5 Topmost Programming Languages used by AI Engineers in 2021

5 Topmost Programming Languages used by AI Engineers in 2021
Apr 09, 2021

As per the PwC estimates, AI will contribute up to USD 15.7 trillion to the global economy and business by 2030.

This shows the pace at which AI is growing. The success potential of AI as defined by PwC offers an excellent array of job prospects for AI engineers.

AI Benefits

Programming language is the base on which AI stands firm, and keeps moving towards superior heights.

Among many programming languages, AI engineers must choose the right one that fits their project's requirements. With the increased applicability of AI, businesses have grown and achieved their complete capabilities. These developments in AI might not have been easy without the improvements in the underlying programming languages. Of all, the 5 most important AI programming languages are detailed below.

1. Python

Python is an object-oriented, plus-interpreted, and high-level programming language with dynamic semantics. The data structure is high-level, built-in and is combined with dynamic binding and dynamic typing. Thus, it is very attractive for Rapid Application Development. In addition to this, it is very promising to use as a glue or scripting language in order to connect existing components. Python's easy to learn simple syntax allows readability and results in cost reduction of program maintenance. Python offers packages and modules which help code reuse and program modularity. Python interpreter and its extensive library are available in binary form or as sources without any extra charges. They can be used for all major platforms and also can be distributed freely.

Python as the No.1 one choice for AI programming

Python language was developed in 1991 and is used in ML and AI projects. In a poll, the results revealed that 57% of programmers prefer to use Python over C++. That is, Python was their most preferred language for the development of AI solutions. Python is easier to learn and thus provides an easy route to enter AI solutions for data scientists and AI programmers.

Strengths of Python

  • Python offers excellent community support, a large set of libraries, and flexibility for AI engineers.

  • Python's certain features offer the benefit of platform independence and extensive frameworks for machine learning and deep learning.

  • Python is concise, short, and readable that expresses much activity in a short and clear code.

  • Python has a highly efficient and rich set of libraries for AI projects.

Python Libraries

The most common libraries for Python are:

  • TensorFlow: for performing tasks related to datasets and workloads related to machine learning

  • PyTorch: for Natural Language Processing (NLP) and computer vision

  • Scikit-learn: for offering training to models in machine learning

  • SparkMLlib: like Machine Learning library (Apache Spark). Used for enabling machine learning for everyone with tools like utilities and algorithms

  • Keras: as the code interface for challenging mathematical operations and calculations

  • MXNet: as the other type of Apache's library for making it easy to learn deep learning workflows

  • Theano: as the library for optimizing, defining and evaluating the mathematical calculations and algorithms.

  • Pybrain: for excellent machine learning algorithms

It is important to note here that Python has exceeded Java and turned out to be the 2nd most popular programming language as per the GitHub contributions. Also, Stack Overflow declares it as the fastest-growing primary language.

2. Java

Java programming is an object-oriented programming language for general-purpose, designed for software development for consumers' electronic devices. For example, electronic devices such as VCRs, TVs, toasters, and others. Java is not linked to any particular operating system or hardware and thus is called a platform-neutral programming language. As it goes for Java, "Write once, run anywhere." Java is one of the best programming languages globally, and has held applicability in the last 20 years.

Strengths of Java

Because of its excellent user-friendliness, flexibility, and independent platform, Java has applicability in programming for AI projects in several methods. Some of them are listed below:

  • TensorFlow: TensorFlow's list of support offered by programming languages includes Java as well. This is used with an API.

  • Kubeflow: It helps easy management and deployment of ML stacks on Kubernetes. This offers ready to apply ML solutions.

  • Deep Java Library: Amazon builds it, and creates and deploys deep learning tasks by applying Java.

  • OpenNLP: OpenNLP of Apache is an ML tool for NLP (Natural Language Processing).

  • Neuroph: It enables the designing of neural networks by applying open-source Java frameworks. This is doable with the support of Neuroph GUI.

  • Java Machine Learning Library: It provides users with many ML algorithms.

3. R

R is a programming language as well as an environment for statistical graphics and computing. R offers many statistical operations (classical statistical tests, linear and nonlinear modeling, classification, time-series analysis, clustering, and others). In addition to this, it offers graphical techniques and is therefore very extensible.

R’s first version was made known in 1993 created by Robert Gentleman and Ross Ihaka. The R Development Core Team at present maintains it. R language is the implementation part of the S language and thus helps develop data analysis and statistical software.

Strengths of R language

  • R language makes it easy to generate well-designed plots. This includes formulae and mathematical symbols at the spots where it is needed.

  • The main feature of R is that it is good at dealing with huge numbers. This feature of R puts it on a better pedestal than even Python. As it is known that Python has a comparatively unrefined package called NumPy.

  • By applying R, the user can tackle several programming paradigms, for instance, vectorial computation, functional programming, and object-oriented programming.

A few of the AI programming language packages built on R are as follows:

  • Gmodels offers a collection of many tools for model fitting

  • ODBC and RODBC as R interface

  • Framework called Tm for text mining applications

  • OneR for the implementation of the One Rule ML classification algorithm. It is helpful for ML models.

Projects of R

COVID- 19's interactive map was created by applying the R language.

Widely applied features offered by R are:

  • Broad types of packages and libraries to extend its functionalities

  • Supportive as well as an active community

  • Able to perform tasks in tandem with Fortran, C and C++.

  • Many packages enable to extend the functionalities

  • Help provided for the making of high – quality graphs

4. Prolog

Prolog (created in 1972) is a logic programming language that plays an essential role in AI. The unique characteristics of Prolog language are that it is mainly intended as a declarative language. In the case of Prolog, logic is being expressed as relations (known as Facts and Rules). Computation or Formulation is performed by running a query related to these relations. Its main capabilities include:

  • Unification (The general idea is, can the provided terms be created to represent the same structure),

  • Backtracking (It is when the task fails, then Prolog traces backward and attempts to satisfy the same structure), and

  • Recursion (The basis as present in the case of every search in the program is known as Recursion).

Strengths of Prolog

  • Prolog creates an exciting tool for the development of AI, mainly Natural Language Processing.

  • Prolog shows the best results when applied for creating chatbots; ELIZA was the first chatbot created using Prolog.

In order to understand Prolog, an AI Engineer must familiarize herself with some of the essential terms. This helps guide it’s working.

  • True statements are defined by Facts.

  • "The statement is defined but additional conditions" are mentioned by Rules.

  • "When the submitted statements stand as per the knowledgebase," it is defined by Goals.

  • Queries are used to define the method of making the statement true and the final analysis of rules and facts.

  • Prolog provides two methods for implementation of AI that have been applied in practice since long and are popular among researchers and data scientists.

  • The Symbolic Approach includes rule-based expert systems, constraint-based approaches, and theorem provers.

  • The Statistical Approach includes data mining, neural nets, ML and several others.

5. Lisp

LISP (List Processing) is an acronym. It is a programming language that got designed for the simple manipulation process of data strings. John McCarthy developed LISP in 1958. It is a commonly applied language for AI programming. It is thus one of the oldest programming languages present and is still in use. In the case of LISP, all computing is presented as a function of at least one object. Objects can be data items (like variables or constants), functions, or data structures. LISP’s capabilities to do computing with symbolic expressions instead of numbers make it easier to work with AI programs.

A shortlist of LISP features that have enabled it to become one of the topmost programming languages for AI projects on ML:

  • Dynamic Object Creation

  • Rapid Prototyping

  • Flexibility

  • Garbage Collection

Final thoughts

Programming languages are the most crucial factors in determining the type of job one will land. Though challenging to decide, interest-based choices can help make the task more productive and rewarding for AI engineers.

Follow Us!

Conversational Ai Best Practices: Strategies for Implementation and Success
Brought to you by ARTiBA
Artificial Intelligence Certification
Conversational Ai Best Practices: Strategies for Implementation and Success

Conversational Ai Best Practices:
Strategies for Implementation and Success

The future is promising with conversational Ai leading the way. This guide provides a roadmap to seamlessly integrate conversational Ai, enabling virtual assistants to enhance user engagement in augmented or virtual reality environments.

  • Mechanism of Conversational Ai
  • Application of Conversational Ai
  • It's Advantages
  • Using Conversational Ai in your Organization
  • Real-World Examples
  • Evolution of Conversational Ai

This website uses cookies to enhance website functionalities and improve your online experience. By browsing this website, you agree to the use of cookies as outlined in our Privacy Policy.

Got it