After a stagnant run for decades, Artificial Intelligence (AI) is going through a period of resurgence and acceleration. 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.
PwC’s recent report “Sizing the Prize,” forecasts GDP to be 14 percent (or $15.7 trillion) higher in 2030 because of AI making it the most significant commercial opportunity in today’s economy.
AI boosts productivity, product quality, and consumption. Dramatic sector gains have been forecasted for financial services, healthcare, and retail. AI development opens up a world of possibilities for businesses, enterprises and software engineers. As a developer or a business looking into AI implementation, you might want to ask yourself which is the best language for AI development. It’s a very valid question for anyone looking for a career in AI as well. This post aims to answer it with the top options for choosing a language for developing AI applications.
The answer to this question will depend on your knowledge and experience, the type of AI project you’re interested in, and current (and future) industry trends. The best (and worst) part about it is that there isn’t a specific AI language dedicated to this technology field. We are spoilt for choice! We need to keep in mind that a language for developing AI needs to support Machine Learning and Deep learning capabilities, the core of AI technology. Here are your options:
Python leads everything else when it comes to programming languages for Artificial Intelligence development due to its unparalleled community support and pre-built libraries (like NumPy, Pandas, Pybrain, and SciPy). Libraries are important as they help expedite AI development. Here are some of the libraries for Python:
- For Machine Learning: scikit-learn
- For Deep Learning: Apache MXNet, PyTorch, and TensorFlow
- For Natural Language Processing (NLP): NLTK, SpaCy.
- Python is the leading development language for NLP because of its simple syntax, structure, and rich text processing tool.
While python is many times 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.
Overall, it makes perfect sense to learn or use Python for AI application development, as it boasts the most comprehensive frameworks for both Deep Learning and Machine Learning. As this highly flexible AI language is platform agnostic, only minor changes are to get it up and running in a new operating system.
The list of Python application is really long. It is used in image processing and graphic design programs, games, web frameworks, enterprise and business applications, and much more. Some of the biggest websites developed in Python include YouTube, Reddit, Quora, Dropbox and Disqus.
R is one of the most effective languages and environment for analyzing and manipulating the data for statistical purposes, it was built for it. R is the language that data scientists love. R can easily produce a well-designed publication-quality plot, including mathematical symbols and formulae where needed. Apart from being a general purpose language, R has numerous packages like RODBC, Gmodels, Class and Tm which are used in the field of machine learning. These packages make the implementation of machine learning algorithms easy, for cracking the business associated problems.
It might not be the best option for developing AI application, as discussed in our post R vs. Python.
R has been drifting as an AI programing language of choice due to the emergence of newer and better alternative and is trending downward. Apart from data scientist, programmers find R a little due to its data frame-centric approach. If you have a dedicated group of R developers, then it can make sense to use the integrations with TensorFlow, Keras, or H2O for research, prototyping, and experimentation, but we hesitate to recommend R for production usage, due to performance and operational concerns. While you can write performant R code that can be deployed on production servers, it will almost certainly be easier to take that R prototype and recode it in Java or Python.
Any discussion for Best Programming Language for AI will be incomplete without talking about 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.
Java is user-friendly, easy to debug and supports almost any imaginable platform. For Natural Language Processing, it’s easy to find enough support from the vibrant community of Java. Seamless access to big data platforms like Apache Spark and Apache Hadoop has cemented Java’s reputation as a language of choice for data analytics related to Artificial intelligence development.
Some of the well-known applications developed in Java are
- WEKA machine learning suite, which is dedicated to machine learning and data mining
- JOONE neural engine for designing, training and testing neural networks
- ALICE (short for artificial linguistic internet computer entity), natural language processing chatterbots
- Robocode, an open source game for learning principles of Java programming
C/C++ is unlikely to be anyone’s first choice when developing an AI application. The tables are turned if you’re working in an embedded environment. The overhead of a Java Virtual Machine or a Python interpreter is too much to handle for an embedded system. In such cases, C/C++ is a life saver. For performance intensive systems, where every bit of the hardware is put to use, then you need to head back to the terrifying world of pointers.
Thankfully, modern C/C++ isn’t as bad as it’s now ancient predecessors. You have a choice of approaches. You can either dive in at the bottom of the stack, using libraries like CUDA to write your own code that runs directly on your GPU, or you can use TensorFlow or Caffe to obtain access to flexible high-level APIs. The latter also allows you to import models that your data scientists may have built with Python and then run them in production with all the speed that C/C++ offers. What can be better!
Rust is changing things dramatically. Keep an eye out for it. It combines the fast C/C++ with type and data safety, Rust is a great choice for achieving production performance without creating security headaches. And a TensorFlow binding is available for it already. Hurrah!
Since the early days of AI development, Lisp has been at the heart of it and that remains true today.
Some consider it to be the best language for AI because it was created by computer scientist and father of AI John McCarthy in 1958 himself. What makes it a highly efficient Artificial Intelligence development application of choice are its unique features that enable the effective processing of symbolic information.
AI developers often turn to Lisp for AI projects that are heavy on ML because it offers rapid prototyping capabilities, support for symbolic expressions, a library of collection types, and is highly flexible and adaptable to their problem-solving needs. Lisp can be described as a practical mathematical notation for computer programs.
It’s also popular among AI programming languages because it allows the easy dynamic creation of new objects, with automatic garbage collection. While the program is still running, you can also enable interactive evaluation of expressions and recompilation of functions or files concurrently.
However, in recent years some of the key features that made it special have migrated into several other languages, so it’s no longer as unique an option in the world of AI.
There are thousands of AI applications developed in Lisp, some of them are –
- American Express Authorizer’s Assistant that checks transactions (credit card)
- METAL, a natural language translation system
- Macsyma, first large computer algebra system
- ACL2, a theorem prover used by AMD