What Is Artificial Intelligence?

Artificial intelligence ( AI ) is among the most exciting and quickly expanding areas of computer science. Even now, it is changing the environment around us. Furthermore, the labor market is as hot as it gets (see Gartner projections).

The goal of AI is to build smart computer systems. In essence, it is the process of creating an autonomous computer system that can learn and function.

Machine learning (ML), nevertheless, focuses on the strategies and tactics employed to enable a computer system to “learn” how to carry out specific actions and even forecast specific results without the need for explicit programming.

A (Very) Brief History of AI

The term “artificial intelligence” was first coined in 1956 by computer scientist John McCarthy, when the field of artificial intelligence research was founded as an academic discipline. In the years since, AI has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter”), followed by new approaches, success and renewed funding.

The majority of AI research’s existence has been split up into subfields that frequently do not interact with one another. These subfields are founded on technological factors, like specific objectives, application domains (like robotics and medicine), and/or implementation strategies. (such as symbolic computation and connectionism).

Today’s and Tomorrow’s AI

Artificial intelligence (AI) is being applied in many different contexts these days, ranging from simpler uses like predictive analytics and self-driving cars to more sophisticated ones like Siri and Alexa.

In the foreseeable future, artificial general intelligence The field’s long-term objectives still include (AGI), also known as strong AI, full AI, or general intelligent action. Common strategies include statistical techniques, computational intelligence, and conventional symbolic AI.

Best Programming Languages for AI Development

Here are the most popular languages used in AI development, along with their key features.

Python

Python is the most popular AI programming language it’s one of the hottest languages going around, and it’s also easy to learn!

Python is an interpreted, high-level, general-purpose programming language with dynamic semantics. Its high-level, built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for rapid application development (RAD), as well as for use as a scripting or glue language to connect existing components together.

Key features of Python:

• It has simple, easy-to-learn syntax that emphasizes readability (and therefore reduces the cost of program maintenance).
• It supports modules and packages (which encourages program modularity and code reuse).
• It has an extensive standard library (available in source or binary form without charge for all major platforms).

What makes Python good for AI:

• It has a rich set of libraries for data analysis and manipulation, such as Pandas, making it easy to work with the data.
• It has a number of libraries specifically for machine learning, such as TensorFlow and Keras.
• It has solid scientific and computation libraries, such as scikit-learn and NumPy.
• It can even be used to program microcontrollers with projects like MicroPython, CircuitPython, and Raspberry Pi.

Example Projects:

• Project: TensorFlow and Keras
  • Description: Open-source libraries for machine learning and deep learning, developed by Google. Used in various applications, from voice/image recognition to drug discovery.

If you’re starting with Python, it’s worth checking out the book The Python Apprentice, by Austin Bingham and Robert Smallshire, as well as other the Python books and courses on SitePoint.

R

R is a programming language and free software environment for statistical computing and graphics that’s supported by the R Foundation for Statistical Computing.

The R language is something of a lingua franca among statisticians, as it’s widely used in official statistics (see uRos2020 and uRos2022), but also on data mining and for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R has an active user base of about two million people worldwide.

Key features of R:

• It features a comprehensive set of software tools for computation, data manipulation, and graphical representation.
• With a vast range of operators for calculations on arrays, lists, vectors, and matrices, it boasts an efficient data processing and storage system.
• It provides a wide range of graphical tools for data analysis and display, which can be used on hardcopy devices or directly on the computer. Interactive web-based interfaces like Shiny are among the interactive tools available.
• With conditionals, loops, user-defined recursive functions, and input/output capabilities (including connections to relational databases), it is a well-developed, straightforward, and consistent programming language.

What makes R good for AI:

• Unlike Python, which was intended to be a general-purpose language, it was expressly created with statisticians in mind.
• Its various properties, including time series analysis, classification, clustering, and linear and nonlinear modeling, are helpful for the development of AI.

Example Projects:

• Project: Microsoft R Server
  • Description: An enterprise-scale server for running and managing R workloads, used in big data analytics, including machine learning projects.

Julia

Julia is a relatively new (launched in 2012), high-level, high-performance dynamic programming language for technical computing, with syntax that’s familiar to users of other technical computing environments.

Key features of Julia:

• It is intended for use in high-performance scientific and numerical computing.
• It is simple to learn and utilize.
• It can call C and Fortran libraries directly, avoiding the need for interface code or wrappers.
• Both distributed and parallel computing can make use of it.
• It has a large standard library that covers machine learning, optimization, and differential equations.

What makes Julia good for AI:

• In addition to being fast, Julia is also very flexible (which makes it easy to experiment with different models quickly).
• It has a number of well-developed machine learning libraries, such as Flux, MLJ, and KNet.

Example Projects:

• Project: Celeste
  • Description: a project that processed petabytes of data from the Sloan Digital Sky Survey with the goal of cataloguing the visible universe. Julia’s capacity for high-performance scientific computing was demonstrated.

C++ and C

C++ is a general-purpose programming language with a bias towards systems programming, and was designed with portability, efficiency and flexibility of use in mind. The main competitor to C++ is Microsoft’s C#, which is very similar in syntax but offers some additional features and benefits like a more managed memory model as well as LINQ (Language Integrated Query) support out of the box, amongst other things.

C++ has also been found useful in widespread domains such as computer graphics, image processing, and scientific computing. Similarly, C# has been used to develop 3D and 2D games, as well as industrial applications.

Key features of C++ and C#:

• They’re both object oriented, supporting data abstraction using classes, objects and inheritance.
• C++ is a compiled language, meaning that it’s converted directly into machine code that can be run on a computer. C# is a managed language and runs on top of a virtual machine, which makes it portable across different platforms.
• C++ offers more control over memory management than C#, but this also means there’s more room for error.

What makes C++ and C# good for AI:

• AI is a common feature in many games, and both languages are utilized extensively in game development.
• Because they are compiled languages and can provide low-level control when needed, they are both high-performance.
• C# has a wide range of available libraries and tooling support from Visual Studio.

Example Projects:

• Project: Game AI in Unreal Engine (C++)
  • Description: The Unreal Engine, widely used for game development, leverages C++ for its AI capabilities, including pathfinding and decision-making in complex environments.
• Project: Microsoft Cognitive Toolkit (C#)
  • Description: A deep learning framework used to train algorithms to think like the human brain, applied in areas such as speech recognition and search relevance.

Java

Java is a versatile and powerful programming language that enables developers to create robust, high-performance applications.

Key features of Java:

• It’s object-oriented (supporting encapsulation, inheritance, and polymorphism).
• It’s platform-independent (bytecode can run on any platform that has a Java Virtual Machine).
• It’s statically typed (ensures type safety at compile time).
• It has a large standard library.
• It supports exception handling (which helps to manage errors and exceptional conditions gracefully) and multi-threading (which enables concurrent execution of multiple parts of a program).

What makes Java good for AI:

• Because of its speed, dependability, and excellent tool support, it is simple to create sophisticated AI systems rapidly and effectively.
• It has been put through its paces in numerous mission-critical applications.
• It can be used for both desktop and mobile apps (using the Android Studio).

Example Projects:

• Project: Deeplearning4j
  • Description: An open-source, distributed deep-learning library for Java and Scala, used in industry for fraud detection, text mining, and image recognition.

JavaScript: A Dynamic Force in AI Development

Despite its roots in web development, JavaScript has emerged as a versatile player in the AI arena, thanks to an active ecosystem and powerful frameworks like TensorFlow.js. As the only programming language native to web browsers, JavaScript enables AI applications to run directly in the user interface, facilitating real-time user interactions and data processing without the need for server-side computation.

Key Features:

• Versatility and Accessibility: With Node.js, JavaScript extends its reach from client-side to server-side, allowing for a unified language across the full stack of development.
• Frameworks and Libraries: TensorFlow.js, Brain.js, and Synaptic provide robust tools for machine learning, neural networks, and deep learning, making advanced AI technologies accessible to web developers.

What Makes JavaScript Good for AI:

• Real-Time Data Processing: Ideal for applications requiring immediate data analysis and feedback, such as interactive web experiences and live data visualization.
• Wide Adoption and Community Support: A vast and active developer community drives innovation and provides extensive resources for learning and troubleshooting

Example Projects:

• Project: Brain.js
  • Description: A JavaScript library for neural networks that enables AI development directly in the browser or on Node.js, making machine learning accessible to web developers.

Scala: Bridging Functional and Object-Oriented Programming for AI

Scala is a programming language that blends object-oriented and functional programming, providing a special set of tools for developing artificial intelligence. Scala is an ideal choice for developing scalable, reliable artificial intelligence systems due to its support for concurrent programming and its ability to manage complicated data types. Because the language is compatible with Java, it can take advantage of the extensive Java library ecosystem, which includes machine learning and artificial intelligence (AI) libraries like Deeplearning4j.

Key Features:

• Hybrid Paradigm: Scala’s integration of functional and object-oriented programming paradigms allows developers to choose the best approach for their task.
• Immutability: Scala encourages the use of immutable objects, which can simplify development and reduce errors in concurrent applications.
• Type Inference: Scala’s sophisticated type inference system reduces verbosity, making code cleaner and more readable.

What Makes Scala Good for AI:

• Scalability: Its name derived from “scalable language”, Scala is designed to grow with the demands of its users, from small scripts to large systems.
• Rich Ecosystem: Scala benefits from its compatibility with Java, providing access to a plethora of libraries and frameworks essential for AI and machine learning projects.

Example Projects:

• Project: Apache Spark
  • Description: A unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning, and graph processing. Scala’s concise syntax is ideal for data science workflows.

Honorary Mentions: Lisp and Prolog

Lisp and Prolog are not as widely used as the languages mentioned above, but they’re still worth mentioning.

Lisp (also introduced by John McCarthy in 1958) is a family of programming languages with a long history and a distinctive, parenthesis-based syntax. It was originally created for artificial intelligence research. Today, Lisp is used in a variety of applications, including scripting and system administration.

Prolog (general core, modules) is a logic programming language from the early ’70s that’s particularly well suited for artificial intelligence applications. Its declarative nature makes it easy to express complex relationships between data. Prolog is also used for natural language processing and knowledge representation.

Comparison of AI Programing Languages

Programming Languages to Avoid in AI Development

These are languages that, while they may have their place, don’t really have much to offer the world of AI.

• Dating back to the 1950s and 1960s, COBOL is an antiquated language. It is not really suited for anything else; it was created for corporate purposes.
• Dating back to 1957, FORTRAN is also considered an old language. Like COBOL, its main use is in science and engineering, and it was not truly meant for other purposes.
• Originating in the 1970s, Pascal is a language that is little used outside of academic settings.
• Python and other languages have mostly replaced Basic, which was created with novices in mind.

That said, you might think that a completely left-of-field language such as JavaScript — which is primarily used for web development — also wouldn’t have much to offer to AI, right? And yet, there are quite interesting active projects — such as TensorFlow.js (backed by Google) — for developing machine-learning models in JavaScript, which can be used directly in the browser or in Node.js. So, who knew?!

Conclusion

AI is real and has already occurred. Along with altering how we live and connect with the outside world and one another, technology is also opening up new business and personal opportunities.

There is no one-size-fits-all fix or magic bullet in IT, as there never is. You will need to experiment when selecting a programming language for AI, and you may need to switch based on the work at hand, the platform that you and your team are utilizing, as well as your own tastes and level of experience.

Nevertheless, because of its user-friendliness, abundance of libraries, and vibrant community, Python is often regarded as one of the greatest AI programming languages. R is a fantastic option for developing AI as well, especially if you want to create statistical models. Due to its efficiency and speed, Julia is becoming more and more popular as a more recent language. Additionally, C++ or C# can be your best option if you are trying to design low-level systems or apps with strict performance requirements.