I. What are the majors in Artificial Intelligence?
Artificial Intelligence (AI) is a major that focuses on the knowledge, technologies, methods, applications, and tools studied in the field of artificial intelligence. With the continuous development and progress of science and technology, the application of artificial intelligence in various fields is becoming increasingly widespread, and the demand for AI majors is also increasing. Below is a detailed introduction to the types of AI majors.
1. Computer Science and Technology Major
Computer science and technology is a discipline related to computer hardware and software. It studies and implements computers through knowledge of algorithms, data structures, and programming languages, providing a foundation for the development of artificial intelligence. At the same time, the computer science and technology major also provides many favorable conditions for the field of artificial intelligence, such as high-performance computing, big data processing, and cloud computing.
2. Data Science and Big Data Technology Major
Data science and big data technology is a new profession designed to meet the demands of the big data era. Its main components include data analysis, data mining, machine learning, and data visualization. For the field of artificial intelligence, data science and big data technology form the foundation for building intelligent systems and conducting deep learning.
3. Intelligent Science and Technology Major
Intelligent Science and Technology is an interdisciplinary field similar to Artificial Intelligence, whose main research areas include digital signal processing, cognitive neuroscience, machine vision, and speech recognition. This major plays a crucial role in the field of artificial intelligence, providing many new methods and ideas.
4. Human-Computer Interaction Major
Human-Computer Interaction (HCI) is a major field that studies the ways humans interact with computers. Its main content includes user experience design, interaction design, and human-computer interfaces. HCI is a crucial component in the field of artificial intelligence, as AI systems require human-computer interaction to achieve communication and exchange.
5. Control Science and Engineering
Control Science and Engineering is a discipline that involves nonlinear control, multivariable control, optimal control, and intelligent control. It primarily studies how to enable systems to achieve desired control objectives under specific conditions. In the field of artificial intelligence, Control Science and Engineering is one of the core theories and technologies researched in reinforcement learning, neural control, and adaptive control.
6. Machine Learning and Artificial Intelligence Major
Machine learning and artificial intelligence is a discipline dedicated to studying how to enable computers to learn and adapt like humans. Machine learning is one of the core technologies in the field of artificial intelligence. Through machine learning, artificial intelligence systems can automatically learn and optimize algorithms, thereby improving the intelligence and efficiency of the system.
7. Software Engineering and Computer Applications Major
Software engineering and computer applications is a new major that emerged to adapt to the development of informatization and digitalization. Its main research areas include software development, enterprise informatization, and quantitative analysis. In the field of artificial intelligence, software engineering and computer applications play a crucial role, providing the necessary development platforms and software tools for the implementation of AI systems.
II. The Shift in Security Perspectives in the Era of Artificial Intelligence
1. Traditional security architecture with obvious shortcomings
The rapid development and widespread application of artificial intelligence have brought new challenges and changes to network and data security. In the age of AI, the concept of network and data security has also undergone significant transformation. With the expansion of application attack surfaces, cybercriminals are launching increasingly sophisticated multi-media attacks. Attackers frequently use automated web crawlers, botnets, and vulnerability scanners to successfully infiltrate IT environments and take over user accounts, thereby stealing data, disrupting business operations, and launching destructive cyberattacks. Faced with complex attacks, traditional stacked, modular, and city-level security architectures suffer from numerous vulnerabilities, poor compatibility, cumbersome and inflexible design, and a lack of efficient collaboration, making them ineffective in responding to increasingly complex attacks.
Cybersecurity often faces more than just network security challenges. The borderless nature of modern applications renders traditional network security protection systems inadequate for new threats. Network security and data security issues are intricately intertwined, and any vulnerability can directly lead to data breaches. Therefore, it is essential to seek integrated solutions that can more comprehensively address everyday security threats—a newer approach that integrates security into the continuous integration/deployment process.
2. The awakening of public awareness of data ownership
Artificial intelligence has significantly enhanced the value of data resources, but it has also highlighted the importance of data ownership issues. At the individual level, data ownership manifests as citizens' data rights, posing a serious challenge to personal privacy protection. At the industry level, data ownership manifests as enterprises' data property rights. Data has become a core asset for enterprises, and data scraping and information gathering have led to the collection, analysis, and utilization of massive amounts of data. This has resulted in data abuse, theft, and misuse, making data ownership issues even more prominent. Like property rights, with the frequent occurrence of data infringement cases and domestic and international data security incidents, citizens and enterprises are increasingly aware of data ownership in the age of artificial intelligence. We need to strengthen data ownership protection and enhance technical means of data protection to ensure the rational use of data resources.
3. Data-driven security strategy innovation
China is undergoing an economic transformation from a demographic dividend to an innovation dividend, and its security is about to enter a new era of data-driven security strategy innovation. Artificial intelligence and data are mutually beneficial and complementary; AI technology endows data security with wisdom, and its development provides underlying general-purpose technical support for data. Data-driven security strategies provide a driving force for development. While the advancement of AI technology will undoubtedly greatly facilitate our lives, the misuse of data and algorithms can also backfire. Our society is more vulnerable and insecure than ever before, and data-driven security strategy innovation has become a crucial issue in this new era.
