Artificial intelligence (AI) has become one of the top agendas for businesses as it offers enhanced customer experience, resilience, and reliability. An increasing number of industries are adopting AI to solve urgent problems, alleviate consumer pain-points faster, and drive digital transformations on a larger scale. From boosting productivity to automating tasks and simplifying processes, AI and machine learning open up a new world of endless opportunities.
Building an AI solution not only requires a clear set of requirements but also the right selection of technologies and AI programming languages that make AI development practically possible and smooth. While there are plenty of programming languages to get you started with developing on AI, no single programming language is a one-stop-solution for AI programming as various objectives require a specific approach for every project. Here we are with the top 8 best languages for AI that are widely used to develop AI applications across a wide range of industry segments.
Python
It’s Python’s user-friendliness more than anything else that makes it the most popular choice among AI developers. It’s also a high-performing and widely used programming language, capable of complicated processes for all kinds of tasks and applications ranging from back end development to data science and machine learning. One of the main reasons Python is so popular in AI development is that it was created as a powerful data analysis tool and has always been popular within the field of big data. Python’s intuitive syntax strives for readability, which streamlines coding and makes the language easier to learn. Thanks to its versatility and ease of use, Python is an excellent choice for both new and seasoned machine learning engineers regardless of programming experience.
Artificial intelligence is difficult enough, so a tool that makes your coding life easier is invaluable, saving you time, money, and patience. According to a Forbes article, “Although, Python has proven to be effective for a myriad of other areas, such as building websites and creating scripts for DevOps, it is with AI/ML where the language has really shined.”
Python also boasts exceptional documentation and community support and integrates easily with other programming languages.
Pros:
- Python is easy to learn and write
- It is faster in development as compared to Java and C++ because of its modular and interactive structure
- It has a large community of users in various parts of the world
- It has feature-rich tools and an extensive library
- It supports neural networks and the development of NLP solutions due to its straightforward function library
- It can easily be integrated with C++, C, Java, Cobra, and other languages
Cons:
- Difficulty in tracking error
- Developers accustomed to Python may find it challenging to switch to other AI programming languages
- Not suitable for mobile and game development
Some of its most popular libraries are:
- TensorFlow – for machine learning workloads and working with datasets
- PyTorch – for computer vision and natural language processing
- scikit-learn – for training machine learning models
- Keras – as the code interface for highly complex mathematical calculations and operations
- SparkMLlib – like Apache Spark’s Machine Learning library, makes machine learning easy for everyone with tools like algorithms and utilities
- Pybrain – for powerful machine learning algorithms
- Theano – as the library for defining, optimising and evaluating mathematical expressions
- MXNet – is another one of Apache’s libraries for easing deep learning workflows
Java
Java by Oracle is probably the most-used language around the globe for different processes and AI is definitely one of them. Java is high-level, class-based, and object-oriented programming language that has created history with its write once, run anywhere (WORA) principle. Over the years, this language has adapted to the latest innovations and technological advancements. The same is true for AI. Using Java for AI development can help you get some scalable AI applications. Also, Java has become a go-to language for mobile app development, which relies heavily on artificial intelligence.
Java is supported by a plethora of machine learning libraries like Weka—which is used for machine learning algorithms, predictive modelling, and more—and Massive Online Analysis, an open-source data mining software. Many popular big data processing tools like Apache Hive, Apache Hadoop, and Apache Spark are written in Java, enabling smooth integration with these key analytic frameworks.
Pros:
- It is portable, and unlike C++, it is easy to use and debug
- Reduces the developer’s workload due to the presence of an automatic memory manager
- It is apt for NLP and neural networks
- It is platform-independent
- It can be easily picked up by amateur AI developers because it is easy to learn
Cons:
- It has less speed in execution and has a longer response time
- Expensive memory management
- It is considered an immature AI programming language as it lacks agility
Some of its most popular libraries are:
- Deep Java Library – a library built by Amazon to create deep learning abilities
- Kubeflow – Kubeflow facilitates easy deployment and management of Machine Learning stacks on Kubernetes, providing ready to use ML solutions
- OpenNLP – Apache’s a Machine Learning tool for processing natural language
- Java Machine Learning Library – Java-ML provides developers with several machine learning algorithms
- Neuroph – Neuroph makes designing neural networks using the open-source framework of Java possible with the help of Neuroph GUI
Prolog
Prolog stands for ‘Programming in Logic.’ It is mainly used for AI and computational linguistics. Comprising mechanisms such as tree-based data structuring, automatic backtracking, and pattern matching, Prolog facilitates flexible frameworks. It is also a declarative and rule-based language that contains facts dictating the AI codebase.
In Prolog, instead of using coded sequences, you simply chart out a basic set of facts, rules, goals, and queries. The locus of Prolog’s expression lies in relationships between facts—otherwise known as rules. To solve a problem, the Prolog interpreter queries the facts and rules represented in a database. In other words, once the user provides the rules and the desired solution, Prolog solves the rest.
The basic tenets of Prolog are as follows:
- facts define true statements
- rules define statements with additional conditions
- goals define where the submitted statements are placed given a knowledge base
- queries define how to make statements true and the final analysis of facts and rules
Prolog might not be as versatile or easy to use as Python or Java, but it can provide an invaluable service.
Pros:
- It enables you to create a database along with running the AI program
- It supports direct linking with C and C++
- It has a built-in capability to handle lists
Cons:
- Despite being an old language, it is not fully standardised and hence, sometimes makes the implementation process cumbersome
Prolog offers two approaches for implementing AI that has been in practice for a long time and is well-known among data scientists and researchers:
- The Symbolic Approach includes rule-based expert systems, theorem provers, and constraint-based approaches
- The Statistical approach includes neural nets, data mining, machine learning and several others
LISP
Lisp (historically stylized as LISP) is one of the oldest languages in circulation for AI development. Originating in 1958, Lisp is short for list processing, one of its original applications. LISP has always been considered a tool for leading companies in AI with its enlarged scope of turning thoughts into reality. The language differentiates itself from other AI languages by eying precision. If your project requires modification of code, problem-solving, rapid prototyping, or dynamic development, Lisp is for you. Some successful projects made with Lisp are Routinic, Grammarly, and DART.
In the present day, the language is just as capable, but because of its difficult syntax and complicated libraries, it’s rare that developers flock to Lisp first. Lisp’s popular libraries for AI and machine learning are CLML, mgl, Antik, and LLA.
Pros:
- It is flexible and extremely fast in prototyping
- It is highly suitable for inductive logic and machine learning projects
- Lisp AI programs are smaller and that is what makes them easier to maintain
Cons:
- Developers can only use it after new hardware and software have been configured
Below are some of the Lisp features that make it one of the best options for AI projects:
- Rapid Prototyping
- Dynamic Object Creation
- Garbage Collection
- Flexibility
Haskell
Haskell is a statically typed and purely functional programming language. What this means, is that Haskell is flexible and expressive. It initially gained popularity in academic circles but slowly found its way into Google and Facebook projects. Haskell’s features include lambda expressions, pattern matching, type classes, list comprehension, and type polymorphism. It is considered a safe programming language due to its flexibility and capability to handle errors. Haskell’s main implementation is the Glasgow Haskell Compiler (GHC). And Haskell’s efficient memory management and type system are major advantages, as is your ability to reuse code. It’s also a lazy programming language, meaning it only evaluates pieces of code when necessary. On the one hand, it can help produce a very smart process. On the other hand, it might end up lagging. Even so, the right setup can make Haskell a decent tool for AI developers.
Haskell’s HLearn library offers algorithmic implementations for machine learning, while its Tensorflow binding supports deep learning. Haskell allows users to code highly expressive algorithms without sacrificing performance, and the language is ideal for projects that involve abstract maths and probabilistic programming. With Haskell, users can represent a model with just a handful of code and read the lines they’ve written like mathematical equations. In this way, Haskell can aptly convey the complexity of a deep learning model with clean code that resembles the model’s actual mathematics.
Pros:
- It is apt for engaging in abstract maths, unlike Java, and leverages common algebraic structures such as monoids and modules
- It enables developers to quickly spot errors in the app during the compile phase of the iteration
Cons:
- It lacks the same level of support enjoyed by the likes of Python and Java
Some of the features offered by Haskell are:
- Strong abstraction capabilities
- Built-in memory management
- Code reusability
- Easy to understand
Julia
Julia is one of the newer languages on the list and was created to focus on performance computing in scientific and technical fields. It is a high-performance and general-purpose dynamic programming language tailored to create almost any application but is highly suited for numerical analysis and computational science. A growing community of support has emerged around the language alongside an expanding number of machine learning libraries like TensorFlow.jl, scikitlearn.jl, Mocha.jl, MLJ.jl, Flux.jl, Turing.jl, and Metalhead. Julia is a top-choice programming language for AI applications that require high-powered numerical computing and is ideal for AI practitioners who already have a background in scripting languages like Python or R.
If you need to make a dynamic interface, catchy graphics, and data visuals, Julia provides you with the right tools for perfect execution. With features like debugging, memory management, metaprogramming, profiling, logging, package management, and asynchronous I/O this language makes AI development a breeze.
Pros:
- Dynamic type system
- Built-in package manager
- Able to work for parallel and distributed computing
- Macros and metaprogramming abilities
- Support for Multiple dispatches
- Direct support for C functions
Cons:
- Developers do not have a wealth of debugging tools at their disposal
Various tools available for working with Julia are:
- Popular editors such as Vim and Emacs
- IDEs such as Juno and Visual Studio
R
R is a comprehensive statistical language that encourages the development of new ideas. Because R can crunch vast data sets with ease, this programming language is widely used for statistical software development, data analysis, and data visualisation. It’s easy to understand and has a lot of packages and material libraries that can speed up the learning process, just like Python. With R, you can work on various paradigms of programming such as functional programming, vectorial computation and object-oriented programming. Although R is not a go-to programming language for deploying machine learning models in production, R is a top tool for exploratory work in the process of model selection. Packages like Gmodels, RODBC, OneR, and Tm allow huge support for Machine Learning processes. When these packages are coupled with a wide range of libraries, the capabilities of the R programming language reach new heights. The programming language is preferred by top AI companies for its ability to solve complex issues.
It is considered a standard language for domains such as biology, sociology, medicine, and finance. R includes user-created packages like graphical devices, tools, import/export capabilities, statistical techniques, etc. With built-in graphic and data modelling support, the language allows developers to work on deep learning moderns without much hassle.
Pros:
- It is relatively easy to understand and -use for data-centric tasks because of the abundance of packages and materials
- It has great support for producing high-quality graphs
- It is particularly good at crunching numbers (or dealing with statistical data) and is often considered a better choice for the same than Python
Cons:
- It lacks the necessary infrastructure for security — which means it cannot embed into web applications
- It is slower when compared to other programming languages like Python
Some of the AI programming packages available for R are:
- Gmodels – provides several tools for the task of model fitting
- TM – a framework used for text mining applications
- RODBC – an ODBC interface for R
- OneR – for implementing One Rule Machine Learning classification algorithm, useful for machine learning models
C++
This may be one of the most popular languages around, but it’s not as effective for AI development as the previous options. The main problem in relation to AI is the syntax of C++. It’s too complicated to quickly create useful coding for machine or deep learning applications. It is, however, perfect for people working in an embedded environment who can’t afford the overhead cost of the Java Virtual Machine. You can build neural networks from scratch using C++ and translate user code into something machines can understand. Come to think of it, many of the most notorious deep and machine learning libraries are written in C++.
The speed of C++ is the greatest benefit since AI development has complex computations and this language can make the calculations faster. It has a low-level memory control feature and carries asset-compelled applications, performance-critical applications, etc. When you consider AI processes, you talk about complex automated solutions to run efficiently. And, C++ won’t disappoint you in that respect. C++ also excels at dynamic load balancing, adaptive caching, and memory management—which is why this language is a top choice for building scalable big data frameworks. Notable C++ libraries include SHARK, which supports supervised learning algorithms like linear regression, and MLPACK, which offers extensible algorithms that users can integrate into scalable ML solutions.
Pros:
- It provides faster execution, and its speed is very much appreciated for time-sensitive AI programming projects
- It has better efficiency of control because of the extensive use of algorithms
Cons:
- Difficult to learn the language because the standard library of C++ is small
- It does not support garbage collection
- Large C++ projects are hard to maintain and are time-consuming to build
Wrapping Up…
While we can’t exactly define what’s best as it’s more subjective than selective, we have tried our best to cover top languages that are used to create AI solutions. The selection of ‘best’ depends on many factors such as business specifics, budget, target audience, market structure, and most importantly why and where you want to implement AI.
At 3point3 labs, we believe every project is unique, which is why our experts always give recommendations based on our clients’ requirements. Research is paramount before picking a suitable AI programming language.
If you are searching for help for your next AI project, please contact us at [email protected], and we will save your time, money, and resources with our top-notch AI development services.
Artificial intelligence (AI) has become one of the top agendas for businesses as it offers enhanced customer experience, resilience, and reliability. An increasing number of industries are adopting AI to solve urgent problems, alleviate consumer pain-points faster, and drive digital transformations on a larger scale. From boosting productivity to automating tasks and simplifying processes, AI and machine learning open up a new world of endless opportunities.
Building an AI solution not only requires a clear set of requirements but also the right selection of technologies and AI programming languages that make AI development practically possible and smooth. While there are plenty of programming languages to get you started with developing on AI, no single programming language is a one-stop-solution for AI programming as various objectives require a specific approach for every project. Here we are with the top 8 best languages for AI that are widely used to develop AI applications across a wide range of industry segments.
Python
It’s Python’s user-friendliness more than anything else that makes it the most popular choice among AI developers. It’s also a high-performing and widely used programming language, capable of complicated processes for all kinds of tasks and applications ranging from back end development to data science and machine learning. One of the main reasons Python is so popular in AI development is that it was created as a powerful data analysis tool and has always been popular within the field of big data. Python’s intuitive syntax strives for readability, which streamlines coding and makes the language easier to learn. Thanks to its versatility and ease of use, Python is an excellent choice for both new and seasoned machine learning engineers regardless of programming experience.
Artificial intelligence is difficult enough, so a tool that makes your coding life easier is invaluable, saving you time, money, and patience. According to a Forbes article, “Although, Python has proven to be effective for a myriad of other areas, such as building websites and creating scripts for DevOps, it is with AI/ML where the language has really shined.”
Python also boasts exceptional documentation and community support and integrates easily with other programming languages.
Pros:
- Python is easy to learn and write
- It is faster in development as compared to Java and C++ because of its modular and interactive structure
- It has a large community of users in various parts of the world
- It has feature-rich tools and an extensive library
- It supports neural networks and the development of NLP solutions due to its straightforward function library
- It can easily be integrated with C++, C, Java, Cobra, and other languages
Cons:
- Difficulty in tracking error
- Developers accustomed to Python may find it challenging to switch to other AI programming languages
- Not suitable for mobile and game development
Some of its most popular libraries are:
- TensorFlow – for machine learning workloads and working with datasets
- PyTorch – for computer vision and natural language processing
- scikit-learn – for training machine learning models
- Keras – as the code interface for highly complex mathematical calculations and operations
- SparkMLlib – like Apache Spark’s Machine Learning library, makes machine learning easy for everyone with tools like algorithms and utilities
- Pybrain – for powerful machine learning algorithms
- Theano – as the library for defining, optimising and evaluating mathematical expressions
- MXNet – is another one of Apache’s libraries for easing deep learning workflows
Java
Java by Oracle is probably the most-used language around the globe for different processes and AI is definitely one of them. Java is high-level, class-based, and object-oriented programming language that has created history with its write once, run anywhere (WORA) principle. Over the years, this language has adapted to the latest innovations and technological advancements. The same is true for AI. Using Java for AI development can help you get some scalable AI applications. Also, Java has become a go-to language for mobile app development, which relies heavily on artificial intelligence.
Java is supported by a plethora of machine learning libraries like Weka—which is used for machine learning algorithms, predictive modelling, and more—and Massive Online Analysis, an open-source data mining software. Many popular big data processing tools like Apache Hive, Apache Hadoop, and Apache Spark are written in Java, enabling smooth integration with these key analytic frameworks.
Pros:
- It is portable, and unlike C++, it is easy to use and debug
- Reduces the developer’s workload due to the presence of an automatic memory manager
- It is apt for NLP and neural networks
- It is platform-independent
- It can be easily picked up by amateur AI developers because it is easy to learn
Cons:
- It has less speed in execution and has a longer response time
- Expensive memory management
- It is considered an immature AI programming language as it lacks agility
Some of its most popular libraries are:
- Deep Java Library – a library built by Amazon to create deep learning abilities
- Kubeflow – Kubeflow facilitates easy deployment and management of Machine Learning stacks on Kubernetes, providing ready to use ML solutions
- OpenNLP – Apache’s a Machine Learning tool for processing natural language
- Java Machine Learning Library – Java-ML provides developers with several machine learning algorithms
- Neuroph – Neuroph makes designing neural networks using the open-source framework of Java possible with the help of Neuroph GUI
Prolog
Prolog stands for ‘Programming in Logic.’ It is mainly used for AI and computational linguistics. Comprising mechanisms such as tree-based data structuring, automatic backtracking, and pattern matching, Prolog facilitates flexible frameworks. It is also a declarative and rule-based language that contains facts dictating the AI codebase.
In Prolog, instead of using coded sequences, you simply chart out a basic set of facts, rules, goals, and queries. The locus of Prolog’s expression lies in relationships between facts—otherwise known as rules. To solve a problem, the Prolog interpreter queries the facts and rules represented in a database. In other words, once the user provides the rules and the desired solution, Prolog solves the rest.
The basic tenets of Prolog are as follows:
- facts define true statements
- rules define statements with additional conditions
- goals define where the submitted statements are placed given a knowledge base
- queries define how to make statements true and the final analysis of facts and rules
Prolog might not be as versatile or easy to use as Python or Java, but it can provide an invaluable service.
Pros:
- It enables you to create a database along with running the AI program
- It supports direct linking with C and C++
- It has a built-in capability to handle lists
Cons:
- Despite being an old language, it is not fully standardised and hence, sometimes makes the implementation process cumbersome
Prolog offers two approaches for implementing AI that has been in practice for a long time and is well-known among data scientists and researchers:
- The Symbolic Approach includes rule-based expert systems, theorem provers, and constraint-based approaches
- The Statistical approach includes neural nets, data mining, machine learning and several others
LISP
Lisp (historically stylized as LISP) is one of the oldest languages in circulation for AI development. Originating in 1958, Lisp is short for list processing, one of its original applications. LISP has always been considered a tool for leading companies in AI with its enlarged scope of turning thoughts into reality. The language differentiates itself from other AI languages by eying precision. If your project requires modification of code, problem-solving, rapid prototyping, or dynamic development, Lisp is for you. Some successful projects made with Lisp are Routinic, Grammarly, and DART.
In the present day, the language is just as capable, but because of its difficult syntax and complicated libraries, it’s rare that developers flock to Lisp first. Lisp’s popular libraries for AI and machine learning are CLML, mgl, Antik, and LLA.
Pros:
- It is flexible and extremely fast in prototyping
- It is highly suitable for inductive logic and machine learning projects
- Lisp AI programs are smaller and that is what makes them easier to maintain
Cons:
- Developers can only use it after new hardware and software have been configured
Below are some of the Lisp features that make it one of the best options for AI projects:
- Rapid Prototyping
- Dynamic Object Creation
- Garbage Collection
- Flexibility
Haskell
Haskell is a statically typed and purely functional programming language. What this means, is that Haskell is flexible and expressive. It initially gained popularity in academic circles but slowly found its way into Google and Facebook projects. Haskell’s features include lambda expressions, pattern matching, type classes, list comprehension, and type polymorphism. It is considered a safe programming language due to its flexibility and capability to handle errors. Haskell’s main implementation is the Glasgow Haskell Compiler (GHC). And Haskell’s efficient memory management and type system are major advantages, as is your ability to reuse code. It’s also a lazy programming language, meaning it only evaluates pieces of code when necessary. On the one hand, it can help produce a very smart process. On the other hand, it might end up lagging. Even so, the right setup can make Haskell a decent tool for AI developers.
Haskell’s HLearn library offers algorithmic implementations for machine learning, while its Tensorflow binding supports deep learning. Haskell allows users to code highly expressive algorithms without sacrificing performance, and the language is ideal for projects that involve abstract maths and probabilistic programming. With Haskell, users can represent a model with just a handful of code and read the lines they’ve written like mathematical equations. In this way, Haskell can aptly convey the complexity of a deep learning model with clean code that resembles the model’s actual mathematics.
Pros:
- It is apt for engaging in abstract maths, unlike Java, and leverages common algebraic structures such as monoids and modules
- It enables developers to quickly spot errors in the app during the compile phase of the iteration
Cons:
- It lacks the same level of support enjoyed by the likes of Python and Java
Some of the features offered by Haskell are:
- Strong abstraction capabilities
- Built-in memory management
- Code reusability
- Easy to understand
Julia
Julia is one of the newer languages on the list and was created to focus on performance computing in scientific and technical fields. It is a high-performance and general-purpose dynamic programming language tailored to create almost any application but is highly suited for numerical analysis and computational science. A growing community of support has emerged around the language alongside an expanding number of machine learning libraries like TensorFlow.jl, scikitlearn.jl, Mocha.jl, MLJ.jl, Flux.jl, Turing.jl, and Metalhead. Julia is a top-choice programming language for AI applications that require high-powered numerical computing and is ideal for AI practitioners who already have a background in scripting languages like Python or R.
If you need to make a dynamic interface, catchy graphics, and data visuals, Julia provides you with the right tools for perfect execution. With features like debugging, memory management, metaprogramming, profiling, logging, package management, and asynchronous I/O this language makes AI development a breeze.
Pros:
- Dynamic type system
- Built-in package manager
- Able to work for parallel and distributed computing
- Macros and metaprogramming abilities
- Support for Multiple dispatches
- Direct support for C functions
Cons:
- Developers do not have a wealth of debugging tools at their disposal
Various tools available for working with Julia are:
- Popular editors such as Vim and Emacs
- IDEs such as Juno and Visual Studio
R
R is a comprehensive statistical language that encourages the development of new ideas. Because R can crunch vast data sets with ease, this programming language is widely used for statistical software development, data analysis, and data visualisation. It’s easy to understand and has a lot of packages and material libraries that can speed up the learning process, just like Python. With R, you can work on various paradigms of programming such as functional programming, vectorial computation and object-oriented programming. Although R is not a go-to programming language for deploying machine learning models in production, R is a top tool for exploratory work in the process of model selection. Packages like Gmodels, RODBC, OneR, and Tm allow huge support for Machine Learning processes. When these packages are coupled with a wide range of libraries, the capabilities of the R programming language reach new heights. The programming language is preferred by top AI companies for its ability to solve complex issues.
It is considered a standard language for domains such as biology, sociology, medicine, and finance. R includes user-created packages like graphical devices, tools, import/export capabilities, statistical techniques, etc. With built-in graphic and data modelling support, the language allows developers to work on deep learning moderns without much hassle.
Pros:
- It is relatively easy to understand and -use for data-centric tasks because of the abundance of packages and materials
- It has great support for producing high-quality graphs
- It is particularly good at crunching numbers (or dealing with statistical data) and is often considered a better choice for the same than Python
Cons:
- It lacks the necessary infrastructure for security — which means it cannot embed into web applications
- It is slower when compared to other programming languages like Python
Some of the AI programming packages available for R are:
- Gmodels – provides several tools for the task of model fitting
- TM – a framework used for text mining applications
- RODBC – an ODBC interface for R
- OneR – for implementing One Rule Machine Learning classification algorithm, useful for machine learning models
C++
This may be one of the most popular languages around, but it’s not as effective for AI development as the previous options. The main problem in relation to AI is the syntax of C++. It’s too complicated to quickly create useful coding for machine or deep learning applications. It is, however, perfect for people working in an embedded environment who can’t afford the overhead cost of the Java Virtual Machine. You can build neural networks from scratch using C++ and translate user code into something machines can understand. Come to think of it, many of the most notorious deep and machine learning libraries are written in C++.
The speed of C++ is the greatest benefit since AI development has complex computations and this language can make the calculations faster. It has a low-level memory control feature and carries asset-compelled applications, performance-critical applications, etc. When you consider AI processes, you talk about complex automated solutions to run efficiently. And, C++ won’t disappoint you in that respect. C++ also excels at dynamic load balancing, adaptive caching, and memory management—which is why this language is a top choice for building scalable big data frameworks. Notable C++ libraries include SHARK, which supports supervised learning algorithms like linear regression, and MLPACK, which offers extensible algorithms that users can integrate into scalable ML solutions.
Pros:
- It provides faster execution, and its speed is very much appreciated for time-sensitive AI programming projects
- It has better efficiency of control because of the extensive use of algorithms
Cons:
- Difficult to learn the language because the standard library of C++ is small
- It does not support garbage collection
- Large C++ projects are hard to maintain and are time-consuming to build
Wrapping Up…
While we can’t exactly define what’s best as it’s more subjective than selective, we have tried our best to cover top languages that are used to create AI solutions. The selection of ‘best’ depends on many factors such as business specifics, budget, target audience, market structure, and most importantly why and where you want to implement AI.
At 3point3 labs, we believe every project is unique, which is why our experts always give recommendations based on our clients’ requirements. Research is paramount before picking a suitable AI programming language.
If you are searching for help for your next AI project, please contact us at [email protected], and we will save your time, money, and resources with our top-notch AI development services.
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