Best programming languages for AI development

The 7 best programming languages for AI development

Are you an AI (artificial intelligence) aspirant who's confused on which programming language to pick for your next project? If so, you've come to the right place, as here we are going to look at the best 4 programming languages for AI development.

·         Python

·           C++

·          Lisp

·          Prolog

·          Java

·          Haskell

·          AIML

Python

Homepage: https://www.python.org
Initial release: 1991, latest release: 2017
OS: cross-platform

 Python takes the first place in the list of AI development languages due to its simple and seamless structure. Simple syntax and rich text processing tool allowed it to become a perfect solution for NLP problems. Programmers can build neural networks in Python, and machine learning with Python is also much easier. 

                           Python is one of the most widely used programming languages in the AI field of Artificial Intelligence thanks to its simplicity. It can seamlessly be used with the data structures and other frequently used AI algorithms. The choice of Python for AI projects also stems from the fact that there are plenty of useful libraries that can be used in AI. For example, Numpy offers scientific computation capability, Scypy for advanced computing and Pybrain for machine learning in Python.

Features:
– short development time (as compared to Lips, Java or C++);
– large variety of libraries;
– high level sytax;
– supposerts object-oriented, functional and procedural styles of programming;
– good for testing algorithms without implementing them.

Java

Homepage: https://www.oracle.com/java/index.html
First release: 1995, latest release: 2014
OS: cross-platform

Java is a great choice. It is an object-oriented programming language that focuses on providing all the high-level features needed to work on AI projects, it's portable, and it offers in-built garbage collection. The Java community is also a plus point as there will be someone to help you with your queries and problems.

                     Java is also a good choice as it offers an easy way to code algorithms, and AI is full of algorithms, be they search algorithms, natural language processing algorithms or neural networks. Not to mention that Java also allows for scalability, which is a must-have feature for AI projects.

 Java AI programming is a good solution for neural networks, NLP and search algorithms. Java is an object-oriented programming language that follows the principle of WORA (“write once, read everywhere”). It runs on all platforms without any additional recompilation due to Virtual Machine Technology. Some more advantages of Java is that this language is easy to use and easy to debug. However, in term of speed, it loses against C++. Java AI programming is a good solution for neural networks, NLP and search algorithms.

Features:
-in-build garbge collection;
– portable;
– easy to code algorithms;
– scalability.

Lisp

Initial release: 1959
Influenced: Python

Lisp fares well in the AI field because of its excellent prototyping capabilities and its support for symbolic expressions. It's a powerful programming language and is used in major AI projects, such as Macsyma, DART, and CYC.

                      The Lisp language is mostly used in the Machine Learning/ ILP sub-field because of its usability and symbolic structure. Peter Norvig, the famous computer scientist who works extensively in the AI field, and also the writer of the famous AI book, “Artificial Intelligence: A modern approach,”.

                 Lisp, being the second oldest programming language in the world (after Fortran), still holds a top position in AI creating due to its unique features. For example, Lisp has a special macro system which makes possible to develop a domain specific level of abstraction and build the next level on it. Lisp in artificial intelligence development is known for its unique flexibility as it adapts to the problem you need to solve on the contrary to the other languages that are chosen because they can complete this or that task. Developers opt for Lisp in machine learning and inductive logic projects.

Features:
– fast prototyping capabilities;
-support for symbolic expressions;
– automatic garbage collection which actually was invented for the Lisp language;
– library of connection types including dynamically-sized lists and hastables;
– efficient coding due to compilers;
– interactive evaluation of components and recompilation of files while the program is running.

Prolog 

Initial release: 1972
Influenced: Mercury, XSB
Dialects: Edinburgh Prolog, ISO Prolog

Prolog stands alongside Lisp when it comes to usefulness and usability. According to the literature, Prolog Programming for Artificial Intelligence, Prolog is one of those programming languagesfor some basic mechanisms, which can be extremely useful for AI programming. For example, it offers pattern matching, automatic backtracking, and tree-based data structuring mechanisms. Combining these mechanisms provides a flexible framework to work with.

Prolog is extensively used in expert systems for AI and is also useful for working on medical projects.

The name of Prolog speaks for itself; it’s one of the oldest logic programming languages. If we compare it with other languages, we can see it is declarative. It means that the logic of any program will be represented by rules and facts. Prolog programming for artificial intelligence can create expert systems and solving logic problems. Some scholars claim that an average AI developer is bilingual – they code both Lisp and Prolog.

Features:
– pattern matching;
– tree-based data structuring;
– good for rapid prototyping;
– automatic backtracking.

C++

Homepage: https://isocpp.org
Initial release: 1983, latest release: 2104
Influenced: Java, Python

The major advantage of C++ for AI is its speed, and one can find C++ among the fastest programming languages in the world. Since AI development demands lots of calculation fast-running programs are of ultimate importance. C++ is highly recommended for machine learning and neural network building.

 C++ is the fastest programming language in the world. Its ability to talk at the hardware level enables developers to improve their program execution time. C++ is extremely useful for AI projects, which are time-sensitive. Search engines, for example, can utilize C++ extensively.

In AI, C++ can be used for statistical AI techniques like those found in neural networks. Algorithms can also be written extensively in the C++ for speed execution, and AI in games is mostly coded in C++ for faster execution and response time.

Features:
-high level of abstraction;
– good for high performance;
– organize data according to object oriented pricniples;
– STL collection.

Haskell

Homepage: https://www.haskell.org
Initial release: 1990, latest release: 2010
OS: cross-platform

Haskell is a purely functional programming language that can boast about its lazy evaluation and type interface features. LogicT monads facilitate expressing non-deterministic algorithms, and algorithms can be expressed in a compositional way.

Features:
– major algorithms available via cabal;
– CUDA binding;
– compiled to bytecode;
– can be executed on multple CPU in cloud.

AIML

Homepage: http://www.alicebot.org/aiml.html
Initial release: 2001, latest release: 2011
Extended from: XML

AIML (Artificial Intelligence Markup Language) is a dialect of XML used to create chatbots. Due to AIML one can create conversation partners speaking a natural language.
The language has categories showing a unit of knowledge; patterns of possible utterance addressed to a chatbot, and templates of possible answers.