Technology surrounds us, and so do the intelligent algorithms that control it. Whether navigating to find your destination, shopping, needing voice assistance, or making online transactions through fraud prevention, it all includes using Artificial Intelligence algorithms. These algorithms are instructions that help an automated system complete a task and are constantly evolving. Programming languages are built for communicating with computers to implement these AI algorithms.
Market research analysis in June 2022 suggests that for 2022-28, the AI market size in the US is estimated to grow to $422 Billion annually. Furthermore, the industrial dynamics reveal that most businesses are expected to invest in technology associates that can produce autonomous machines/robotics that can comprehend instructions/environment and interact or sense accordingly.
The AI industry has a prosperous future yet can be complicated, demanding a specific skill set. For high-end AI systems, it is essential to be proficient in the following programming languages:
For AI, Java is among the top user-friendly language. Moreover, it offers AI libraries for application development.
An AI Java developer can have the following advantages:
- Simplicity of debugging
- Data representation through graphics
- Project simplification and enhanced interactions
- Maintainability, security, and portability
- Easy to code and learn
- Garbage collector (Built-in)
- Widget toolkit
- Easy implementation for large-scale projects
- Reliable Java community and 24/7 support
For AI business solutions, it is used for developing genetic programming, machine learning-oriented solutions, neural networks, and robotic systems.
It is among the top-notch languages for AI-based solutions and is considered a general-purpose language. Top software projects, including Google, Facebook, and Youtube, are written in Python codes.
It has been demonstrated to be the top-notch language for AI due to the following reasons:
- Simple and easy coding
- A vast library system that has pre-written codes
- Better data handling/transformation
- Flexible for selecting/combining programming styles, for example, Object-oriented or functional styles
- Availability of custom-made Python packages, including Nimoy, Spicy
- Visualization tools support data representation
- Ease of altering code/debugging
- Fast project development
C++ evolved from the C language and a general-purpose programming language developed in 1979 and efficient for time-sensitive AI projects. It encompasses both low and high-level programming.
Following are a few aspects of C++ in AI projects:
- It is complex and has additional syntax rules and program conventions
- It is statically typed, hence proven to be fast in code compilation
- It does not provide a Garbage collector for memory management
- The best application for C++ in AI is in developing embedded systems and hardware for AI projects.
R language evolved from language S and was released in 2000. In AI, it is mainly used for data representations, data analysis, and machine learning.
Following are some aspects of R:
- Use machine learning functions and statistics
- Predicts new data through previous results
- Applicable for big data and presents large datasets into smaller datasets
- Provides an experimental web interface for data analysis
- Project development can be documented
Prolog is short for Programming in logic that investigates an association between fact, rule, and conditional statement for finalizing an objective.
The main aspects are:
- Less effort in programming
- Easy pattern matching
- Simpler for list-based algorithms
- In AI, it is best suited for backtracking and search problems
- Advantageous for AI applications that require focus on the issue, rather than particular computer commands
- Makes use of recursive constructs in developing significant AI real-life problems