Top 5 Programming Languages for Artificial Intelligence in 2020
With the rising popularity of AI, the demand for talented and skilled Artificial Intelligence Engineers also increases. Artificial intelligence is the future, so many software developers decide to improve their skills in this field. And what programming language will be the best for creating AI-based solutions? Check out our subjective list of the top 5 programming languages for artificial intelligence in 2020!
Originally published at https://websensa.com on June 16, 2020.
Over the last few years, Python is still the best and most popular language for programming AI and ML-based solutions. It is a leader of most of the rankings in this category. Its most significant competitive advantage is its simplicity of use and the vast number of available machine learning libraries and frameworks. Python is also one of the easiest programming languages, so it is a popular choice not only for programmers but also for mathematicians, physicists, or analysts related to artificial intelligence.
Popularity of Python
According to Stack Overflow Survey , Python is the fourth most popular programming language overall and also the fastest-growing major programming language. Moreover, it is the leader among the most popular machine learning languages on Github .
Pros of using Python for AI:
- easy to learn and read
- a vast number of machine learning libraries and useful tools
- popularity and large community
- many useful machine-learning repositories
- easy to integrate with other programming languages
Cons of using Python for AI:
- speed limitations
- not suitable for mobile and game development
- design limitations
Useful AI & ML libraries for Python:
- TensorFlow — fast, flexible library for working with datasets and machine learning
- PyTorch -a machine learning framework that speeds up the path from research prototyping to production deployment
- Scikit-Learn — a library focussed on data modeling. It provides simple integration with other ML programming libraries
- Keras — an open-source library for machine learning and deep learning, which is used for complex mathematical calculations and fast datasets processing
Java is one of the most popular and best-paid programming languages in the world. It is a general-purpose programming language, which means it is very flexible. It is much more challenging to learn than Python and requires much more time for learning and coding in this language. Java is a statically-typed language, so it is relatively easy to debug. Software developers love it for its high user-friendliness and ability to work on most platforms. Moreover, Java is a very safe and scalable language, and it is an excellent solution for large scale projects.
It is worth noticing that the newest version of Java has improved a few useful features for machine learning, such as new string methods, new file methods, and pattern recognition methods.
Popularity of Java
According to the Stack Overflow Survey , Java is the 5th most popular technology and the 3rd most popular backend programming language in the world. Currently is in the 4th place of the top machine learning languages on the Github ranking . Although its popularity has declined in recent years, it is still a stable language with a strong market position.
Pros of using Java for AI:
- easy to implement on various platforms
- easy to debug
- popularity and large community
- a lot of open-source libraries
- good for mobile applications
Cons of using Java for AI:
- needs a JVM to function
- a high entry threshold
Useful AI & ML libraries fo Java:
- Weka — an easy-to-use library for data analysis, data mining, and predictive modeling
- Massive Online Analysis (MOA) — library for machine learning on data streams in real-time, especially useful for large datasets and the Internet of Things (IoT)
- Java-ML — a collection of machine learning algorithms for feature selection, data preprocessing, classification, and clustering
- dynamic websites,
- standard web applications,
- progressive web applications.
- works well with other applications
- good speed
- a lot of useful fast-growing ML libraries
- popularity and community support
- lack of debugging facility
- client-side security
- browser support (JS is interpreted differently in different browsers)
- TensorFlow.js — a popular ML library for training and using machine learning models directly in the browser.
- Brain.js — an open-source JS library for running and processing neural networks.
- ml.js — a group of repositories and tools for ML, including regression algorithms, artificial neural networks, supporting libraries for statistics, and many more.
R is a dynamically typed language, considered one of the best programming languages used for statistics, predictive analysis, and other activities related to data science. R is easy to understand, especially for people who programmed in another language. It does not require complex knowledge because it has a lot of ready to use packages, libraries, and materials that can help you in almost every step of the software development process.
Popularity of R
According to the Stack Overflow Survey , only 5.8% of software developers know R. It ranked at 17th place on the list of most popular technologies overall. Despite its low popularity, R often appears on the lists of the best programming languages for artificial intelligence. For instance, it is on the 8th place among the top machine learning languages on Github .
Pros of using R for AI:
- good for statistics and analysis
- good for crunching huge numbers
- a lot of useful libraries, frameworks, and AI programming packages
- allows working on various paradigms of programming
Cons of using R for AI:
- speed limitations
- not beginner-friendly (recommended as a second programming language)
Useful AI & ML libraries for R:
- Dplyr — a powerful library with a simple syntax, which is used in the process of data manipulation
- Ggplot2 — an old, extensive library for visualization and graphical representation of data
Popularity of Go
Go is another niche language, but, as I mentioned before, it is one of the fastest-growing technologies! Comparing 2018 and 2019, the popularity of Go increased by 147%, according to Github report . It is a relatively young language, so it could be really useful in the future!
Pros of using Go for AI:
- good speed
- embedded testing environment
- a smart standard library
- good for infrastructure projects
Cons of using Go for AI:
- relatively few libraries
- youth and low popularity
Useful AI & ML libraries for Go:
- GoLearn — a new library for machine learning, including a few useful methods and algorithms such as neural networks or and logistic regressions
- GoML — a library used for generalized linear models, logistic regression, perceptron, text classification, and many more
Today we can find plenty of programming languages that can be used for various AI-based solutions, but we need to know that any single programming language is a one-stop-solution for AI. Today’s IT market requires a specific approach for every project, so before you decide to use or learn one of them, you should consider which one will best meet your expectations.
 The State of the Octoverse: machine learning | Github
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 Top five programming languages for AI and machine learning you should learn this year | ITProPortal