


AIoT revolution: How artificial intelligence and the Internet of Things are changing our world
The rapid growth of the Internet of Things (IoT) is driven by the declining cost of sensors, the proliferation of connected devices, and advances in artificial intelligence (AI). The Internet of Things is a network of embedded sensors, software, electronic devices, and connected physical objects (vehicles, equipment, buildings, etc.) that allows these objects to collect and exchange data. According to a recent McKinsey report, IoT could have an annual economic impact of up to $12.6 trillion by 2030.
While the Internet of Things is still in its infancy, AIoT represents the next wave of IoT, where artificial intelligence is used to turn data into insights and actions. AIoT has the potential to transform industries and society, and is already starting to have an impact. This article will explore the principles, benefits, and current uses of AIoT.
What is the Internet of Things?
The Internet of Things (IoT) is a system of connected devices (things) that collect and exchange data. The data collected by these devices can be used to automate processes, increase efficiency and make better decisions.
IoT devices are often equipped with sensors that can detect various environmental conditions, such as temperature, light, and sound. These sensors can also track the location of devices and detect when they are being used. IoT devices can connect to the Internet using a variety of technologies, such as WiFi, Bluetooth, and cellular networks. It is estimated that 25 billion devices will be connected to the Internet of Things in 2021.
IoT devices can be used to automate a variety of tasks. For example, a sensor that detects when a door is opened can be used to turn on a light, or a sensor that detects when a car is parked can be used to automatically open a garage door. IoT devices can also be used to collect data for analysis, for example, sensors that track the number of people entering a store can be used to analyze customer traffic patterns.
"AI" in AIoT
AI plays a very important role in AIoT. Without artificial intelligence (AI), the IoT would be just a bunch of devices connected to the internet and collecting data. However, artificial intelligence can interpret all this data and turn it into useful insights.
Let’s take a smart home as an example to further explain this.
Suppose you have a connected thermostat in your home, and that thermostat collects data about how the temperature in your home changes over time. Artificial intelligence can take this data and use it to improve the efficiency of heating and cooling systems. AI can do this through trial and error, constantly learning and improving its algorithms.
For example, if the AI notices that the temperature in your home is too cold, it can adjust the settings on your heating system accordingly. The AI also takes into account other factors, such as weather and time of day. This enables AI to continuously optimize the performance of your heating and cooling systems, saving you money in the process.
Benefits of AIoT
AIoT is still in its early stages of development, but it is already starting to change the way we live and work.
The benefits of AIoT are many:
- At home, AIoT-enabled devices can control the temperature, turn on lights, and open doors.
- In the workplace, AIoT can monitor employee productivity, safety, and regulatory compliance.
- For consumers, AIoT can make life easier and more convenient.
- For society as a whole, AIoT can help us better manage resources and protect the environment.
As the technology matures, we can expect to see more amazing and transformative AIoT applications in our homes, workplaces, and communities.
Benefits of AIoT for Business
AIoT is a growing field with many potential benefits. Enterprises that adopt AIoT can improve their efficiency, decision-making, customization, and security. Let’s take a closer look at its benefits for businesses:
- Improving Efficiency: By combining artificial intelligence with IoT, businesses can automate tasks that would otherwise have to be performed manually and process. This frees up employees to focus on more important tasks and increases overall productivity.
- Improved decision-making: By collecting data from a variety of sources and analyzing it using artificial intelligence, businesses can gain insights they would otherwise not have access to, from product development to marketing. Can help companies make smarter decisions.
- Better customization: Businesses can use data collected from IoT devices to create customized products and services based on customer needs and preferences. This increases customer satisfaction and loyalty.
- Reduce costs: Businesses can reduce labor costs by automating tasks and processes. Additionally, AIoT can help businesses reduce energy costs by optimizing resource usage.
- Improve safety: By monitoring conditions and using artificial intelligence to identify potential hazards, businesses can take steps to prevent accidents and injuries from occurring.
Industry-specific advantages of AIoT
AIoT has the potential to transform industries and create new business opportunities:
- In the healthcare industry, AIoT can be used to monitor patient health, predict disease outbreaks, and improve treatment effectiveness.
- AIoT can be used in manufacturing to optimize production lines, reduce waste and improve quality control.
- In the retail industry, AIoT can help personalize the shopping experience, improve customer service, and prevent fraud.
AIoT in today’s world
AIoT is used in many ways in today’s world.
Smart Home
One way AIoT is used is in smart homes. Devices in the home, such as thermostats, lights and security cameras, can be connected to the internet and controlled using smartphones or other devices. Artificial intelligence can be used to automate some of these tasks, such as turning off lights or heating when no one is home.
Smart Cars
Another way AIoT is used is in self-driving cars. AI processes data from the car’s sensors, such as cameras, radar and lidar, to steer the car. Cars can also connect to the internet to receive traffic and road updates.
Smart Healthcare
AIoT is also used in healthcare. It is estimated that over the next seven years, more than 30% of IoT devices will be used exclusively in healthcare. Artificial intelligence can be used to:
- Process medical images, such as X-rays and MRIs, to diagnose disease
- Track a patient’s health data, such as heart rate and blood pressure, and report any changes if there are any Remind doctors
- Provide patients with information and support
- Help develop new drugs
Smart cities
AIoT has the most promise One of its applications is in the field of smart cities. Smart city projects are already underway around the world, and AIoT is expected to play an important role in these projects. AIoT can be used to help manage traffic congestion, optimize energy use and improve public safety.
Wearable Technology
AIoT is useful for wearable technology because it can help track and predict user needs and preferences. For example, if a user is wearing a smartwatch, AIoT can learn about the user's daily routine and suggest various activities that the user might enjoy. Additionally, AIoT helps improve the overall efficiency of wearable technology.
Summary
AIoT can make our lives easier in many ways. From controlling the temperature in our homes to providing us with directions, AIoT is slowly becoming integrated into our daily lives. Despite some concerns about privacy and security, the potential benefits of AIoT appear to outweigh the risks. As we become more reliant on technology, AIoT could play a larger role in our lives in the future.
The above is the detailed content of AIoT revolution: How artificial intelligence and the Internet of Things are changing our world. 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

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

In the world of front-end development, VSCode has become the tool of choice for countless developers with its powerful functions and rich plug-in ecosystem. In recent years, with the rapid development of artificial intelligence technology, AI code assistants on VSCode have sprung up, greatly improving developers' coding efficiency. AI code assistants on VSCode have sprung up like mushrooms after a rain, greatly improving developers' coding efficiency. It uses artificial intelligence technology to intelligently analyze code and provide precise code completion, automatic error correction, grammar checking and other functions, which greatly reduces developers' errors and tedious manual work during the coding process. Today, I will recommend 12 VSCode front-end development AI code assistants to help you in your programming journey.
