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.įrameworks in the form of Tensorflow, PyTorch and Keras have their primary implementations written for Python. 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. Efficiency is key, and Python delivers in this respect. 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.Īt the foundational level, Python packages like NumPy, Scikit Learn, Matplot Lib and Pandas give the programming language capabilities to perform many calculations as efficiently as possible.Įfficiency is extremely important in artificial intelligence programming it is not uncommon to perform millions of calculations when training a neural network. 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. PYPL measures the popularity of programming languages based on google searches for tutorials of that language. This popularity is corroborated by the PYPL rankings, where Python is number 1 with an over 10% gap from the number 2 spot, Java. We can observe from reliable sources such as the 2021 Stack Overflow Developer Survey that Python stands as the number 3 most popular technology period, only with HTML and JavaScript surpassing it in the rankings. 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. Python: The Dominant AI Programming Language Like traditional functions, neural nets accept inputs (akin to function arguments) and generate an output (akin to the return value of a function). 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. 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. These three fields are often combined to achieve a particular task, and collectively form the field of Machine Learning. article generation, text to speech, English to Chinese, etc. Natural Language Processing comprises mathematical models for language that identify and learn patterns of and between different languages. This approach is heavily relied upon in robotics, with companies like Boston Dynamics effectively demonstrating what can be achieved with Reinforcement Learning. Reinforcement Learning (RL)Ī 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. Utilising deep neural networks to generate or derive information from text, images, video, etc. The AI applications we interact with today are mostly derived from 3 subfields: Deep Learning (DL) Instead, AI can be thought of as a series of mathematical models that can be applied to a range of programming languages.īefore discussing the best programming languages used for AI development, it is important to understand what we want to program when creating AI applications. There is not one specific programming language for AI. The discipline has come a long way since being first introduced by John Mccarthy and co in the late 70s. 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.ĪI 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 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.Ī 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.
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