Technology peripherals
AI
Emerging technologies in 2024: IoT, cybersecurity and artificial intelligence transforming industries
Emerging technologies in 2024: IoT, cybersecurity and artificial intelligence transforming industries
In 2024, IoT systems will gradually be integrated into critical infrastructure and transformed by cybersecurity, artificial intelligence, and other emerging technologies.
In this article, I will delve into the impact of artificial intelligence and machine learning (ML) in smart IoT systems. With the rise of edge computing and the integration of blockchain, the security of the system has been enhanced. In addition, the introduction of ultra-thin smart transportation labels and the application of the SGP.32 standard have also brought new development opportunities to IoT systems. Finally, we explore the emerging role of the Internet of Things in sustainable development. Through in-depth research on these aspects, we can better understand the transformation of smart IoT systems.
Pay more attention to IoT cybersecurity
By 2024, IoT devices will become part of important systems such as smart cities. At the same time, the widespread adoption of technologies such as 5G, eSIM, iSIM and satellite connectivity has increased the importance of cybersecurity measures. These advancements make IoT devices more versatile and efficient, but also require greater attention to the protection of data integrity and device security.
To meet these needs, there is increasing emphasis on deploying advanced encryption and strict security protocols. These measures ensure that data transmitted between IoT devices and central systems is protected. In addition, continuous monitoring and real-time threat detection with the help of artificial intelligence and machine learning are likely to become standard practice, enabling timely identification and response to potential security vulnerabilities and maintaining the integrity and reliability of IoT networks.
AI and ML enable smart IoT systems
Artificial intelligence and machine learning are revolutionizing the IoT space, and they add value to IoT applications such as predictive maintenance and energy management by analyzing massive amounts of data in real time. new capabilities. This synergy, combined with a centralized IoT management platform, results in unprecedented operational efficiencies.
By 2024, the convergence of artificial intelligence and machine learning will be more deeply applied in IoT infrastructure. By combining the analytical power of AI with the data collection and monitoring capabilities of IoT, we will build a smarter and more responsive IoT ecosystem. Such systems will be able to gather operational insights more efficiently, enabling smarter IoT systems.
Edge computing enhances IoT performance
Edge computing is a method of processing data closer to the source, revolutionizing the performance of IoT. With this approach, latency is significantly reduced, which is critical for real-time applications such as self-driving cars, industrial automation, and augmented reality. These advancements are particularly relevant in areas such as smart cities, healthcare, manufacturing, and retail, where they can facilitate instant data analysis and improve service quality.
Looking to the future, the combination of artificial intelligence and machine learning with edge computing will be further enhanced, enabling edge devices to make complex decisions autonomously. At the same time, with the popularization of 5G networks, communication between devices will be faster and more efficient, thereby accelerating data processing. In addition, the role of edge computing in reducing energy consumption and carbon emissions will be highlighted, further promoting the cultivation of a more sustainable IoT ecosystem.
Blockchain for IoT security
With the increase in the number of sensitive data processed by IoT devices, the role of blockchain in IoT security has become increasingly prominent. The decentralized nature of blockchain can enhance data integrity and become an important component in preventing IoT network security threats. Integration with artificial intelligence (AI) and machine learning (ML), in particular, represents important progress in building resilient IoT infrastructure.
This combination is expected to form a stronger and more secure IoT ecosystem in 2024 and beyond, especially as the IoT attack surface expands. In this context, blockchain’s ability to ensure the authenticity and security of data transactions across the network is crucial, providing a powerful solution to the ever-changing challenges of IoT security.
Ultra-thin, low-power smart shipping labels
Ultra-thin, low-power smart shipping labels will debut in early 2023, our own smart shipping labels equipped with printed, eco-friendly batteries , has eSIM functionality and supports up to 1,000 messages on LTE-M, NB-IoT and 2G networks.
By 2024, these types of tags will become even more prolific as they serve as advanced tracking devices for both large and small items. They monitor location, temperature and package integrity in real-time to ensure safe and efficient transportation.
Due to their adaptability to various logistics needs, from tracking small documents to large assets, these smart labels not only increase supply chain efficiency but are also aligned with sustainable development goals and represent IoT-driven asset management significant progress.
Integrating SGP.32 into the IoT ecosystem
The SGP.32 standard will be integrated into the IoT ecosystem in 2024, heralding major advancements in device functionality and application efficiency. By providing superior geolocation services, SGP.32 is critical for use cases that require high positioning accuracy, such as precision agriculture.
Additionally, the integration of SGP.32 plays a key role in expanding the use of esim in IoT devices. This is particularly beneficial for global IoT deployments as it simplifies the complexities associated with device management in different regions. Features such as remote configuration and profile exchange inherent in eSIM technology help improve operational efficiency.
This development is not just a technological leap; It is a strategic enabler of a more efficient, globally connected, responsive IoT ecosystem. The impact of integrating SGP.32 will be felt in various fields, making a significant contribution to the overall development and effectiveness of IoT applications.
IoT’s driving force for sustainable development continues to grow
Finally, through 2024, IoT will continue to play a key role in driving sustainability across industries. Advanced, energy-efficient sensors combined with artificial intelligence are revolutionizing resource management by enabling precise monitoring and control. This technological synergy significantly reduces waste and optimizes energy use.
In industries such as manufacturing, IoT adoption is accelerating through tightening global regulations that require more sustainable practices and a better ecological footprint. IoT technology not only improves operational efficiency but also promotes environmental management. The implementation of smart systems in areas such as energy management and waste reduction is evidence of the growing impact of IoT in creating a more sustainable future.
As the world grapples with environmental challenges, the integration of the Internet of Things in sustainability efforts is becoming increasingly important, marking a new era in which technology and ecology harmoniously intersect.
The above is the detailed content of Emerging technologies in 2024: IoT, cybersecurity and artificial intelligence transforming industries. 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
1386
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
