9 principais linguagens de programação para inteligência artificial

Top 9 Programming Languages for Artificial Intelligence

June 1, 2024 Roberto Magalhães

If your company wants to integrate Artificial Intelligence, there are a few languages you should seriously consider adding to your developer's toolkit.

Artificial Intelligence is on everyone's mind, especially companies looking to accelerate growth beyond what they have previously managed to achieve. With AI, your company can save time and money by automating and optimizing normally routine processes. Once AI is implemented, you can be confident that these tasks will be performed faster, more accurately and reliably than a human can accomplish.

Additionally, AI is exponentially faster at making business decisions based on information from multiple sources (such as customer information or collected data). AI can serve as chatbots, in mobile and web applications, in analytical tools to identify patterns that can serve to optimize solutions for any process and the list goes on. In fact, there is very little that AI cannot power.

But to employ artificial intelligence in your company's systems and services, you'll need software engineers who are up to the task. Additionally, these developers will need to know the best languages to use for AI.

What languages are these? There are several that can serve to make your AI integration dreams come true. Let's dive in and take a look at 9 of the best languages available for Artificial Intelligence.

#1Python

Although Python was created before AI became crucial for businesses, it is one of the most popular languages for Artificial Intelligence. Python is the most used language for machine learning (which lives under the umbrella of AI). One of the main reasons why Python is so popular in AI development is that it was created as a powerful data analysis tool and has always been popular in the big data field.

As for modern technology, the most important reason why Python is always ranked at the top is that there are specific AI frameworks that have been created for the language. One of the most popular is TensorFlow, which is an open source library created specifically for machine learning and can be used for training and inference of deep neural networks. Other AI-centric frameworks include:

scikit-learn – for training machine learning models.
PyTorch – visual and natural language processing.
Keras – serves as a code interface for complex mathematical calculations.
Theano – library for defining, optimizing and evaluating mathematical expressions.

Python is also one of the easiest languages to learn and use.

#2 Lisp

Lisp has existed since the 1960s and has been widely used for scientific research in the areas of natural languages, theorem proofs and solving artificial intelligence problems. Lisp was originally created as a practical mathematical notation for programs, but it ended up becoming the main choice for developers in the field of AI.

Although Lisp is the second oldest programming language still in use, it includes several features that are essential for successful AI projects:

Rapid prototyping.
Dynamic object creation.
Mandatory garbage collection.
Data structures can be executed like programs.
Programs can be modified like data.
Uses recursion as a control structure and not as iteration.
Great symbolic information processing capabilities.
Read-Eval-Print-Loop for easy interactive programming.

Most importantly, the man who created Lisp (John McCarthy) was very influential in the field of AI, so much of his work has long been implemented.

#3Java

It goes without saying that Java is an important language for AI. One reason for this is the prevalence of the language in mobile app development. And given the number of mobile apps leveraging AI, it's a perfect fit.

Not only does Java work with TensorFlow, it also has other libraries and frameworks designed specifically for AI:

Deep Java Library – a library built by Amazon to create deep learning skills.
Kubeflow – Enables you to deploy and manage machine learning stacks on Kubernetes.
OpenNLP – a machine learning tool for natural language processing.
Java Machine Learning Library – provides various Machine Learning algorithms.
Neuroph – makes it possible to design neural networks.

Java also makes use of simplified debugging and its easy-to-use syntax offers graphical presentation of data and incorporates WORA and Object-Oriented standards.

#4 C++

C++ is another language that has been around for a while but is still a legitimate candidate for AI use. One of the reasons for this is the flexibility of the language, which makes it perfectly suited for resource-intensive applications. C++ is a low-level language that provides better handling for the AI model in production. And while C++ may not be the first choice for AI engineers, it cannot be ignored that many of the deep and machine learning libraries are written in C++.

And because C++ converts user code into machine-readable code, it is incredibly efficient and high-performance.

AI speech recognition implementation.
Deep learning libraries – for example, MapReduce, mlpack, and MongoDB.
C++ Builder – a rapid application development environment.

#5R

R may not be the perfect language for AI, but it is fantastic at crunching very large numbers, which makes it better than Python at scale. And with R's integrated functional programming, vector computing and Object Oriented Nature, it becomes a viable language for Artificial Intelligence.

R also has some packages designed specifically for AI:

gmodels – provides several tools for the task of model tuning.
TM – a framework used for text mining applications.
RODBC – an ODBC interface.
OneR – enables the implementation of the One Rule Machine Learning classification algorithm.

#6 Julia

Julia is one of the newest languages on the list and was created to focus on performance computing in scientific and technical areas. Julia includes several features that directly apply to AI programming:

Common numeric data types.
Arbitrary precision values.
Robust mathematical functions.
Tuples, dictionaries and code introspection.
Integrated package manager.
Dynamic type system.
Ability to work for parallel and distributed computing.
Macros and metaprogramming capabilities.
Support for multiple dispatches.
Support for C functions.

Julia can also be integrated with TensorFlow.jl, MLBase.jl and MXNet.jl.

#7 Haskell

A functional, readable, statically typed language, Haskell offers a number of features that make it a solid choice for AI programming. On the one hand, it allows developers to describe algorithms explicitly and succinctly. It also provides type safety and seamless multicore parallelism. Additional notable features include:

Lazy evaluation capabilities – allow definitions of infinite data structures.
HLearn Library – includes implementations of machine learning algorithms.
Ideal for machine learning.

#8 Prologue

Easy pattern matching and list handling are notable features of Prolong, which stands for logic programming. These features make logical language a good choice for AI. Prolong is especially ideal in cases where developers need to focus on problems because the language can run the program using its search tools. In particular, the facets and tools that make Prolong a reliable AI language are:

Declarative nature – allows programmers to declare rules and facts when writing AI programs.
Intelligent database recovery.
Natural language processing.
Simple to use systems.
Tree-based data structuring.
Representation of knowledge.

#9 Scale

Scala is an easy-to-use and reliable language, but that's only part of the reason developers apply it to AI. It's a good option for building machine learning algorithms and gaining insights from large data sets, as well as managing complex content in general. It also has features like:

Smile – a data science library with algorithms for actions like classification.
An abundance of frameworks and libraries like BigDL and Breeze.

What programming languages are not suitable for AI?

Many programming languages are commonly used for AI, but there are some that are not suitable for it. Perl is an example of a programming language that is not typically used for AI because it is a scripting language.

Conclusion

If your company needs to add Artificial Intelligence development services, you need to start the process of integrating one or more of these languages. With the right development team, there's no limit to what AI can do to help accelerate your company's growth.

Common questions

Can you use JavaScript for machine learning and artificial intelligence?

JavaScript is one of the best languages for web development, but it is not particularly known for machine learning and AI. There is growing interest in using JavaScript for data science, but many believe this is due to the language's popularity rather than its suitability.

Is Python enough to learn AI?

Python is one of the most important languages to get started in machine learning and AI, but if you want to specialize, you'll often need to supplement your Python skills with those in one of the other major programming languages.

What is the best language for AI?

There is no single “best” language for AI. It depends on what type of AI model you plan to implement. For example, in the case of machine learning, you can choose Python, which is a great choice for this subset of AI.

Which is better for AI, Python or Java?

Python and Java are widely used languages for AI. Choosing between programming languages depends on how you plan to implement AI. For example, in the case of data analysis, you would probably choose Python. However, given the popularity of AI for mobile applications, Java, which is often used in this case, may well be the best language for this type of program.

If you liked this article, check out one of our other articles on AI.