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   In the era of digitization and intelligentization, real-time data-driven approaches have emerged as a significant trend across many fields. In intelligent systems, perception refers to the system's ability to sense the environment or user inputs, cognition denotes the system's capacity to understand and process the perceived information, and interaction signifies the system's ability to exchange information and engage with users or the environment. Real-time data-driven approaches and intelligent perception-cognition-interaction are pivotal directions in the development of intelligent technologies, playing crucial roles in driving digital transformation and enhancing the level of system intelligence.

   From the perspective of neuroscience and psychology, structural-functional mapping can be used to study the structure and function of the brain, as well as the relationship between the brain and behavior. Through biomimetic design, structural-functional mapping provides important data support and theoretical basis for brain-like machine learning. By conducting in-depth research on the structure and function of the brain, we can better understand the neural structure and information processing mechanisms of the human brain, thereby providing guidance for the design and implementation of brain-like machine learning. At the same time, brain-like machine learning can also utilize the data from structural-functional mapping to optimize and improve its algorithms and models, enhancing learning efficiency and accuracy.

   Both bionic brain-like neural networks and multi-layer variational neural networks are significant research directions in the field of neural networks. Bionic brain-like neural networks focus on mimicking the structure and function of human brain neural networks, achieving intelligent processing and learning capabilities similar to those of the human brain. In contrast, multi-layer variational neural networks combine the advantages of variational inference and deep neural networks to capture complex features of data and generate new data samples. Although they differ in application fields and specific implementations, both provide new ideas and methods for the development and application of neural networks.

   Deep learning has made significant progress in various fields. It is a subfield of machine learning, with its core idea being to simulate the working of human brain neurons by constructing multi-layer neural network models, thereby enabling computers to learn autonomously and extract high-level features from data. Unlike traditional machine learning methods, the key characteristic of deep learning lies in its ability to gradually abstract data into increasingly high-level feature representations through layered non-linear transformations, demonstrating excellent performance in complex tasks. The basic principles of deep learning include Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Generative Adversarial Network (GAN). There exists a close and mutually reinforcing relationship among deep learning, big data, and large-scale computation. Big data provides abundant training data and computational resources for deep learning; large-scale computation accelerates the training process of deep learning models, supporting larger and deeper neural network models; and deep learning, in turn, extracts valuable information from big data to support areas such as business decision-making, prediction, image and video processing, natural language processing, and IoT data analysis.

   With the continuous advancement of technology and the expansion of application scenarios, the process of intelligence and smartification will exhibit broader development prospects. In the future, intelligent systems will become more intelligent, autonomous, and user-friendly, better adapting to and satisfying the needs of human society. Meanwhile, the process of intelligence and smartification will also propel human society towards a more intelligent, efficient, and sustainable direction. Understanding and shaping this process requires comprehensive consideration of multiple aspects and factors. By strengthening interdisciplinary research, promoting technological innovation, paying attention to social needs and ethics, and formulating policies and regulations, we can facilitate the healthy, orderly, and sustainable development of the intelligence and smartification process.

   Machine intelligence is a form of intelligence created by humans through technological means. It relies on algorithms, data, and computational resources to simulate certain human intelligent behaviors, such as learning, reasoning, and decision-making. With the continuous advancement of technology, machine intelligence has demonstrated capabilities that surpass humans in certain domains, such as large-scale data processing and rapid decision-making. However, machine intelligence still lacks the consciousness, emotions, and creativity possessed by humans. We need to strengthen interdisciplinary research to explore the complementarity and synergy between human intelligence and machine intelligence. We should promote technological innovation to enhance the level of intelligence and application capabilities of machine intelligence, while ensuring its development within a safe and controllable framework. Additionally, we must pay attention to ethical issues to ensure that the development of machine intelligence aligns with social ethics and moral norms. Lastly, we should strengthen education and training to improve human qualities and abilities, in order to adapt to the development needs of the era of machine intelligence.

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