Programming Languages for AI
Artificial intelligence, or AI, is a very hot topic. From professors worried about students using it to write term papers to businesses figuring out how to incorporate AI to optimize business practices ranging from customer service to product design, AI is being discussed everywhere. Some AI chatbots are easy enough for everyone to use, but knowing how to program and effectively use AI is a more advanced skill. Designing AI utilities requires understanding how to use programming languages.
C++
The reason C++ has remained popular for so long as a basic coding language is that it's also popular for use in AI. It's a very flexible coding language, which makes it perfect to use when coding applications and programs that use lots of resources at the same time.
- Many existing machine learning libraries were written for C++.
- C++ is very efficient because it automatically rewrites user inputs into code that is machine-readable.
- C++ is used for speech recognition in AI.
Haskell
Haskell is a good fit for AI in many different ways. It's a coding language that's both readable and functional. Programmers in Haskell can succinctly describe algorithms using the language.
- The HLearn library has existing implementation frameworks for machine learning algorithms.
- Haskell is the preferred language for data structures with no defined limits.
Java
Java is perhaps the most used coding language for the creation of apps, so it makes sense that it's also widely used for coding AI applications.
- Java also works with TensorFlow.
- There are lots of content repositories and existing frameworks for coding AI.
- Java's syntax means it's easy to use for coding complex data as graphical interfaces.
Julia
Julia is a far newer coding language than some of the others commonly used in AI programming. It was purposefully designed for use in technical and scientific fields.
- Julia was designed for dealing with different types of numeric data.
- It is more adaptable for dealing with math and also capable of more robust functioning.
- It supports C language functions.
- Programmers use Julia for both metaprogramming and macroprogramming.
Lisp
Lisp debuted in the 1960s and was used for very complex coding needs, including solving theorem proofs and training computers to answer questions in natural language. It's the second oldest coding language still commonly used today. However, some of its features make it particularly useful to people coding AI applications.
- Lisp is good at handling symbolic information.
- It automatically collects stray code as garbage.
- Lisp excels at dynamic object creation.
- It's one of the fastest languages for rapid prototyping.
Prolog
Prolog is a logic language. Its name comes from the phrase "programming in logic."
- Developers like working with Prolog because the language can execute commands from the search tool function.
- Programmers can easily establish rules when writing AI applications.
- It's one of the best languages for establishing rules for extracting information from databases.
- Data is structured using a tree, meaning it's easy to find issues within the code.
Python
Python is used for many other reasons other than AI. However, it's one of the most commonly used languages for a variety of AI programming needs. It's most commonly used for machine learning applications. Other reasons Python is perhaps the most popular AI coding language include:
- There are many AI-specific frameworks developed for the language.
- Python is easy to learn.
- There are lots of resources available to coders, like TensorFlow, an open-source code repository full of information about neural networks and machine learning.
R
R's strength lies in its scalability. It's very good at working with large numbers and data sets. R is object-oriented, which also makes it very useful for AI.
- There are special tools designed for R to be used in AI programming, which include models, RODBC, and TM.
- One use for R in AI is for text mining.
- R also excels at vectorial computation.
Scala
Beginners find Scala to be a great tool to help them get into AI coding. In fact, software developers of all experience levels appreciate the ease of using Scala.
- Scala excels at helping users analyze large amounts of data.
- It's easy to use for managing large amounts of complicated content.
- There are established, open-source algorithms for classification functions.
- Various open-source libraries and frameworks are available for developers to use when applying Scala to AI functions.
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