Is it easy to find employment for a bachelor's degree in artificial intelligence for male stude

 

1. Ranking of the Best Schools in Artificial Intelligence

Original title: What does the undergraduate major in artificial intelligence study? A complete list of 440 AI major universities → | Editor: Heart of Machines, Datawhite The article is a bit long, directory preview: Professor Liu Zhiyuan from Tsinghua University answers questions about various universities that offer artificial intelligence (if there is your school! Leave a message in the comment section! Catching an alumni is an opportunity)

2. What should I learn in the field of artificial intelligence

In the increasingly popular era of computer science and artificial intelligence, many people are curious and longing for these two majors. Professor Liu Zhiyuan from Tsinghua University recently shared some content on Zhihu to help candidates choose their majors more rationally. He hopes that more candidates who truly like CS/AI can choose the right majors from their schools.

3. Recommended schools for postgraduate entrance exams in artificial intelligence

Professor Liu Zhiyuan answered questions about what artificial intelligence is, what to learn, how to learn, and where to learn. What is artificial intelligence? Artificial Intelligence (AI) is a young discipline that has been around for only 65 years since the name AI was officially proposed at the Dartmouth Conference in 1956; It has only been 70 years since Alan Turing proposed the Turing test in 1950 to determine whether a machine can think.

4. Artificial Intelligence Professional Courses

The 70 year development history of AI has brought together the efforts of scholars from different fields such as mathematics, computer science, logic, philosophy, neuroscience, linguistics, etc. It is a typical interdisciplinary. At the same time, from the overall perspective, AI is still the main branch of computer science and technology. What is AI? In short, the discipline of artificial intelligence utilizes computers to achieve human intelligence.

5. What are the employment directions and prospects for artificial intelligence majors

There is no universally recognized definition or boundary for human intelligence, and in fact, it has changed with the development of AI. After a certain human skill is mastered by computers, people often no longer believe that it represents human "true" intelligence. For example, after IBM Deep Blue defeated human chess champion Kasparov in 1997, there were comments that IBM computers were just violent searches, not real intelligence, that's not thinking! This phenomenon is also known as "AI Effect".

6. Postgraduate entrance examination subjects for artificial intelligence major

So, artificial intelligence always focuses on those human intelligence abilities that have not yet been cracked by computers. Simple human intelligence has been solved, such as counting ability with calculators, data memory and queries with databases, chess ability with chess software, and the remaining difficult advanced intelligence. Simply put, if we consider the brain as a black box, it can accept stimuli from the external world, The brain processes these signals to generate output feedback, and human intelligence is reflected in the corresponding "stimulus feedback".

7. Direction of Artificial Intelligence Graduate Entrance Examination

In view of the complexity of different stimulus signals and feedback processing, there are many specialized fields under AI to carry out relevant research and exploration. At present, the recognized core topics of AI include: machine learning, computer vision, natural language processing, speech processing, knowledge representation and calculation, reasoning and planning, etc., and on this basis, it supports many important application scenarios such as unmanned driving, robots, etc.

8. Graduate Entrance Examination for Artificial Intelligence

Machine learning: aims to equip computers with the ability to automatically learn, solve tasks such as classification, clustering, regression, and association analysis. Currently, the mainstream is to automatically learn and summarize rules from large-scale data, so as to predict new data. It is also known as statistical machine learning. Simply put, machine learning is a technology that automatically summarizes rules from a large amount of "stimulus feedback" data.

9. Employment direction of artificial intelligence major

Natural language processing: It aims to make computers understand and process human language differently from artificially designed programming languages such as C++and Java. Human language is the product of nature, so it is also known as "natural language". Human language is also typical unstructured data, which is composed of words. How to understand the meaning of a sentence, an article or even a book is also a challenge for AI.

Which school is the best for artificial intelligence majors

Because language is a unique carrier for human beings to transmit rich information and knowledge, express complex thoughts and emotions, and even is considered an important tool for human thinking, natural language processing is closer to human advanced cognitive intelligence, and there are many important open problem knowledge representation and calculation: human knowledge of the world accumulates into knowledge, which is the basis for human understanding of external information and realizing various intelligent capabilities.

In recent years, with the widespread application of knowledge graphs, they have become a key issue of concern in the research and industrial fields. Due to their youth, these directions are full of vitality. On the one hand, the latest technology is increasingly far-reaching in affecting all aspects of human social life, and at the same time, the disciplinary system and technical framework are rapidly changing and innovating. Nowadays, many contents of textbooks ten years ago seem outdated.

From the perspective of disciplinary settings, domestic universities issue degrees in accordance with the "Catalogue of Degree Granting and Talent Training Disciplines" issued by the Ministry of Education. The initial primary discipline of computer science and technology was "Computer Science and Technology", which consists of three secondary disciplines: "Computer System Architecture", "Computer Software and Theory", and "Computer Application Technology". Among them, "Computer System Architecture" corresponds to high-performance computing (supercomputing) and computer network architecture (Internet), Later, a first level discipline of "Cyberspace Security" was established separately; 'Computer Software and Theory' corresponds to software engineering and computer theoretical science, and later established a separate first level discipline called 'Software Engineering'; And "computer application technology" corresponds to various types of computer application technologies, which are largely advancing along the route from informatization to automation and then to intelligence. It can be imagined that if the current wave of AI can continue for a few years, the establishment of a separate "artificial intelligence" level discipline is also within reach.

From the perspective of research configuration, AI research teams are mainly distributed in information science related departments such as computer, automation and electronic engineering, which is closely related to the origin of AI. The founders of computer, Turing and von Neumann, Weiner, the founder of the main theoretical basis of automation, "cybernetics," and Shannon, the founder of the main component of electronic engineering and signal processing, "information theory," all contributed ideas to the creation of AI.

Therefore, the Computer Department mainly studies AI from the perspective of theory of computation and computer application, the Automation Department understands AI from the perspective of automatic control, and the Electronic Engineering Department interprets AI from the perspective of signal processing (regarding the visual, text, auditory and other modal understanding problems AI cares about as signal processing). Of course, there are also scholars engaged in artificial intelligence exploration in philosophy, brain and other fields.

However, overall, since the core goal of artificial intelligence is to explore how to transform human intelligence into computable problems, it mainly falls within the field of computer science? As mentioned earlier, artificial intelligence is generally still a topic of computer application. Although many universities both domestically and internationally have opened artificial intelligence classes and majors in the past two years, there is still no consensus on the curriculum design.

We can analyze the "Nanjing University Artificial Intelligence Undergraduate Education and Training System" released by the School of Artificial Intelligence, Nanjing University, which is the precursor of the domestic AI undergraduate education system

As a comparison, here is a list of course selection guidelines for the Department of Computer Science and Technology at Tsinghua University, with limited courses related to artificial intelligence highlighted in red boxes.

It can be seen that the main contents of AI need to learn include: basic mathematics courses: calculus (or mathematical analysis), algebra and geometry, discrete mathematics (or mathematical logic, graph theory, etc.), probability theory, which are graduate courses in Tsinghua CS and Nantah AI.

Discipline basic courses: Tsinghua CS and Nanjing University AI both need to learn the basics of programming, data structure, introduction to artificial intelligence, computer principles, digital circuits, system control, new machine learning, knowledge representation, computer vision, natural language processing as the discipline basic courses, which are both senior elective courses or graduate courses in Tsinghua CS; Tsinghua CS needs to learn additional circuit principles, signal processing, operating systems, Compilers: Principles, Techniques, and Tools, formal languages and automata, which are listed as professional elective courses by Nanjing University AI.

Professional elective courses: Nanjing University AI has established many AI related professional elective courses, such as automatic planning, probability graph modeling, reinforcement learning, neural networks, deep learning, etc., all of which are graduate courses in the field of artificial intelligence at Tsinghua CS; However, many cross courses such as cognitive science, neuroscience, computational finance, computational biology, computational linguistics and so on set up by Nantah AI are scattered in various departments in Tsinghua.

From this, it can be concluded that compared to the computer science major, the overlap in the core courses of the AI undergraduate major is much greater than the difference. It can be seen that Nanjing University has invested a lot of effort in the construction of the AI curriculum system, which is very in line with the current development characteristics of AI. Therefore, returning to this question, what does artificial intelligence learn?.

The suggestion is to take the core computer courses (basic mathematics courses, basic subject courses) as the main line of the subject, machine learning, knowledge representation, computer vision, natural language processing as the subject characteristics, and subject intersection as the auxiliary. Therefore, we can also say that whether it is in the newly established AI major represented by the AI School of Nanjing University, or the computer major represented by the Computer Department of Tsinghua University, You can complete the learning of basic knowledge of artificial intelligence.

The difference lies in that the former is preset as a basic course for the subject, while the latter becomes one of the optional directions for senior students in computer application technology (computer system architecture, computer software and theory, computer application technology). The following is a list of professional restricted courses for this direction, with over half of the courses being AI related

Artificial Intelligence Undergraduate Professional Knowledge System and Curriculum Design

Author: Zheng Nanning Pricing: 129 yuan Content Introduction: This book focuses on the professional connotation, positioning, and knowledge system of artificial intelligence undergraduate talent cultivation in higher education institutions. It sets up professional course groups such as Mathematics and Statistics, Science and Engineering, Computer Science and Engineering Core, Artificial Intelligence Core, Cognitive and Neuroscience, Advanced Robot Technology, Artificial Intelligence and Society, Artificial Intelligence Tools and Platforms, etc, The focus was on introducing the overview and knowledge points of each course in the eight major course groups, laying the foundation for cultivating engineers with scientific literacy in terms of knowledge and abilities.

This book can provide reference and guidance for various schools to build a wide range of interdisciplinary course systems for artificial intelligence majors, and also provide guidance for graduate students to learn how to learn artificial intelligence? The Constitution of Tsinghua University clearly proposes a three-in-one education model of "value shaping, ability cultivation, and knowledge imparting". Professor Liu Zhiyuan believes that this is the best description of the way to cultivate high-level AI talents.

There is no need to say much about imparting knowledge. Teachers and students impart knowledge and dispel doubts. Through course lectures and extracurricular practice in university, they become proficient in computer and artificial intelligence theory and technology. Each student's grades in a course reflect the level of professional knowledge mastery, and the vast majority of students can understand the importance of course learning.

However, the path of a university is not limited to this, otherwise it would be just a professional technical school. Building on knowledge transfer requires the cultivation of abilities, which is particularly important for CS/AI majors. Computer and artificial intelligence are very young disciplines, and they are in a period of rapid development. The updating of subject knowledge is fast, and most of the latest knowledge cannot be timely deposited into textbooks in a short period of time.

The knowledge that enters textbooks often has a significant distance from practical application scenarios. Many CS/AI high-tech companies themselves are at the forefront of the discipline and urgently need talents with fast learning and independent problem-solving abilities. On the one hand, students are required to consciously establish the concept of lifelong learning and have strong independent learning abilities; On the other hand, students are required to pay attention to exercising their research and innovation abilities through laboratory research and other means.

CS/AI students need to actively participate in the entire process of scientific research, establish professional interests, cultivate independent learning ability, self-learning habits, problem-solving awareness, and the ability to independently solve open problems. This is the only way for universities to cultivate high-level talents in CS/AI. Therefore, the ability of university teachers to conduct high-level original research in CS/AI also determines the level of their ability to cultivate students.

The final layer of value shaping may be the most mysterious, but more importantly, when a person's knowledge and abilities are determined, the direction of their efforts and persistence ultimately determine their height of growth. Finding the direction of their perseverance in the field of art is the process of value shaping. This process is not just a simple and crude indoctrination and preaching can be achieved. It requires high-level teachers to teach and learn together, and ambitious students to work together and strive, There are senior students from all walks of life who strive as exemplary models, and there are practical platforms outside of school and overseas to broaden their horizons.

Practice leads to true knowledge. Only by listening, watching, and thinking more can one find the direction they like and strive for, and also have more perseverance. Therefore, whether it's artificial intelligence, computer science, or any other major, if one wants to cultivate oneself as a worthy talent in this field, they need to strive to improve themselves from the three levels of knowledge, ability, and value.

List of Universities Majoring in Artificial Intelligence (440)

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