Artificial intelligence hasn’t developed its own language yet, but even with using existing programming languages humanity has achieved great results. Just recollect the 2015 breakthrough of AlphaGo. It was the first time when a machine managed to beat a human being in the most difficult board game Go, which demands a high level of abstract thinking.
Let’s have a closer look at means that make artificial intelligence real.
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.
Features:
– short development time (as compared to Lips, Java or C++);
– large variety of libraries;
– high level sytax;
– supposrts object-oriented, functional and procedural styles of programming;
– good for testing algorithms without implementing them.
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.
Features:
-high level of abstraction;
– good for high performance;
– organize data according to object oriented pricniples;
– STL collection.
Lisp
Homepage: http://lisp-lang.org/
Initial release: 1959
Influenced: Python
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
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.
Java
Homepage: https://www.oracle.com/java/index.html
First release: 1995, latest release: 2014
OS: cross-platform
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.
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. To know how it works check out this article about building a chatbot.
So, the matter of best-something is rather philosophical in any sphere, and AI development is not an exception. There are a lot of factors influencing the choice of programming languages for an AI project. It depends on functions you need to create, usage and even your taste in some cases. However, more and more AI programmers are using Python as it’s a simple and powerful tool, while C++, Prolog and Lisp can be called runners-up in this race.
The article was originally posted at https://qubit-labs.com/ai-programming-languages-choose/.
