In artificial intelligence, you can find many tools to build your project. You might wonder which standard programming and scripting languages you can use for AI development when getting started.
Further on in this article, we will overview each of these four languages and their strengths and weaknesses. Additionally, we will also provide information on when it’s most appropriate to use each for AI development.
Is Java Useful for Building AI?
Java is a household name for many, and this familiar language finds itself used across a whole swath of different industries. Is it a suitable choice for constructing AI?
Java is typically useful for building AI. Particularly, Java shines as a language when developers use it to construct machine learning tools, neural networks, and intelligent search solutions. On the other hand, this language is less useful when speed is a primary concern.
Let’s move on to the essential benefits of Java and scenarios where it’s a good idea to consider using it when building an AI project.
Advantages of Java
When learning Java, you’re likely to come across a few significant benefits it has over other major programming languages.
For example, as you can read on Oracle’s official website, Java’s design philosophy emphasizes low overhead costs for business, innovative technology development, and wide use across multiple industries and large enterprises.
Additionally, Java is a general-purpose language, so a significant advantage it offers is practical use across several unique types of programming fields. W3Schools explains how this usability applies to every kind of software development.
As a result of these advantages, Java is one of the most common choices for general development and has significant benefits in AI development.
Using Java for Artificial Intelligence
You’re likely wondering: When should you use Java for AI development, and what separates it from the other common languages in this field?
Java provides multiple popular and innovative tools for AI programming. According to Intel, one of these tools is Project Panama’s Vector API, which was a joint development venture by both Oracle and Intel.
This Vector API provides convenient vectorization that Intel says will benefit artificial intelligence and related industries such as finance and scientific research.
Another mainstream AI use for Java is the development of neural networks, which the National Initiative for Cybersecurity Careers and Studies explains can be implemented using the Neuroph framework.
Can Python Be Used for AI Programming?
Python has transitioned into one of the most popular, well-known, and sought-after languages among both developers and employers in recent years. Is it practical for AI-based software development?
You can use Python for AI programming and development. As the language gains popularity, it becomes more attractive to many AI developers due to its widespread and well-documented tools and libraries and its simplicity and ease of use.
Before I explain the AI benefits offered by Python, I’ll first give a more fundamental explanation of why developers might want to use this language.
Why Programmers Use Python
As I already touched on, Python has numerous software libraries maintained by various organizations and individuals.
Python’s general-purpose nature allows these libraries to offer valuable features across many unrelated fields significantly. According to Python’s website, these potential applications include web development, scientific research, education, software development, etc.
You will likely find several Python libraries and toolkits available for each of these fields. Some examples include Twisted Python (for web development), SciPy (for scientific research), and pyqt (for GUI development).
Developers can also integrate Python into existing frameworks and software tools, such as HTML and CSS, JSON, and GTK+.
Python and Machine Learning
While the benefits above help explain Python’s popularity, they don’t specifically address why developers might use the language for artificial intelligence projects.
As Python Basics addresses, there are a few key reasons AI developers are often inclined to use Python. Python is useful in machine learning contexts due to its easy syntax and usability, extensive software libraries, and streamlined debugging process.
Additionally, Python offers a large and active community of users and developers. AI developers often find that they can rely on these Python enthusiasts when they are stuck on their project, need a specific software tool, or want to receive an expert’s feedback on their works-in-progress.
Should C++ Be Used To Program AI?
C++ is widely known as an older, commonly used language with a reputation for stability, versatility, and extensive documentation. However, this doesn’t say anything directly about its effectiveness in developing AI.
You should use C++ to program AI in many cases. These cases include building artificial neural networks, AI programs where a fast runtime is crucial, and video game development. Due to its steep learning curve, this language is not usually ideal for inexperienced AI developers.
So, what are the most significant benefits of C++ over other programming languages, particularly in the case of artificial intelligence? Let’s break it down.
Benefits of Using C++
One of the main reasons C++ has lasted so long as a global programming language is that it offers multiple crucial advantages over newer, potentially more user-friendly languages.
As the free training website Learn C++ points out, one notable example of these advantages is the language’s speed. Since C++ is a lower-level language (i.e., it works more closely with a device’s hardware than interpreted languages such as Python), it can process data faster than many programming languages.
While this speed is always beneficial, it becomes particularly appealing when working with AI, a data-heavy computing field requiring a quicker response time than some other programming categories.
Another desirable benefit of learning C++ is its wide library availability.
Because C++ has existed for so long, numerous software libraries have entered the development scene to make executing tasks (including machine learning) faster and easier. While this can also be said for other languages, the longevity of these libraries offers credibility that newer languages may lack.
C++ Libraries for Machine Learning
What are some instances of software libraries you can use when building machine learning applications with C++?
One example of a C++ machine learning library is Shark. According to the project’s official website, developers created Shark as a toolbox for developing AI applications with C++.
Features offered by the Shark project include machine learning algorithms, neural networks, and optimization methods (both linear and nonlinear). It’s also a cross-platform library so that developers can build applications with Shark across multiple operating systems.
Another C++ library available for AI development is MLPACK. As the software’s documentation explains, it features a division into bindings and a C++ library. The library should be used by developers when using more complicated or specific AI tools.
TensorFlow.js also allows programmers to view demonstrations of the software’s machine learning models directly in their web browser, without requiring an initial download or installation process.
Instead, developers should compare and contrast each language’s features, support, community, and ease of use with the criteria required by their unique project.
Fortunately, extensive documentation is available online for each of these languages, so comparing their pros and cons is not an especially difficult process.