Artificial Intelligence (AI) is a new field of technology science that studies and develops theories, methods, technologies, and application systems used to simulate, extend, and enhance human intelligence.

Artificial intelligence is a branch of computer science that attempts to understand the essence of intelligence and produce machines that can respond in ways similar to human intelligence. Research in this field includes robotics, language recognition, image recognition, natural language processing, and expert systems. Since its inception, theories and technologies in artificial intelligence have matured, and its application areas have continuously expanded. It can be imagined that future technological products brought by artificial intelligence will serve as “containers” of human wisdom. AI can simulate the information processes of human consciousness and thought. Although artificial intelligence is not human intelligence, it can think like a human and may even surpass human intelligence.

Artificial intelligence is a highly challenging science. People engaged in this work must understand computer science, psychology, and philosophy. AI is an extensive science composed of various fields, such as machine learning and computer vision. Generally speaking, a major goal of AI research is to enable machines to perform complex tasks that typically require human intelligence. However, different eras and individuals have different understandings of what constitutes “complex work.”

Definition Analysis

The definition of artificial intelligence can be divided into two parts: “artificial” and “intelligence.” The term “artificial” is relatively easy to understand and is less controversial. Sometimes we need to consider what can be manufactured by human effort or whether human intelligence is sufficiently advanced to create artificial intelligence. Overall, an “artificial system” refers to what is commonly understood as an artificial system.

Regarding what constitutes “intelligence,” there are many questions. This involves other issues such as consciousness, self, and mind (including unconscious thought). The only intelligence that humans fully understand is human intelligence, which is a widely accepted view. However, our understanding of our own intelligence is very limited, and we have limited knowledge of the necessary elements that constitute human intelligence, making it difficult to define what constitutes “artificial” intelligence. Therefore, research in artificial intelligence often involves studying human intelligence itself. Other forms of intelligence, such as that of animals or other artificial systems, are also generally considered related research topics in AI.

Artificial intelligence has received increasing attention in the field of computer science and has been applied in robotics, economic and political decision-making, control systems, and simulation systems.

Professor Nelson defined artificial intelligence as “the discipline concerning knowledge – how to represent knowledge, acquire knowledge, and use knowledge.” Another professor from MIT, Winston, stated, “Artificial intelligence is the study of how to enable computers to perform intelligent tasks that only humans could do in the past.” These statements reflect the fundamental ideas and content of the AI discipline, which studies the laws governing human intelligent activities, constructs artificial systems with certain intelligence, and researches how to enable computers to accomplish tasks that previously required human intelligence, essentially studying how to use computer hardware and software to simulate certain intelligent behaviors of humans.

Artificial intelligence is a branch of computer science and has been regarded as one of the three cutting-edge technologies in the world since the 1970s (space technology, energy technology, and artificial intelligence). It is also considered one of the three cutting-edge technologies of the 21st century (genetic engineering, nanoscience, and artificial intelligence). This is because it has developed rapidly over the past thirty years, gaining widespread application in many fields and achieving fruitful results. AI has gradually become an independent branch, forming a system in both theory and practice.

Artificial intelligence studies how to enable computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It mainly includes the principles of computer implementation of intelligence, manufacturing computers that resemble human brain intelligence, and enabling computers to achieve higher-level applications. AI will involve various disciplines such as computer science, psychology, philosophy, and linguistics. It can be said that it encompasses almost all fields of natural and social sciences, with the relationship between AI and cognitive science being one of practical and theoretical application. AI is at the technical application level of cognitive science and is an application branch of it. From the perspective of cognition, AI is not limited to logical thinking; it must also consider imaginative and inspirational thinking to promote breakthrough development in artificial intelligence. Mathematics is often regarded as the foundational science of many disciplines, and mathematics also enters the fields of language and thought. The AI discipline must also borrow mathematical tools. Mathematics not only plays a role in standard logic and fuzzy mathematics but also enters the field of AI, where they will promote each other’s faster development.

Research Value

For example, heavy scientific and engineering calculations that were once the domain of the human brain can now be performed by computers, which can do so faster and more accurately than humans. Therefore, contemporary society no longer regards such calculations as “complex tasks requiring human intelligence.” This illustrates that the definition of complex work changes with the development of the times and technological advancement, and the specific goals of AI science naturally evolve with these changes. AI continues to make new progress while also shifting towards more meaningful and challenging goals.

Typically, the mathematical foundation of “machine learning” is “statistics,” “information theory,” and “control theory,” along with other non-mathematical disciplines. This type of “machine learning” heavily relies on “experience.” Computers need to continuously acquire knowledge and learning strategies from the experience of solving a class of problems, applying experiential knowledge to solve similar problems, and accumulating new experiences, much like ordinary humans. We can refer to this type of learning as “continuous learning.” However, humans not only learn from experience but also create, which can be termed “leap learning.” In certain contexts, this is referred to as “inspiration” or “insight.” Historically, the most challenging aspect for computers to learn has been “insight.” More rigorously, computers struggle to learn “qualitative changes that do not rely on quantitative changes” and find it difficult to transition directly from one “quality” to another or from one “concept” to another. For this reason, the term “practice” here does not equate to human practice. The human practice process includes both experience and creativity.

This is what intelligent researchers dream of achieving.

In 2013, data researcher S.C Wang from the Dijing Data Research Center developed a new data analysis method that derived a new way to study the properties of functions. The author found that this new data analysis method provided a pathway for computers to learn to “create.” Essentially, this method offers a relatively effective way to model human “creativity.” This approach is granted by mathematics, a capability that ordinary people do not possess but computers can. Consequently, computers can not only excel in calculation but can also create due to their proficiency in computation. Computer scientists should decisively restrict the overly comprehensive operational capabilities of computers that excel in “creation”; otherwise, computers may one day “reverse capture” humanity.

Upon reviewing the derivation process and mathematics of the new method, the author expanded their understanding of thought and mathematics. Mathematics is concise, clear, reliable, and strongly patterned. The history of mathematics development is illuminated by the creativity of mathematical masters. This creativity is presented in various mathematical theorems or conclusions, with the greatest characteristic of mathematical theorems being that they are based on fundamental concepts and axioms and expressed in a patterned linguistic form that contains rich information in a logical structure. It can be said that mathematics is the discipline that most purely and straightforwardly reflects (at least one category of) creativity patterns.

Since the advent of computers, humanity has had a tool that can simulate human thought. In the years that followed, countless scientists have worked tirelessly towards this goal. Today, artificial intelligence is no longer the exclusive domain of a few scientists; computer science departments in almost all universities worldwide have researchers studying this discipline. University students studying computer science must also take courses in this area. Through collective efforts, computers now seem quite intelligent. For instance, in May 1997, IBM’s Deep Blue computer defeated chess master Garry Kasparov. Many may not notice that in some instances, computers help humans perform tasks that were once solely human responsibilities, leveraging their speed and accuracy to assist humanity. Artificial intelligence remains at the forefront of computer science, and programming languages and other computer software exist because of advancements in AI.

On March 4, 2019, during the second session of the 13th National People’s Congress, spokesperson Zhang Yesui announced that legislation closely related to artificial intelligence had been included in the legislative plan.

Scientific Introduction

Practical Applications

Machine vision, fingerprint recognition, facial recognition, retinal recognition, iris recognition, palm print recognition, expert systems, automatic planning, intelligent search, theorem proving, gaming, automatic programming, intelligent control, robotics, language and image understanding, genetic programming, etc.

Disciplinary Scope

Artificial intelligence is an interdisciplinary field at the intersection of natural and social sciences.

Involved Disciplines

Philosophy and cognitive science, mathematics, neurophysiology, psychology, computer science, information theory, control theory, and indeterminacy theory.

Research Scope

Natural language processing, knowledge representation, intelligent search, reasoning, planning, machine learning, knowledge acquisition, combinatorial scheduling problems, perception problems, pattern recognition, logic programming, soft computing, imprecise and uncertain management, artificial life, neural networks, complex systems, genetic algorithms.

Consciousness and Artificial Intelligence

Artificial intelligence, in essence, is a simulation of the information processes of human thought.

The simulation of human thought can be approached in two ways: structural simulation, which mimics the structural mechanisms of the human brain to create “brain-like” machines; and functional simulation, which temporarily sets aside the internal structure of the human brain and simulates its functional processes. The emergence of modern electronic computers represents a simulation of the functional aspects of human brain thought, simulating the information processes of human brain thought.

Weak artificial intelligence is rapidly advancing, especially after the 2008 economic crisis, as the US, Japan, and Europe hope to achieve re-industrialization through robots. Industrial robots are developing faster than ever before, further driving breakthroughs in weak AI and related fields. Many tasks that previously required human involvement can now be accomplished by robots.

Strong artificial intelligence, however, remains at a bottleneck and still requires the efforts of scientists and humanity.

The Important Role of Artificial Intelligence is to Supplement Human Abilities

Speaker:Bart Selman, Image from:Tuchong

The perceptual ability is the reason for AI’s power.

We see autonomous systems controlled by artificial intelligence technology beginning to emerge in society, including self-driving cars, drones, and virtual assistants. Almost everywhere you interact with technology, AI is integrated.

This marks a genuine shift in academic research communities from academia to the real world, and the reason is simple: machines can finally “hear” and “see” this world, which is a significant advancement.

The field of artificial intelligence began in the late 1950s, around 60 or 70 years ago. In the early 1960s, AI researchers primarily focused on whether computers could see, which essentially meant having a camera to interpret images and recognize objects. This is something humans can do naturally, but it is very challenging for machines. We have spent over 50 years researching this, and only now have we developed systems that can genuinely interpret images, see their surroundings, and recognize objects, which are all part of human perception.

As time has progressed, we have developed various technologies, reasoning, planning, decision-making, and more. These technologies were developed in the absence of hearing and seeing; computers could not hear or see us, so the ability to hear and see represents an incredible change that will alter how we build these systems.

Self-driving cars without cameras

Stanford’s Stanley

In 2005, Stanford University’s Stanley demonstrated impressive autonomous driving technology. This self-driving car could not see anything; Stanley was not equipped with a camera. When developers asked if they should install a camera on the car, the response was – no need, it would be useless.

So how did Stanley operate? It used GPS and light technology, a mechanism similar to lasers for object detection, but it was not true computer vision.

Now, this situation has completely changed. Tesla and other autonomous driving technologies are based on computer vision, and cars are beginning to have the capability to “see.” Artificial intelligence systems are gradually becoming rooted in the human world, beginning to understand us and interact with us, which is key to making AI technology effective – AI must interact with humans, understand how humans view the world, understand how humans interact with the world, and understand human intentions and emotions; all of this is becoming possible.

This transformation has made AI a new technology in our society, as you can see at this conference, which will drive the transformation of education, and this is the direction we are working towards.

Let me briefly introduce why such changes are happening in this field.

2005’s computer vision image recognition task

This is a computer vision image recognition task from 2005; you take a photo like this, and we see a lamp here. It’s not easy to spot a camera, and there is a half-human sculpture here.

When we look at the photo, we can recognize the objects, but this is very difficult for machines. This is what we call labeled data, the result of human labeling.

In 2005, what could computer vision achieve? You can see that the lamp is completely broken, and the light is almost connected to the statue, with various objects mixed together. So once we start interpreting objects from this image, recognizing them becomes very challenging. Visually, computers cannot recognize what is in the image.

If we jump 15 years later, this is the technology we now have for self-driving cars. I want to show you this image, which is very similar to the labeling problem I just presented; we must label the road and sidewalks. You can see the labeled image, almost perfectly labeled, achieving a superhuman level, meaning that computer vision has surpassed human capabilities.

Another surprising fact is that we now have traffic sign recognition technology that exceeds human capabilities; humans find it difficult to recognize all traffic signs, while computers do not.

Deep Learning in Self-Driving Cars

Your computer can read these traffic signals, even in very poor driving conditions, with frames around all moving cars; this was completely impossible ten years ago.

This is what we envisioned: cars can observe their surroundings in real time, with better visibility than human drivers. We expect the number of car accidents to decrease by 90%, or even 95%. In the US, approximately 30,000 people die in traffic accidents each year, and we hope to reduce this number to below 5,000 or even 3,000.

AI Drives Rapid Evolution of Human Society

The Stanley self-driving car was born in 2005; it could not fully match human drivers, but it was a significant milestone.

The milestones of artificial intelligence began in the late 1990s.

IBM Watson played a popular game in the US; it was a system capable of defeating the best human players. This was a surprising event, as people believed it was a very challenging game, and the human world champion enjoyed high prestige. However, the IBM Watson system could defeat human opponents.

From 2011 to 2012, we entered the era of deep learning. Deep learning pioneers Geoffrey Hinton, Yoshua Bengio, and Yann LeCun received the Turing Award, the highest honor in computer science. They opened a new chapter in deep learning for artificial intelligence, enabling computer vision and speech recognition.

AlphaGo defeated human players, and Go is a game that is several orders of magnitude more difficult than chess. At that time, we did not expect this to happen within 10 to 20 years, but with deep learning, it occurred in 2016.

The current president of the American Artificial Intelligence Association, Yolanda Gil, and I led the “20-Year Roadmap for American Artificial Intelligence Research,” involving nearly a hundred top AI researchers. I want to emphasize that healthcare is a driving force behind business, scientific discovery, and social justice, and education and training are critical drivers of this roadmap.

We propose establishing a large research center; the US is currently doing this, and many countries, including China, are establishing their own research centers to accelerate the development of these fields. We refer to this as a task-driven AI center, one of whose missions is centered around education.

The 20-Year Community Development Roadmap for American AI Research

We all believe that education can benefit from artificial intelligence, with a focus on personalized and customized learning. AI systems can learn from students and understand their status, identifying areas for improvement and focusing training and education on these areas, thereby becoming a highly focused teacher working alongside human instructors.

I want to emphasize the collaborative aspect; AI systems alone are insufficient; there must be collaboration between human teachers, AI systems, and students, and this collaboration will transform education.

Many tasks are so-called data-driven, meaning that having a million labeled photos is enough to train an AI system. However, in education, another factor is also crucial. The world is not solely about data; knowledge is more important. What is the difference between data and knowledge? Data includes labeled images, charts, payrolls, and various surveys, which are all basic data.

Knowledge, on the other hand, is processed data; Newton’s laws are an example of knowledge, and the entire field of physics is based on a few fundamental principles, yet incredible results arise from them. Mathematics is driven by some fundamental principles, but a rich mathematical system consequently emerges.

Artificial Intelligence and Lifelong Education and Training

In a sense, knowledge is a more compact form of data. Education ultimately revolves around knowledge, and we are witnessing a transformation in this regard. Data-driven approaches are essential, but the technologies that complement deep learning methods are increasingly driven by knowledge and reasoning, making it a very exciting research field.

I am about to assume the presidency of the Artificial Intelligence Development Association, which is undertaking a significant award selection process to recognize enterprises and research that contribute to social welfare through the application of AI technologies. Beyond educational equity, it can also improve living standards.

In this field, we will continue to see many new developments, with the key being the complementarity to humanity and AI systems. This is the pioneering era of artificial intelligence and education, and their integration will be one of the most exciting new developments of our time.

Speaker: Bart Selman (Elected President of AAAI, Professor at Cornell University), Image from: Tuchong

Attachment 1

In recent years, domestic universities have responded to the Ministry of Education’s “Higher Education Artificial Intelligence Innovation Action Plan” by establishing AI-related majors, including several top universities that have been researching AI for many years. Below, Feng Huajun will summarize several universities leading the AI major in China.

1. Tsinghua University: National Key Laboratory of Intelligent Technology and Systems

Tsinghua University’s Department of Computer Science and Technology (referred to as Computer Department) was established in 1958. In the evaluations conducted by the National Degree and Graduate Education Development Center in 2006 and 2012, it ranked first with a total score of 100.

The Intelligent Technology and Systems Laboratory is based in the Department of Computer Science and Technology at Tsinghua University, mainly engaged in basic and forward-looking research on artificial intelligence (fundamental principles and methods), intelligent information processing, intelligent robotics, and interdisciplinary research with cognitive neuroscience and psychology, as well as application research and system integration related to these theories.

The laboratory has undertaken several national key scientific research tasks, with some research reaching international levels, such as the “Offline Handwritten Chinese Character Recognition System and Method with Interactive and Self-Learning Functions” and “Hierarchical Problem Solving Theory and Application in Artificial Intelligence,” which have won national science and technology progress awards and natural science awards.

2. Peking University: Department of Intelligent Science

The Department of Intelligent Science was established in July 2002, mainly engaging in basic and applied research in intelligent perception, machine learning, data intelligent analysis, and intelligent computing, focusing on theories, methods, and major field applications.

The Department of Intelligent Science relies on the National Key Laboratory of Visual and Auditory Information Processing, aiming to achieve highly intelligent machine perception systems and is internationally leading in biometric recognition research.

Under the leadership of renowned software and AI experts, Academician He Xingu and Changjiang Distinguished Professor Zha Hongbin, the Department focuses on research in machine vision, machine hearing, intelligent systems, and the physiological and psychological foundations of intelligence.

With a technology core formed by researchers from Peking University, the automatic fingerprint identification system developed by Peking University is the only one in China that can compete with foreign systems, providing comprehensive solutions for public security applications and holding the highest market share for fingerprint automatic identification technology products in China.

3. Fudan University: Brain-like Intelligence Science and Technology Research Institute

The Brain-like Intelligence Science and Technology Research Institute at Fudan University was established in March 2015 as an independent secondary research institution within Fudan University. Its predecessor was one of the first interdisciplinary international research centers at Fudan University – the Computational Systems Biology Research Center, established in 2008.

The institute is currently building five core functional platforms and an international cooperation research and development center, mainly including:

1. A neuromorphic computing simulation platform focused on studying the multi-information feedback processing mechanisms of advanced cognitive functions of the brain;

2. An intelligent diagnosis and treatment data demonstration platform based on a multi-scale, multi-center major brain disease database and algorithm development;

3. A comprehensive biomedical imaging platform providing experimental technical support for biomedical translational research and information industry intelligence, relying on a cluster of high-end medical imaging equipment;

4. A brain-like intelligent software and hardware development platform targeting the development of machine learning algorithms such as deep learning, reinforcement learning, and self-organizing learning, as well as wearable devices, brain-like chips, and health service robots;

5. A brain-like intelligence industrialization platform integrating incubation acceleration, industry alliance, and investment fund, providing application technology resources and incubation services for brain-like intelligence innovation projects and enterprises;

6. Building brain-like intelligence international cooperation nodes and talent training centers based on existing international cooperation data and academic resources from the European Human Brain Project, US Brain Project, etc.

4. Zhejiang University: Artificial Intelligence Research Institute

Zhejiang University established the Artificial Intelligence Research Institute as early as the 1980s, with its first director being the renowned computer scientist He Zhijun, known as the “pioneer of artificial intelligence research in China,” affiliated with the College of Computer Science and Technology.

From 1981 to the present, the AI Research Institute at Zhejiang University has witnessed and participated in a series of changes in artificial intelligence. During He Zhijun’s tenure, AI research was still in the traditional stage dominated by rules, logic, and symbols. When Pan Yunhe became the second director, AI began to converge with computer-aided design and graphics.

Now, as AI enters the big data stage, Zhejiang University has established a significant advantage in the field of computer vision.

In terms of talent output, beyond internet technology companies like Alibaba and NetEase, Zhejiang enterprises such as Hikvision, Zhejiang Wangxin, and Zhejiang Dahua have also been early participants in AI research and development, making Zhejiang University an important partner and talent reserve.

5. Shanghai Jiao Tong University: Intelligent Human-Computer Interaction Research Institute

The Intelligent Human-Computer Interaction Research Institute is affiliated with the Department of Computer Science and Engineering, with a long-term goal of exploring the mechanisms of human brain intelligent information processing and cognitive processes, providing new computational structures and algorithms for intelligent information processing, and developing natural and friendly human-computer interaction systems.

Additionally, Shanghai Jiao Tong University established the Key Laboratory of Intelligent Computing and Intelligent Systems in collaboration with Microsoft in September 2005, aiming to achieve the shared mission of “enabling future computers and robots to see, hear, learn, and communicate with humans in natural language.”

Representative achievements include a multimodal fatigue driving detection system based on brain-machine interaction, a brain function rehabilitation training platform based on EEG, and a cognitive intelligent human-machine spoken dialogue system.

6. Xi’an Jiaotong University: Artificial Intelligence and Robotics Research Institute

The Artificial Intelligence and Robotics Research Institute was established in 1986, evolving from the computer control teaching and research section of the automatic control major. The institute supports the National Engineering Laboratory for Visual Information Processing and Application and has collaborated with internationally renowned scholars to establish the International Research Center for Cognitive Science and Engineering under the support of the Ministry of Education and the State Administration of Foreign Experts Affairs’ “Higher Education Discipline Innovation and Intelligence Introduction Plan.”

As a national key discipline in “Pattern Recognition and Intelligent Systems”, the institute recruits doctoral students in control science and engineering and is a component of the postdoctoral mobility station in automation disciplines.

In scientific research, under the leadership of academic leader and director Academician Zheng Nanning, the institute mainly conducts intelligent information processing research based on computer vision and pattern recognition, aligning with the forefront of disciplinary development.

7. Northwestern Polytechnical University: Audio, Speech, and Language Processing Group

The Audio, Speech, and Language Processing Group (ASLP@NPU) at Northwestern Polytechnical University is affiliated with the Shaanxi Provincial Key Laboratory for Speech and Image Information Processing.

The research group was established in 1995 and has rapidly developed over the past decade, forming major research directions in human-computer speech interaction, speech and audio signal processing, emotional and audiovisual multimodal processing, and multimedia content analysis and retrieval.

The laboratory has engaged in extensive and in-depth research collaborations with famous IT companies and several startups, including Baidu, Sogou, Alibaba, Microsoft, Tencent, IBM, Samsung, Huawei, ZTE, Xiaomi, JD.com, Yunzhisheng, Chumenwentong, Roobo, and Harman, establishing the “Northwestern Polytechnical University-Tencent Media Information Technology Joint Laboratory” and the “Northwestern Polytechnical University-Yunzhisheng Intelligent Speech Interaction Joint Laboratory” with the prominent startup Yunzhisheng.

8. Huazhong University of Science and Technology: School of Automation

The School of Automation at Huazhong University of Science and Technology was formed by merging the original Department of Control Science and Engineering and the Institute of Image Recognition and Artificial Intelligence in 2013.

Pattern recognition and intelligent systems are important secondary disciplines of the first-level discipline of automation. To date, the Department of Automation has undertaken over a hundred national, defense, and industry projects in the original “Image Recognition and Artificial Intelligence Research Institute” and “Control Science and Engineering Department”.

9. Xiamen University: Department of Intelligent Science and Technology

In December 2006, Xiamen University officially established the “Intelligent Science and Technology” undergraduate program with the approval of the Ministry of Education, and in June 2007, the “Department of Intelligent Science and Technology” was officially established. In September 2007, the department welcomed its first undergraduate students.

The department currently undertakes multiple national 863 projects, National Natural Science Foundation projects, and Fujian Provincial Science and Technology Fund projects, with three platforms: the “Fujian Provincial Key Laboratory of Brain-like Intelligent Systems”, the “Fujian Provincial Key Laboratory of Intelligent Information Technology in Universities”, and the “Xiamen University Language Technology Center”.

Additionally, there are several research labs such as “Artistic Cognition and Computation”, “Natural Language Processing”, “Intelligent Multimedia Technology”, “Artificial Brain Laboratory”, and “Intelligent Traditional Chinese Medicine Information Processing”, providing necessary support for cultivating high-quality students.

10. University of Science and Technology of China: School of Computer Science and Technology

The University of Science and Technology of China established its computer major when it was founded in 1958. The supporting laboratories include the National High-Performance Computing Center (Hefei), the Anhui Provincial Key Laboratory of High-Performance Computing, the Anhui Provincial Key Laboratory of Computing and Communication Software, the Multimedia Computing and Communication Ministry of Education-Microsoft Key Laboratory, the USTC Supercomputing Center, and the Information Science Laboratory.

Among them, the Multimedia Computing and Communication Ministry of Education-Microsoft Key Laboratory mainly conducts research in human-machine natural language communication, semantic computing, and data mining.

In the area of human-machine natural language communication, the focus is on Chinese information processing, human auditory-visual mechanisms, and linguistic phonetics.

In the area of semantic computing and data mining, the focus is on natural language-driven computing, semantic annotation of multimedia content, automatic question answering, semantic social networks, data and knowledge engineering, and semantic computing in privacy protection and management.

11. Nanjing University of Science and Technology: School of Computer Science and Engineering

The School of Computer Science and Engineering at Nanjing University of Science and Technology was established in 1953, evolving from the simulation computer research group of Harbin Military Engineering Institute. In December 2005, it was renamed the School of Computer Science and Technology, and in May 2012, it was renamed to its current name.

In the fields of computer science and artificial intelligence technology, the school has well-organized discipline laboratories and platforms, including the “Key Laboratory of High-Dimensional Information Intelligent Perception and Systems” of the Ministry of Education, the “Key Laboratory of Social Security Information Perception and Systems” of the Ministry of Industry and Information Technology, the “Jiangsu Provincial Key Laboratory of Social Public Security Image and Video Understanding”, the “Jiangsu Provincial 2011 Collaborative Innovation Center for Social Public Security Technology”, and the “Key Laboratory of Social Public Security” of the Jiangsu Provincial Public Security Department, as well as the “High-Dimensional Information Intelligent Perception and Systems” 111 innovative intelligence introduction base supported by the Ministry of Education and the State Administration of Foreign Experts Affairs.

At the same time, the school has established a series of industry-university-research collaborative innovation platforms with well-known domestic enterprises, including the Nanjing University of Science and Technology-CAS Smart City Big Data Joint Laboratory and the Deep City Institute-Nanjing University of Science and Technology Big Data Technology Joint Laboratory.

Besides the above universities, other institutions with years of accumulated professional strength are also joining the ranks of establishing AI colleges.

12. Chinese Academy of Sciences: Artificial Intelligence Technology Academy

The Artificial Intelligence Technology Academy of the University of Chinese Academy of Sciences was established on May 28, 2017, as the first new type of academy in China to comprehensively carry out teaching and research work in the field of AI technology.

The academy focuses on the international scientific frontier and has six teaching and research offices: pattern recognition, foundations of artificial intelligence, brain cognition and intelligent medicine, intelligent human-computer interaction, intelligent robotics, and intelligent control.

It has research institutions such as the National Key Laboratory of Pattern Recognition, the National Key Laboratory of Complex Systems Management and Control, the National Engineering Technology Research Center for Special Integrated Circuit Design, and the Key Laboratory of Molecular Imaging of the Chinese Academy of Sciences.

13. Xi’an University of Electronic Science and Technology: Artificial Intelligence Academy

On November 2, 2017, the Xi’an University of Electronic Science and Technology officially unveiled the Artificial Intelligence Academy, the first entity directly under the Ministry of Education dedicated to training high-end talent in the field of artificial intelligence, innovative result development, and high-level team cultivation.

14. Chongqing University of Posts and Telecommunications: Artificial Intelligence Academy

On February 7, 2018, Chongqing University of Posts and Telecommunications partnered with iFlytek to establish the Artificial Intelligence Academy, which will start enrolling students this year. This initiative aims to leverage its own professional research strength while fully utilizing the advantageous resources of enterprises to lay out the infinitely developing high-tech field of artificial intelligence, making the future development of Chongqing University worth looking forward to!

15. Nanjing University: Artificial Intelligence Academy

On March 6, 2018, Nanjing University officially established the Artificial Intelligence Academy under the Department of Computer Science and Technology. It is reported that the Nanjing University Artificial Intelligence Academy is a cooperative project with the Nanjing municipal government, on par with the School of Computer Science and the School of Software.

The dean is Professor Zhou Zhihua, who has been engaged in AI research for over 20 years and is an influential scientist on the international academic stage in artificial intelligence. He is also the first Chinese scholar to be elected as a fellow of the five major international academic societies: AAAI, ACM, AAAS, IEEE, and IAPR.

16. Harbin Institute of Technology: Artificial Intelligence Research Institute

On May 5, 2018, Harbin Institute of Technology officially established the Artificial Intelligence Research Institute. The researchers, administratively belonging to their respective departments, have a common research target and unified physical space. Researchers will build around four levels: theory, technology, platform, and application, across seven directions.

Summary:

The Chinese artificial intelligence market is experiencing remarkable growth, accompanied by a surge in demand for talent in the AI field.

The demand for talent is particularly strong in the foundational layer of AI, especially in algorithms, machine learning, GPU, and intelligent chips, showing a more significant talent gap compared to the technical and application layers.

The surge in market demand and the introduction of various favorable policies indicate a bright future for AI in China. However, talent cultivation is not an immediate process; continuous investment in education from the government, universities, and enterprises is needed to gradually fill the talent gap in artificial intelligence and enable rapid development of the AI industry.

As the college entrance examination approaches, I wish all candidates good results and hope they can enter their desired universities and institutions!

Attachment 2

Seven Habits of Efficient Learning

Learning requires a gradual improvement in one’s learning ability through review. In exam preparation, there are often two misconceptions: one is solely focusing on doing problems; some students, as long as they get the correct result, do not consider other related issues; the other is emphasizing classroom learning efficiency while neglecting after-class practice. Ti Fen Gao Zhong has summarized seven learning habits for everyone.

Habit 1: Don’t Expect Anyone to Push You Forward

If you don’t move forward, who will push you? Therefore, a proactive attitude is essential for achieving personal vision. We often say, “I can’t… because of genetics…”; “I’m late because…”; “I didn’t complete my plan because…” We are always looking for excuses or complaining, wasting our lives in dissatisfaction. The difference between humans and animals is that humans can actively create and realize dreams to enhance our quality of life.

Effective individuals take responsibility for their actions and choices in life, autonomously choosing their attitude and methods to cope with external environments; they strive to achieve things they can control rather than passively worrying about things that are uncontrollable or difficult to control; they improve their effectiveness through effort, thereby expanding their areas of concern and influence. A positive mindset allows you to have “the freedom to choose.”

Habit 2: Learn to Make Good Plans

We often lose direction on the path of life, consuming our lives in wandering and confusion. Effective individuals understand how to design their futures. They carefully plan who they want to become, what they want to do, and what they want to possess, clearly writing it down to guide their decisions.

Therefore, “beginning with the end in mind” is the principle of self-leadership. This ensures that one’s actions align with their goals and are not influenced by others or external environments. Why do many successful people feel lost? Many people work hard without discovering their ultimate life goals, merely busying themselves without realizing their deepest desires and without examining their life beliefs: what do you truly want to do? What is most important in your life? What is the focus of your life? Only by establishing life goals that align with your values can you concentrate your willpower and fully commit to realizing them, thus achieving the greatest satisfaction within.

Habit 3: Learn to Give Up What You Won’t Do

Everyone’s time is limited, so focus on important things, meaning those you find valuable and that contribute to your life’s value and highest goals; do less urgent things, which are those you or others think need immediate resolution. The fire department’s greatest contribution should be fire prevention, not just responding to fires. Therefore, “putting first things first” is the principle of self-management.

Effective individuals typically have a small number of very important urgent crisis events they need to deal with immediately; they focus their work on important but not urgent matters to maintain a balance of effectiveness and efficiency. “Effective management” is prioritizing the most important matters. Leaders must first determine what is important, then they should grasp the priorities and keep them at the forefront to avoid being swayed by feelings, emotions, or impulses. To concentrate on current priorities, one must first eliminate distractions from secondary matters and be brave enough to say “no.”

Habit 4: Stay Away from Disputes

Those who understand mutual benefit view life as a stage for cooperation rather than a battleground. Generally, people approach matters with a binary mindset: either strong or weak, either winning or losing. In fact, the world provides everyone with enough standing space; another’s gain is not your loss. Therefore, “win-win thinking” has become the principle applied in interpersonal leadership.

We have been involved in various competitions and exams since childhood, cultivating a competitive mindset of “you win, I lose, you die, I live.” Imagine, who would willingly accept defeat in a competition?

Establishing win-win thinking means continually seeking mutual benefits in interpersonal interactions to reach agreements that satisfy both parties and commit to cooperation. Those with win-win thinking often possess three personality traits: integrity, maturity, and an abundance mindset. They are loyal to their feelings, values, and commitments; they have the courage to express their thoughts and feelings and can view others’ thoughts and experiences with an open and understanding attitude; they believe the world has enough resources and space for everyone to share.

Habit 5: Learn to Empathize

“Understanding others and understanding oneself” is the principle of communication. Consider, if a doctor prescribes medication without diagnosis, who would dare to follow? When communicating with others, we often make the mistake of jumping to conclusions without understanding the situation. Therefore, I must emphasize that “understanding others” and “expressing oneself” are indispensable elements of effective interpersonal communication. First, understand the other party, then strive to have them understand you; this is the key to effective interpersonal communication, shifting away from the tendency to hastily suggest or solve problems.

One must cultivate the habit of empathetic communication. To seek understanding from others, one must first understand them. Everyone wishes to be understood and is eager to express themselves, yet often neglects listening. Effective listening not only helps acquire a wide range of accurate information but also fosters emotional accumulation between both parties. When our cultivation reaches a level where we can manage ourselves, maintain a peaceful mindset, withstand external disturbances, and draw from diverse opinions, our interpersonal relationships will elevate to a new level.

Habit 6: 1 + 1 Can Be Greater Than 2

If we integrate win-win thinking, empathetic communication, and the principle of synergistic effects, we can not only dissolve resistance but even turn resistance into support. “Synergistic effects” refer to the principle of creative cooperation. Collaborative brainstorming is immensely powerful. Many natural phenomena demonstrate that the whole is greater than the sum of its parts.

When different plants grow together, their roots intertwine, improving the soil quality, leading to more prosperous growth than if they grew alone; two bricks can bear more weight together than their individual capacities combined. These principles also apply to humans, although there are exceptions. Only when everyone opens their hearts and respects differences can we achieve collective strength.

Habit 7: A Balanced Life of Body and Mind

The body, mind, and will are the foundations for achieving our goals, so regularly exercising both body and mind allows us to take on greater challenges, and introspection makes our intuition increasingly sensitive. When we improve both aspects in a balanced manner, we enhance the effectiveness of all habits. Thus, we will grow, change, and ultimately succeed. The most worthwhile investment in life is in self-improvement, and possessing strong thinking, learning, creativity, and adaptability skills will enable us to remain undefeated.

The seven habits mentioned above are interrelated. The first three habits focus on ourselves, establishing goals that we must strive to achieve, emphasizing personal cultivation and shifting from dependence to independence to achieve “personal success”; the fourth, fifth, and sixth habits, which establish win-win scenarios, empathetic communication, and collaborative brainstorming, will promote team communication and cooperation; while the seventh habit encompasses the previous six, urging us to improve from the body and mind. By cultivating these habits, we can gradually achieve substantial transformation and become truly effective individuals.

Source: Sohu Education

Attachment 3

Exciting Quotes for College Entrance Examination

• Students in their third year of high school are under intense pressure, and even their emotions fluctuate. Parents need to maintain a good mindset during this time; otherwise, they may become more anxious than their children, which can negatively affect them.

• Some parents want to achieve their own goals through their children, but children have their own objectives. If parents impose their goals on their children excessively, it may backfire.

• Parents should be versatile, ensuring that their children are filled with positive energy at every stage of their third year. Regardless of the situation, parents should maintain a calm and firm mindset.

• During the third year, not only students are anxious, but parents are also. I advise all parents of third-year students to communicate and interact more with their children, acting as their learning assistants, admissions advisors, and life caretakers.

• High school students’ romantic relationships often stem from emotional needs and communication, serving as a psychological ally. Relationships during the third year should be handled based on the situation; first, understand the child, second, avoid taking drastic measures that push the child to the opposing side, and third, provide guidance.

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Editor: rm