Which programming language, such as python and java, is suitable for AI ?, Python artificial intelligence

Which programming language, such as python and java, is suitable for AI ?, Python artificial intelligence

Google's AI beat a GO master. It is a way to measure the sudden and rapid development of artificial intelligence. It also reveals how these technologies develop and how they can develop in the future.

Artificial intelligence is a future technology and is currently working on a set of tools. A series of advances have occurred over the past few years: self-driving beyond 300000 miles without accidents and driving legally in three states has ushered in a milestone of self-driving; IBM Waston beat the Jeopardy two-time championship; Statistical Learning Technology from consumer interest to complex datasets with trillions of records of images for pattern recognition. These developments will inevitably increase the interest of scientists and masters in artificial intelligence. This will also enable developers to understand the real nature of the application.

The first thing to note when developing these products is:

Which programming language is suitable for AI?

Every programming language you are familiar with can be an AI development language.

Artificial intelligence programs can be implemented in almost all programming languages. The most common ones are: Lisp, Prolog, C/C ++, and Java recently.

LISP

Advanced languages such as LISP are favored in artificial intelligence, because after years of research in colleges and universities, they chose quick prototypes instead of quick execution. Some features such as garbage collection, dynamic types, data functions, unified syntax, interactive environment, and scalability make LIST very suitable for AI programming.

PROLOG

This language can be effectively combined with the traditional advantages of LISP, which is very useful for AI. Its advantage is to solve "logic-based problems ". Prolog provides solutions for logic-related problems, or its solutions have concise logic features. Its main disadvantage (to be blunt) is that it is difficult to learn.

C/C ++

Like a cheetah, C/C ++ is mainly used when the execution speed is high. It is mainly used for simple programs and Artificial Intelligence statistics, such as neural networks. Backpropagation only uses a few pages of C/C ++ code, but it requires a speed, even if programmers can only increase the speed by a little.

JAVA

A newcomer, Java uses several concepts in LISP, the most obvious of which is garbage collection. Its portability makes it suitable for any program, and it also has a set of built-in types. Java is not as fast as LISP and Prolog, but it is best if portability is required.

PYTHON

Python is a language compiled with LISP and JAVA. According to Norvig's comparison of Lips and Python, these two languages are very similar to each other, with only a few minor differences. JPthon also provides a way to access the Java image user interface. This is why Peter norvig chose to use JPyhton to translate the program in his artificial intelligence book. JPython allows him to use a portable GUI demonstration and a portable http/ftp/html library. Therefore, it is ideal for AI.

Advantages of using Python in AI over other programming languages

High-quality documents

Platform-independent. It can be used in every * nix version.

Easier and faster than other Object-Oriented Programming Languages

Python has many image enhancement libraries such as Python Imaging Libary, VTK and Maya 3D Visualization Toolkit, Numeric Python, Scientific Python, and many other tools available for numerical and Scientific applications.

Python is well designed, fast, robust, portable, and scalable. Obviously, these are very important factors for AI applications.

It is useful for a wide range of programming tasks for scientific purposes, regardless of small shell scripts or the entire website application.

Finally, it is open-source. You can get the same community support.

Python library for AI

Overall AI Library

AIMA: Python implements the algorithm "Artificial Intelligence: a modern method" from Russell to Norvigs.

PyDatalog: logic programming engine in Python

SimpleAI: Python implements artificial intelligence algorithms described in the book "Artificial Intelligence: a modern method. It focuses on providing an easy-to-use library with good documentation and testing.

EasyAI: a python engine for double AI games (negative maximum value, replacement table, game solution)

Machine Learning Library

PyBrain is a flexible, simple, and effective algorithm for machine learning tasks. It is a modular Python Machine Learning Library. It also provides a variety of predefined environments to test and compare your algorithms.

PyML is a bilateral framework written in Python, focusing on SVM and other kernel methods. It supports Linux and Mac OS X.

Scikit-learn is designed to provide simple and powerful solutions that can be reused in different contexts: machine learning is a versatile tool for science and engineering. It is a python module that integrates classic machine learning algorithms. These algorithms are closely related to the python Science Package (numpy, scipy. matplotlib.

MDP-Toolkit is a Python Data Processing framework that can be easily expanded. It collects supervised and unsupervised learning computing rice and other data processing units, which can be combined into a data processing sequence or a more complex feed-forward network structure. The implementation of new algorithms is simple and intuitive. Available algorithms are constantly increasing, including signal processing methods (principal component analysis, independent component analysis, and slow Feature Analysis), flow pattern learning methods (Locally Linear embedding), and centralized classification, probability methods (factor analysis, RBM), data preprocessing methods, and so on.

Natural Language and text processing Library

NLTK open-source Python module, linguistic data and documentation, used to research and develop natural language processing and text analysis. Windows, Mac OSX, and Linux are supported.

Case

I did an experiment, a software that uses artificial intelligence and Iot for employee behavior analysis. The software provides a useful feedback to employees through their emotional and behavioral distractions, thus improving management and work habits.

Use the Python Machine Learning Library, opencv, and haarcascading concepts for training. A sample POC was established to detect the underlying emotions passed back by wireless cameras positioned in different locations like happiness, anger, sorrow, disgust, suspicion, contempt, ridicule, and surprise. The collected data is concentrated in apsaradb, and even the entire office can be retrieved by clicking a button on the Android device or desktop.

Developers have made progress in in-depth analysis of facial emotions and mining more details. With the help of deep learning algorithms and machine learning, you can help analyze the individual performance of employees and appropriate feedback from employees/teams.

Conclusion

Because python provides a good framework like scikit-learn, it plays an important role in AI: Machine Learning in Python, achieving most of the requirements in this field. One of the most powerful and easy-to-use tools for data-driven documents in D3.js. Processing framework, its rapid prototyping makes it an important language that cannot be ignored. AI requires a lot of research, so there is no need to require a 500KB Java sample code to test new hypotheses. Almost every idea in python can be quickly implemented through 20-30 lines of code (the same is true for JS and LISP ). Therefore, it is a very useful language for artificial intelligence.

The above is all the content of this article. I hope it will be helpful for your learning and support for helping customers.