Today, Artificial Intelligence (AI) has become one of the hot topics and most extensive research fields; every giant tech company and even startups are working on it. It’s a very broad topic ranging from basic calculators and self-steering technology to self-aware robots that can radically change the future. Developing systems that equal or exceed human intelligence is the crux of AI.
According to Mark Cuban (American businessman and investor), AI will make the world’s first trillionaire. ‘The first trillionaire is going to be someone who masters Artificial Intelligence and all its derivatives and implements it in ways we never thought of,’ he said.
If you are an AI aspirant confused about which coding language to select for your next big project, you are landed at the correct destination. Below we’ve shown which programming language is best for developing AI software.
One can use C# on Microsoft’s .NET suite to develop high-level machine learning models. It’s an object-oriented language that contains several useful tools for developing AI apps.
The ML.NET, a cross-platform machine learning framework, makes it easy to develop and integrate custom machine learning models into .NET apps. You don’t even require any prior machine learning experience to do it.
You can even use pre-built AI models, such as vision and speech recognition, intelligent search, and emotion and sentiment detection.
.NET also supports the F# programming language — a succinct, robust, and performant language. Its type system and special features like Units of Measure and Type Providers are a perfect match for machine learning and data science.
Benchmark times relative to C (C performance = 1.0, smaller is better)
Julia is designed to deal with high-performance numerical analysis and computational science without the typical requirement of separate compilation. It includes a type system with parametric polymorphism and multiple dispatches as its core programming paradigm.
Julia’s deep mathematical roots and comprehensive customizability make it very friendly to work with for a data analyst. With Julia, it’s quite easy to translate algorithms from research papers into code with no loss in translation – thus decreasing model risk and improving safety.
However, the most important factor is the community – It’s an open-source language, licensed under the liberal MIT license. Julia runs on almost all types of hardware out there, including IBM, Intel, NVIDIA, and ARM.
Since Julia combines the ease of use and familiar syntax of Matlab, Python, and R with the speed of C++, developers no longer need to estimate models in one language and reproduce them in a faster production language. This reduces error and saves time and cost.
This language is now used by the world’s biggest companies. IBM and Julia computing, for instance, have analyzed eye fundus images and developed deep-learning solutions that provide better eye diagnosis and care to thousands of rural Indians. It is also widely used in astronomy, robotics, network security, parallel supercomputing, and financial modeling and management.
Haskell in strong static typing, a non-strict programming language developed in 1990. Since there are not many Haskell developers, private companies are reluctant to try Haskell.
One thing that Haskell is perfect at is abstraction (abstract mathematics, not like Java OOP). It allows expressive and efficient libraries to express AI algorithms. For example, HLearn uses well-known algebraic structures (modules, monoids, etc.) to express and boost the speed of simple machine learning algorithms.
Although you can write these algorithms in any language, Haskell makes them more expressive than others while maintaining decent performance. For instance, faster cover trees is written in Haskell.
Haskell supports embedded domain-specific languages, which is a famous area of programming language research applicable to a large number of domains, including artificial intelligence. Specifically, it is a good host for probabilistic programming and helps developers catch errors at compile time. If you are interested, you can check out Hakura, a research project creating embedded probabilistic programming.
The language has CUDA binding and is compiled to bytecode. Since it is functional and stateless, code can be easily executed on different CPUs in the cloud. If we talk about industry adoption, Facebook uses Haskell to fight spam.
A high-level, event-driven, interpreted programming language that is mostly used to make webpages interactive and create online programs and games.
- ConventJS: A library for implementing deep learning – train convolutional neural network in the browser. It supports fully connected layers as well as nonlinear neural network modules, classification, and regression cost functions.
- Synaptic: A neural network library for node.js. Its generalized algorithm is architecture-free, which lets you develop and train almost all types of first and second-order neural network architectures.
- Mind: It uses matrix implementation to process training data. You can completely customize the network topology and upload/download minds that have already been learned.
In short, you don’t have to reinvent the wheel – just determine what type of ‘learning’ the AI will do.
Prolog is a logic programming language and semantic inference engine associated with computational linguistics and artificial intelligence. It has a flexible and powerful framework that is widely used for theorem proving, non-numerical programming, natural language processing, and AI in general.
It’s declarative language with formal logic. AI developers value it for its pre-designed search mechanism, non-determinism, backtracking mechanism, recursive nature, high-level abstraction, and pattern matching.
Prolog is well suited for problems involving structured objects and relations between them. For instance, in Prolog, it is easier to express spatial relationships between objects, like a green triangle is located behind the blue one. It is also simple to state a general rule – if object A is closer to the person than object B, and B is closer than C, then A should be closer than C.
Prolog’s nature makes it simple and straightforward to implement facts and rules. If fact, everything in Prolog is a fact or a rule. It allows you to query the database even when you have thousands of these facts and rules.
Prolog supports the development of graphical user interfaces and administrative and network applications. It is well suited for projects like voice control systems and filling templates.
The best thing about the Java language is its Java Virtual Machine technology, which allows you to create a single app version for all Java-supported platforms. Its strengths are transparency, maintainability, and portability.
What are the benefits of developing AI in Java? Well-supported large-scale projects, better user interaction, debugging ease, facilitated visualization, and incorporation of Swing and Standard Widget Toolkit.
The major advantage is its versatility – if you are a beginner, there are thousands of useful tutorials available on the internet (for free) that make your learning easier and more effective.
Some of the well-known applications developed in Java are
- WEKA machine learning suite, which is dedicated to machine learning and data mining
- JOONE neural engine for designing, training, and testing neural networks
- ALICE (short for artificial linguistic internet computer entity), natural language processing chatterbots
- Robocode, an open-source game for learning principles of Java programming
Lisp is one of the oldest (developed in 1958) and most prominent languages created by Dr. John MaCarthy, who coined the term ‘Artificial Intelligence.’ Although it is not used much these days, the language is both flexible and extendable.
It was originally developed for Lambda Calculus computation, and since its inception, it has evolved a lot. The language introduced many ideas in computer science, such as recursion, dynamic typing, higher-order functions, automatic storage management, self-hosting compiler, and tree data structure.
Lisp is used for developing Artificial Intelligence software because it supports the implementation of a program that works with symbols very well. Processing symbolic expression is what Lisp is good at.
Also, Lisp consists of a macro system, a well-developed compiler that can produce efficient code, and a library of collection types, including hashtables and dynamic-size lists.
There are thousands of AI applications developed in Lisp; popular names include-
- American Express Authorizer’s Assistant that checks transactions (credit card)
- METAL, a natural language translation system
- Macsyma, the first large computer algebra system
- ACL2, a theorem prover used by AMD
C++ is faster than other languages – its ability to communicate at the hardware level allows you to improve code execution time. It is extremely useful for artificial intelligence projects that are time-sensitive. It can be used for statistical AI approach like those found in neural networks.
With faster execution time and OOP principles, C++ makes itself a good candidate for AI programs. In fact, a vast portion of machine learning and deep learning libraries are written in C/C++. It offers APIs for the same and wrapper for other programming languages.
If you want to have control over runtime and performance, C++ is obviously a good choice here. The templates are safer (type safety) to use, and they provide a better way for generalizing APIs. Although templates are a powerful technique that can simplify most things, they require more time and experience to decide when their usage is appropriate.
The language overrides the complexities of 3D games, optimizing resource management and facilitating multiplayer with networking. A real-world example is the science fiction game Doom 3, which uses C++ and the Unreal Engine, a suite of game development tools (written in C++). Microsoft Windows, Mac OS, Adobe Photoshop, Maya 3D software, CAD, and Mozilla Firefox are a few famous applications of C++.
Python is focused on DRY (don’t repeat yourself) and RAD (rapid application development). Developed in the early 1990s, Python has become one of the fastest-growing programming languages because of its scalability, adaptability, and ease of learning.
Python has hundreds of libraries that make any type of project possible, whether it is a mobile app, web app, data science, or artificial intelligence. For example, ‘Numpy’ for scientific computation, ‘Pybrain’ for machine learning, ‘Scipy’ for advanced computing, and ‘AIMA’ for artificial intelligence.
Python’s holistic language design, the balance of low-level and high-level programming, modular programming, and testing frameworks make it different from other languages.
The other major benefit is fast prototyping. AI is about 80% research. In Python, almost all ideas can be quickly validated via 30-40 lines of code.
The list of Python applications is really long. It is used in image processing and graphic design programs, games, web frameworks, enterprise and business applications, and much more. Some of the biggest websites developed in Python include YouTube, Reddit, Quora, Dropbox, and Disqus.
Read: 50 Shades of Computer Programming Laws
Smalltalk features dynamic runtime typing methodology and maneuverability. In this language, there is no concept of integers, characters, and booleans — all these are treated as objects. In fact, classes and metaclasses are treated as objects.
These objects perform three major operations — receive messages from itself or other objects; send messages to itself and other objects; and reference other objects.
Since it has influenced several other languages (including Java, Python, C, and Ruby) and has an efficient computational power, it has been widely used in neural networks, machine learning, simulations, genetic algorithms, and numerous other industrial fields. Many web-developing firms use Smalltalk on a very large scale.
11. Wolfram Language
Wolfram Language is a multi-paradigm programming language that focuses on rule-based programming, functional programming, and symbolic computation. It can employ arbitrary data and structures.
Wolfram makes it possible to express complex notions in computational form. To do this, it integrates high-level forms (such as Molecule, GeoPolygon, or Image) with advanced superfunctions (like ApplyReaction or ImageIdentify).
Overall, it represents everything (including documents, code, data, graphics, and interfaces) as symbolic expressions, making it easier to develop intelligent systems with any data type.
12. Artificial Intelligence Markup Language
AIML is an XML dialect meant to build artificial intelligence applications. It makes it easier to develop AI apps, while keeping the implementation easy to understand and highly maintainable.
It contains a set of rules that define the conversational capabilities of the chatbot. The more rules we add in AIML, the smarter the chatbot is.
AIML interpreters are available in C#, Pascal, C++, Python, Java, Ruby, and other programming languages. Moreover, a W3C XML Schema and semi-formal specification for AIML are also available.
More to Know
What are the top AI fields?
The five most demanding and high-paying AI fields are:
- Machine learning
- Data Science
- Natural Language Processing
- Business Intelligence System
The most popular AI projects
Hundreds of companies across the world have been working on AI for more than a decade. Some of the notable, non-classified AI projects are:
- Google Brain combines machine learning research with large-scale computing resources and information systems
- AlphaGo plays the Chinese board game Go
- Libratus is designed to play Poker and intended to be generalizable to other systems
- Serenata de Amor is built to analyze public spending and identify discrepancies
- Melomics is an AI program for composing unique music instead of mimicking musicians
- ChatGPT is a chatbot fine-tuned with supervised and reinforcement learning methods
Machine learning market size
According to the Precedence Research report, the global market size of machine learning as a service will exceed $305.6 billion by 2030, growing at a CAGR of 39.3% from 2023 to 2030.
The major factor behind this growth includes the increasing demand for smart tools like facial recognition, data visualization, predictive analytics, and deep learning models. Moreover, many tech companies are strengthening their deep learning capabilities to drive innovations that are likely to drive machine learning market growth across regions and industries.
12 Best Machine Learning Tools
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