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Role of Python in Artificial Intelligence (AI)

Role of Python in Artificial Intelligence

Python and Artificial Intelligence(AI) - How do they relate?

Python is one of the most popular programming languages used by developers today. Guido Van Rossum created it in 1991 and ever since its inception has been one of the most widely used languages along with C++, Java, etc.

In our endeavour to identify what is the best programming language for AI and neural network, Python has taken a big lead. Let us look at why Artificial Intelligence with Python is one of the best ideas under the sun.

Features and Advantages of Python

Python is an Interpreted language which in lay man’s terms means that it does not need to be compiled into machine language instruction before execution and can be used by the developer directly to run the program. This makes it comprehensive enough for the language to be interpreted by an emulator or a virtual machine on top of the native machine language which is what the hardware understands.

It is a High-Level Programing language and can be used for complicated scenarios. High-level languages deal with variables, arrays, objects, complex arithmetic or Boolean expressions, and other abstract computer science concepts to make it more comprehensive thereby exponentially increasing its usability.

Python is also a General-purpose programming language which means it can be used across domains and technologies.

Python also features dynamic type system and automatic memory management supporting a wide variety of programming paradigms including object-oriented, imperative, functional and procedural to name a few.

Python is available for all Operating Systems and also has an open-source offering titled CPython which is garnering widespread popularity as well.

Let us now look as to how using Python for Artificial Inelegance gives us an edge over other popular programming languages.

AI and Python: Why?

The obvious question that we need to encounter at this point is why we should choose Python for AI over others.

Python offers the least code among others and is in fact 1/5 the number compared to other OOP languages. No wonder it is one of the most popular in the market today.

  • Python has Prebuilt Libraries like Numpy for scientific computation, Scipy for advanced computing and Pybrain for machine learning (Python Machine Learning) making it one of the best languages For AI.
  • Python developers around the world provide comprehensive support and assistance via forums and tutorials making the job of the coder easier than any other popular languages.
  • Python is platform Independent and is hence one of the most flexible and popular choiceS for use across different platforms and technologies with the least tweaks in basic coding.
  • Python is the most flexible of all others with options to choose between OOPs approach and scripting. You can also use IDE itself to check for most codes and is a boon for developers struggling with different algorithms.

Decoding Python alongside AI

Python along with packages like NumPy, scikit-learn, iPython Notebook, and matplotlib form the basis to start your AI project.

NumPy is used as a container for generic data comprising of an N-dimensional array object, tools for integrating C/C++ code, Fourier transform, random number capabilities, and other functions.

Another useful library is pandas, an open source library that provides users with easy-to-use data structures and analytic tools for Python.

Matplotlib is another service which is a 2D plotting library creating publication quality figures. You can use matplotlib to up to 6 graphical users interface toolkits, web application servers, and Python scripts.

Your next step will be to explore k-means clustering and also gather knowledge about decision trees, continuous numeric prediction, logistic regression, etc.

Some of the most commonly used Python AI libraries are AIMA, pyDatalog, SimpleAI, EasyAi, etc. There are also Python libraries for machine learning like PyBrain, MDP, scikit, PyML.

Let us look a little more in detail about the various Python libraries in AI and why this programming language is used for AI.

Python Libraries for General AI

  • AIMA – Python implementation of algorithms from Russell and Norvig’s ‘Artificial Intelligence: A Modern Approach.’
  • pyDatalog – Logic Programming engine in Python
  • SimpleAI – Python implementation of many of the artificial intelligence algorithms described on the book “Artificial Intelligence, a Modern Approach”. It focuses on providing an easy to use, well documented and tested library.
  • EasyAI – Simple Python engine for two-players games with AI (Negamax, transposition tables, game solving).

Python for Machine Language (ML)

Let us look as to why Python is used for Machine Learning and the various libraries it offers for the purpose.

  • PyBrain -  A flexible, simple yet effective algorithm for ML tasks. It is also a modular Machine Learning Library for Python providing a variety of predefined environments to test and compare algorithms.
  • PyML – A bilateral framework written in Python that focuses on SVMs and other kernel methods. It is supported on Linux and Mac OS X.
  • Scikit-learn – Scikit-learn is an efficient tool for data analysis while using Python. It is open source and the most popular general purpose machine learning library.
  • MDP-Toolkit – Another Python data processing framework that can be easily expanded, it also has a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. The implementation of new algorithms is easy and intuitive. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, and Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.

Python Libraries for Natural Language & Text Processing

  • NLTK – Open source Python modules, linguistic data and documentation for research and development in natural language processing and text analytics with distributions for Windows, Mac OSX, and Linux.

Python over other popular languages

Let us now see where Python stands with another computer language for AI like C++ and Java.

Python vs. C++ for AI

  • Python is a more popular language over C++ for AI and leads with a 57% vote among developers. That is because Python is easy to learn and implement. With its many libraries, they can also be used for data analysis.
  • Performance wise C++ outperforms Python. This is because C++ has the advantage of being a statically typed language and hence there are no typing errors during runtime. C++ also creates more compact and faster runtime code.
  • Python is a dynamic (as opposed to static) language and reduces complexity when it comes to collaborating meaning you can implement functionality with less code. Unlike C++, where all significant compilers tend to do specific optimisation and can be platform specific, Python code can be run on pretty much any platform without wasting time on specific configurations.
  • With the rise of GPU-accelerated computing offering capabilities for parallelism which has led to the creation of libraries such as CUDA Python and cuDNN, Python has the edge over C++.  This means is that more and more of the actual computing for machine learning workloads is being offloaded to GPUs – and the result is that any performance advantage that C++ may have is becoming increasingly irrelevant.
  • Python wins over C++ regarding simplicity of code, especially amongst new developers. C++ being a lower-level language requires more experience and skill to master.
  • Python’s simple syntax also allows for a more natural and intuitive ETL (Extract, Transform, Load) process, and means that it is faster for development when compared to C++, allowing developers to test machine learning algorithms without having to implement them quickly.

Between C++ and Python, the latter has more edge and is more suitable for AI. With its simple syntax and readability promoting the rapid testing of complex machine learning algorithms and a thriving community bolstered by collaborative tools like Jupyter Notebooks and Google Colab, Python wins the crown.

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LISP for AI

Before we deal with Java, let us look at LISP with AI and how they are compatible with each other. LISP is favoured in AI because after many years of research in various universities fast prototyping was chosen over fast execution. Garbage collection, dynamic typing, functions as data, consistent syntax, interactive environment, and extensibility are some of its feature that makes the language suitable for AI programming.

Java with AI

To master how to program Artificial Intelligence in Java, It is essential to know where it stands in comparison to Python.

  • Java is a compiled language whereas Python is an interpreted language.
  • The two languages are also written differently. A structure in Java is enclosed in braces. Python uses indentation to perform the same tasks.
  • Java is also performance wise slower, and for developing high-end applications in AI, Python is more preferred by developers.

Java Artificial Intelligence Library is Java’s answer to Python but is still less accessible to developers for apparent reasons. The Java Norvig Russell modern approach to AI has paved the way for many to sit back and notice why it could be the best language for a neural network.

Case Study

An experiment to bring AI to use with an Internet of Things was done to make an IoT Application for employee behavioural analytics. The software provides useful feedback to employees through employee emotions and behaviour analysis, thus enhancing positive changes in management and work habits.

Using python machine learning libraries, opencv and haarcascading concepts for application training, a sample POC was built to detect basic emotions like happiness, anger, sadness, disgust, suspicion, contempt, sarcasm and surprise through wireless cameras attached at various bay points.

The data collected was fed to a centralised cloud computing database where daily emotional quotient within the bay or even the entire office could be retrieved at the click of a button either through an android device or desktop.

Developers are making gradual progress in analysing further complex points on facial emotions and mine more details with the help of deep learning algorithms and machine learning which can help analyse individual employee performance and support in proper employee/team feedback.

Conclusion

Python plays a vital role in AI coding language by providing it with good frameworks like scikit-learn: machine learning in Python, which fulfils almost every need in this field and D3.js – Data-Driven Documents in JS, which is one of the most powerful and easy-to-use tools for visualisation.

Other than frameworks, its fast prototyping makes it an important language not to be ignored. AI needs a lot of research, and hence it is necessary not to require a 500 KB boilerplate code in Java to test a new hypothesis, which will never finish the project. In Python, almost every idea can be quickly validated through 20-30 lines of code (same for JS with libs). Therefore, it is a pretty useful language for the sake of AI.

Thus it is quite evident that Python is the best AI Programming Language under the sun. Apart from being the best language for artificial intelligence, Python is useful for many other objectives.

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