Artificial Intelligence (AI) is one of the most transformative and impactful domains in tech. AI has permeated various aspects of everyday lives, including popular virtual personal assistants like Siri and Alexa. Behind the scenes of these groundbreaking applications are powerful computer science languages that enable the development and execution of AI algorithms.
This article will explore the most useful computer science languages for AI development and cover how these languages are the building blocks for creating intelligent systems and machine-learning models.
What is AI development?
Before going deeper into the specific programming languages used in AI development, it is important to understand what AI development entails. AI is all about making computers smart enough to perform tasks that typically require human intelligence. This includes recognizing images, understanding spoken language, and making predictions. AI development thus involves creating software, algorithms, and systems to learn from data, adapt to new information, and make decisions.
Who are AI developers?
AI developers are professionals who specialize in creating and implementing AI systems. They possess a unique skillset that combines expertise in computer science, data science, and domain-specific knowledge, allowing them to develop AI solutions tailored to specific applications.
Here are several crucial concepts that AI developers should acquaint themselves with.
Algorithms are the heart of AI. Therefore, AI developers must have a deep understanding of algorithms, which are instructions for solving specific tasks or problems. Machine learning algorithms, in particular, play a crucial role in training AI models to recognize patterns and make predictions based on data.
Data science is a fundamental component of AI development. AI developers must be proficient in data collection, preprocessing, analysis, and visualization. They must understand how to extract meaningful insights from large datasets and use these insights to train machine-learning models.
Deep learning is, in layman’s terms, teaching a computer to think like a brain. It is a special type of AI inspired by how the human brain works. Deep learning algorithms are like neural networks, which are made up of artificial ‘neurons’ that process information. Developers use deep learning to tackle complex tasks, such as understanding natural language (like Siri or Google Assistant) or recognizing objects in images.
Six top programming languages for AI
The choice of programming language plays a pivotal role in shaping the development landscape. Each language brings its own advantages and capabilities, making it crucial for developers to stay informed about their options.
Those who are eager to explore these languages further and gain expertise in AI development should consider looking into Baylor University’s comprehensive computer science program. The program offers comprehensive coursework that familiarizes students with computer science languages, ensuring they have a solid foundation in AI development. The online master’s in computer science combines academic rigor with the benefits of virtual learning. Successful applicants hold GPAs of at least 3.0, three recommendation letters, and are familiar with at least one of the languages mentioned below.
Python is one of the most prominent and widely adopted programming languages in AI. Its simplicity, readability, and extensive libraries make it a favorite among AI developers.
With its versatility and community support, Python has become the go-to language for prototyping AI models, building Natural Language Processing (NLP) applications, and developing computer vision systems. Its ease of use and the availability of numerous resources make Python an ideal starting point for anyone looking to embark on an AI development journey.
List Processing (LISP) is one of the oldest programming languages significantly contributing to AI. Developed in the late 1950s, LISP was specifically designed for symbolic processing and manipulation. It excels in tasks that involve symbolic reasoning, knowledge representation, and rule-based AI systems.
Although LISP is not as commonly used in contemporary AI development as Python or other modern languages, it remains influential in certain AI research areas. Understanding LISP can provide valuable insights into the historical foundations of AI and its symbolic reasoning roots.
At its core, Programming in Logic (Prolog) is based on formal logic and rules, making it particularly well-suited for applications involving rule-based and expert systems. Prolog programs are built around a knowledge base of facts and rules, and queries can be posed to this knowledge base to derive logical conclusions. This makes Prolog essential for tasks like NLP, rule-based expert systems, and semantic web applications.
One of the standout features of Prolog is its built-in backtracking mechanism, which allows it to explore multiple possible solutions to a problem. This feature is valuable for problems with multiple valid solutions or where the optimal solution is not immediately apparent.
Java is a versatile and widely used programming language that has made significant inroads into AI. Java programs can run on various operating systems and hardware platforms, making it an excellent choice for AI applications that must be deployed across diverse environments. This portability is particularly valuable for AI projects that require integration with existing systems or applications that need to run on various devices.
R is a specialized programming language and environment primarily designed for statistical computing and data analysis. R’s strength lies in its extensive collection of packages and libraries tailored for statistical analysis and data visualization. This makes it a powerful tool for AI developers working on projects that involve data preprocessing.
C++ programs can execute at near-native speeds, making it a preferred choice for AI developers working on tasks where performance is critical. This includes applications such as real-time computer vision, robotics control, and high-frequency trading algorithms. C++’s ability to interact with hardware and system-level libraries also makes it suitable for developing AI software that interfaces with sensors, actuators, and other external devices.
Despite its strengths, C++ can be more complex than languages like Python. Development may require more effort and attention on memory management and low-level details.
Navigating AI programming
AI is a dynamic and multifaceted domain. As the field continues to advance, developers armed with suitable languages and expertise will be at the forefront of shaping the future of technology and innovation.
The choice of programming language is a critical decision that shapes the capabilities and potential of a developer’s AI endeavors. Ultimately, the ideal programming language for an AI project depends on the specific requirements, expertise, and project goals.