Programming languages are notoriously versatile, each capable of great feats in the right hands. AI (artificial intelligence) technology also relies on them to function properly when monitoring a system, triggering commands, viewing content, etc.
As a programmer, you need to get to know the best languages for developing AI. Below are 10 options to consider and how they can benefit your smart projects.
It’s the usability of Python more than anything else that makes it the most popular choice among AI developers. Having said that, it is also a highly performant and widely used programming language capable of complex processes for all kinds of tasks and platforms.
Artificial intelligence is difficult enough, so a tool that makes your coding life easier is invaluable, saving you time, money, and patience.
Another benefit to consider is the unlimited library and forum support. If you can build desktop apps in Python with the Tkinter GUI library, imagine what you can build using machine learning libraries like NumPy and SciPy.
Here is another programming language that appeals to AI programmers with its flexibility, ease of use and broad support. Java is not as fast as other coding tools, but it is powerful and works well with AI applications.
A key feature is its cross-platform compatibility, so you don’t have to rewrite the code every time you use a different system. Java also produces good visuals. You don’t have to worry so much about the quality of your AI graphics.
There’s more coding involved than Python, but Java’s overall AI results clearly make it one of the best programming languages for this technology.
Its AI capabilities primarily involve interactivity that works seamlessly with other source code, like CSS and HTML. It can handle front-end and backend functions, from buttons and multimedia to data storage.
Thanks to Scala’s powerful features, such as high performance features, flexible interfaces, model matching and navigation tools, its efforts to impress programmers are paying off. It is now one of the best languages to use for AI development.
Another benefit to keep in mind is the Scaladex, an index containing all of the available Scala libraries and their resources.
Developed in the 1960s, Lisp is the oldest programming language for the development of AI. It’s very smart and adaptable, especially good at problem-solving, writing code that changes, creating dynamic objects, and rapid prototyping.
There are downsides, however. Lisp’s syntax is unusual compared to modern computer languages, which makes it more difficult to interpret. Relevant libraries are also limited, let alone programmers to advise you.
Despite its flaws, Lisp is still in use and deserves a review to find out what it can offer your AI projects. Grammarly, DART and Routinic are some of its successes.
If you’re working with an AI that involves analyzing and representing data, R is your go-to programming language. It is an open source tool capable of processing data, automatically applying it as you want, reporting patterns and changes, helping with predictions, etc.
In addition to serving primarily statistical functions, R is a difficult language to learn and must be combined with other reliable tools to produce comprehensive software and a productive workflow for your business.
But, what R lacks in scope, it makes up for it with high compatibility and great styling when creating visuals like charts and tables no matter how detailed they are.
For a more logical way to program your AI system, take a look at Prolog. Software that uses it follows a basic set of facts, rules, goals, and queries instead of coded instruction sequences.
Prolog can understand and match models, find and structure data in a logical way, and automatically go back through a process to find a better path. Overall, the best way to use this language in AI is problem solving, where Prolog is looking for one solution, or several.
Hence its use in chatbots and virtual assistants like IBM’s Watson. Think how simple yet useful these forms of intelligent communication are. Prolog may not be as versatile or easy to use as Python or Java, but it can provide an invaluable service.
Julia is another premium product that just hasn’t achieved the status or community support it deserves. Despite everything, its characteristics do not disappoint. This programming language is useful for general tasks, but works best with numbers and data analysis.
It offers several tools to create a dynamic interface and impressive graphics to visualize your data, for example. There is also memory management, metaprogramming and debugging for more efficiency.
In terms of AI capabilities, Julia is ideal for any machine learning project. Whether you want pre-built models, help with algorithms, or playing around with probabilistic programming, a range of packages await, including MLJ.jl, Flux.jl, Turing.jl, and Metalhead.
If you want pure functionality above all else, Haskell is a good programming language to learn. Getting started with AI development can take some time, in part due to limited support.
But, its abstraction capabilities make it very flexible, especially when it comes to handling errors. Efficient memory management and Haskell’s typing system are major advantages, as is your ability to reuse code.
It’s also a lazy programming language, which means it only evaluates chunks of code when needed. On the one hand, it can help produce a very smart process. On the other hand, he could end up falling behind. Even so, the right setup can make Haskell a decent tool for AI developers.
It might be one of the more popular languages, but it’s not as good for AI development as the previous options. The main problem with AI is the syntax of C ++. It’s too complicated to quickly create useful coding for machine or deep learning applications.
At the same time, we wouldn’t have TensorFlow, a key machine learning framework, without C ++. It remains a powerful and flexible tool for software design, so you should keep it in mind for some tasks and alongside other tools.
For example, search engines like Google use its memory capacity and fast functions to ensure short response times and an efficient ranking system. In other words, feel free to use C ++, but do it with care.
Explore what you can create with AI programming
There are several programming languages to choose from for AI development, depending on the ease or technicality of your process. Another factor to consider is which system works best for the software you are designing.
One way to approach the question is to look at popular apps that are already available. What do they do? How do their basic or interactive functions work? What languages do they use? The more you know, the more ideas you will have.
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