Top 9 AI Programming Languages for 2024 and Beyond

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AI Programming Languages

Introduction:-

Artificial intelligence has gained the favored spotlight amongst businesses and many other industries. Now, every business is looking forward to applying it in their operations to embark on overall growth. This has paved the way for software development companies to deliver various AI-powered services and solutions. If you are a company looking forward to integrating AI, adding a few AI programming languages to the developers’ toolkit will become necessary. Ensure you have a software engineer who knows about AI coding tools and is potent enough to carry out tasks using the best programming language for AI. In this blog, we have briefly described the top 9 AI programming languages to look for in 2024.

9 Best Programming Languages for AI Development

Which are the most prevalent programming languages for AI development? There are several that can make it possible to integrate AI into your project. We have compiled a list of the top 9 programming languages for AI.

Best AI Programming Languages

#Python

Python is one of the most popular programming AI languages adopted for Artificial Intelligence. The extensive support of pre-existing libraries and frameworks makes it an ideal choice for AI development. It includes go-to frameworks and libraries like Tensor, PyTorch, and Scikit-learn.

They all serve different purposes,

  • TensorFlow is a supreme machine learning framework widely used for building and training deep learning models, primarily for neural networks.
  • PyTorch is a deep learning framework for building and training neural networks, mainly in research and prototyping.
  • Scikit-learn is a machine-learning library for data analysis and modeling. It performs activities like classification, regression, clustering, and dimensionality reduction.

PROS:

  1. Consists of a rich ecosystem of libraries and frameworks
  2. Large & active community support
  3. Ensures code readability & maintainability

CONS:

  1. Extensive capabilities in Python results in a steep learning curve
  2. Syntax can be verbose, which makes code complex.

#Lisp

Lisp is the second oldest programming language, having a long history in AI development. It is well-known for its symbolic reasoning capabilities. Moreover, Lisp is known for its immense flexibility and ability to turn thoughts into reality.

A few features of Lisp are:-

  • Dynamic object creation
  • Rapid prototyping
  • Developing programs using data structures
  • Enabling mandatory garbage collection

Some common purposes served by Lisp are:-

  • Web Development is possible using frameworks such as Hunchentoot and Weblocks.
  • Lisp has strong Artificial Intelligence and Symbolic Reasoning capabilities.
  • Lisp servers can be employed to develop complex enterprise applications, including those that require complex and rule-based processing.

PROS:

  • Reasoning capabilities make Lisp suitable for AI tasks involving rule-based systems.
  • It allows high degrees of flexibility in programming.

CONS:

  • Syntax is particularly unconventional and may take time.
  • Fewer resources and community support

#Java

Java is one of the most popular server-side programming languages. Its core portability feature makes it an ideal choice for developing AI applications. Few well-known libraries or frameworks, including Apache OpenNLP and Deeplearning4j, are available for AI development.

Java can work with various AI libraries and frameworks, including TensorFlow.

  • Deep Java Library
  • Kubeflow
  • OpenNLP
  • Java Machine Learning Library
  • Neuroph

PROS:

  • Highly portable across various platforms
  • Java’s object-oriented makes it easier to work with
  • It is widely used in enterprise environments

CONS:

  • More verbose compared to contemporary programming languages
  • High memory consumption
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#C++

C++ is one of the programming languages known for its high-performing capabilities. Its flexibility is one of the reasons why it is best suited for resource-intensive applications. C++ low programming level makes it perfect for handling AI models. Many libraries, including TensorFlow and OpenCV, offer APIs to machine learning and computer vision applications.

C++ is capable of converting user code to machine-readable code, leading to more efficient and highly performing,

  • Different deep learning libraries, such as MapReduce, mlpack, and MongoDB are available.
  • C++ Builder offers an environment for developing applications rapidly.
  • AI speech recognition implementation.

PROS:

  • Highly efficient and performing, ideally suited for AI computational tasks
  • Provides control to developers to manage resource management

CONS:

  • Increased learning curve for beginners
  • It leads to memory-related errors if not handled carefully

#R

R is popularly known for its statistical computing and data analysis; it is not the best programming language for AI, but is good at crunching numbers. Few in-built object-oriented, vectorial computation, and functional programming make it an ideal choice for Artificial Intelligence.

You might find it helpful to check out these R packages:

  • Gmodels package offers tools for model fitting.
  • Tm is a framework ideally suitable for text-mining applications.
  • OneR algorithm is utilized to accomplish the One Rule Machine Learning classification.

PROS:

  • Designed for statistical computing, it is suitable for data analysis and statistical modeling
  • it offers powerful visualization libraries for developing interactive plots
  • It caters to data processing for AI

CONS:

  • It is not well-supported
  • R can be slow and also has a steep learning curve

#Julia

Julia is one of the latest programming languages for AI development. The dynamic interface and arresting graphics for data visualization make it a developer’s go-to choice. The functions, including memory management, debugging, and metaprogramming of the mix, make it a hype.

Some of the features it consists of are as follows:

  • Parallel & distributed computing
  • Dynamic type system
  • Supports for C functions

Pros:

  • Offers high-performance numerical computing & solid support for machine learning
  • Focuses on ease of use for numerical and scientific computing

Cons:

  • Steep learning curve
  • It is a new programming language with limited community support

#Haskell

Haskel is an all-purpose statistically-type and purely functional programming language with a type interface. Its comprehensive capabilities make it an ideal choice for AI application development.

Some of the features of Haskell are:-

  • Statically typed
  • Every function is in the mathematical sense, which implies Purely functional
  • There is no necessity for explicitly documenting every type in a Haskell program
  • Haskell lends itself well to concurrent programming due to its explicit handling of effects
  • Active range of packages available on public package servers

Pros:

  • Emphasized code correctness
  • Commonly used in teaching & research

Cons:

  • Challenging to learn; moreover, extremely confusing

#Prolog

Prolog is known for its logic-based programming language. It is associated with computational linguistics and artificial intelligence. The common application of this programming language is symbolic reasoning and rule-based systems.

Some essential elements of Prolog that are commonly used:

  • Facts: Define the assumption for true statements
  • Rules: Define the relationship between different facts
  • Variables: Define representation of the value which the interpreter can determine
  • Queries: Define to find the solutions to the query

Pros:

  • It is a declarative language well suited for AI development.
  • It is used for the foundation of AI development as it is logic-based programming.

Cons:

  • Steep learning curve
  • Small community of developers

#Scala

Scala is a modern, high-level, general-purpose programming language that supports both object-oriented & functional programming. It is ideal for teaching programming to freshers.

Some of the core features of Scala are given below:-

  • Gives priority to interoperability
  • Allows to build safe systems by default
  • Lazy evaluation
  • Pattern matching
  • Advanced type system supporting

Pros:

  • Consists of suitable features for AI development
  • Integrates well with Java & has a large number of developers
  • JVM Scala provides interoperability with Java

Cons:

  • Complex & challenging to learn
  • Primarily used for data processing and distributed computing

Final Thoughts on AI Programming Languages

Choosing the best coding languages for artificial intelligence is essential for your project requirements. The developer must remember the project specifications or the type of software development before selecting the appropriate programming language. In the above blog, we have curated the list of AI programming languages, their specifications, and pros & cons. This will ideally help you make the right choice for your project.

If you have your project requirements, reach out to us to get custom AI development services with suitable AI programming for your project.

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Deepali Medchal
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Articulate & piquant content creator committed to her passion for writing.

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