9 Best Programming Languages for AI in 2024

#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

#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 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