As the demand for AI among companies and developers is continuously increasing, several programming languages have emerged as popular choices for the Artificial Intelligence Development field. In that case, the choice of the best Programming language for the Artificial Intelligence field has become a very important decision.
If you are working on a new artificial intelligence project and still have not decided which language you should use to program it, then you are at the right place. Artificial Intelligence is a branch of engineering that basically aims to create computers that can think intelligently, in a similar manner to the intelligent humans think.
Understanding the concept of AI programming languages
Artificial intelligence programming involves using one or more programming languages, AI frameworks, and code editors to develop AI applications that perform tasks with little or no human intervention.
AI coders design and develop machine learning algorithms. These algorithms are then trained on large datasets, allowing them to analyze patterns, learn, solve problems, and even make some decisions.
AI programming is a broad term and involves the following components:
- Machine learning. Machine learning involves creating and training models to learn, predict, and make decisions. Among other use cases, machine learning is useful in creating recommendation engines that can analyze customer data and recommend items that match their tastes and preferences.
- Deep learning. AI programming also involves developing neural networks for modeling complex relationships and achieving advanced learning capabilities.
- Natural language processing. With natural language processing, AI programmers create and train computer models capable of processing and responding to human language (both written and spoken). Businesses use natural language processing to create customer service chatbots, digital assistants, and voice-operated GPS systems.
- Computer vision. This field involves creating computer systems that can extract meaningful information from videos and images. Computer vision has many use cases, including object detection, image classification, and object tracking. In the automotive industry, computer vision enhances safety by enabling cars to detect objects, thus assisting drivers with navigation.
- Data processing. The accuracy of AI applications depends on the quality of data used for training. Apart from laying the foundation, AI programmers must process and clean data to ensure it’s suitable for training.
- Robotics. This branch of engineering involves designing, developing, and operating robots. AI programmers assist in creating and training models, allowing bots to get smarter and perform tasks more accurately.
- Deployment. Once they’ve finished creating models and applications, AI programmers must deploy the programs on cloud, hybrid, or on-premise environments. AI professionals are also involved in debugging and optimizing the performance of AI applications.
Roadmap to learning AI programming languages
Artificial intelligence can be a complex field, requiring developers to have solid technical skills. Knowing how to code, clean, and prepare data for modeling is essential. Understanding statistical concepts (e.g., linear algebra, calculus, vectors, and matrices) can also help an AI programmer be successful.
Apart from understanding AI fundamentals, programmers must have excellent critical thinking, problem-solving, and communication skills. Being a team player is also essential; while often siloed, programmers rarely work in a complete void and collaboration between team members and departments is key to meeting objectives.
The five most important programming languages in AI are Python, C++, R, MATLAB, and Java. For most people, the first programming language to learn is Python. It’s easy to learn, extremely adaptable, and has numerous libraries specifically for machine learning. For those reasons and more, it is the de facto coding language in AI. What to learn next depends on your career goals.
5 AI programming languages you should know
Python
The best all-around programming language for AI, Python is a popular, general-purpose programming language that is relatively easy to learn. Its simplicity lends itself to AI development, and the AI community has adopted Python as its de facto language.
Python is popular for several important reasons, which include:
- It’s easy: Compared to other coding languages, Python has a simple syntax (the words, symbols, and expressions you’ll type to create programs). This means you’ll have more time to devote to the stuff that matters: Looking at data and tuning your models.
- It’s open source: Anybody can adapt, update, or add to the code that underlies Python. As a result, many members of the Python community have built frameworks and libraries that make it adaptable to nearly any machine learning or data science task.
- It’s versatile: Your operating system supports Python, whether you use iOS, Windows, or Linux. What’s more, you don’t need to modify a Python program much to get it to run across platforms.
If your job involves building machine learning models and working with lots of data, Python is for you. However, you may want to specialize in a second language if you work in data analysis, AI infrastructure, or plan on doing more intensive programming outside of your core AI work.
Also, Python has limitations when it comes to performing complex mathematical and statistical functions. It also runs slowly compared to languages like C++ and Java.
Java
A fast, versatile programming language that is useful for building scalable AI infrastructure, Java is similar to Python in many ways. It is popular, open-source, and has many frameworks and toolkits specifically for machine learning and data science. Java is also much older, and therefore it has legacy buy-in from many organizations.
It is also more technically complex than Python. This means it is more difficult to learn than Python and R, but it can execute programs much more efficiently.
Everything Python can do, Java can do just as well, and in some cases, maybe better. For instance, it has frameworks for data science, classification, deep learning, and more. Java has more rules than Python, which makes it more difficult to break or misuse the code. It is useful for building full-stack, back-end, large-scale infrastructure for deploying machine learning models.
If not for its complexity, Java might be the dominant AI coding language. However, you probably only need to learn it if you are targeting a role-building AI infrastructure or deploying machine learning products. For beginners, Python is a safer bet.
Java’s downside is its steeper learning curve when compared to Python (though not as steep as C++). Writing programs in Java also takes more time than Python, and those programs often require more debugging due to their complexity. These are speed bumps that slow down the rapid prototyping of machine-learning models.
Finally, Java’s community isn’t as active as Python’s when it comes to developing AI-focused tools, and as a result, Java is useful for a narrower range of machine learning and data science tasks.
C++
C++ is one of the most popular languages for general-purpose applications and is the best programming language for AI infrastructure. It is the backbone of operating systems like Windows, iOS, and Linux; apps like Spotify and Photoshop; and sites like YouTube. It also powers video games, banking systems, and more, and is an essential language for anyone working in self-driving cars or robotics.
C++ is a compiled language. It doesn’t require an interpreter program, which adds processing overhead. In practical terms, programs written in C++ are fast and efficient. It also isn’t well suited for most data scientists or machine learning engineers, but it is essential for some disciplines within AI.
- If your role involves building or maintaining AI infrastructure — the core software libraries that others use to deploy models or analyze data — then you should absolutely learn C++.
- Most teams working on robotics and self-driving cars use C++ for their production code due to its speed and efficiency.
- Most jobs in the chip and hardware industry call for C++.
- It’s important to learn C++ if you want to get involved in the open-source community. Many of the most popular Python frameworks and toolkits are programmed in C++.
C++ programs may be efficient to run, but writing them is complex — with a capital C and two pluses. Writing a program in C++ takes time, debugging it often takes even more time, and rewriting it every time you adjust your hyperparameters takes more time than you think it will. C++ is also notoriously difficult to learn.
MATLAB
MATLAB is more than a programming language, it’s a five-part system that consists of a language, development environment, graphics visualizer, math library, and interface for writing programs in other languages. It focuses on matrix computation; matrices are arrays of numbers, and the ability to compute them well is central to many machine learning and data science applications.
MATLAB is excellent for working with numerical arrays. It is also excellent for many other mathematical operations, and it has built-in features for implementing machine learning models. These features give it a leg up over Python, which requires add-on toolkits and frameworks for both mathematical functions and model implementation. In terms of speed, it is fast and easily outpaces Python in many operations.
Some employers and AI teams prefer MATLAB either because they are legacy users or because their goals require more mathematical oomph. However, the biggest drawback to MATLAB is the cost: You have to pay a fee to access the system and possibly for additional functionality depending on your goal. This barrier to entry has partly contributed to MATLAB’s declining popularity among AI builders; Python, after all, is free and open-source.
Lastly, MATLAB’s syntax is difficult to learn compared to Python’s.
R
R is the best programming language for data analysis, as it was built specifically for statistical analysis. It excels at finding patterns in data and deriving insights from model outputs. For obvious reasons, R also appeals to machine learning engineers and data scientists who use it for statistical analysis, data visualizations, and similar projects.
Like Python, it is open-source, and the community has created a number of frameworks and libraries for AI tasks.
R is a great tool for data analysis, data science, and adjacent professions, but it is often used by academics. You might also be required to learn R if you get a job working in finance, and for teams that use it in their legacy software.
In terms of complexity, beginners will find R more difficult to learn than Python. R has more built-in features for crunching numbers than Python, but it also tends to lag when processing projects that use too much data at once.
Benefits of AI programming
Starting with improved efficiency in risk and fraud mitigation, here are the benefits of AI programming to organizations:
- Enhanced efficiency. AI programming allows organizations to automate repetitive and monotonous processes, freeing employees from low-value tasks and allowing them to focus on other core activities.
- Data analysis and insights. AI programming allows organizations to create machine learning models that can process and analyze large datasets. As a result, organizations can access valuable insights that inform decision-making and promote strategic initiatives.
- Personalization and customer experience. Understanding customer needs and wants allows organizations to deliver better products and services. By embracing AI coding, organizations can analyze customer data and identify unique behaviors, thus delivering more personalized experiences. Businesses can also boost customer satisfaction and engagement using chatbots, virtual assistants, and data-driven recommendations.
- Risk mitigation and fraud detection. AI programming also facilitates algorithms and models capable of detecting anomalies and patterns indicative of fraud. Organizations can use this information to mitigate threats and safeguard sensitive information, including financial transactions.
Programming languages not suited for AI development
Php, Ruby, C, Perl, and Fortran are some examples of languages that wouldn’t be ideal for AI programming.
These languages have many reasons why you may want to consider another. A language like Fortran simply doesn’t have many AI packages, while C requires more lines of code to develop a similar project. A scripting or low-level language wouldn’t be well-suited for AI development.
Many of these languages lack ease-of-life features, garbage collection, or are slower at handling large amounts of data. While these languages can still develop AI, they trail far behind others in efficiency or usability.
How to learn AI programming languages
While artificial intelligence was once a subject only the most advanced computer science researchers were familiar with, AI concepts are becoming more commonplace throughout the programming field.
There are many ways to learn artificial intelligence concepts, including traditional college degree programs, independent study, and coding boot camps. Keep in mind that before you dive into AI-related topics, it’s good to have a foundational understanding of programming knowledge, as artificial intelligence builds on existing fundamentals.
That said, coding bootcamps are a great choice for those who want to learn web programming quickly through hands-on experience.
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