


Artificial intelligence is in the ascendant. How is the development progress of intelligent security?
As an industry where artificial intelligence has already had market space, the security industry has a clearer understanding and more urgent demand for the development of artificial intelligence. Artificial intelligence is promoting the third technology in the security industry after high-definition and networking. change.
In the context of the rapid development of artificial intelligence, the security industry has begun a new intelligent journey around AI. In this journey, how is the development progress of intelligent security?
"Progress"
Edge computing promotes the development of edge intelligence
Edge computing refers to the edge of the network close to the source of things or data On the other hand, an open platform that integrates the core capabilities of network, computing, storage, and applications provides edge intelligence services nearby to meet the key needs of industry digitalization in terms of agile connection, real-time business, data optimization, application intelligence, security, and privacy protection. In one sentence, edge computing can be understood as referring to computing procedures completed at the edge close to the data source.
With the continuous advancement of technology, the concept of "edge intelligence" emerged as the times require. It proposes a new model: allowing every edge device in the Internet of Things to have data collection, analysis and calculation, communication, and important functions. of intelligence. The new intelligent edge computing also takes advantage of the capabilities of cloud computing. It uses the cloud to securely configure, deploy and manage edge devices on a large scale, and can allocate intelligent capabilities according to device types and scenarios, so that intelligence can be integrated between the cloud and the edge. Flow between spaces and get the best of both worlds.
Edge intelligence has become a general trend. With the advent of the Internet of Everything era, the amount of picture and video data generated by front-end equipment in the field of computer vision is huge. If all of it is gathered into the cloud computing data center for intelligent analysis, it will bring unlimited bandwidth requirements and real-time requirements for communication. pressure. This requires providing edge intelligence services nearby and gradually migrating artificial intelligence computing power or inference capabilities from the cloud to the edge, which will help relieve the pressure on transmission links.
Deep learning construction promotes the development of AI-City
As a natural training ground and application field for artificial intelligence technology, the security industry has an urgent need for the implementation of artificial intelligence. In recent years, with the emergence of "brains" such as "city brain", "traffic brain", and "police brain", artificial intelligence deep learning technology combined with multi-dimensional perception has promoted the further development of AI-City.
The main research areas of deep learning are in speech recognition and vision, and applying deep learning to various directions can make different technological innovations in different fields. For the security industry that has mastered many video image resources, the combination of deep learning and security has a relatively high degree of compatibility, that is, the analysis of images and videos, including: image analysis; face recognition; word processing.
Deep learning in the security industry mainly focuses on four major areas: volume analysis, vehicle analysis, behavior analysis, and image analysis. With breakthroughs in deep learning algorithms, intelligent analysis technologies such as target recognition, object detection, scene segmentation, and character and vehicle attribute analysis have all made breakthrough progress.
"Obstruction"
Artificial intelligence security is short of "core"
In the security industry, the chip can be said to run through the whole process, from the front end to the back end , from transmission, recording to storage, security without the "core" is bound to be incomplete.
The field of security video surveillance has massive data, which can provide enough scenarios for deep learning training; in addition, in recent years, the development of intelligent algorithms has relied on massive big data to achieve important breakthroughs in speech recognition and vision. , presenting faster iterations. The implementation of artificial intelligence in the security field requires processing chips with sufficiently powerful computing capabilities. However, at the chip level, there is no artificial intelligence security application chip that fully meets actual needs.
It is difficult to put aside human intervention
Although artificial intelligence has completed some bluestone bridges that humans cannot do, the large-scale application of artificial intelligence has not yet come, and human intervention is needed to distinguish Differences between nearly similar objects.
Judging from actual cases, when a video of a single scene is extracted, by searching for pictures, the related pictures can be quickly revealed, and based on this, the trajectory of the criminal suspect can be discovered, and finally However, experts frankly pointed out that this process relies on artificial intelligence algorithms and is difficult to put aside human intervention, and it is still inseparable from the analysis and judgment of video criminal investigators.
Conclusion: Nowadays, the security industry has entered the era of data explosion. Faced with the explosive growth of data volume, traditional intelligent algorithms can no longer meet the needs of deep data value mining. . The deepening and deepening of artificial intelligence research has brought more changes to the security industry than imagined, and there are more and more application scenarios where it can play a role.
The above is the detailed content of Artificial intelligence is in the ascendant. How is the development progress of intelligent security?. 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

AI Hentai Generator
Generate AI Hentai for free.

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

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

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

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

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

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
