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The 5 Best Programming Languages for AI Development

Ever wonder what the 5 best programming languages for AI development are? In this article, we will look at 5 artificial intelligence languages that the best artificial intelligence developers use to build innovative and powerful AI applications.

Successful businesses stay abreast of the latest trends in technology and strategically adopt and implement these technologies to excel in competitive markets. One of the most promising and influential new technologies is artificial intelligence (AI). The AI software market is growing rapidly and will achieve a global revenue of some $126 billion by 2025 Artificial intelligence has become a crucial component of commercial systems that stream data, automation technologies, and a broad range of customer solutions. Much of the modern high-tech atmosphere that surrounds software development nowadays consists of artificial intelligence languages, newly launched frameworks, and complex algorithms. While there are many types of artificial intelligence languages, some are superior to others – below, we highlight the 5 best programming languages for AI development.

Here’s a summary of five high-level languages that can make developing AI software solutions go smoother and faster.

Python

5 best programming languages for ai development

Python is the most widely used programming language around the world. Artificial intelligence developers use the Python programming language in many different industries when it comes to providing AI development services. Python can be used for machine learning, web development, data analysis, and more.

Many businesses also use Python-based services to process gigabytes of data to gain intelligence on specific topics to keep ahead of the competition. Moreover, Python allows developers to create AI-based digital assistants to improve the customer experience. For example, by allowing online shoppers to easily place orders and get updates.

Some notable python libraries for AI development services are:

  • NumPy
  • SciPy
  • Pandas
  • matplotlib.

The following are some other AI-centric frameworks:

  • Scikit-learn
    • Efficient tool to predict and analyze data
  • PyTorch
    • Free open-source framework based on Computer vision and NLP
  • Keras
    • An API with multiple backend support serves as a code interface for advanced mathematical calculations.
  • Theano
    • A library to optimize and evaluate critical mathematical expressions.

Anaconda is the best approach to get started with deep learning on Windows or Linux, with simple access to popular libraries like TensorFlow and Keras from within the Anaconda environment; PyCharm IDE provides full support for Python data science applications. Python is used by leading tech companies such as Google, Facebook, Nasa Uber, Netflix, and many more.

C++

C++ is a general-purpose programming language that was formed in the early 1980s. The language has many uses but it is mainly used in AI development. C++ helps artificial intelligence developers to create better algorithms and code faster than using other languages. With rich libraries, C++ is a favorite and highly popular language among scientists and developers. It is used to create apps, games, and websites, among many other services. Because of its object-oriented structure, it’s simple to construct reusable code modules utilized in several projects or systems.

R

R is a statistical computing and graphics programming language. It offers a user-friendly platform and an effective environment for data analysis and graphical modeling.

The R programming language is useful in a variety of fields, including;

  • Ecology.
  • Evolutionary Biology.
  • Bioinformatics.
  • Computational molecular biology/genomics (e.g., gene prediction).
  • Geoscience (e.g., climate modeling).
  • Statistics (e.g., hypothesis testing in social sciences).
  • And more.

The language features powerful tools and applications, such as:

  • Neural networks.
  • Support vector machines (SVM).
  • Random forests.
  • Clustering algorithms.

It’s compatibility with other languages gives developers flexibility when it comes to providing AI development services.

Java

AI development services
Java allows developers to create AI systems that were previously difficult to implement due to the time-consuming manual coding involved with machine learning. It is a versatile programming language that allows developers to build a wide range of software applications and websites. Java possesses artificial intelligence development tools to build scalable machine learning, neural networks, deep understanding, and other cloud-based computer vision programs.

The most popular AI development frameworks universally adopt Java-like TensorFlow, Caffe2, PyTorch and Keras.

  • Deep Java Library
    • Amazon offers a library to help people understand more deeply.
  • Kubeflow
    • Makes ML workflows simple and saleable.
  • Open NLP
    • Allows computers to understand human text and spoken words.
  • Java Machine Learning Library
    • Compatible with several ML algorithms.
  • Neuroph
    • An object-oriented framework that deals with neural networks.

More than an estimated 8 billion software developers use Java’s rich set of libraries and APIs to support AI development services. Both large enterprises and small startups prefer Java for flexible app development and backend processing power. The demand is so high it has been ranked as the most in-demand programming language among employers worldwide.

Lisp

Lisp is an artificial intelligence language with a lot of power. It was developed in the 1950s as a practical computing language. The language is unique in that it contains an interactive read-eval-print loop (REPL), which makes coding both fun and more efficient. Lisp is a general-purpose computer language that prioritizes readability and efficiency in programs. Some of the other benefits that Lisp offers include:

  • It is highly portable and works with almost any operating system and hardware platform.
  • An extensive library of standard packages (Lisp libraries) makes AI application development easier.
  • The language is embedded in popular frameworks such as Open AI Gym, Keras, and TensorFlow.

Because it enables functional programming methodologies, Lisp is highly suited for creating large and complicated applications.

Conclusion

Machine learning algorithms use artificial intelligence to help them learn from data, identify patterns, and make predictions about future events. Smartphones, social networks, and other websites constantly collect information and make decisions based on this data. AI algorithms improve the efficiency and accuracy of these processes and therefore play a crucial role in helping organizations to achieve digital transformation.

There are many programming languages that developers use to build AI solutions. But the 5 best programming languages for AI development discussed in this article allow developers to work more efficiently while building robust and powerful AI solutions.

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