Which programming language is suitable for artificial intelligence?

Use the Python machine learning Library, OPENCV and haarcascading concepts to train. A sample POC was established to detect the transmission of the base emotion by placing the wireless camera in different locations like happiness, anger, sadness, disgust, suspicion,

Google's AI defeated a go master, a way of measuring the sudden and rapid development of AI, and revealing how these technologies evolved and how they could evolve in the future. AI is a futuristic technology and is currently working on its own set of tools. A series of developments have taken place over the past few years: an accident-free driving of more than 300000 miles and legal driving in three states ushered in a milestone in automatic driving; IBM Waston defeated the Jeopardy two-year championship; Statistical learning techniques are pattern recognition from the consumer's interest to a complex set of data in trillions of images. These developments are bound to increase the interest of scientists and giants in AI, which also allows developers to understand the true nature of creating AI applications. The first thing to note in developing these things is: Which programming language is suitable for artificial intelligence? Every programming language you are proficient in can be the language of AI development. AI programs can be implemented in almost all programming languages, most commonly: lisp,prolog,c/c++, recently Java, and recently Python. Lisp High-level languages such as Lisp have been favored in artificial intelligence because they have chosen rapid prototyping after years of research in universities and abandoned rapid execution. Some of the features of garbage collection, dynamic typing, data functions, unified syntax, interactive environments, and extensibility make list ideal for AI programming. PROLOG This language has an effective combination of Lisp high-level and traditional advantages, which is very useful for AI. Its advantage is to solve the "logic-based Problem". Prolog provides a solution to a logic-related problem, or its solution has a concise logic feature. Its main drawback (with all due respect) is that it is difficult to learn. C + + Like a cheetah, C + + is primarily used when the speed of execution is high. It is mainly used for simple programs, and statistical artificial intelligence, such as neural networks, is a common example. BackPropagation only uses a few pages of C + + code, but requires speed, even if the programmer can only raise a little bit of speed is good. Java Newcomers, Java uses several of the concepts in Lisp, most notably garbage collection. Its portability allows it to be applied to any program, and it has a built-in type. Java does not have Lisp and Prolog advanced, and it is not as fast as C, but it is best if portability is required. Python Python is a language compiled with Lisp and Java. By comparing lips and Python in the Norvig article, the two languages are very similar to each other, with only a few small differences. There is also the Jpthon, which provides access to the Java image user interface. This is the reason why Peternorvig chose to use Jpyhton to translate the program in his AI books. Jpython allows him to use portable GUI demonstrations, and portable http/ftp/html libraries. Therefore, it is well suited as an AI language. The benefits of using Python on artificial intelligence over other programming languages High-quality documentation Platform-independent, can be used on every *nix version now and other object-oriented programming languages are easier and faster than learning Python has many image enhancement libraries like Python Imaging libary,vtk and Maya 3D Visualizer, Numeric python, scientific python and many other tools available for numerical and scientific applications. Python is designed to be very good, fast, rugged, portable, and extensible. This is obviously a very important factor for AI applications. It is useful for a wide range of programming tasks for scientific purposes, from small shell scripts to entire Web site applications. Finally, it is open source. Can get the same community support. Ai's Python library The overall AI Library Aima:python implements the "AI: a Modern approach" algorithm from Russell to Norvigs The logical programming engine in Pydatalog:python Simpleai:python implements the artificial intelligence algorithms described in the book "Artificial Intelligence: a modern approach". It focuses on providing an easy-to-use library with good documentation and testing. Easyai: A python engine for two-person AI games (negative maxima, substitution tables, game resolution) Machine Learning Library Pybrain is a flexible, simple and effective algorithm for machine learning tasks, which is a modular Python machine learning library. It also provides a variety of pre-defined environments to test and compare your algorithms. PYML a bilateral framework written in Python that focuses on SVM and other kernel methods. It supports Linux and Mac OS x. Scikit-learn is designed to provide a simple and powerful solution that can be reused in a variety of contexts: machine learning is a versatile tool for science and engineering. It is a Python module that integrates classic machine learning algorithms that are closely linked to the Python Science Pack (numpy,scipy.matplotlib). Mdp-toolkit This is a python-data-processing framework that can be easily extended. It collects supervised and unregulated learning to calculate rice and other data processing units that can be combined into data processing sequences or more complex feedforward network structures. The implementation of the new algorithm is simple and intuitive. The available algorithms are increasing steadily, including signal processing methods (principal component analysis, independent component analysis, slow feature analysis), Flow learning methods (local linear embedding), centralized classification, probabilistic methods (factor analysis, RBM), data preprocessing methods, and so on. Natural language and Text processing library NLTK Open source Python modules, linguistic data and documentation for research and development of natural language processing and text analysis. There are Windows,mac OSX and Linux versions. Case Did an experiment, a software that uses artificial intelligence and the Internet of Things to do employee behavioral analysis. The software improves management and work habits by providing a useful feedback to employees about their emotional and behavioral distractions. Use the Python machine learning Library, OPENCV and haarcascading concepts to train. A sample POC was established to detect the transmission of the underlying emotions through the placement of wireless cameras in different locations like happiness, anger, sadness, disgust, suspicion, contempt, sarcasm and surprises. The collected data is concentrated in the cloud database, and even the entire office can be retrieved by clicking on a button on an Android device or desktop. Developers are making progress in analyzing the emotional complexities of the face and digging into more detail. With the help of in-depth learning algorithms and machine learning, you can help analyze employee individual performance and appropriate employee/team feedback. Conclusion Python has an important role to play in AI because it provides a good framework like Scikit-learn: machine learning in Python, which achieves most of the requirements in this area. D3.js JS is one of the most powerful and easy-to-use tools for visualizing data-driven documents. Processing framework, its rapid prototyping makes it an important language not to be ignored. AI requires a lot of research, so there's no need to ask a 500KB Java boilerplate code to test the new hypothesis. Almost every idea in Python can be implemented quickly through 20-30 lines of code (JS and Lisp are the same). Therefore, it is a very useful language for AI.

Which programming language is suitable for artificial intelligence?