Technology peripherals
AI
What is edge artificial intelligence and how to implement edge artificial intelligence
What is edge artificial intelligence and how to implement edge artificial intelligence
Edge AI is the abbreviation of edge artificial intelligence. It is the next development frontier of the Internet of Things system. So, what is edge artificial intelligence? How to realize edge artificial intelligence? Follow the editor to read below Let’s read this article!
What is edge artificial intelligence
Simply put, edge artificial intelligence refers to the application of artificial intelligence directly on edge devices Artificial intelligence is used in the form of machine learning algorithms that run. Machine learning is a broad field that has made tremendous progress in recent years. It is based on the principle that computers can autonomously improve their performance on a given task by learning from data, sometimes even beyond human capabilities.
Today, machine learning can perform many advanced tasks, including but not limited to:
● Computer vision: image classification, object detection, semantic segmentation.
● Speech recognition, natural language processing, chatbots, translation.
● Weather and stock market prediction and recommendation system.
● Anomaly detection and predictive maintenance.
So machine learning has been around for so long, what makes edge AI suddenly so special? To explain this better, let’s first look at what edge in edge AI actually means .
Edge Computing and Cloud Computing
Essentially, edge computing and cloud computing are designed to do the same thing, which is to process data, run algorithms, etc. However, the fundamental difference between edge computing and cloud computing is where the computing actually occurs.
In edge computing, information processing occurs on-site and on distributed IoT devices actively deployed or at the edge. Some examples of edge devices are smartphones, and various microcontrollers. However, in cloud computing, the same information processing occurs in a centralized location, such as a data center.
Traditionally, cloud computing has dominated the IoT space. Since it is powered by data centers that naturally have greater computing power, IoT devices at the edge can simply transmit local data and maintain their key characteristics of low power consumption and affordability. While cloud computing remains a very important and powerful tool for the IoT, edge computing has received increasing attention recently for two important reasons.
● Hardware on edge devices becomes more powerful while remaining cost competitive.
● Software is increasingly optimized for edge devices.
This trend is making huge headway, and it is now possible to run machine learning on edge computing devices, whereas machine learning has long been reserved for cloud computing due to high computing requirements! So, edge computing Artificial intelligence was born.
Edge Artificial Intelligence: Bringing the Cloud to the Edge to Grow the Internet of Things
With edge artificial intelligence, IoT devices are becoming increasingly smarter. Through machine learning, cutting-edge devices are now able to make decisions, make predictions, process complex data, and manage solutions.
For example, edge IoT devices can process operating conditions and predict whether a machine will malfunction, which allows companies to perform predictive maintenance and avoid greater losses and costs in the event of a complete failure.
On the other hand, security cameras equipped with edge artificial intelligence will no longer just capture video, but will be able to recognize humans and calculate foot traffic. Or, with facial recognition, it's even possible to identify exactly who passed through a certain area and when.
As machine learning advances, many exciting possibilities will now extend to edge devices as well. But the key to this paradigm shift is clear, and there are reasons why cloud computing capabilities are more marginalized than ever.
Benefits of edge artificial intelligence
1. Reduce latency
The most direct benefit of processing information at the edge is that there is no need to transmit data between clouds. Therefore, delays in data processing can be significantly reduced.
In the previous example of preventive maintenance, edge AI-enabled devices will be able to respond immediately, such as shutting down damaged machines. If we use cloud computing to execute machine learning algorithms, we will lose at least one second in the transmission of data to the cloud. While this may not sound important, when it comes to operating critical equipment, every achievable margin of safety is worth pursuing!
2. Reduce bandwidth requirements and costs
In Less data is transmitted between edge IoT devices, resulting in lower network bandwidth requirements and therefore lower costs.
Take the image classification task as an example. Due to reliance on cloud computing, the entire image must be sent for online processing. But if you use edge computing instead, you no longer need to send that data. Instead, we can simply send the processed result, which is typically several orders of magnitude smaller than the original image. If we multiply this effect by the number of IoT devices in the network, it could be as many as thousands or more.
3. Improve data security
Reducing data transmission to external locations also means fewer open connections and fewer opportunities for cyber attacks. This allows edge devices to operate securely, avoiding potential interception or data leakage. Additionally, since data is no longer stored in a centralized cloud, the consequences of a single breach are greatly mitigated.
4. Improve reliability
Due to the distributed nature of edge artificial intelligence and edge computing, operational risks can also be distributed throughout the network. Essentially, even if a centralized cloud computer or cluster fails, individual edge devices are able to maintain their functionality because the computing process is now independent of the cloud! This is especially important for critical IoT applications, such as healthcare.
Why Edge AI Matters
While the practical benefits of edge AI are clear, its intrinsic impact may be more elusive.
Edge artificial intelligence changes the way we live
Edge artificial intelligence represents the first wave of truly integrating artificial intelligence into daily life. While AI and machine learning research has been around for decades, we are only now starting to see their practical applications in consumer products. Self-driving cars, for example, are the product of advances in edge artificial intelligence. Edge AI is slowly but surely changing the way we interact with our environment in many ways.
Edge artificial intelligence makes artificial intelligence affordable to the people
The use and development of artificial intelligence is no longer exclusive to research institutions and large enterprises. Because edge AI is designed to run on relatively affordable edge devices, it’s easier than ever for anyone to understand how AI works and develop one that works for them.
More importantly, edge AI enables educators around the world to bring artificial intelligence and machine learning into classroom learning in tangible ways. For example, by providing students with hands-on experience using edge devices.
Edge Artificial Intelligence Challenges the Way We Think
It is often said that the potential of artificial intelligence and machine learning is only limited by human creativity and imagination, but as machine learning becomes more and more As we become more advanced, many tasks that were once only possible by humans will become automated, and our inherent concepts of productivity and purpose will be seriously challenged.
While I can’t be sure what the future will bring, I am generally optimistic about what edge AI will bring because I believe it will push us to find more creative and fulfilling jobs. For example, AI-embedded edge devices can monitor PPE in real time, including hard hat compliance in the work environment, and send safety and maintenance signals to anyone who violates PPE. Computer vision combined with machine learning can automate the PPE compliance monitoring process.
Another example is that AI-integrated cameras can alleviate bottlenecks and choke points in cities that often hinder traffic. Traffic jams occur mainly due to ignoring certain factors, such as the distance between two moving vehicles, traffic lights, street signs, pedestrians at intersections, etc. Intelligent transportation systems are the main application areas of computer vision, including vehicle classification, traffic violation detection, traffic flow analysis, parking lot detection, license plate recognition, pedestrian detection, traffic sign detection, collision avoidance, road condition monitoring, etc., as well as in-car driver attention force detection.
Through this article, I believe everyone has understood what edge artificial intelligence is and what it means for the Internet of Things and even the future of mankind. I hope that the above can give you some help.
The above is the detailed content of What is edge artificial intelligence and how to implement edge 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
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
1378
52
This article will take you to understand SHAP: model explanation for machine learning
Jun 01, 2024 am 10:58 AM
In the fields of machine learning and data science, model interpretability has always been a focus of researchers and practitioners. With the widespread application of complex models such as deep learning and ensemble methods, understanding the model's decision-making process has become particularly important. Explainable AI|XAI helps build trust and confidence in machine learning models by increasing the transparency of the model. Improving model transparency can be achieved through methods such as the widespread use of multiple complex models, as well as the decision-making processes used to explain the models. These methods include feature importance analysis, model prediction interval estimation, local interpretability algorithms, etc. Feature importance analysis can explain the decision-making process of a model by evaluating the degree of influence of the model on the input features. Model prediction interval estimate
Identify overfitting and underfitting through learning curves
Apr 29, 2024 pm 06:50 PM
This article will introduce how to effectively identify overfitting and underfitting in machine learning models through learning curves. Underfitting and overfitting 1. Overfitting If a model is overtrained on the data so that it learns noise from it, then the model is said to be overfitting. An overfitted model learns every example so perfectly that it will misclassify an unseen/new example. For an overfitted model, we will get a perfect/near-perfect training set score and a terrible validation set/test score. Slightly modified: "Cause of overfitting: Use a complex model to solve a simple problem and extract noise from the data. Because a small data set as a training set may not represent the correct representation of all data." 2. Underfitting Heru
Transparent! An in-depth analysis of the principles of major machine learning models!
Apr 12, 2024 pm 05:55 PM
In layman’s terms, a machine learning model is a mathematical function that maps input data to a predicted output. More specifically, a machine learning model is a mathematical function that adjusts model parameters by learning from training data to minimize the error between the predicted output and the true label. There are many models in machine learning, such as logistic regression models, decision tree models, support vector machine models, etc. Each model has its applicable data types and problem types. At the same time, there are many commonalities between different models, or there is a hidden path for model evolution. Taking the connectionist perceptron as an example, by increasing the number of hidden layers of the perceptron, we can transform it into a deep neural network. If a kernel function is added to the perceptron, it can be converted into an SVM. this one
The evolution of artificial intelligence in space exploration and human settlement engineering
Apr 29, 2024 pm 03:25 PM
In the 1950s, artificial intelligence (AI) was born. That's when researchers discovered that machines could perform human-like tasks, such as thinking. Later, in the 1960s, the U.S. Department of Defense funded artificial intelligence and established laboratories for further development. Researchers are finding applications for artificial intelligence in many areas, such as space exploration and survival in extreme environments. Space exploration is the study of the universe, which covers the entire universe beyond the earth. Space is classified as an extreme environment because its conditions are different from those on Earth. To survive in space, many factors must be considered and precautions must be taken. Scientists and researchers believe that exploring space and understanding the current state of everything can help understand how the universe works and prepare for potential environmental crises
Implementing Machine Learning Algorithms in C++: Common Challenges and Solutions
Jun 03, 2024 pm 01:25 PM
Common challenges faced by machine learning algorithms in C++ include memory management, multi-threading, performance optimization, and maintainability. Solutions include using smart pointers, modern threading libraries, SIMD instructions and third-party libraries, as well as following coding style guidelines and using automation tools. Practical cases show how to use the Eigen library to implement linear regression algorithms, effectively manage memory and use high-performance matrix operations.
Explainable AI: Explaining complex AI/ML models
Jun 03, 2024 pm 10:08 PM
Translator | Reviewed by Li Rui | Chonglou Artificial intelligence (AI) and machine learning (ML) models are becoming increasingly complex today, and the output produced by these models is a black box – unable to be explained to stakeholders. Explainable AI (XAI) aims to solve this problem by enabling stakeholders to understand how these models work, ensuring they understand how these models actually make decisions, and ensuring transparency in AI systems, Trust and accountability to address this issue. This article explores various explainable artificial intelligence (XAI) techniques to illustrate their underlying principles. Several reasons why explainable AI is crucial Trust and transparency: For AI systems to be widely accepted and trusted, users need to understand how decisions are made
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
Is Flash Attention stable? Meta and Harvard found that their model weight deviations fluctuated by orders of magnitude
May 30, 2024 pm 01:24 PM
MetaFAIR teamed up with Harvard to provide a new research framework for optimizing the data bias generated when large-scale machine learning is performed. It is known that the training of large language models often takes months and uses hundreds or even thousands of GPUs. Taking the LLaMA270B model as an example, its training requires a total of 1,720,320 GPU hours. Training large models presents unique systemic challenges due to the scale and complexity of these workloads. Recently, many institutions have reported instability in the training process when training SOTA generative AI models. They usually appear in the form of loss spikes. For example, Google's PaLM model experienced up to 20 loss spikes during the training process. Numerical bias is the root cause of this training inaccuracy,
