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which are the best programming languages for the development of artificial intelligence projects ?

 

The choice of programming language for the development of artificial intelligence projects

If you are developing a new artificial intelligence project, but still have not decided which language to use for programming, then you will know now.



Artificial intelligence is a branch of engineering, and its basic purpose is to enable computers to think intelligently in a similar way to intelligent humans. The following are the top languages ​​most commonly used to make AI projects:

 

1. Python

Due to its simplicity, Python is considered to be the first in the list of all AI development languages. The grammar belonging to python is very simple and can be easily learned. Therefore, many AI algorithms can be easily implemented in it. Compared with other languages ​​(such as Java, C++ or Ruby), Python requires a shorter development time. Python supports object-oriented, functional and process-oriented programming styles. There are many libraries in python, which makes our task easier. For example: Numpy is a python library that can help us solve many scientific calculations. In addition, we have Pybrain, which is used to use machine learning in Python.

 

2. R

R is one of the most effective languages ​​and environments for statistical analysis and manipulation of data. Using R, we can easily generate well-designed publication quality maps, including the required mathematical symbols and formulas. In addition to being a common language, R has many software packages such as RODBC, Gmodels, Class and Tm, which are used in the field of machine learning. These software packages make it easy to implement machine learning algorithms, thereby solving business-related problems.

3. Lisp

 Lisp is one of the oldest and most suitable languages ​​in artificial intelligence development. It was invented in 1958 by John McCarthy, the father of artificial intelligence. It has the ability to effectively process symbol information.

It is also known for its excellent prototyping capabilities and easy dynamic creation of new objects, with automatic garbage collection. Its development cycle allows interactive evaluation of expressions and recompilation of functions or files while the program is still running. Over the years, due to advancements, many of these features have been migrated to many other languages, thus affecting the uniqueness of Lisp.

4. Prolog

When we talk about the development of the AI ​​field, this language coexists with Lisp. The functions it provides include effective pattern matching, tree-based data structure and automatic backtracking. All these features provide a surprisingly powerful and flexible programming framework. Prolog is widely used in the design of medical projects and expert AI systems.

5. Java

Java can also be considered a good choice for AI development. Artificial intelligence has a lot to do with search algorithms, artificial neural networks and genetic programming. Java provides many benefits: easy to use, easy to debug, package services, simplify the work of large projects, graphical representation of data, and better user interaction. It also contains Swing and SWT (Standard Widget Toolkit). These tools make graphics and interfaces look more attractive and complex.

Just like Google at this year's I/O conference, the issuers were convened to jointly contribute to the development community. At the same time, the Model Play team is also issuing an AI model call-up order to developers around the world, soliciting deep learning models based on TensorFlow that can be run on the Google Coral Dev Board to encourage more developers to participate in activities and to collaborate with thousands of people around the world. Thousands of AI developers, sharing ideas and ideas.

Model Play is an AI model sharing market for AI developers around the world. Model Play not only provides a platform for global developers to display and communicate with AI models, it can also be used with the Coral Dev Board with Edge TPU to accelerate ML inference, preview the effect of model operation on mobile phones in real-time, and help AI expand from prototypes to products.

Developers can either publish the AI ​​models they have trained, or they can subscribe and download the models they are interested in to retrain and expand their AI creativity and realize the idea-prototype-product process. Model Play also presets various commonly used AI models, such as MobileNetV1, InceptionV2, etc., and supports the submission and release of retrainable models to facilitate users to optimize and fine-tune their business data.

The future development of artificial intelligence, big data, cloud computing, and the Internet of Things is worthy of attention. They are all cutting-edge industries. The multi-Intelligent Era focuses on the introduction and scientific spectrum of artificial intelligence and big data.

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