real advantages on the server side due to deployment models with Node.js (again compared to Python options). AI applications based on JavaScript are therefore likely to continue to be browser-based.
Swift
Swift for TensorFlow combines the latest and best features of TensorFlow with the advantages of Python libraries that can be easily integrated – just as if you were using Python itself.
The fastai team is currently working on a Swift version of its popular library – and has numerous optimizations in sight, especially in connection with the LLVM compiler. There is no “production ready” yet, but the next generation of deep-learning development work could be based on it – so you should definitely keep an eye on Swift.
R
R is the programming language of choice for data scientists. Developers from other areas, however, may find the coding language confusing because of its dataframe-centric approach.
For a team of passionate R developers, it may make sense to use integrations with TensorFlow, Keras or H2O for research and prototyping. In terms of performance, however, R is only conditionally recommended for productive use. Although high-performance R code can be used productively, in most cases it should be easier to reprogram the R-prototype in Java or Python.
AI programming – further options
Of course, the four programming languages mentioned above are not the only options for programming artificial intelligence. The following two coding languages could also be of interest for your AI projects, depending on their purpose:
Lua
Some years ago Lua was considered the “next big thing” in the field of artificial intelligence. This was mainly due to the Torch Framework – one of the most popular machine learning libraries – both for productive use and for research purposes. If you dive into older DeepLearning models, you will often find numerous references to Torch and Lua source code.
It might be useful to get some knowledge about the Torch API, which has some similarities to PyTorch’s base API. However, if you don’t feel the need to delve into historical research for your applications, you can do without Lua.
Julia
Julia is a high-performance programming language that focuses on numerical calculations. Therefore it fits perfectly into the mathematically oriented world of artificial intelligence.
Julia may not be the most popular coding language at the moment, but wrappers like TensorFlow.jl and Mocha offer good deep learning support. If the relatively small ecosystem is not an exclusion criterion for you and you want to benefit from Julia’s focus on high-performance computing, you should take a look.