What major does artificial intelligence belong to?
What major does artificial intelligence belong to?
Currently belonging to the computer science major, artificial intelligence is a branch of computer science that attempts to understand the nature of intelligence and produce a new intelligent machine that can respond in a manner similar to human intelligence. , research in this field includes robotics, language recognition, image recognition, natural language processing and expert systems, etc.
The definition of artificial intelligence
The definition of artificial intelligence can be divided into two parts, namely "artificial" and "artificial intelligence" intelligent". "Artificial" is easier to understand and less controversial. Sometimes we have to consider what is possible for humans to create, or whether humans are intelligent enough to create artificial intelligence, etc. But in general, "artificial systems" are artificial systems in the usual sense.
There are many questions about what “intelligence” is. This involves other issues such as consciousness (CONSCIOUSNESS), self (SELF), thinking (MIND) (including unconscious thinking (UNCONSCIOUS_MIND)), etc. It is a generally accepted view that the only intelligence that people understand is their own intelligence. However, our understanding of our own intelligence is very limited, and our understanding of the necessary elements that constitute human intelligence is also limited, so it is difficult to define what "artificially" manufactured "intelligence" is. Therefore, research on artificial intelligence often involves the study of human intelligence itself. Other intelligence related to animals or other artificial systems is also generally considered to be a research topic related to artificial intelligence.
Artificial intelligence has received more and more widespread attention in the computer field. And it is applied in robots, economic and political decision-making, control systems, and simulation systems.
Professor Nelson gave this definition of artificial intelligence: "Artificial intelligence is a subject about knowledge-how to represent knowledge and how to obtain knowledge and use knowledge." And another one from the Massachusetts Institute of Technology in the United States Professor Winston of the college believes: "Artificial intelligence is the study of how to make computers do intelligent work that only humans could do in the past." These statements reflect the basic ideas and basic content of the artificial intelligence discipline. That is, artificial intelligence is the study of the laws of human intelligent activities, the construction of artificial systems with certain intelligence, and the study of how to let computers complete tasks that previously required human intelligence. It is also the study of how to use computer software and hardware to simulate certain human intelligences. Basic theories, methods and techniques of behavior.
Artificial intelligence is a branch of computer science. Since the 1970s, it has been known as one of the world's three cutting-edge technologies (space technology, energy technology, and artificial intelligence). It is also considered to be one of the three cutting-edge technologies (genetic engineering, nanoscience, and artificial intelligence) in the 21st century. This is because it has developed rapidly in the past thirty years, has been widely used in many subject areas, and has achieved fruitful results. Artificial intelligence has gradually become an independent branch, both in theory and practice. into a system.
Artificial intelligence is the study of using computers to simulate certain human thinking processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.). It mainly includes the principles of computer realization of intelligence and the manufacture of products similar to the human brain. Intelligent computers enable computers to achieve higher-level applications. Artificial intelligence will involve disciplines such as computer science, psychology, philosophy and linguistics. It can be said that almost all disciplines of natural science and social science have its scope far beyond the scope of computer science. The relationship between artificial intelligence and thinking science is the relationship between practice and theory. Artificial intelligence is at the technical application level of thinking science. It is an application branch of it. From a thinking point of view, artificial intelligence is not limited to logical thinking. Only by considering image thinking and inspired thinking can we promote the breakthrough development of artificial intelligence. Mathematics is often considered to be the basic science of many disciplines. Mathematics has also entered the fields of language and thinking. Artificial intelligence Intelligent disciplines must also borrow mathematical tools. Mathematics not only plays a role in standard logic, fuzzy mathematics, etc., but when mathematics enters the artificial intelligence discipline, they will promote each other and develop faster.
Recommended tutorial: "PHP"
The above is the detailed content of What major does artificial intelligence belong to?. 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
1387
52
Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions
Jun 28, 2024 am 03:51 AM
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
Context-augmented AI coding assistant using Rag and Sem-Rag
Jun 10, 2024 am 11:08 AM
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
Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations
Jun 11, 2024 pm 03:57 PM
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
Seven Cool GenAI & LLM Technical Interview Questions
Jun 07, 2024 am 10:06 AM
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
Five schools of machine learning you don't know about
Jun 05, 2024 pm 08:51 PM
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
To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework
Jul 25, 2024 am 06:42 AM
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
SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time
Jul 17, 2024 pm 06:37 PM
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
SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc.
Aug 01, 2024 pm 09:40 PM
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