Top languages for Artificial Intelligence development

Artificial Intelligence is one of the hottest technologies currently. From work colleagues to your boss, chances are that most (yourself included) wish to create the next big AI project.

Artificial Intelligence is a vast field and with thousands of languages to choose from, it can get a bit difficult to pick the language that will bring the most value to your project. For anyone wanting to dive in the AI space, the initial stage of choosing the right language can really decelerate the development process. Moreover, making a right choice about the language for the Artificial Intelligence development depends on your skills and needs. Following are the top 5 programming languages for Artificial Intelligence development:

1.Python

Python, hands down, is the number one programming language when it comes to Artificial Intelligence development.

Not only is it one of the most popular languages in the field of data science, machine learning, and Artificial Intelligence in general, it is also popular among game developers, web developers, cybersecurity professionals and others. It offers a ton of libraries and frameworks in Machine Learning and Deep Learning that are extremely powerful and essential for AI development such as TensorFlow, Theano, Keras, Scikit Learn, etc. Python is the go-to language for AI development for most people, novices and experts alike.

Pros

It’s quite easy to learn due to its simple syntax. This helps in implementing the AI algorithms in a quick and easy manner.

Development is faster in Python as compared to Java, C++ or Ruby.

It is a multi-paradigm programming language and supports object-oriented, functional and procedure-oriented programming languages.

Python has a ton of libraries and tools to offer. Python libraries such as Scikit-learn, Numpy, CNTK, etc are quite trending.

It is a portable language and can be used on multiple operating systems namely Windows, Mac OS, Linux, and Unix.

Cons

Integration of the AI systems with non-Python infrastructure. For e.g. for an infrastructure built around Java, it would be advisable to build deep learning models using Java rather than Python.

If you are a data scientist, a machine learning developer or just a domain expert like a bioinformatician who hasn’t yet learned a programming language, Python is your best bet. It is easy to learn, translate equations and logic well in few lines of code and has a rich development ecosystem.

2. C++

C++ comes second on the list when it comes to top 5 programming languages for Artificial Intelligence development.

There are cases where C++ supersedes Python even though it is not the most common language when talking about AI development. For instance, when working with an embedded environment where you don’t want a lot of overhead due to Java Virtual Machine or Python Interpreter; C++ is a perfect choice. C++ also consists of some popular libraries and frameworks in AI, machine learning and deep learning namely, Mlpack, shark, OpenNN, Caffe, Dlib, etc.

Pros

Execution in C++ is very fast which is why it can be the go-to language when it comes to AI projects that are time-sensitive.

It offers substantial use of algorithms.

It uses statistical AI techniques quite effectively.

Data hiding and inheritance make it possible to reuse the existing code during the development process.

It is also suitable for machine learning and Neural Networks.

Cons

It follows a bottom-up approach and this makes it very complex for large-scale projects.

If you are a game developer, you’ve already dabbled with C++ in some form or the other. Given the popularity of C++ among developers, it goes without saying, that if you choose C++, it can definitely kickstart your AI development process to build smarter, more interactive games.

3. Java

Java is a close contender to C++. From Machine Learning to Natural language processing, Java comes with a plethora of libraries for all aspects of Artificial Intelligence development. Java has all the infrastructure that you need to create your next big AI project. Some popular Java libraries and frameworks are Deeplearning4j, Weka, Java-ML, etc.

Pros

Java follows the once Written Read/Run Anywhere (WORA) principle. It is a time-efficient language as it can be run on any platform without the need for re-compilation every time because of Virtual Machine Technology.

Java works well for search algorithms, neural networks, and NLP.

It is a multi-paradigm language i.e. it supports object-oriented, procedure-oriented and functional programming languages.

It is easy to debug.

Cons

As mentioned, Java has a complex and verbose code structure which can be a bit time-consuming as it increases the development time.

If you are into development of software, web, mobile or anywhere in between, you’ve worked with Java at some point, probably you still are. Most commercial apps have Java baked in them. The familiarity and robustness that Java has to offer is a good reason to pick Java when working with AI development. This is especially relevant if you want to enter well-established domains like banking that are historically built on top of Java-based systems.

4. Scala

Just like Java, Scala belongs to the JVM family. Scala is a fairly new language in the AI space but it’s finding quite a bit of recognition recently in many corporations and startups.

It has a lot to offer in terms of convenience which is why developers enjoy working with it. Also, ScalaNLP, DeepLearning4j, etc are all tools and libraries that make the AI development process a bit easier with Scala. Let’s have a look at the features that make it a good choice for AI development.

Pros

It’s good for projects that need scalability.

It combines the strengths of Functional and Imperative programming models to act as a powerful tool which helps build highly concurrent applications while reaping the benefits of an OO approach at the same time.

It provides good concurrency support which helps with projects involving real-time parallelized analytics.

Scala has a good open source community when it comes to statistical learning, information theory and Artificial Intelligence in general.

Cons

Scala falls short when it comes to machine learning libraries.

Scala consists of concepts such as implicits as well as type classes. These might not be familiar to programmers coming from the object-oriented world.

The learning curve in Scala is steep.

Even though Scala lacks in machine learning libraries, its scalability, and concurrency support makes it a good option for AI development. With more companies such as IBM and lightbend collaborating together to use Scala for building more AI applications, it’s no secret that Scala’s use for AI development is on constant demand in the present as well as for the future.

5. R

R is a language that’s catching up in the race recently for AI development. Primarily used for academic research, R is written by statisticians and it provides basic data management which makes tasks really easy. It’s not as pricey as statistical software namely Matlab or SAS, which makes it a great substitute for this software and a golden child of data science.

Pros

R comes with plenty packages that help boost its performance. There are packages available for pre-modeling, modeling and post modeling stages in data analysis.

R is very efficient in tasks such as continuous regression, model validation, and data visualization.

R being a statistical language offers very robust statistical model packages for data analysis such as caret, ggplot, dplyr, lattice, etc which can help boost the AI development process.

Major tasks can be done with little code developed in an interactive environment which makes it easy for the developers to try out new ideas and verify them with varied graphics functions that come with R.

Cons

R’s major drawback is its inconsistency due to third-party algorithms.

Development speed is quite slow when it comes to R as you have to learn new ways for data modeling. You also have to make predictions every time when using a new algorithm.

R is one of those skills that’s mainly demanded by recruiters in data science and machine learning. Overall, R is a very clever language. It is freely available, runs on server as well as common hardware. R can help amp up your AI development process to a great extent.

Other languages worth mentioning

There are three other languages that deserve a mention in this article: Go, Lisp and Prolog. Let’s have a look at what makes these a good choice for AI development.

Go

Go has been receiving a lot of attention recently. There might not be as many projects available in AI development using Go as for now but the language is on its path to continuous growth these days. For instance, AlphaGo, is a first computer program in Go that was able to defeat the world champion human Go player, proves how powerful the language is in terms of features that it can offer.

Pros

You don’t have to call out to libraries, you can make use of Go’s existing machine learning libraries.

It doesn’t consist of classes. It only consists of packages which make the code cleaner and clear.

It doesn’t support inheritance which makes it easy to modify the code in Go.

Cons

There aren’t many solid libraries for core AI development tasks.

With Go, it is possible to pull off core ML and some reinforcement learning tasks as well, despite the lack of libraries. But given other versatile features of Go, the future looks bright for this language with it finding more applications in AI development.

Lisp

Lisp is one of the oldest languages for AI development and as such gets an honorary mention. It is a very popular language in AI academic research and is equally effective in the AI development process as well. However, it is not such a usual choice among the developers of recent times. Also, most modern libraries in machine learning, deep learning, and AI are written in popular languages such as C++, Python, etc.

But I wouldn’t write off Lisp yet. It still has an immense capacity to build some really innovative AI projects, if take the time to learn it.

Pros

Its flexible and extendable nature enables fast prototyping, thereby, providing developers with the needed freedom to quickly test out ideas and theories.

Since it was custom built for AI, its symbolic information processing capability is above par.

It is suitable for machine learning and inductive learning based projects.

Recompilation of functions alongside the running program is possible which saves time.

Cons

Since it is an old language, not a lot of developers are well-versed with it. Also, new software and hardware have to be configured to be able to accommodate using Lisp.

Given the vintage nature of Lisp for the AI world, it is quite interesting to see how things work in Lisp for AI development. The most famous example of a lisp-based AI project is DART (Dynamic Analysis and Replanning Tool), used by the U.S. military.

Prolog

Finally, we have Prolog, which is another old language primarily associated with AI development and symbolic computation.

Pros

It is a declarative language where everything is dictated by rules and facts. It supports mechanisms such as tree-based data structuring, automatic backtracking, nondeterminism and pattern matching which is helpful for AI development. This makes it quite a powerful language for AI development.

Its varied features are quite helpful in creating AI projects for different fields such as medical, voice control, networking and other such Artificial development projects.

It is flexible in nature and is used extensively for theorem proving, natural language processing, non-numerical programming, and AI in general.

Cons

High level of difficulty when it comes to learning Prolog as compared to other languages.

Apart from the above-mentioned features, implementation of symbolic computation in other languages can take up to tens of pages of indigestible code. But the same algorithms implemented in Prolog results in a clear and concise program that easily fits on one page.

So those are the top programming languages for Artificial Intelligence development. Choosing the right language eventually depends on the nature of your project.

If you want to pick an easy to learn language go for Python but if you are working on a project where speed and performance are most critical then pick C++. If you are a creature of habit, Java is a good choice. If you are a thrill seeker who wants to learn a new and different language, choose Scala, R or Go, and if you are feeling particularly adventurous, explore the quaint old worlds of Lisp or Prolog.

Why is Python so good for AI and Machine Learning? 5 Python Experts Explain

Top 6 Java Machine Learning/Deep Learning frameworks you can’t miss

15 Useful Python Libraries to make your Data Science tasks Easier