The top 7 programming languages in 2020

Which programming language is best? This question may never have an answer. Turnip and cabbage have their own love. AI engineers and scientists can choose the most suitable programming language from many programming languages ​​according to the needs of the project.

1. Python

Python is the most powerful language that can be read.

Python was developed in 1991, and a poll showed that when developing AI, more than 57% of developers use Python as the programming language of choice instead of C++. Because it is easy to learn, Python makes it easier for programmers and data scientists to enter the world of developing AI.

Python is an "experiment" of how many degrees of freedom a programmer needs. Too free, no one can read other people's code; too free, there will be less expressive power.

Using Python, you not only get excellent community support and extensive library collection, but also enjoy its flexibility. Perhaps the biggest benefit you get from Python is platform independence and a broad framework for deep learning and machine learning.

The fun of coding in Python is that you can see short, concise, highly readable classes that can express a lot of behavior with a small amount of clear code (instead of annoying the reader with a lot of code).

2. Java

Write once and run anytime.

Java is recognized as one of the best programming languages ​​in the world, and its use in the past 20 years is the best proof.

With its user-friendliness, flexibility, and platform independence, Java has participated in the development of AI in various ways, such as:

TensorFlow-Java with API is also listed in the programming languages ​​supported by TensorFlow. Although not as feature-rich as other fully supported languages, it does support Java and is improving rapidly.

Deep Java Library-A library developed by Amazon that uses Java to create and deploy deep learning capabilities.

Kubeflow-Kubeflow makes it easier to deploy and manage machine learning stacks on Kubernetes and also provides ready-made ML solutions.

OpenNLP-Apache's OpenNLP is a machine learning tool for natural language processing.

Java Machine Learning Library (Java Machine Learning Library)-Java-ML provides developers with a variety of machine learning algorithms.

Neuroph-Neuroph uses the Neuroph GUI to design neural networks using the Java open source framework.

If Java can be garbage collected, most programs will delete themselves during execution.

3. R

Ross Ihaka and Robert Gentleman released the first version of the R language in 1995. It is now maintained by the R development core team. R is an implementation of the S programming language for statistical software development and data analysis.

The basic feature of R is that it is good at handling large amounts of data. Compared to the incomplete NumPy package in Python, R is a better choice; you can use R to handle various programming paradigms, such as functional programming, vector computing, and object-oriented programming Wait.

Support the generation of high-quality graphics.

4. Prolog

Abbreviation for Logic Programming. Prolog first appeared in 1972 and is suitable for the development of artificial intelligence, especially natural language processing. Prolog is most suitable for creating chat bots. ELIZA is the first chat bot ever created with Prolog.

The first successful chatbot.

In order to understand Prolog, you must be familiar with some basic terms that guide the work of Prolog:

Fact defines the correct statement;

Rules (Rule) define statements with additional conditions;

Goal defines the location of the submission of statements according to the knowledge base;

Query defines how to make your statement correct, and the final analysis of facts and rules.

Prolog provides two ways to implement AI. These two methods have been implemented for a long time and are widely known among data scientists and researchers:

Symbolic methods include rule-based expert systems, theorem proofs and constraint-based methods;

Statistical methods include neural networks, data mining, machine learning, and other methods.

5. Lisp

Use Lisp coding to create a perceptron with n inputs and m units.

Abbreviation for List Processing. This is the second oldest programming language after Fortran. It is also known as one of the founding languages ​​of AI, created by John McCarthy in 1958.

Lisp is used to implement impossible languages.

Lisp is a practical mathematical notation that can be programmed, and it quickly became the AI ​​programming language of choice for developers. Lisp has become one of the best choices for machine learning AI projects because of its unique features:

Rapid prototype creation;

Create dynamic objects;

Garbage collection


With the significant improvements of other competing programming languages, other languages ​​have integrated some features unique to Lisp. Famous projects involving Lisp are Reddit and HackerNews.

Speaking of Lisp, this is the most beautiful language in the world-at least before Haskell.

6. Haskell

Haskell was founded in 1990 and named after the famous mathematician Haskell Brooks Curry. Haskell is a purely functional and statically typed programming language, used with lazy calculations and short codes.

Haskell is a very safe programming language. Compared with other programming languages, Haskell rarely makes errors, so it provides more flexibility in handling errors. Even if an error occurs, most non-syntax errors can be caught at compile time (not run time). Features provided by Haskell include:

Strong abstraction ability;

Built-in memory management;

Code reusability;

Easy to understand.

SQL, Lisp, and Haskell are the only programming languages ​​I have seen that can spend time thinking instead of typing. ——Philip Greenspun

Haskell's features help increase programmer productivity. Haskell is very similar to other programming languages, but only used by a small number of developers. Challenges aside, as the usage of the developer community increases, it can prove that Haskell is as good as other competing languages ​​for AI.

7. Julia

Julia is a high-performance general-purpose dynamic programming language that can create almost any application, but it is most suitable for numerical analysis and computational science. Tools used with Julia also include:

Popular editors like Vim and Emacs;

IDEs like Juno and Visual Studio.

Julia source code organization.

There are some features in Julia that make it an important choice for AI programming, machine learning, statistics, and data modeling. These features are:

Dynamic type system;

Built-in package manager;

Ability to perform parallel and distributed computing;

Macro and meta programming ability;

Support multiple dispatch;

C functions are directly supported.

Julia is built to eliminate the weaknesses of other programming languages. After being integrated with other tools (such as TensorFlow.jl, MLBase.jl, and MXNet.jl), it can also be used for machine learning. The scalability of Julia can do more thing.

Google Trends-the usage trend of Julia.

to sum up

AI engineers and scientists can choose from a variety of programming languages ​​according to the needs of the project. Every AI programming language has advantages and disadvantages. With the continuous improvement of these languages, AI development will soon have a more comfortable experience, so that more people will join this wave of innovation. Excellent community support enables new people to work better, and community contributions to packages and extensions make everyone’s work easier.