Artificial intelligence, machine learning (ML), and deep learning (DL), are currently in the focus of many enterprises to simplify various business processes. Moreover, with cloud-based AI solutions becoming an easy way for companies to deploy AI to the average user, they have also opened up new possibilities in the age of mobile computing.
Some of the biggest companies in the world, including Google, Facebook, Amazon, and Microsoft, have now set their foot in the AI market. These companies not only deploy AI solutions for their products but also provide tools and frameworks designed to program AI. Google, in particular, has open-sourced many of its prominent advancements in the AI space, showing the rising adoption of AI by software engineers.
As ML algorithms are built to improve upon past iterations, machine learning is the focus of AI today. However, the tools required to develop these algorithms are not commonly known. In this article, we’ll take a look at the various AI programming languages and their strengths and weaknesses.
Table of Contents
How AI Programming Works
Developing artificial intelligence is not as obscure as it sounds. At its base, AI is a connected chain of algorithms designed to perform a specific task. An algorithm is simply a way for users to tell computers how to conduct any given task.
For example, a short algorithm can be written to determine the largest of three numbers. In this algorithm, the computer is asked to compare all three numbers with each other and output the number, which is greater than the other two. AI algorithms are a more specialized type of algorithms.
When it comes to AI, more of these algorithms are strung together to execute more complex processes. However, some AI algorithms allow computers to learn from themselves and improve upon their past iterations. This is more commonly referred to as machine learning.
Creating a Workflow
AI programming, therefore, functions similarly to programming non-AI algorithms but differs primarily in its complexity and learning aspect. There is no need to explicitly state the input and output parameters to an AI algorithm. However, a traditional algorithm requires the user to provide all the information required to solve the problem.
As AI is a combination of many algorithms, developers need to focus on creating the flow of information and knowledge from one to another. Moreover, these algorithms need to perform more complex tasks. So, the developer also has to orchestrate the flow of the algorithms. This is known as creating the workflow for the program.
Training the AI Model
The algorithmic workflow is designed to perceive and process information like a human mind. This workflow, which is the initial state of the AI program, is then applied to a dataset pertinent to the problem trying to be solved.
Upon ingesting the dataset, the algorithm or AI model recognizes data patterns and tries to learn from it. When the second iteration of the algorithm is run on the dataset, it brings forward the knowledge it had in the first iteration, slowly improving at solving the problem. This process is then repeated until the model gains a certain degree of accuracy. This process is known as training the model.
To ensure that the model tries to find the most optimal solution, usually a concept known as ‘cost function’ is implemented. A cost function determines how effective an algorithm is at a given task by gauging the efficiency of the solution.
Once the model has completed training and has learned how to solve the problem on its own, it can then be deployed for use. This approach results in algorithms that have a high degree of accuracy and efficiency over human labor.
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The languages used to create these complex algorithms are similar to the ones used for creating other computer programs. However, a concept known as ‘frameworks’ has recently come to the forefront. Frameworks are built upon existing programming languages but provide unique functionality that allows the creation of AI algorithms.
Top AI Programming Languages
Various general-purpose and specialized languages can be used to develop AI algorithms today; the most popular one being Python, a general-purpose programming language. Python is used by AI enthusiasts, mainly due to its powerful yet simple syntax formats, which makes the language equally accessible to beginners and experienced individuals.
R, a statistical computing programming language, is also popular for developing AI algorithms. R is a good fit for modern AI as it can handle large amounts of data easily. Moreover, statistics is an important part of designing an AI model, making it the second, most widely-used programming language for AI.
Apart from these, programming mainstays, such as C++ and Java, are also used for AI programming. These languages are used when there is a specific need that can only be executed by using these languages. Another popular choice for AI programming is Scala, a general-purpose programming language with an object-oriented approach.
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1. Python
Python is one of the most popular go-to choices for AI programmers. Python has a variety of features that make it well-suited for AI programming. Firstly, the language is easy to learn and read. The creator of Python designed the language so that it can be easily read; a far cry from the jumbled lines of code visible in previous environments. The names of the functions and the code itself are written in plain English, thus lowering the learning curve for new users.
Python is powerful and versatile. The language can be used for a variety of applications from building web pages to creating AI. It is compatible with most platforms and supports many programming methods. Owing to these features, Python allows AI developers to focus on creating the algorithm workflow rather than writing the code or debugging it.
One of the biggest selling points of Python for AI above other programming languages is the vast breadth of support of AI libraries. Libraries are sets of functions that make it easier to implement certain concepts. These libraries can add specialized AI functionality to general-purpose Python languages. Let’s delve deeper into the most popular AI libraries for Python.
TensorFlow
TensorFlow is a Python library developed and open-sourced by Google to be used for AI programs. The library is used to write AI programs that utilize machine learning. It also has support for implementing neural networks and makes up most of Google’s production AI services. TensorFlow is commonly used by many AI practitioners owing to its capability to parallelize workloads and scale easily. It is heavily backed by Google with a healthy developer community.
SciKit-Learn
SciKit-Learn is another Python library which handles an important part of an AI workflow – the data. SciKit-Learn provides functions for classification, model selection, and data pre-processing. It is commonly used for data mining and analysis applications. This open-source library is used to wrangle data and manage it in a way that is easy for the algorithm to ingest.
Pybrain
Pybrain, which stands for Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library, is a modular library created for AI beginners. It contains algorithms for neural networks and reinforcement learning that can be simply added and used in conjunction with Python. It is also commonly used to train and deploy common AI algorithms quickly.
2. R
R is a programming language that is commonly used in data science, a profession that makes heavy use of AI. Data science involves the practice of processing and analyzing data to find patterns with the help of AI, using statistics and mathematics. The software features an extensive selection of libraries for data science tasks, such as data transformation, preprocessing, and analysis.
Most benefits of R lies in its statistical data processing capabilities. They include linear and nonlinear modeling, time-series analysis, clustering, and visualization. It can also effectively store and access data, thus making it a great choice for building machine learning algorithms.
R is not recommended to beginners in programming or AI, as it has a steep learning curve. However, in enterprise settings where large amounts of data are handled, the payoff to learning R is sizeable.
3. Java
Java is another commonly used languages for AI programming. First released over 20 years ago in 1995, Java is used by AI programmers due to its ‘write once, run anywhere’ approach to programs. Java is designed to have the least number of dependencies, which means that the requirements to run it on any platform is low.
Java stands apart from other programming languages due to its unique virtual machine, known as Java Virtual Machine (JVM). JVM acts as an intermediary between the code written in Java and the machine on which it runs. This is one of the reasons why Java is portable and easy to run.
Many popular libraries are also found in Java, such as Deeplearning4j, an open-source library focusing on deep learning applications, and Neuroph, a library for building neural networks using Java. The programming language also has powerful capabilities for data processing, thus allowing for complete management of the data pipeline in a corporate environment.
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4. Scala
Scala or ‘scalable language’ is a general-purpose language that was created to improve upon Java, while still maintaining its most powerful feature; the JVM. Thus, Scala is completely interoperable with Java. The code written in Scala is completely executable in Java and vice-versa. Scala offers interoperability with the entire JVM ecosystem, and even features the same syntax as Java.
However, Scala has numerous improvements over Java; the most significant one being the ability to concurrently conduct many operations at the same time. This increases the capability to parallelize compute-heavy operations, allowing the overall run time of the program to reduce. Scala also has access to the entire array of libraries for Java, making it an obvious choice for those already working in Java.
5. C++
C++ is an object-oriented, general-purpose programming language. It has been the mainstay of computer science professionals for over 30 years due to its powerful capabilities and compiled language feature. Owing to its compiled language, any commands written in C++ are directly conveyed to the CPU on a computer, eliminating delays along the way.
Due to this, C++ is generally used in situations where the computing power is low. This includes AI deployed away from server farms and AI deployed on edge devices, such as cars and IoT devices. However, C++ suffers from some shortcomings that prevent its adoption in the AI world.
The code of C++ is not easy to read like Python, and it comes with a smaller selection of libraries for specialized tasks. Moreover, C++ has a high learning curve, with similar benefits as Python. This leaves C++ delegated to tasks that directly interface with the CPU.
Apart from the languages mentioned above, Lisp is also gaining a lot of popularity today. When asked why, Daniel VivonaOpens a new window , CEO of UDX InteractiveOpens a new window , says, “Lisp and its variants are languages that give the programmer a lot of power. Its increasing popularity is a reflection of the maturation of the AI field. For large projects that have research teams available or many experienced AI programmers, Lisp is an excellent language to use.
However, as a game development tool, I think Python is more useful. On game development teams it’s very important to have mobility of your code. It needs to be easily communicated, picked up, and understood by development team members, from experienced to newcomers, in multiple physical locations.
Many studios use game development engines, such as Unreal, Unity, or Lumberyard (or a mix) requiring some form of integration. Lisp doesn’t yet offer (from what we’ve seen) a simple approach to handle these constraints or integrations. While there are some strides in a few variants, it’s still a much more difficult language to use and maintain.
In the future, I see some incredible development with languages like Lisp and Python to grow directly in the game engines.â€
Closing Thoughts for Techies
AI programming is now becoming more accessible than ever due to a large number of libraries and accessible learning material about the subject. For popular languages, such as Python and Java, training material is abundant on the Internet, making it even easier for beginners to get into AI programming.
Many new entrants towards programming AI come from a business or commerce background, which introduces difficulty when trying to grasp concepts of computer science. This makes Python the go-to choice for any AI beginner, as it is easy to read, use, and learn.
Creating AI programs also requires knowledge of human cognitive systems, as it becomes easier to design algorithms that follow similar processes. This enables a better understanding of the procedure as a whole.
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