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8 Best Artificial Intelligence Programming Language in 2018

[Estimated read time: 8 minutes]

Today, Artificial Intelligence (AI) has become on the hot topics and biggest research fields, every giant tech company and even startups are working on. 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 who is confused on 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.

8. Julia

JuliaBenchmark 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 dispatch 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 paper 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 model in one language and reproduce them in a faster production language. This reduces error, and saves time and cost.

 

The programming language is now used by the world’s biggest companies. In 2016, IBM and Julia computing analyzed eye fundus images and developed a deep learning solutions that provide better eye diagnosis and care to thousands of rural Indians.

 

7. Haskell

Haskell in strong static typing, 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 express AI algorithms. For example, HLearn that uses well known algebraic structure (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 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 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 an 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.

6. JavaScript

A high-level, event-driven, interpreted programming language that is mostly used to make webpages interactive and creating online programs, including games.

In JavaScript, it’s not essential to learn conversation model. Learn the data in server side and then call the learner by Ajax to predict. There are numerous libraries to develop your leaner. We are summarizing three of them –

ConventJS: A library for implementing deep learning – train convolutional neural network in 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 that 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 learned.

In short, you don’t have to reinvent the wheel – just determine what type of ‘learning’ the AI will do.

5. Prolog

simulating turing machine on prolog

Prolog is a logic programming language and semantic inference engine, associated with computational linguistic 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 a declarative language with formal logic. AI developers value it for its pre-designed search mechanism, nondeterminism, 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 object, like green triangle is 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 fact or a rule. It allows you to query the database even when you have thousand of these facts and rules.

Prolog supports development of graphical user interface, administrative and network applications. It is well suited for projects like voice control systems and filling templates.

4. Java

Robotcode

The best thing about Java language is its Java Virtual Machine technology that enables you to create a single app version, which will run on all Java-supported platforms. Its strengths are transparency, maintainability and portability.

What are the benefits of programming 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 makes 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

3. Lisp

Lisp is one of the oldest (developed in 1958) and prominent language created by the 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 program that computes with symbols very well. Symbolic expression and computing with those is what Lisp is good at.

Also, Lisp consists of a macro system, 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, some of them are –

  • American Express Authorizer’s Assistant that checks transactions (credit card)
  • METAL, a natural language translation system
  • Macsyma, first large computer algebra system
  • ACL2, a theorem prover used by AMD

2. C++

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++, and offers APIs for the same and wrapper for other programming languages.

If you want to have a control over runtime and performance, C++ is obviously a good choice here. The templates are more safe (type safety) to use and they provide a better way for generalizing APIs. Although templates are a powerful technique that can simplify most of the 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 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, Mozilla Firefox are a few famous applications using C++.

Read: 50 Shades of Computer Programming Laws

1. Python

Python is focused on DRY (don’t repeat yourself) and RAD (rapid application development). Developed in early 1990s, Python has become one of the fastest growing programming languages because of scalability, adaptability and ease of learning.

Python has hundreds of libraries that make any type of project possible, whether it is 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, balance of low-level and high-level programing, modular programming and testing frameworks, makes it different from the other language. The next advantage is fast prototyping. AI is about 80% research. In Python almost all idea can be quickly validated via 30-40 lines of code.

Read: 10+ best programming software for writing code

The list of Python application 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.

5 comments

  1. I respect no list of programming languages that doesn’t include F#. With the exception of C++ (speed), F# blows away all of these languages, for AI and pretty much anything else.

    Pick any language on this list that you like — I don’t care which — and I guarantee you I can write code that is cleaner, has a higher signal-to-noise ratio (i.e. fewer brackets, fewer parentheses, less punctuation, etc.), is more mathematically fluid, has fewer side effects, and is less bug prone, using F#. 100% guaranteed.

    I’ve been using it religiously for over five years, and I’ve never known a better language. Again, the only language that has anything on it is C++, for sheer speed. F# has been described as the greatest programming language since C++, and I would agree with that assessment. ( http://www.red-gate.com/simple-talk/opinion/geek-of-the-week/don-syme-geek-of-the-week/ )

  2. Interesting list. I expected to see Python come up as it shows up in much of my research into AI. I’m happy to see C++ here as it is my bread & butter. I quite honestly had not thought of using Javascript for AI programming but the article makes an interesting case.
    @MiloDC – I personally have never used F# but have come across it from time to time and your point is intriguing to me. I will have to check it out further.

  3. I think it’s also worth mentioning Clojure that brings the power of Lisp to the JVM. Cortex https://github.com/thinktopic/cortex library is a good place to start.

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