Which are the Best Language for Artificial Intelligence Apps Development
What is Artificial Intelligence?
Today Artificial Intelligence is a very popular field. Artificial Intelligence (AI) is the study of computer science which focuses on developing software or machines that exhibit human intelligence. John McCarthy is the father of Artificial Intelligence. Artificial Intelligence is the sub-part of computer science. It’s main aim to perform activities that are normally done by the user. It studies how people’s brain think, works, learn and make a decision. AI is a very broad subject and is used in all fields such as computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology.
Goals of AI
- To create expert systems which exhibit intelligent behavior to learn, demonstrate, explain, and give advice its users.
- Implement Human Intelligence in Machines will enable to understand, think, learn, and behave like humans.
What is the Artificial Intelligence Markup Language?
Artificial Intelligence Markup Language (AIML) is an XML based popular language used to create Artificial Intelligence Applications. It is also used to store data to drive a dialog engine.
Typical AIML syntax consists of <topic> tags that contain <category> tags. Each of these <category> tags contain a <pattern> tag, an optional <that> tag and a <template> tag. Below is an example:
<aiml>
<topic name=”*”>
<category>
<pattern>HELLO</pattern>
<that>*</that>
<template>Hi! How are you?</template>
</category>
</topic>
</aiml>
Artificial Intelligence Programming for Beginners
In AI it is really hard to refer one single programming language. There are lots of languages for artificial intelligence that can be used, but not every programming language offers you the best value for your time and effort.
Below is some programming language for artificial intelligence which shows which is the best for developing AI software.
- Python
- Java
- Lisp
- Prolog
- C++
- Haskell
Python: Python is the best language to learn AI because its libraries are better suited to Machine Learning. Python is a simple, easy to learn, powerful, high level and object-oriented programming language. Python is an object-oriented programming language created by Guido Rossum in 1989.
Advantages of Python Programming Language over the Other Programming Languages for AI
- Less Code: Python helps in easy writing and execution of codes.
- Prebuilt Libraries: Python has a lot of libraries for every need of your AI project. Python has many image intensive libraries like Python Imaging Library, VTK and Maya 3D Visualization Toolkits, Numeric Python, Scientific Python and many other tools available for numeric and scientific applications.
- Python is platform independent
- Compared to other programming languages, it allows more run-time flexibility
- Python supports functional and structured programming as well as OOP
Java: Java Programming language also a good option for AI development. AI Programming with Java has various benefits such as It is easy to use debugging is easy, simplified work with large-scale projects, facilitated visualization, better user interaction. Another reason for programming AI in Java is the incorporation of Swing and SWT (the Standard Widget Toolkit). These features make graphics and interfaces look appealing and sophisticated.
Java is not as high-level as Lisp or Prolog, and not as fast as C, making it best when portability is paramount.
Lisp: LISP is a general-purpose programming language and is the second-oldest programming language still in use. For developing Artificial Intelligence software Lisp is used because it helps for the implementation of a computer program with symbols very well. Also, Lisp consists of a macro system, a well-developed compiler that can produce efficient code, and a library of collection types, including hash tables and dynamic-size lists.
Prolog: Prolog is a logic programming language that was invented by Alain Colmerauer and Phillipe Roussel. Prolog is one of those programming languages for some basic mechanisms, which can be extremely useful for AI programming. Prolog is a flexible and powerful framework which is used for theorem proving, non-numerical programming, natural language processing, and AI. Prolog supports the development of graphical user interface, administrative and network applications. It is well suited for projects like voice control systems and filling templates.
C++ : C++ is an object-oriented programming language. C++ is faster than other languages. C++ is highly recommended for machine learning and neural network building. The C++ Standard Library giving a rich set of functions manipulating files, strings, etc. C++ is a superset of C, and that virtually any legal C program is a legal C++ program.
Haskell: Haskell is one of the leading languages for teaching functional programming, enabling students to write simpler and cleaner code. One thing that Haskell is perfect at is an abstraction. It allows expressive and efficient libraries express AI algorithms. Haskell is in many respects a very safe language. Haskell programming language helps to work on a major piece of logic and mathematics.
If you want to learn Artificial Intelligence then get more familiar with machine learning and you will be ready to understand artificial intelligence too. Before choosing a programming language it is important to check whether the programming language can be utilized or not. Currently, Python is more popular among all above languages as it is viable to use for most of the AI subfields. Java and C++ both languages are also good for AI development.
Artificial Intelligence using Python
Python is a great choice for Artificial Intelligence due to several reasons. Python is a simple and easy to learn; even if you’re not aware of Python, you can speed up your programming quickly if you’ve ever used any other language like C-Programming. Other reason to used Python, Python has a great community, which provides a good documentation and available 24X7 to solve your queries.
Brush up Your Python Skill:
If you want to build AI in Python, it is bit hard and will take some time to understand it. The time needed all depends on your programming skill, experience etc. You have to brush up your programming skill for that first of all you have to install Python. You can install it using Anaconda, it is open source analytics platform. If you have experience on working with Python, still you should check python documentation for the latest updates in technology.
Basic Machine Learning Skills:
After that, you have to pick up your machine learning skills. It is impossible to learn machine learning in a short period of time, if you want to the deep understanding of machine learning then you have to spend hundreds of hours for that.
Learn more about Python packages:
After getting the basic knowledge of Python and Machine Learning you should learn Python libraries. There is the number of open source Python libraries which is used to build AI.
Let’s see some basic libraries
- Numpy – Numpy is mainly a container of generic data which contain an N-dimensional array object, random number capabilities, Tools for integrating C/C++ code, Fourier transform and other functions. It is useful for scientific computing.
- Pandas – Pandas is a Python data analysis open source library, including structures such as data frames that provide users with easy-to-use data structures and analytic tools for Python.
- Matplotlib – Matplotlib is another library you will like. It is 2D plotting library used to create publication quality figures. Matplotlib has a great advantage as it used in graphical user interface toolkits, web application servers, and Python scripts.
- Scikit-learn – Scikit-learn is an open source machine learning algorithms used for data analysis and data mining tasks. It also beneficial for commercial use.
Advanced Machine Learning Topics with Python
After learning with sci-kit-learn, you can move towards in depth with various common and useful algorithm. You can use k-means clustering one of the best machine learning algorithms. It is the simple method to solve unsupervised learning problem. Apart from that, you can learn other common and well-known machine learning algorithms such as investigated a powerful ensemble technique (random forests) and examined some additional machine learning support tasks (dimensionality reduction, model validation techniques).
If you want to learn more about AI then you have to take more attention on deep machine learning frameworks such as Caffee and a Python library Theano.
You can learn more about Python AI libraries such as AIMA, pyDatalog, SimpleAI, EasyAi, etc. There are also Python libraries for machine learning: PyBrain, MDP, sci-kit, PyML.
I can’t say it’s easy to work with AI using python but if you follow the above steps and learn more about Machine Learning Algorithms and libraries then you will get success definitely.
Best Artificial Intelligence Software
- Braina:
Braina is a one of the best Artificial Intelligence software which allows you to interact with your PC using voice commands. This AI software used to develop different products. It also supports multiple languages. Braina is used for windows operating system. Braina isn’t a Siri or chat-bot, its priority is to be super practical and to assist you in doing tasks. Using Braina you’ll be able to sort commands or speak thereto, Braina gives you the particular result as per your wish. Using the Braina app for Android You can send voice commands to your PC using Wi-Fi network.
- Alice (Artificial Linguistic Internet Computer Entity) :
Alice is a free natural artificial intelligent language used for processing chatting and robotics program with a real person over the internet. ALICE perform a quick action, such as it gives an immediate response to the chat when the user begins typing a conversion. ALICE is also called as Alicebot or Alice. The ALICE program used artificial intelligence markup language (AIML), which helps to specify conversation rules. Other developers wrote free and open sources of ALICE in multiple programming languages.
- AlphaGo:
- ELIZA
- OpenNN