AI is a huge technology. That's why a lot of developers simply don't know how to get started and choose the best programming language. Also, personally, I've met a bunch of people who have no coding background whatsoever, yet they want to learn artificial intelligence.
Most aspiring AI developers wonder: what languages are needed to create an AI algorithm? So, I've decided to draw up a list of programming languages my friends-developers use to create AIs.
Python is one of the most popular programming language thanks to its adaptability and relatively low difficulty to master. Python is quite often used as a glue language that puts components together.
Why do developers choose Python to code AIs?
Python is gaining unbelievably huge momentum in AI. The language is used to develop data science algorithms, machine learning, and IoT projects. There are a few reasons for this astonishing popularity:
- Less coding required. AI has a lot of algorithms. Testing all of them can make into a hard work. That's where Python usually comes in handy. The language has "check as you code" methodology that eases the process of testing.
- Built-in libraries. They proved to be convenient for AI developers. To name but a few, you can use Pybrain for machine learning, Numpy for scientific computation, and Scipy for advanced computing.
- Flexibility and independence. A good thing about Python is that you can get your project running on different OS with but a few changes in the code. That saves time as you don't have to test the algorithm on every OS separately.
- Support. Python community is among the reasons why you cannot pass the language by when there's an AI project at stakes. The community of Python's users is very active - you can find a more experienced developer to help you with your trouble.
- Popularity. Millennials love the language. Its popularity grows day-to-day, and it's only likely to remain so in the future. There are a lot of courses, open source projects, and comprehensive articles that'll help you master Python in no time.
C++ is a solid choice for an AI developer. To start with, Google used the language to create TensorFlow libraries. Though most developers have already moved on to using "easier" programming languages such as Python, still, a lot of basic AI functions are built with C++. Also, it's quite an elegant choice for high-level AI heuristics.
To use C++ to develop AI-algorithms, you have to be a truly experienced developer with no rush pressing on you. Otherwise, you might have a bit of tough time trying to figure out a complicated code few hours before due date of the project.
A reason for Lisp's huge AI momentum is its power of computing with symbolic expressions. One can argue that Lisp is a bit old-fashioned, and it might be true. These days, developers mostly use younger dynamic languages as Ruby and Python. Still, Lisp has its own powerful features. Let's name but a few of those:
- Lisp allows you to write self-modifying code rather easily;
- You can extend the language in a way that fits better for a particular domain thus creating a domain specific language;
- A solid choice for recursive algorithms.
Should you take an in-depth course to learn Lisp? Not necessarily. However, knowing as much as basic principles is pretty much enough for AI developers.
Being one of the most popular programming languages in overall development, Java has also won its fans hearts as a fit and elegant language for AI development.
Why? I asked some developers I know use Java about it. Here are the reasons they've given to back of their fondness of the language:
- It has impressive flexibility for data security. With GDPR regulation and overall concerns about data protection, being able to ensure of client's data security is crucial. Java provides the most flexibility in creating different client environments, therefore protecting one's personal information.
- It is loved for a robust ecosystem. A lot of open sources projects are written using Java. The language accelerates development a great deal comparing to its alternatives.
- Low cost of streamlining.
- Impressive community. There are a lot of experienced developers and experts in Java who are open to sharing their knowledge and expertise. Also, there's but a ton of open source projects and libraries you can use to learn AI development.
Prolog is a less popular and mainstream choice as the previous ones we've been discussing. However, you shouldn't dismiss it simply because it doesn't have a multi-million community of fans.
Prolog still comes in handy for AI developers. Most of those who start using it acknowledge that it's, at no doubt, a convenient language to express relationships and goals.
- You can declare facts and create rules based on those facts. That allows a developer to answer and reason different queries.
- Prolog is a straightforward language that for a problem-solution kind of development.
- Another good news is that Prolog supports backtracking so the overall algorithm management will be easier.
Similar to Lisp, the wide use of SmallTalk was a common practice in 70s. Now, it loses its momentum in favor of Python, Java, and C++. However, SmallTalk libraries for AI are currently appearing at a rapid pace. Obviously, there aren't as many as those for Python and Java.
Yet, highly underestimated as for now, the language keeps evolving through its newly developed project Pharo. Here are but a few innovations it made possible:
- Oz - allows an image to manipulate another one;
- Moose - an impressive tool for code analysis and visualization;
- Amber (with Pharo as the reference language) is a tool for front-end programming.
R is a must-learn language for you if any of your future projects make use of data and require data science. Though speed might not be R's most prominent advantage, it does almost every AI-related task you can think of:
- creating clean datasets;
- split a big data set into a few training sets and test sets;
- use data analysis to create predictions for the new data;
- the language can be easily ported to Big Data environments.
Sometimes R does things a bit differently from the traditional way. However, among its advantages, one has to name the little amount of code and interactive working environment.
Haskell is quite a good programming language to develop AI. It is a fit for writing neural networks, graphical models, genetic programming, etc. Here are some features that make the language a good choice for AI developers.
- Haskell is great at creating domain specific languages.
- Using Haskell, you can separate pure actions from the I/O. That enables developers to write algorithms like alpha/beta search.
- There are a few very good libraries - take matrix for an example.
This was my list of programming languages that come in handy for AI developers. What are your favorites?