What programming language is used for artificial intelligence?
Since AlphaGo has beaten all the chess players in the world without any opponent, artificial intelligence has been unrivaled in the limelight. At the just past IT Leaders Summit, the three BAT bosses were optimistic about the future development of artificial intelligence. At the beginning of this year, Baidu made a big move and bet on artificial intelligence in medical care. Therefore, at this summit, Robin Li also stated that the Internet is an appetizer and artificial intelligence is the main course.
Artificial intelligence is a very broad field, and many programming languages can be used for artificial intelligence development, so it is difficult to say which language must be used to develop artificial intelligence. Having more choices also means there are pros and cons, and not every programming language can save developers time and energy. So we have compiled 5 programming languages that are more suitable for artificial intelligence development, hoping to be helpful to you.
Python
Python is one of the most widely used programming languages in the field of artificial intelligence due to its simplicity and ease of use. It can be seamlessly integrated with data structures and other commonly used used together with AI algorithms.
The reason why Python is used in AI projects is actually based on Python. Many useful libraries can be used in AI, such as
Numpy provides scientific computing capabilities, Scypy’s advanced computing and Pybrain’s Machine learning.
In addition, Python has a large number of online resources, so the learning curve is not particularly steep.
Recommended courses: Python Tutorial.
Java
#Java is also a good choice for AI projects. It is an object-oriented programming language that focuses on providing all the advanced features required on AI projects, it is portable, and it provides built-in garbage collection. In addition, the Java community is also a plus. A complete and rich community ecosystem can help developers query and solve problems anytime and anywhere.
For AI projects, algorithms are almost the soul. Whether it is a search algorithm, a natural language processing algorithm or a neural network, Java can provide a simple coding algorithm. In addition, Java’s scalability is also one of the necessary features for AI projects.
Lisp
Lisp has emerged in the field of AI due to its excellent prototyping capabilities and support for symbolic expressions. As a language designed for artificial intelligence, LISP is the first declarative functional programming language, which is different from imperative procedural C, Fortran and object-oriented structured programming languages such as Java and C#. .
Lisp language is mainly used in the machine learning/ILP subfield because of its usability and symbolic structure. In his book "Artificial Intelligence: A modern approach", the famous AI expert Peter Norvig explains in detail why Lisp is one of the top programming languages for AI development. Interested friends can check it out by themselves.
Prolog
Prolog is comparable to Lisp in terms of usability. According to the article "Prolog Programming for Artificial Intelligence", Prolog is a logic programming language, mainly for some basic Mechanism for programming, which is very effective for AI programming. For example, it provides pattern matching, automatic backtracking and tree-based data structuring mechanisms. Combining these mechanisms can provide a flexible framework for AI projects.
Prolog is widely used in AI expert systems and can also be used in medical project work.
C
C is the world’s fastest programming language, and its ability to communicate at the hardware level allows developers to improve program execution times. C is time-sensitive, which is very useful for AI projects, for example, search engines can use C extensively.
In AI projects, C can be used for statistics, such as neural networks. In addition, algorithms can also be widely and quickly executed in C. The AI in the game is mainly coded in C for faster execution and response time.
Summary:
In fact, choosing a programming language for the artificial intelligence AI project depends largely on the sub-field. For programming The choice of language should start from the overall situation and not only consider some functions. Among these programming languages, Python is gradually becoming the number one AI programming language because it is suitable for most AI sub-fields. Lisp and Prolog are effective in some AI projects because of their unique functions, and their status is temporarily difficult to achieve. shake. The advantages of Java and C will continue to be maintained in AI projects.
The above is the detailed content of What programming language is used for artificial intelligence?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP

Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application

To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text

Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A

According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year

Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S