Popular AI Programming Languages To Design AI Apps in 2024

According to IBM, Artificial Intelligence is what makes computers, robots, and other machines imitate certain abilities of human intellect like learning, reasoning, recognizing objects, problem-solving, and decision-making. According to International Data Corporation, the compound annual rate at which AI market share to increase by $76.44 billion by 2025

One can say that AI is the larger aspect of machine learning and deep learning and the three are kind of interconnected. The credit of the expansion happening in the area of AI in recent times goes to the availability of extensive data and advanced machines that can process this data swiftly and accurately.

So, let’s know more about artificial intelligence and some popular AI programming languages that support AI-based app development in 2024.

6 Best AI Coding Languages in 2024

Here are the top 6 AI programming languages in 2024 that play a significant role in the development of AI tools.

Python

Python serves as the base language of several technologies and plays a special role in implementing AI apps. Introduced in 1991, Python today enjoys extensive community support. Python is the preferred choice of programming language for AI/ML services.

Merits of Python:

• One of the fastest-growing coding languages in the world, Python is easy to learn.
• It is still a readable language with impressive data visualization features.
• It is world-renowned for its versatility and simple syntax.
• Natural Language Processing, General AI, Neural Networks, and Sentimental Analysis is largely backed by Python Development Services.
• It also has applicability in the fields of analytics, statistics, and mathematics.
• It has a wide existing framework for Machine Learning and Deep Learning.
• Python offers multiple libraries including Pytorch, Pybrain, TensorFlow, Scikit-learn, Theano, Keras, and more.

What can you do with Python?

• Helpful in writing bot programs.
• With a strategic road-mapping method, Python helps to create dependable chatbots.
• Aids in designing interactive, modular, and dynamic codes.

Java

In the case of app development company, Java falls second to Python in the list of trending coding languages for AI-based tools. Java was at the forefront of AI and ML apps previously and is recently replaced by Python as the leading language for AI app building.

Merits of Java:

• Java is used with Search Algorithms, Neural Networks, and Natural Language Processing.
• Java can deliver promising results in the case of AI development of Next-Gen Apps.
• It offers easy debugging.
• Platform is independent.
• Java can support the development of AI apps for larger projects.
• It has an in-built garbage collector along with a good ability of graphical data representation.
• Extensive libraries available with Java include Apache Jena, Deep Java Library, TensorFlow, PowerLoom, Jenetics, Eye, d3web, Tweety, ApacheOpenNLP, etc.
• It is user-friendly and flexible.

What can you do with Java?

• Creating chatbots is easy with Java.
• WEKA is a Machine Learning Suite developed with Java, which facilitates companies with data mining, predictive analytics, and BI.
• AI-enabled Java-based games are also easy to create. One such example would be that of Robocode.

R

R is basically a statistical language created from Data Science and Deep Learning. It helps to design statistical apps and is highly suited for implementation in the field of business statistics.

It can be used to manipulate data to further generate statistical reports.

Here are some of its essential benefits:

• R, in combination with Python, can assist in the development of statistical apps.
• Generates valuable business insights with the help of AI.
• Utilized in Predictive Analysis, Statistical Computations, Numerical Analysis, Neural Networks, and Machine Learning.
• It is open-source and highly compatible with big data.
• The platform is independent and easy to integrate.
• It has several machine learning operations.

What can you do with R?

• R offers a strong package ecosystem for AI/ML development services like Carat, Random Forest, RPart, DataExplorer, MICE, Dplyr, etc.
• R sees major implementations in finance, eCommerce, banking, consulting, and healthcare.

C++

One of the fastest and oldest coding languages, C++ has been the 9th most preferred choice of developers for making AI apps. The development of AI and ML-based applications with C++ have covered a significant distance so far.

Merits of C++:

• Utilizes object-oriented characteristics.
• It is portable and scalable.
• Multi-Paradigm with memory management features.
• It enables faster code execution.
• Multiple libraries offered by C++ are Shark, Boost, Torch, TensorFlow Lite, and MLpack.
• Facilitates saving cost of JVM when developers are using an embedded development environment.

What can you do with C++?

• It is useful for coding resource-intensive applications.
• You can develop AI-enabled tools for healthcare, robots, speech recognition, games, and computer vision.

Lisp

The second oldest programming language after Fortran, Lisp was created in 1958 by John McCarthy. It was actively used in the 1980s by programmers. List Processing is otherwise known as Lisp. Lisp has laid the founding stones of AI long back.

Merits of Lisp:

• It is built as a mathematical notation for programs.
• It is enabled with a garbage collection feature.
• Is flexible and user friendly.
• It facilitates rapid prototyping.
• It is capable of dynamic object creation.
• It supports the implementation of software that can compute using symbols
• Lisp is a general-purpose but dynamically typed programming language.

What can you do with Lisp?

• Typical areas of implementation are planning systems, knowledge representation, machine translation, logic languages, rewrite systems, and more.

AIML (Artificial Intelligence Markup Language)

Artificial Intelligence Markup Language is used as a coding language for Artificial Intelligence (AI) and Machine Learning (ML). Created as a free software community between 1995 to 2002 by Richard Wallace, AIML is the XML dialect.

Merits of AIML:

• AIML translators are available in Java, Python, Ruby, C++, C#, and many more.

What can you do with AIML?

• It helps in designing natural language software agents.

Key Areas of Artificial Intelligence Application

We may still not be in times where the implementation of AI is as advanced as depicted in the Avengers: Age of Ultron or The Matrix Trilogy; however, currently, we can see the following practical uses of AI in everyday life.

Speech recognition

STT(speech-to-text) is an AI technology that is the principal driving force behind the software, voice-controlled remotes, voice-enabled GPS, voice-driven virtual keyboards, and many other tools. STT helps in recognizing spoken words and transforms them into digitized texts.

Speech-to-Text, Speechnotes, ListNote are few examples of voice recognition software.

Also Read: AI Apps For Android

NLP ( Natural Language Processing)

NLP guides software apps and machines to comprehend, interpret, and generate human text. NLP utilizes sentimental analysis to identify mood, attitude, anger, etc.

Practical applications of NLP are Siri and Alexa.

Image recognition

Identification and classification of objects, people, written texts, and also actions fall under image recognition in AI. It is driven by deep neural networks.

Examples of image recognition include fingerprint ID systems, medical image analysis, self-driving cars, etc.

Autopilot mode

Although, this has been in use for quite a long time in flying passenger and air force aircrafts. In modern times, autopilot utilizes a combination of GPS, sensors, image recognition, collision avoidance technology, robotics, and NLP.

Robots for house cleaning

Starting from vacuum cleaning floors to mopping floors while avoiding hurdles on the path, taking the most effective route of cleaning, and determining the area of the room is what AI-enabled house cleaning robots can do in today’s time.

An example under this category would include iRobot’s Roomba vacuum.

Product recommendations

eCommerce websites utilize neural networks for recommending products to users based on their previous purchase activities, geography, time, weather, and various other factors. Such real-time recommendations can boost sales..

Amazon & eBay largely rely on this method of sales.

Virus & spam protection

Antivirus and spam tools employ deep neural networks to identify and render new viruses ineffective.

Norton, Mcafee and many other antivirus applications utilize AI-based virus detection methods.

Automatic stock trading

AI can help automate stock trading to optimise stock portfolios. It can particularly be useful in day trading.

Some of the popular AI-enabled stock trading apps are TrendSpider, Trade ideas, EquBot, Blackboxstocks, etc.

Ride-sharing services

The cab service providers use AI to calculate the ETA, connect passengers with the nearest driver, and do a lot more.

Examples of ride-sharing services include Uber, Lyft, Ola, etc.

Also Read: Things to Know About Node JS Stream In Detail

Harikrishna Kundariya

CEO, eSparkBiz

Harikrishna Kundariya, a marketer, developer, IoT, chatbot and blockchain savvy, designer, co-founder, Director of eSparkBiz @Software Development Company where you can Hire Software Developers. His 14+ experience enables him to provide digital solutions to new start-ups based on Web app development.