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"artificial intelligence technology"
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Simply AI : facts made fast
Covering a broad range of fields within AI - from computing and mathematics to politics and philosophy - entries demystify what artificial intelligence is and how it works, how it has dramatically changed how we live, and how it might evolve in the future. Everyone is talking about AI, but this book helps to explain each individual aspect of AI more clearly than ever before.
Book
Design and innovation of audio IoT technology using music teaching intelligent mode
The traditional music teaching model cannot meet the needs of modern music teaching. Therefore, this paper combines machine learning technology and Internet of Things audio technology to improve the music audio recognition algorithm. In order to ensure the smoothness between music frames and the continuity of speech, when the remaining length of the music audio stream tail is less than one frame length, it is directly discarded and no framing processing is performed. The main end of the integrated platform can communicate with the sub-end systems of each smart music classroom through a standard communication protocol by the front communication server, and access the information and data of each device subsystem in the smart music classroom. In addition, this paper combines the needs of music teaching to construct the layout of the smart music classroom and the software system architecture. After constructing the system, this paper designs experiments to verify the performance of the teaching system constructed in this paper. The experimental research results show that the audio technology based on the Internet of Things proposed in this paper can play an important role in music teaching.
Journal Article
Applied artificial intelligence : a handbook for business leaders
This bestselling book gives business leaders and executives a foundational education on how to leverage artificial intelligence and machine learning solutions to deliver ROI for your business.
Book
Basketball motion video target tracking algorithm based on improved gray neural network
This article takes the basketball game video with high attention in sports video as an example to analyze the feature extraction of basketball game video, improve the gray neural network algorithm, and disassemble the basketball video. Moreover, this paper takes basketball, basket, and athletes as the feature extraction objects. Considering that the basketball is a round sphere and the object in the image is a circle, as well as the edge is added to the original image and saved. In addition, this paper combines the improved gray neural network algorithm to construct a basketball motion video target tracking algorithm. Finally, this paper designs experiments to verify the performance of this method. The experimental test results show that this method can effectively recognize basketball gestures with high recognition accuracy, which provides a new method for basketball posture recognition.
Journal Article
Everything about you
Freya has a new virtual assistant. It knows what she likes, knows what she wants and knows whose voice she most needs to hear: her missing sister's. It adopts her sister's personality, recreating her through a life lived online. But this virtual version of her sister knows things it shouldn't be possible to know. It's almost as if the missing girl is still out there somewhere, feeding fresh updates into the cloud. But that's impossible. Isn't it?
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Sports video athlete detection based on deep learning
The deep fusion of sports and machine vision has become a research hot spot in sports video target detection, athlete state recovery and sports promotion. On the basis of in-depth study, it can detect a large number of sports videos, complete the drawing and analysis of human body detection model, and detect and evaluate the posture of corresponding athletes in the video, which can save a lot of costs and maximize the more professional training of athletes. In order to solve the above problems, this paper innovatively completes the automatic language description of sports video based on time-sharing memory algorithm. Its principle is to realize the accurate decomposition of athletes' sports data through the mapping relationship between the corresponding letter sequence and video sequence in time-sharing memory. In order to capture the key posture of athletes' sports video, this paper innovatively proposes an object extraction algorithm based on athletes' skeleton motion enhancement. In practical application, based on the key pose capture, it is necessary to train the depth selection network in time to extract the key pose of the skeleton. Based on this network, it can enhance the key posture of bone information and accurately express its related features. After extracting the actual athlete's bone information, we need to fine-tune the training network to realize the accurate recognition of key features. Based on the above key algorithms, this paper designs a sports video athlete detection system based on deep learning and makes an experimental research on the related sports video. The experimental results show that the detection accuracy of athletes' sports video is improved by nearly 10% compared with the traditional convolution network recognition algorithm, so the algorithm has obvious advantages in recognition accuracy.
Journal Article
Novacene : the coming age of hyperintelligence
\"The originator of the Gaia theory offers the vision of a future epoch in which humans and artificial intelligence together will help the Earth survive. James Lovelock, creator of the Gaia hypothesis and the greatest environmental thinker of our time, has produced an astounding new theory about future of life on Earth. He argues that the Anthropocene;the age in which humans acquired planetary-scale technologies is, after 300 years, coming to an end. A new age the Novacene has already begun. In the Novacene, new beings will emerge from existing artificial intelligence systems. They will think 10,000 times faster than we do and they will regard us as we now regard plants. But this will not be the cruel, violent machine takeover of the planet imagined by science fiction. These hyperintelligent beings will be as dependent on the health of the planet as we are. They will need the planetary cooling system of Gaia to defend them from the increasing heat of the sun as much as we do. And Gaia depends on organic life. We will be partners in this project. It is crucial, Lovelock argues, that the intelligence of Earth survives and prospers. He does not think there are intelligent aliens, so we are the only beings capable of understanding the cosmos. Perhaps, he speculates, the Novacene could even be the beginning of a process that will finally lead to intelligence suffusing the entire cosmos. At the age of 100, James Lovelock has produced the most important and compelling work of his life.\"--Publisher's website.
Book
Sports training auxiliary decision support system based on neural network algorithm
In order to improve the effect of sports training auxiliary decision, this paper combines the needs of sports training auxiliary system to carry out functional analysis and improve the traditional machine learning algorithm. The domain adversarial neural network based on maximum entropy loss combines the ability of maximum entropy loss to process misclassified samples and uses classification loss and domain adversarial loss to solve the problem of inconsistent edge distribution of category features between domains. Moreover, this paper takes sports decision as the core and introduces tasks of different difficulty and video training into research. In addition, this paper uses simulation software to measure the correctness of sports training in different scenarios and the data of the response latency and applies the neural network algorithm to the construction of the sports training auxiliary decision system. Finally, this paper designs experiments to study sports training recognition and sports training decision-making and builds an intelligent system through a simulation platform. The experimental research results show that the system constructed in this paper has a good sports training auxiliary decision function. The reliability of the method in this article can be verified in practice in the future.
Journal Article
The AI conundrum : harnessing the power of AI for your organization-profitably and safely
\"Demystifies AI for business professionals, highlighting its strengths, weaknesses, and real-world applications, while providing actionable insights for responsible implementation and risk mitigation\"-- Provided by publisher.
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Research on simulation of 3D human animation vision technology based on an enhanced machine learning algorithm
This paper provides an in-depth analysis and study of the simulation of 3D human animation visualization techniques by enhancing machine learning algorithms. Based on the statistical analysis of the data obtained from different measurement methods, the extraction of human body feature parameters based on millimeter-wave point cloud data is realized, and the 3D reconstruction and simulation of the human body are realized using parametric human modeling software. In video-based action recognition, most methods are data-driven and use deep networks to automatically learn features of the entire video image. In this process, specific research on human actions is not included or reflected. However, human action recognition is a processing of the semantic level of video content. Realizing universal human action recognition requires a semantic understanding of human behavior. Firstly, the geometric feature analysis of the 3D scanned human model is performed to extract the human body shape characteristic parameters, and the research on the analysis and estimation methods of body shape characteristic parameters is carried out to establish the human body shape parameter relationship model; then, the millimeter-wave point cloud is calculated and measured, the Li group features extracted using the group skeletal representation model with high data dimensionality, to be able to process the high-dimensional data, while reducing the complexity of the recognition process and speeding up the computation, feature learning and classification are performed with convolutional neural networks. To verify the better library portability and robustness of the method in this paper, the method was tested on a self-built human action database in the laboratory, and an average recognition rate of 97.26% was achieved. Meanwhile, this paper investigates the natural interaction application of virtual characters in a virtual learning environment based on human action recognition. Four testers tested the virtual human–computer interaction system of this paper, respectively, and the final test results show that the system has flexibility and stability.
Journal Article