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AI Programming Basics–Build and Develop Your AI Skill Set

Updated: Mar 17, 2022

AI programming is the cornerstone for every AI platform and application. Boost your AI knowledge with an overview of programming and languages.

Artificial intelligence (AI) is an ever-growing field that holds nearly limitless potential. In both popular culture and business, existing use cases merely scratch the surface of what developers believe is possible with future AI technology.

Despite massive growth potential, companies and developers alike face serious limitations. According to recent surveys, 26% of companies struggle to find a starting point for their AI journeys. Maintaining a competitive edge is difficult without an understanding of basic fundamentals.

In this guide, we’ll discuss how AI programming is the cornerstone for all future AI initiatives and how companies can leverage programming languages to develop an AI game plan.

What is AI programming?

AI programming is the process of bringing artificial intelligence ideas to fruition, either manually or automatically with written code. During the development process, computer scientists and engineers implement a combination of software, custom coding, and programming strategies to build AI technology and machines.

Programming is the backbone of all artificial intelligence. Without it, machines and systems can’t function as intended. As developers build an AI program, they instruct the system how to collect data, respond to it, and generate new information.

Building a complete AI system

Learning how to program AI is the first step to building an AI system. From initial idea to conception, constructing an AI system involves a few critical steps.

1. First, developers must identify the pain point that the AI tool intends to solve. This involves collecting and preparing data so that it’s ready for analysis by the AI platform.

2. Second, a builder must choose and train the algorithm of choice. Here, the computer model learns how to respond based on reinforcement and structured learning.

3. Third, the builder must choose the most fitting and suitable AI programming language for a given application.

4. Finally, a developer selects the platform on which the artificial intelligence runs. Options include SaaS platforms, freestanding databases, or cloud-based infrastructure.

AI applications for all industries

Artificial intelligence is no longer reserved for cutting edge technology firms. The applications are more diverse than ever before, and more individuals experience AI as part of daily life.

Common applications that require knowledge of AI programming include:

  • Intuitive messenger services and chatbots

  • Automated or self-driving vehicles

  • Apps and machines that respond to voice commands

  • Predictive online ad targeting

  • Personalized customer support services

Top 6 AI programming languages

In computer science, a programming language is the formal set of instructions that a system depends on for activity and output. These languages dictate how a system responds to and uses algorithms.

In artificial intelligence, there are several programming languages. For the purpose of this guide, we’ll focus on six of them. Keep in mind that each option has its own unique benefits, drawbacks, and ideal applications.


Python is one of the most popular languages for AI programming due to its relative simplicity and ease of use. It’s also an ideal choice for beginner programmers because it has pre-existing libraries, extensive tutorials, and online resources.

As an AI programming language, Python is open-source; this encourages broad application, usage, and experimentation from many types of users.

Developers may favor Python over other types of languages because it includes English keywords instead of complicated syntax. This feature makes Python easier to read, write, and design. Additionally, it’s a language that can be used for large enterprise and commercial applications.


C++ is an AI programming language that’s well-recognized for its speed and quick response to various inputs. As a result, it’s used for fast and quickly-changing applications, including Google Chrome and many computer video games. Its response rate is particularly useful for artificial intelligence tools that need to respond quickly and automatically.

C++ is also a budget-friendly option, coming in at a lower cost than languages like Python. C++ reuses and inherits programs, which means that it can save developers time as well as money.

A downside to C++ is that it contains complicated syntax that’s more difficult for a beginner or average user to learn. Although it’s an ideal fit for large enterprise applications, developers should not expect to have the same resources or support as with Python.


Although Java is one of the most popular programming languages around the world, its presence as an AI language is still in the early stages. Fortunately, Java has several unique qualities that make it suitable for artificial intelligence. These benefits include the WORA (Once Written Read/Run Anywhere) principle, which makes it portable for any platform.

Java is relevant to the following types of AI projects and applications.

  • Neuro-linguistic programming

  • Search result algorithms

  • Neural networks

Even though Java contains some C++ syntax, it is often much slower than C++ and lagging in response time. Although it’s a portable programming language (a great benefit to large enterprise systems), it may require more fixes when used with older systems.


LISP is short for “list programming,” and it was first introduced in the 1950s. In terms of AI programming languages, LISP is among the oldest. Its history has allowed for extensive development, but it still falls behind other languages in terms of wide scale application.

Some of the hesitance at using LISP is because of its lengthy and quirky syntax. Even though it can be flexible, LISP is most helpful in situations with inductive logic or machine learning.

Modern AI developers often flock to programming languages that have a wider variety of libraries and learning resources. Because it lacks these features, LISP falls behind popular languages like Python.


Prolog is similar to LISP in that it is a primary computer language for AI. Prolog is constructed using three main elements: facts, rules, and goals. Once an engineer defines each of these elements, Prolog is capable of recognizing and reading the relationship between them in order to reach a conclusion.

Developers might favor Prolog for use cases that involve:

  • Database development

  • Pattern matching

  • List handling

One of the major drawbacks of Prolog as a language is that it’s not as standardized as others. When engineers run into bugs or language issues, the fixes may be elaborate. Despite this flaw, Prolog is a practical choice for logic-based AI tools like chatbots.


R was a programming language originally released in the 1990s. It’s currently maintained as a free software by the R Development Core Team. Although not as popular as some of the more advanced languages, R is useful for AI programming because of its ability to compute huge numerical data sets at one time.

Additionally, R is a useful language for data miners and statistical analysts who use research that is influenced or built by artificial intelligence. Computer scientists can even extend R’s functionalities with other applications and languages, including C++.

How to choose the best programming language for AI

Unfortunately, choosing the perfect AI programming language is impossible. This is because each separate language has uses for which it is the most appropriate at a given time.

To determine the best AI programming software for a particular need, focus on details such as:

  • Primary purpose of the application (or the reason why you want it to exist)

  • Budget for a specific AI project

  • Target audience and skills of the end user

  • Need for future flexibility and adaptability

Once you answer these questions honestly, consider the experience of your development team in conjunction with the perks and downsides of each language.

What does an AI programmer do?

AI programmers are the individuals responsible for writing custom code for artificial intelligence applications. They also build, maintain, and debug applications to ensure seamless usage. Corporations and businesses may hire engineers with AI skills or recruit general developers who can tackle AI-specific challenges.

What skills are important?

Regardless of the business need, all AI developers should possess a few common characteristics in order to succeed.

  • Working knowledge of artificial intelligence use cases

  • Understanding of existing programming languages

  • Ability to adapt to different learning methods that are essential for machine learning, deep learning, and neural networks

  • Strong skills in mathematics, data analytics, and algorithms

How difficult is AI programming for beginners?

Although artificial intelligence projects are new territory, developers who have a strong background in programming languages should be able to easily make the jump. As is true for any type of programming, adequate training can make a huge difference.

As professionals become more comfortable using, designing, and developing AI applications, the need for hands-on experiences is vital for growth. Beginner AI programmers should pursue experiences that promote this growth through tangible building and testing.

AI residency programs

To help new programmers quickly learn the ropes for careers in machine learning, AI residency programs are on the rise.

The Google AI Residency Program is one of the most prestigious and advanced training courses available. This 18-month residency includes research and training in deep learning, machine learning, and application development. It’s just one of many emerging programs for future AI developers.

Takeaways to consider

As organizations and companies learn more about the foundational components of AI programming, they can become industry trailblazers, innovators, and thought leaders within the global economy.

From an individual standpoint, technology professionals who learn AI programming basics now may become innovators in their own right. As the need and desire for artificial intelligence grows, the demand for these skills is only expected to increase.

Now is the perfect time to start your AI programming journey with the right skills, tools, and knowledge to make strides in this untapped arena.

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