AI Programming: 5 Most Popular AI Programming Languages

AI programming is an elevation of technology that has brought efficiency and optimum benefits to different companies’ operations and people’s lives. AI has brought another level of smart technology to different industries, and the prospects of its potential still grow with the expectation that it will reach human intelligence. This article seeks to clarify the appropriate artificial intelligence programming languages and how ai programming has become advantageous for various industries.

List of the Content

• What is AI programming
• Top AI programming languages
• AI programming benefits in software development
• How to adopt artificial intelligence programming
• Best practices for AI programming
• Conclusion

WHAT IS AI PROGRAMMING

Developers are willing to explore, experiment and implement AI capabilities to satisfy more of the human and organizational necessities. After all, necessity is the mother of invention. Therefore, we can experience impressive tech advancements due to the wide adoption of AI programming. Artificial intelligence has already covered needs in numerous fields. 

• Availability of an individual’s virtual assistant with information about the person’s daily life routine, thus facilitating their day-to-day goals and needs.
• Availability of multiple voice-based gadgets. That is where most basic items such as cars and television are customized to allow their listening and providing solutions to individuals.
• Replacement of credit cards with facial recognition technology thanks to biometrics capabilities.
• AI creates media platforms in which the viewer or listener can choose the specifications of their needs, such as the genre of music to listen to.
• Empathetic computers, so-called smart devices, no longer provide a single and simple question and discrete response; instead, they offer human-like responses. These are comprehensive feedback for queries and questions and even sensible solutions.
• AI in healthcare provision; healthcare adopts AI systems, for instance, in diagnostic specialties.
• Provision of news and other reports by AI; this is where systems provide individuals with demanded and comprehensive information work. 

We’ve just gathered some good examples, but there is always more to discover and create. AI programming is a field that is growing extensively and at a rapid pace. If earlier, most of us learned about AI from frequent talks by individuals in the tech world, such as Elon Musk and Mark Zuckerberg. Now developers introduce AI to almost every developing project. In the next years, AI technology will be at a more tangible level to many individuals and impact our lives on the core basis. And due to the statistics, the revenues from the artificial intelligence software market are estimated to increase to $126 billion by 2025. 

More and more companies plan to adopt AI across various business verticals. They have the opportunity to present new solutions as well as update existing approaches. Along with the product creation, they have the chance to optimize the development process, streamline planning, obtain new knowledge and improve user experience. That definitely results in better productivity, high quality, and speed improvements.

Just like in custom software development, a developer has a variety of languages to use in writing AI. However, there is no perfect programming language to point to as the best programming language used in artificial intelligence. The development process depends on the desired functionality of the developed AI application. AI has achieved biometric intelligence, autopilots for self-driving cars, and other applications requiring different artificial intelligence coding languages for their development projects. So let’s proceed with discovering the most popular AI programming languages.

POPULAR AI PROGRAMMING LANGUAGES

Debates about the best programming language for AI never stop. Because of that, we decided to compare languages commonly used for artificial intelligence projects to outline the pros and cons of each one.

Java, Python, Lisp, Prolog, and C++ are major AI programming languages used for artificial intelligence capable of satisfying different needs in developing and designing software. It is up to a developer to choose which AI languages will gratify the desired functionality and features of the application requirements.

PYTHON

Python is among developers’ favourite AI programming languages in development because of its syntax simplicity and versatility. Python is very encouraging for machine learning for developers as it is less complex compared to C++ and Java. It is also a very portable language used on platforms including Linux, Windows, Mac OS, and UNIX. It is likable of its features such as Interactive, interpreted, modular, dynamic, portable, and high level, making it more unique than Java.

Moreover, Python is multi-paradigm programming supporting object-oriented, procedural, and functional programming styles. AI programming with Python supports neural networks and the development of NLP solutions thanks to its simple function library and, more so, ideal structure.

Advantages

• Python has a wide variety of libraries and tools.
• Supports algorithm testing without having to implement them.
• Python supporting object-oriented design increases a programmer’s productivity.
• Compared to Java and C++, Python is faster in development.

Drawbacks

• Developers accustomed to using Python face difficulty adjusting to completely different syntax when they try using other languages for AI coding.
• Unlike C++ and Java, Python works with the help of an interpreter, which makes compilation and execution slower in AI development.
• Not suitable for mobile computing. Python is unsuitable for AI meant for mobile applications due to its weak language for mobile computing.

C++

C++ is the fastest computer language. Its speed is appreciated for AI programming projects that are time-sensitive. It provides faster execution and has less response time which is applied in search engines and the development of computer games. In addition, C++ allows extensive use of algorithms and is efficient in using statistical AI techniques. Another important factor is that C++ supports the re-use of programs in development due to inheritance and data-hiding, thus efficient in time and cost-saving. C++ is appropriate for machine learning and neural network.

Advantages

• Good for finding solutions for complex AI problems.
• Rich in library functions and programming tools collection.
• C++ is multi-paradigm programming that supports object-oriented principles and is thus useful in achieving organized data.

Drawbacks

• Poor in multitasking; C++ is suitable only for implementing core or the base of specific systems or algorithms.
• It follows the bottom-up approach, thus, highly complex, making it hard for newbie developers to use it for writing AI programs.

JAVA

Java is another programming language to answer ‘which computer language is used for artificial intelligence?’ Java is also a multi-paradigm language that follows object-oriented principles and the principle of Once Written Read/Run Anywhere (WORA). It is an AI programming language that can run on any platform that supports it without recompilation.

Java is one of the most commonly used and not just in AI development. It derives a major part of its syntax from C and C++ in addition to its lesser tools. Java is not only appropriate for NLP and search algorithms but also neural networks.

Advantages

• Very portable; it is easy to implement on different platforms because of Virtual Machine Technology.
• Unlike C++, Java is simple to use and even debug.
• Java has an automatic memory manager, which eases the work of the developer.

Disadvantages

• Java is, however, slower than C++; it has less speed in execution and more response time.
• Though highly portable, Java would require dramatic changes in software and hardware to facilitate on older platforms.
• Java is a generally immature programming AI language as there are still some developments ongoing.

LISP

Lisp is another language used for artificial intelligence development. It is a family of computer programming languages and is the second oldest programming language after Fortran. Lisp has developed over time to become a strong and dynamic language in coding.

Some consider Lisp as the best programming language for AI due to the favour liberty it offers developers. Lisp is used in AI because of its flexibility for fast prototyping and experimentation, which in turn facilitates Lisp to grow to a standard AI language. For instance, Lisp has a unique macro system that facilitates the exploration and implementation of different levels of Intellectual Intelligence.

Unlike most AI programming languages, Lisp is more efficient in solving specific as it adapts to the needs of the solutions a developer is writing. It is highly suitable for inductive logic projects and machine learning.

Advantages

• Fast and efficient in coding as compilers instead of interpreters support it.
• An automatic memory manager was invented for Lisp; therefore, it has a garbage collection.
• Lisp offers specific control over systems resulting in their maximum use.

Drawbacks

• Few developers are well acquainted with Lisp programming.
• Being a vintage programming language artificial intelligence, Lisp requires the configuration of new software and hardware to accommodate its use.

PROLOG

Prolog is one of the oldest programming languages, thus suitable for the development of programming AI. Like Lisp, it is also a primary computer language for artificial intelligence. It has mechanisms that facilitate flexible frameworks developers enjoy working with. It is a rule-based and declarative language as it contains facts and rules that dictate its artificial intelligence coding language.

Prolog supports basic mechanisms such as pattern matching, tree-based data structuring, and automatic backtracking essential for AI programming. Other than its extensive use in AI projects, Prolog is used to create medical systems.

Advantages

• Prolog has a built-in list handling essential in representing tree-based data structures.
• Efficient for fast prototyping for AI programs to be released modules frequently.
• Allows database creation simultaneous with the running of the program.

Drawbacks

• Despite Prolog’s old age, it has not been fully standardized in that some features differ in implementation, making the developer’s work cumbersome.

WHAT BENEFIT DOES AI PROGRAMMING INTRODUCE IN SOFTWARE DEVELOPMENT?

Artificial intelligence definitely has an influence on software development as businesses become more interested in AI programming languages. And it undoubtedly impacts how teams build apps and how users interact with them. AI helps developers handle various activities and establish new skills to advance products. The distinct benefit remains the opportunity to support the efficient work of developers.

It’ll be interesting to discover how AI works to developers’ advantage and how it helps deal with common software development challenges.

Accurate project estimates

One of the common challenges in software development is providing accurate estimates. It requires great expertise, context awareness and project familiarity. As a result, AI allows developers to apply historical data from previous projects and facilitate the estimation of budgets and deadlines. Considering various scenarios and possible challenges, they are capable of including better assessments, user stories, feature descriptions, etc. It enables developers to anticipate the required budget and workload.

Innovative design

Project planning also requires specialized experience and knowledge of software design. Sometimes, it is challenging to transform customer requirements into a good-looking product with an excellent user experience. However, AI tools can help automate different tasks regarding software design and its implementation steps. For example, it becomes easier to present visual interfaces using AI design assistants and other tech innovations. It greatly helps learn customer preferences and transform them into functional and well-designed software.

Quality code

The quality of code impacts the implementation of software and its long-term maintenance. Every team realizes the need to stay up-to-date and apply the latest tech approaches. AI programming definitely takes the top position here. Developers use artificial intelligence to produce better code, optimize operations and gain new knowledge. Also, AI solutions are great for boosting the accuracy and speed of the coding process. For example, AI-enabled coding supports error detection when it occurs. It enables better productivity and increases the software value.

Automated security

Software security is among the main concerns for many development teams. They look for new approaches to discern inappropriate behaviours, avoid delayed notifications, deliver advance warnings, etc. Therefore, incorporating AI tools becomes quite essential. They are used to study data and advance AI coding due to more accessible and faster detection of security issues. At the same time, it helps to save time in reviewing executable files.

Quality assessment

It’s important to mention the AI importance in software testing and the ability to ensure the quality assessment procedure. The quality assurance specialists greatly benefit from the involvement of AI tools. It considerably improves the quality of testing, bug finding and fixing. Besides, it’s typically done before the product is delivered to the market. That allows teams to streamline the development process and present high-quality products.

Analytics automation

The AI-based techniques also cover the automation of data analytics. It helps teams automate, speed up and simplify the data analysis process. That supports valuable data insights and strategic decision-making. AI/ML algorithms are used in autonomous monitoring and analyzing large volumes of data. Besides, it presents new solutions for user engagement, software optimization and insight generation.

Strategic decision making

Whether creating a new product or advancing the existing solution, the team has to come up with a number of strategic decisions. For better efficiency, they can apply AI platforms to accumulate the previous experience in order to reduce project misunderstandings and prioritize the needs. Constant reviewing of the software performance allows them to enhance it to meet the rising business needs.

AI programming has become an inevitable part of the software industry. A variety of AI-powered tools helps streamline the development process, collaborate efficiently with the team, gain valuable insights, and create awesome market products.

HOW TO ADOPT AI PROGRAMMING FOR YOUR BUSINESS

AI programming has presented numerous advantages for businesses of different sizes and specifications. That’s why AI integration remains among the top requirements across many industries. According to the survey, 61% of respondents reported the broad adoption of AI, including increased investments in it. That is obviously explained by the significant value they gain with artificial intelligence. 

It’s impossible to implement any of the following use cases without AI integration. Of course, the companies can choose among different AI programming languages, tools and approaches. Moreover, AI use cases vary from project to project and always need to have well-outlined specifications. The list of sampled AI use cases includes: 

• Smart product development
• Smart service development
• Process automation
• Data efficiency
• Intelligent business processes
• Repetitive task automation

That are just a few examples, whereas businesses continuously need to extend their project specifications and define the accurate requirements. In order to simplify the adoption of AI, it’s important to evolve the step-by-step strategy. Besides, AI integration can be started only after careful analysis and detailed planning of the future product. We’ve prepared a guide that can be useful in adopting artificial intelligence programming for your business.

1. Have strategic goals

Any technology innovations have to be implemented to cover business-specific needs. Besides, the strategic approach is really essential here. The primary steps aim at connecting goals with future solutions powered by AI.

2. Set clear objectives

This stage requires lots of planning and analysis of the smallest details. It’s important to answer all the business questions and define clear objectives. Also, it’s a good idea to track progress as the team identifies KPIs.

3. Define use cases

AI adoption can cover a number of different use cases. Therefore, the team specifies requirements and completes tasks through separate use cases. That helps to reach better results within each case and complements the overall project success.

4. Choose AI approaches

In discovering the opportunities presented by programming AI, teams have to select the approaches suited to project-specific needs. The team has to be aware of all the tech-related implications, requirements and possible challenges. 

5. Apply the up-to-date technologies

Artificial intelligence evolves extremely fast and shapes various innovations. Therefore, it’s required to stay up-date and choose AI programming languages that support business needs and keep on constant advancements.

6. Identify the required skillset

The well-defined project requirements allow the company to cooperate better with chosen specialists. The team composition is formed based on the product needs, workload and necessary skills. It helps the business to form a professional team that knows how to implement ideas into workable solutions. 

7. Start implementation

Like any other project, AI adoption has to go through the entire development process. It definitely covers the design, development and testing phases. That’s a vital stage to implementing the AI goals into the valuable market product.

8. Change management

After successfully implementing AI in software development, the team has to be ready to support further maintenance. AI programming keeps pace with the constant advancements, so businesses have to manage changes efficiently and be ready for continuous improvements.

The AI adoption varies from project to project. Thus it’s essential to follow good market practices as well as evolve the project-specific strategy. Careful planning and a well-organized development process enable to turn primary goals into valuable solutions. Artificial intelligence develops at a rapid pace, thus introducing more and more opportunities to grasp.

BEST PRACTICES FOR AI PROGRAMMING

AI programming has become an essential aspect of modern-day technology. As it continues to advance, it is crucial to implement the best practices to ensure the effectiveness and efficiency of the AI system. Let’s outline some of these AI practices that developers should consider to optimize the functionality of their solutions.

• One of the crucial practices in AI programming is a clear problem definition and objective. Defining the problem and objectives upfront is vital to developing an effective AI system. The objectives should align with the business goals, and possible biases should be considered. This approach helps ensure that the system solves the right problem.
• Another essential practice is the quality and relevance of data. High-quality and relevant data are fundamental to the effectiveness of programming. Data collection and cleaning and data augmentation techniques are essential to ensure the AI system has access to the right data to provide accurate results.
• Choosing the right algorithm for the problem is also a required practice in AI programming. Different algorithms have various strengths and weaknesses, and selecting the right algorithm for a specific problem is crucial. Customizing algorithms for specific problems helps improve the performance of the AI system.
• Accurate testing and validation ensure the AI system’s effectiveness and identify any problems before deployment. Different types of testing and validation techniques are used to ensure the accuracy of AI solutions.
• Proper documentation and version control ensures that the code is well-documented and that different versions of the code can be tracked. This approach helps developers keep track of changes and maintain the AI system efficiently.
• Finally, ethical considerations are really vital practices in AI programming. Developers should ensure that their AI systems do not discriminate or introduce bias. Transparency and accountability are essential to ensure that the AI system operates ethically and effectively.

Implementing good practices is crucial to ensuring that the AI system operates effectively and efficiently. By considering the abovementioned practices, developers can optimize their AI systems to meet their intended objectives.

CONCLUSION

When it comes to keeping up with technology, every individual, business person and organization does not want to be left behind. The emergence of AI technology is bringing changes that will permeate the core of our lives; therefore, understanding and using AI technology would be the best strategy right now.