- April 11, 2022
- Posted by: Shalini R
- Category: Artificial Intelligence
Irrespective of whether you realize it, artificial intelligence is becoming more and more a part of your daily life. In 2022 alone, Gartner estimates that AI software will be valued at $62 billion, an increase of 21% from the year before. It is possible to use artificial intelligence in a wide range of fields like customer service, marketing, and e-commerce as well as education, travel, and even hospitality. More than half of the world’s e-commerce businesses are now using artificial intelligence (AI) to enhance the client experience. To assist you to make an educated selection regarding the best AI programming languages for 2022, we’ve compiled a list of reasons why each language was chosen by developers.
AI systems that are easy to use and provide a magical experience to their users are dependent on the choice of programming language. Among the many programming languages available, each has its advantages and disadvantages. When it comes to your AI project, the programming language you choose relies on what you need. Certain AI programming languages thrive in manipulating massive amounts of data and crunching enormous numbers, while others excel at natural language programming. Each language has unique strengths and weaknesses that might help you identify which one is best suited for your project.
Consider the top AI programming languages for the year 2022. Developers have continually preferred these languages, and as a result, their use and community have risen. Developers’ preference for a programming language indicates that it is reliable and simple to use.
It doesn’t matter how you look at it, Python is the finest AI programming language out there. Being around for such a long time and having continuously done well as a general-purpose programming language that can be used for both front-end and back-end development is a plus. New and experienced developers alike can benefit from its ease of learning and ability to generate clean, simplified code. Python is a very legible and easy-to-learn programming language with a well-defined syntax and simple English keywords.
Python is a strong contender in the artificial intelligence arena because of its large array of pre-designed libraries that are especially valuable for AI development. Scikit-learn, a Python library for artificial intelligence (AI) methods like regression and classification, does an excellent job. A similar level of sophistication is seen in deep learning packages like Keras, Caffe, and TensorFlow, making AI development in Python a breeze. Python is one of the most approachable programming languages to deal with because of libraries like NumPy, SciPy, Matpolib, SimpleAI, and others.
Python has an advantage in natural language processing, which is so important in today’s user experience, owing to technologies like SpaCy and NTLK.
Python is an excellent programming language for AI development because of its easy syntax, plentiful libraries, vibrant community, and simple code. Code complexity may be handled by the simple syntax and an arsenal of libraries, allowing developers to concentrate on the more essential areas of development such as creativity, problem-solving, and design thinking.
There are many different reasons why Java is a good AI development language in 2022. A concise syntax and easy debugging make Java an excellent object-oriented programming language. Java continues to win over developers with each new generation, with a proven track record in software development, mobile app development, and now even AI development.
Rapid execution and fast runtimes are two of Java’s superpowers, which are essential for artificial intelligence development. All major analytical frameworks can be seamlessly integrated with their high output value.
Java’s Virtual Machine Technology makes it very simple to implement on a wide range of systems. Using the write-once, run-anywhere technique, you may execute your AI application on any platform once it has been created and built on one of those platforms. There are many open-source tools for big data that are written in Java.
Easy usage, rapid debugging, portable memory management, and adaptability are some of Java’s greatest features like an AI programming language. Data analysis, natural language processing, deep learning, machine learning, and much more may all benefit from it.
In addition to providing enough assistance for novice developers, the Java community also provides lots of opportunities for creative enrichment for experienced developers from across the globe.
With a large number of applications in artificial intelligence research, C++ is yet another extremely trustworthy object-oriented programming language worth learning. Complex machine learning models are particularly interested in it because of their fast-processing speed, which enables them to run efficiently. A lot of great things can be done with C++. It creates a little amount of code, enabling modules to execute faster. Because it is statically typed, runtime type mistakes are not an issue. There are other characteristics in C++ that make it a popular choice for AI programmers, such as dynamic load balancing, adaptive caching, and memory management.
It’s a little-known fact that C++ is the fastest programming language. Developed in 1983, C++ has a very important place in AI programming. The ability to move quickly is unquestionably advantageous.
There is a learning curve for C++, which may be discouraging for newcomers to AI programming, however, if you get beyond the learning curve, C++ has several distinct benefits over other AI programming languages.
A small number of machine learning and deep learning libraries built in C++ are included in the standard C++ library package. Linear regression and other methods of supervised learning are both supported by SHARK. It is possible to combine MLPACK’s extendable algorithms into large-scale ML applications.
Julia, a new AI programming language developed by MIT in 2012, is capable of handling complex numerical analyses and massive data sets with ease, making it a useful tool for researchers and scientists. All of the constraints of current AI development were taken into account when MIT developers built Julia. Fast, powerful, script-like syntax and a slew of other features make it an excellent tool for creating cutting-edge artificial intelligence programs.
A thriving community and an ecosystem of machine learning libraries, such as TensorFlow.jl, Scikitlearn.jl, Flux, and Mocha.jl have formed around Julia even though it is still a relative newcomer to the developing world of programming languages.
Julia has an intriguing property in that it can readily convert methods from research papers into code, reducing model risk and increasing safety. It’s a high-performance AI programming language designed for current AI applications, and it’s best suited to developers with a Python or R background.
The winning qualities of Haskell are its safety and quickness. In the field of AI programming, Haskell is a contemporary, functional programming language that has a wide range of benefits. Type classes allow the overloading of operators in a type-safe manner. Lambda expressions, type classes, pattern matching, type polymorphism, and list comprehension are some of the other features available. There are many good reasons why Haskell is a good language for research, teaching, and business. Haskell is one of the safest AI programming languages because of its flexibility and error handling capabilities.
Since 1972, Prolog has been a part of the computing landscape. A more accurate description given by experts is that it is a well-aged and mature AI programming language rather than an old one. Natural language processing is a logical, declarative programming language that can be used to write programs. As a result, IBM Watson employs Prolog to parse natural language to answer human-generated inquiries.
Prolog’s pattern matching, metalevel reasoning, and tree-based data structure are just a few of the features that set it out as a top AI programming language. Natural language processing, computer vision, and intelligent database search all benefit from pattern matching characteristics.
List management is another skill that Prolog excels at. Using Prolog’s recursive list handling, you can solve problems faster and analyze data more effectively. This results in enhanced application performance.
The programmer sets a set of rules, or ‘facts,’ as well as the final objective in Prolog AI programming. After finding the link between the two, Prolog uses pattern matching to deliver the results you want.
In AI programming, LISP is probably the first language. John McCarthy, the man behind LISP, created the phrase “artificial intelligence,” which has now become a household name. With automated garbage collection, rapid prototyping skills and dynamic object creation, and support for symbolic expressions, it is one the most versatile programming languages available today. The early AI programs, such as ELIZA, were written in LISP.
Since its inception, LISP has lost some of its appeals due to some of its inherent weaknesses over time. But it did set the groundwork for early AI research and is now a wonderful language to learn to understand how AI progressed.
As you can see, there are a plethora of AI programming languages available to programmers in the year 2022. For your next AI project, you’ll have to decide which of these is most suited for you and your background and degree of AI programming knowledge. The engineers at Aelius Venture are delighted to help you figure out which of these is ideal for your project, and we’d be happy to examine your concept and decide which is the greatest match for you.