Programming Languages for AI
- [Nebraska State - Forbes]
- Overview
The AI, as we see it today, is called “narrow AI” or “weak AI.” That is because it can only perform narrow tasks such as facial recognition, an internet search or driving a car. The long term goal is to create “general AI” or “strong AI,” which would be able to outperform a human in any cognitive task.
Some examples of existing AI are:
- spam filters
- voice to text features
- Siri, Cortana
- smart searches
- personalized ad targeting
- automated customer support
- chatbots
There are many more examples. AI has brought technology to another level.
- The Programming Languages for AI and Machine Learning
If you are interested in AI, working on your own AI projects, then you will need to know what the most popular AI programming languages are. There are quite a few AI programming languages, and there is none of them that can be called “the best ai programming language.” They all have their pros and cons, and following are some of them:
Python, Scala, Java, R, Javascript, Lisp, C++, and Prolog
- Advantages of Python
- General-purpose language: Python is regarded as a better choice if your project demands more than just statistics. For instance — designing a functional website. Smooth Learning Curve — Python is easy to learn and easily accessible which enables you to find the skilled developers on a faster basis.
- The bulk of Important libraries: Python basts countless libraries for munging, gathering, and controlling the information. Take an occasion of Scikit-realize which comprises tools for information mining and investigation to support the incredible AI convenience utilizing Python. Another bundle called Pandas gives engineers superior structures and data examination devices that help to diminish the improvement time. If your development team demands one of the major functionalities of R then RPy2 is the one to go for.
- Better Integration: Generally, in any engineering environment, the Python integrates better than R. Thus, regardless of whether the designers attempt to exploit a lower-level language like C, C++ or Java, it generally gives better joining different components with Python wrapper. Additionally, a python-based stack is anything but difficult to incorporate the remaining task at hand of data researchers by bringing it easily into creation.
- Boosts Productivity: The syntax of Python is exceptionally decipherable and like other programming languages, however unique in relation to R. In this way, it guarantees high profitability of the development groups.
