Suppose you are trying to collect data for a phone application that monitors heart rate. You are able to come up with some handwritten rules or formulae through an empirical study. But the observation requires an impossible duration and then you begin to notice the problem is too complex, as the heart rate data keeps changing. The handwritten code is suboptimal. How can you cater to such a complex problem? This dilemma can be solved by AI programming.
But here comes another problem! With a plethora of languages that are used to give instructions to machines, how do you pick the best programming language for AI? There are a bunch of options to consider before we answer this query.
In this article, we will explore six popular AI programming languages that can help you kickstart your AI journey, but first, let’s understand the concept behind AI programming.
AI comprises 4 categories; machine learning (supervised and unsupervised), deep learning, natural language processing, and robotics. Let’s see how these subsets can be achieved with the languages we discuss in the upcoming sections of the article.
What Is AI Programming?
The incorporation of the human brain blueprint into writing and designing computer programs to accomplish tasks is known as AI programming. But why was there a need to integrate artificial intelligence in an already successful field?
A comparative analysis between regular programming and AI programming highlights some deficiencies in traditional programming. The fact that programs crash or come to a halt due to bugs, invalid instructions, or incorrect address values, is one factor that shows traditional programming requires the incorporation of AI.
AI programming does not require all possible scenarios in a problem to be defined. It is proficient in learning from historical data, identifying patterns, devising solutions to problems, and then using the correct formula to solve similar problems in the future; this way, there are fewer crash reports. The algorithm can learn from non-linear or linear data to form complex relationships, update as more information becomes available, and give scalable solutions derived from large data sets.
Simply put, conventional programming was the saturation of human capabilities and excellence, and AI programming is the tool that was needed to break the glass ceiling.
Here are three features of AI programming that can empower programmers to reach new heights.
1. Learning Processes
An algorithm, which is a problem-solving formula, is used to acquire data and convert it into actionable information. This particular form of information enables decision making and problem-solving. That’s right! AI programming allows computers to solve their own problems by using accurate, credible, and relevant information.
2. Reasoning Process
When you are programming, you find yourself analysing your every decision. Similarly, AI programing coined in the framework of the human mind does the same. According to the outcome it must reach, it selects the appropriate algorithm. It is as simple as using the correct formula in Mathematics to reach the desired answer.
3. Self-Correcting Process
Just like humans correct their mistakes, smart machines are well-versed in self-evaluation and correction. Self-programming AI can figure out that it has made a mistake when an unsought outcome is obtained. It will learn from the mistake instead of coming to an abrupt halt. This allows it to consistently improve.
6 Languages You Can Use for AI Programming
Now that we have discussed some basic concepts, let’s talk about the language options you have for programming AI if you are a novice at this niche.
Choosing a language is a critical decision to make. Why? Because AI has an abundance of benefits but it is not an easy technology to use and develop. Secondly, the success of your entire project is dependent on the choice you make. Too much pressure? Read on to put an end to your ambiguity.
Python, an interpreted object-oriented language, is one of the most sought after languages when it comes to programming AI.
The vast resources, in the form of libraries, give programmers the advantage of pre-written codes, configuration data, templates, help data and superior visualisation tools. Some specific libraries for Python can be used for specific subsets of AI.
PyBrain is a library for machine learning that not only offers powerful algorithms but also a predefined environment to test them in, in terms of compatibility and scalability. You can also compare algorithms against each other to select the best one.
Nerolab is another library where coders can find neural networks and frameworks to achieve deep learning and train algorithms.
For natural language processing, python AI programming provides a framework called Gensim, to extract semantic topics from documents and process raw text. Similarly, libraries and frameworks can be found for robotics as well, an area that is in its early stages.
Python is an easy language to pick up, even for beginners, due to its resemblance to English, Thus, one can learn AI programming rather than only focusing on conceptualising the language. Its syntax is also simplistic, enabling a programmer to work with complex systems. The modular architecture creates further simplifications by dividing the functionality of the program into modules that contain resources to execute singular aspects of a function, one at a time.
Python offers great flexibility with not just programming styles but also with its easy integration with various other programming languages such as C++. This versatile language is operational on various platforms too so you can start your python AI programming journey on Windows, Linux, Unix or Macintosh amongst various other platforms.
Python plays a key role in the growing global community of data scientists and programmers who contribute to the consistent development of the language. This also opens up avenues for help and guidance for novices. For AI programming, Python is also great for teamwork. Due to its readability, colleagues can easily understand each other’s work.
When it comes to analyzing and manipulating data for statistical analysis, R is a clear winner. It is an interpreted language that has procedural meaning. The code organisation allows code reuse through modularity, giving functions to applications by working together.
As machine learning involves large amounts of data, R is competent in crunching data and reorganising it. It can also break up large datasets into simple test sets as an added ease to the programmer. It is a known statistical language that specialises in data analysis and visualisation. This specialisation has made it a go-to language for most statistical purposes in AI programming.
R is also robust in mathematical capabilities, to the extent that programmers have commended it to be on par with MATLAB. It can utilise vectors, linear algebra, and matrices to process data rapidly, which is a much-needed attribute for machine learning.
R is an open-source language, which means novices can benefit from pre-written codes that have been contributed by the R developer community. R offers various packages that can be utilised for the supervised machine learning subset. Let’s see what packages this language has to offer.
One package is CARET, which stands for classification and regression training. When a programmer is building AI models, this package facilitates training and prediction. This is how software built with AI programming can formulate smart predictions for users at the frontend.
With Kernlab, one is equipped to execute projects based on kernel-based learning algorithms. These projects identify the patterns and relations in the data. This is how software built with a kernel method can pin the relation between client and server and identify patterns such as listening habits in music applications.
Furthermore, R can create a testing interface for a programmer to test data analysis that creates synergy between humans and machines. R has complex ways of achieving outputs and can be a little difficult to get a grasp of at first. However, once you are proficient enough, you will find it is easy to use it for programming AI applications.
C++ is a general-purpose compiled language that offers tons of advantages. This statically typed language declares and determines its variables at compile-time, and shows errors and inconsistencies. This feature can help in algorithm model development because it makes it easier to work with relational databases.
Compiled languages enjoy some high-quality optimisation features. The fact that this language is directly converted into machine code that is easily executed can save you precious time.
However, one thing C++ is really known for is game AI programming. Enemies, or the opponents in the game, are the most paramount elements, followed by game graphics, interactive elements, and overall gaming experience. C++ can be used to code game opponents that get stronger with every level.
In order to do this, data is collected from the user to understand their strategies and then this is coded into the game software to teach the opponent how to combat better. The algorithms that C++ derives are robust in handling varying scenarios.
C++ is also compatible with other languages, such as Python or R, and is often used as a background language with these two. It has its fair share of libraries and resources such as C++ Boost for mathematical operations. However, AI programming for beginners would require more packages that other programming languages for AI offer already.
MATLAB is a proprietary programming language, meaning it is domain-specific, rendering it to extract optimal solutions to a specific class of problems. Problem-solving is one of the key attributes of AI programming. With its arsenal of mathematical resources, MATLAB is fully equipped to qualify as one of the best programming languages for AI.
MATLAB is an AI programming language that can be used to teach students mathematical fundamentals such as calculus and statistics. These numerical coding languages come with resourceful packages that are known to surpass the quality of other AI programming language’s resources.
This AI programming language has a toolbox that enables symbolic computing which uses algebraic computation to develop algorithms for the manipulation of mathematical expressions. It is an interpreted language so variables do not have to be defined unless the programmer wants to treat them as symbolic objects.
The square identity matrix of n size can be generated for matrices of any size with numerous zeros. It has simple functions to generate mathematical formulas. MATLAB uses array programming, which means solutions are obtained by giving the application of operations an entire set of values at once, using this linear data structure. This makes it possible to solve complex computing problems involving vectors and matrices as dimensioning is not required. This allows the programmer to develop neural networks that are capable of deep learning. Programmers train the neural network via representational learning through image or speech data inputs.
MATLAB is able to handle a heavy load of input due to its sophisticated mathematical capabilities. It has libraries that offer a large computational algorithms collection, ranging from basic functions like sum, sine, cosine, and complex mathematics, to more superior functions like matrix inverse and matrix eigenvalues. MATLAB libraries can be integrated with other languages as well, which can be a benefit for novices.
Prolog is a logic computing language that is well-known for incorporating artificial intelligence technology. Logic computing is one of the paradigms of AI programming that expresses facts and rules about problem domains. This gives it complex problem-solving capabilities similar to MATLAB, to some extent.
Another paradigm of Prolog is declarative programming that builds the structures of the program by expressing the logic of computing without the description of control flow (the order in which individual statements or functions are called or executed).
These two paradigms empower Prolog to perform string searching algorithms to locate where strings (patterns) can be found within a larger dataset. Secondly, tree-based data structures, which are non-linear structures that identify a hierarchy, can also be identified.
The hierarchy system leads to the development of neural networks. Several neural nets are piled on top of each other to create a hierarchy for more accurate classifications and predictions.
With these capabilities, Prolog can be used to develop natural language processing as it can make sense of human languages by understanding the connection between words in a sentence in multiple human languages.
Prolog also excels in its capabilities of spatial knowledge due to its quality of understanding relationships between objects. So, for instance, it can make sense of Object A being behind Object B that is parallel to Object C. Such technology is also used when developing intelligent commuting applications that can give real-time updates of traffic.
Java is a cross-platform language that you can utilise to learn AI programming. This paradigm gives you the freedom to work on multiple platforms by transferring your work. However, you might be required to compile your code according to each platform.
Another feature that can help budding AI programmers is the garbage collector. This is a memory management system that enables Java to manage objects for you. With a decluttered system, the programmer can work on other key aspects such as the visualisation of the project by making use of Java’s widgets.
This language is able to handle large scale projects that can employ neural networks and machine learning. It has specific libraries for neural languages, such as Neuroph, which is an open-source framework to create and use neural networks.
The goldmine for this language is the Java Virtual Machine (JVM) that can optimize computational performance and thus rank it above many languages. It has a low demand for resources because of its inbuilt memory management system. This way, it can handle large amounts of data and compute it faster than many languages.
Java is considered an easy language and many enterprises use it due to its optimisation abilities which make it easy to handle artificial intelligence projects.
Factors That Can Affect Your Language Selection
Now that we are at the end of the article, as promised, let’s discuss what the best AI programming language is.
At this point, we would like to point out that the whole concept of “best AI programming language” is quite unrealistic. Language selection is a very subjective decision. There are three factors that need to be taken into consideration before a language can be selected.
By asking yourself the following questions, you can discover what the best AI language is for you.
1. What is my background?
Analyse your background and categorise it into one of the following realms: mathematics, science, engineering.
If you have a strong math background, then chances are, you might have been taught vectorial concepts via languages such as MATLAB. If science is a disciple you identify your background with, then perhaps languages such as R would be more appropriate for you. A person with an engineering background might resonate better with Java.
There is a strong overlap of concepts in programming languages so you can use languages from other disciplines if you understand its prerequisite concepts. Otherwise, you can also focus on utilising the language you are already well-versed in instead of learning a new language.
2. What is my proficiency level?
This is the level of expertise you are on. As a beginner of AI, it is strongly advised that you start with an easy-to-grasp language such as Python that comes with a large library resource.
If you are an intermediate-level developer, then you can handle more complex languages such as R or Prolog, and then comes C++. Once you reach a superior level of expertise, the opportunities are endless, as all language entry barriers will be lifted.
You may even make use of multiple languages for a single project.
3. What is the nature of my project?
This question seeks to understand the type of project you are executing. What type of software are you designing? Are you making an insight application such as a diagnostic system for healthcare? Perhaps Python can help in the creation of superior algorithms.
Are you developing games? C++ is the most well-known language for the game development industry. Its graphic development and machine learning abilities will help you in producing classic end results.
Do you require spatial knowledge? Perhaps you are working on an application that can be used for architecture in designing floor plans. You can then invest in learning Prolog.
The nature of projects is endless and once you know what it is you want to achieve, you can simply match your demands to the features of any language. This will help in finding you a compatible match.
Once you ask yourself these questions, you will be well-equipped to identify what the best AI programming language is for you. And once you make up your mind, there are plenty of AI programming tutorials along with other resources you can find online to begin your learning process.
So what programming language is used for AI? It should be the one that fulfils your requirements!
AI programming opens up a whole world of new possibilities for programmers, app owners and end-users alike. Using artificial intelligence technology for programming has led to various successful products and services in all walks of life.
The languages available to dive into the world of AI programming are many and varied. This field is open to novices who can take advantage of easy languages such as Python to learn AI programming in no time. Though artificial intelligence technology is not easy to employ, it is, undoubtedly, the gateway to acceleration and success.