What is Artificial General Intelligence (AGI)? A Comprehensive Guide - AI Encyclopedia

Artificial General Intelligence (AGI) is a theoretical form of intelligence that enables machines to possess broad cognitive abilities comparable to or even surpassing those of humans. It allows machines to learn, understand, reason, and solve problems in diverse tasks and environments, not limited to specific domains. The realization of AGI will mark a fundamental shift in artificial intelligence from a tool-like, domain-specific approach to a universal, general one, with far-reaching potential impacts that could radically transform scientific research, economic development, social services, and even global governance. However, the development of AGI still faces numerous technological bottlenecks, ethical dilemmas, and potential risks, requiring global cooperation to ensure its safety, controllability, and benefit for all humanity.

Definition of AGI

Artificial General Intelligence (AGI) is a form of artificial intelligence possessing broad cognitive abilities comparable to or even surpassing those of humans. It can exhibit intelligent behavior in a variety of tasks and environments, not limited to specific domains. The goal of AGI is to create intelligent systems capable of understanding, learning, reasoning, and adapting to new situations, with capabilities not limited to predefined tasks but capable of handling virtually any intellectual task that humans can perform. Unlike the current mainstream "narrow AI" or "weak AI," AGI pursues generalized intelligence, demonstrating human-like adaptability and learning abilities across multiple domains and tasks. AGI systems not only perform tasks but, more importantly, understand the problems they face and possess the ability to make independent decisions. This intelligent agent is expected to integrate multiple capabilities, such as automated reasoning, knowledge representation, automated planning, autonomous learning, and natural language communication, to achieve set goals. AGI is also known as Strong AI, Full AI, or Human-Level AI.

The difference between AGI and Artificial Intelligence in the Narrow Sense (ANI)

Artificial General Intelligence (AGI) and Artificial Narrow Intelligence (ANI), also known as Weak AI, differ fundamentally in the breadth, depth, and autonomy of their intelligence. ANI is the most common type of AI today, focusing on solving problems in specific domains or tasks, such as image recognition, speech recognition, specific applications in natural language processing (such as machine translation or sentiment analysis), or chess-playing programs. These systems are trained with large amounts of data and algorithms to achieve specific goals, but they can only excel in their predefined domains, performing poorly or failing completely in other domains.

For example, an ANI system that excels at Go cannot directly transfer its capabilities to driving a car or making medical diagnoses. Models like ChatGPT, Midjourney, and Meta AI, while powerful, are still examples of weak or narrow AI, lacking true human-level intelligence.

In contrast, AGI possesses broad cognitive abilities, enabling it to learn and adapt to diverse tasks and environments, much like humans, and perform any intellectual task a human can perform. The core difference in AGI lies in its "generality" and "autonomy." First, in terms of task and problem understanding, AGI needs not only to perform tasks but also to understand the problems it faces, making it more adaptable and survivable. Second, in terms of decision-making, AGI needs to be able to make independent, context-based decisions, a crucial factor in its greater reliability. Finally, in terms of intelligence level, AGI is considered a form of general artificial intelligence, roughly equivalent to a computer possessing all the intellectual capabilities of an average person, including the ability to communicate using natural language, solve problems, reason, and perceive the environment, placing it at the same or higher level of intelligence as an average person. In short, ANI is a "specialist," while AGI strives to be a "generalist."

Core characteristics of general artificial intelligence

Generality

This is the most prominent feature of AGI. A key advantage of human intelligence is its versatility; we can learn mathematics, languages, and art, and combine knowledge from different fields to solve complex problems. AGI mimics this ability, enabling it to "learn by analogy" like humans.

For example, a versatile AGI system can not only perform complex scientific calculations, but also understand literary works and even create music.

Professor Zhu Songchun, director of the Beijing Institute of General Artificial Intelligence, proposed that general artificial intelligence needs to meet three basic conditions, the first of which is "being able to complete an unlimited number of tasks".

Autonomy

AGI systems are expected to think independently and make autonomous decisions, completing tasks with little or no human intervention. This includes the ability to execute pre-set instructions, and more importantly, to understand task objectives, analyze environmental information, and formulate action plans based on their own learning and experience. Autonomy means that AGI can proactively identify problems, set goals, and actively seek solutions, rather than passively waiting for instructions.

The second condition proposed by Professor Zhu Songchun is "being able to proactively and autonomously discover tasks in a scenario, that is, 'having a keen eye for tasks'".

For example, an autonomous AGI robot can not only clean a room according to instructions, but also actively identify the degree of dirtiness in the room, plan a cleaning path, and deal with unexpected situations during the cleaning process, such as avoiding obstacles or replenishing cleaning agents.

Adaptability and Learning ability

This is the foundation for AGI to achieve versatility and autonomy. AGI needs to possess strong learning capabilities, able to learn from experience, extract patterns from data, and continuously update and improve its knowledge and skills. This includes existing AI technologies such as supervised learning, unsupervised learning, and reinforcement learning, and may also involve more advanced learning forms, such as meta-learning (learning how to learn) and transfer learning (applying knowledge learned in one domain to another). Adaptability requires AGI to quickly adapt to new and unknown environments and tasks, adjusting its behavioral strategies to cope with changes.

Comprehension ability and Reasoning ability

AGI needs to process information, but more importantly, it needs to truly understand the meaning of that information, including information from multiple modalities such as language, images, and sound. AGI needs to possess common sense knowledge and be able to perform logical reasoning, causal inference, and abstract thinking.

For example, when reading an article, AGI needs to understand the intentions, emotions, and implicit information behind the text, not just recognize words and sentence structures. When solving problems, AGI needs to be able to analyze the essence of the problem, use existing knowledge to reason, and find effective solutions.

The Development History of General Artificial Intelligence

The Proposal and Early Exploration of the AGI Concept

The concept of Artificial General Intelligence (AGI) did not emerge overnight; it has been developed alongside the entire field of artificial intelligence, acquiring different connotations and expectations at different stages. Early explorations of artificial intelligence, even before the formal birth of the term "artificial intelligence," already contained an aspiration for general intelligence.

The Dartmouth Conference of 1956Widely considered the beginning of the field of artificial intelligence, the conference aimed to explore how to enable machines to think, learn, and solve problems like humans. Early AI researchers, such as Alan Turing, Marvin Minsky, and John McCarthy, envisioned creating machines with human-level intelligence. Turing's "Turing Test," proposed in his 1950 paper "Computing Machinery and Intelligence," while not directly defining AGI, set an important benchmark for the goal of AGI by judging whether machines can exhibit intelligent behavior indistinguishable from humans. Herbert A. Simon predicted in 1965 that "machines will be able to do any job a human can do within twenty years."

In the late 1970s and early 1980s, AI research gradually shifted towards more specific and practical fields, with expert systems becoming a research hotspot. These systems attempted to encode human expert knowledge in specific domains into computers to solve specific problems. Although expert systems achieved success in some areas, they were essentially "narrow AI," lacking general applicability. This led to the first "winter" in artificial intelligence research (1974-1980), with mainstream research methods gradually shifting from general purposes to domain-specific approaches.

The term "Artificial General Intelligence" (AGI) itself was originally coined by...Mark Gubrud in 1997Proposed within the context of discussions on nanotechnology and international security, this concept describes systems that can rival or even surpass the human brain in complexity and speed, enabling them to acquire, manipulate, and reason about everyday knowledge and function in areas requiring human intelligence.

Around 2001, some artificial intelligence researchers, such as Ben Goertzel, Shane Legg, and Peter Voss, began to promote and use the concept of AGI (Artificial General Intelligence) in order to return to the original vision of artificial intelligence—creating machines with general intelligence. They believed that mainstream AI focused too much on specific applications and neglected the core issue of general intelligence. The explicit introduction of the AGI concept marked a rethinking and refocusing of the direction of artificial intelligence development, distinguishing it from the then-mainstream research on "weak AI" or "applied AI."

Between approximately 2004 and 2007, a renewed call for research into general-purpose systems emerged both within and outside the mainstream AI field, with topics such as "integrated AI," "general-purpose system," and "human-level AI" gradually gaining attention.

After 2008, the emergence of academic conferences and organizations such as the AGI series of conferences, Advances in Cognitive Systems, and the IEEE Task Force on Towards Human-like Intelligence marked the further clarification and development of AGI as an independent research direction.

Different technical approaches and major research schools

Symbolism

Also known as logicism, the psychological school, or the computer science school, symbolism is based on the assumption of physical symbolic systems, which posits that intelligent behavior can be achieved through symbolic manipulation. Symbolists attempt to simulate human cognitive processes by constructing systems based on logical reasoning and knowledge representation. They believe that the core of intelligence lies in the manipulation and reasoning of abstract symbols; general intelligence can be achieved as long as human knowledge and reasoning processes can be formalized into symbols and rules. Early expert systems and knowledge graphs are manifestations of symbolic thought. While symbolism has achieved success in handling explicit rules and structured knowledge, it has encountered challenges in dealing with perception, learning, and the uncertainty and ambiguity of the real world—the so-called "knowledge acquisition bottleneck" and "framing problem."

Connectionism

Also known as the biomimetic or physiological school of thought, connectionism draws inspiration from the structure and function of the human brain's neural networks. Connectionists believe that intelligence arises from the interconnections and parallel processing between a large number of simple processing units (neurons). They simulate the brain's learning and cognitive processes by constructing artificial neural networks (ANNs). Deep learning is a prime example of the tremendous success of connectionism in contemporary times, particularly in areas such as image recognition and natural language processing. However, current deep learning models largely rely on large amounts of labeled data for training and still have limitations in interpretability, robustness, and common-sense reasoning, falling short of true AGI (Advanced Generative Intelligence).

Behaviorism/Embodied cognition

Emphasizing the importance of agent-environment interaction, perception, and action, this school of thought argues that intelligence cannot remain merely at the level of abstract symbolic manipulation or neural network computation; it requires real-time interaction between the physical body and the environment to arise and develop. Embodied cognition theory posits that cognitive processes are profoundly influenced by body form, sensorimotor abilities, and the way an agent interacts with its environment. Therefore, the realization of AGI requires constructing embodied agents capable of perceiving the environment, taking action, and learning from interactions. Research in robotics, reinforcement learning, and other fields is closely related to this school of thought. This school emphasizes the importance of a "world model," meaning that the agent needs to construct an internal representation of the environment and utilize these representations for planning and decision-making.

Besides the main schools of thought mentioned above, there are other research directions and theories, such as:

Evolutionary ComputationDrawing inspiration from biological evolution, we optimize and design intelligent systems through operations such as selection, crossover, and mutation.

Bayesian NetworksThis provides a framework for uncertainty reasoning based on probabilistic graphical models;

Integrative ApproachesProjects such as OpenCog attempt to combine the advantages of different AI methodologies, such as combining the reasoning ability of symbolic logic with the pattern recognition ability of neural networks, to achieve more comprehensive intelligence.

Cognitive Architectures(Such as ACTR, SOAR, LIDA) to construct a unified computational model based on the principles of cognitive science to simulate various abilities of the human mind;

AI intelligent agentCombining large language models and reinforcement learningAI AgentIt is considered an essential path to AGI (Agent General Intelligence), where agents can understand instructions, formulate plans, and execute complex tasks. Current research trends in AGI increasingly reflect the characteristics of multidisciplinary integration and the fusion of multiple technologies, such as combining deep learning with symbolic reasoning, or combining reinforcement learning with cognitive architecture, to overcome the limitations of single methods and move towards true general intelligence.

Research progress in general artificial intelligence

progress

Currently, research on Artificial General Intelligence (AGI) is in a vibrant but also challenging phase. Breakthroughs in generative AI technologies, exemplified by Large Language Models (LLMs), have prompted many researchers and technology companies, such as OpenAI, DeepMind, Google, Baidu, and iFlytek, to actively explore paths towards AGI. These companies have launched a series of models considered "close to AGI level," such as GPT-4, Claude 3, and Sora. These models have demonstrated remarkable capabilities in various areas, including natural language understanding and generation, image generation, code writing, and multi-task processing, surpassing the average human performance in certain specific tasks.

OpenAI internally divides the path to AGI into five levels, believing that its current AI models (such as GPT-4) are still at the L1 level (chatbot), but it expects to reach the L2 level (reasoner) soon, with the ability to solve basic problems at the PhD level.

bottleneck

Constraints of computing power and energyTraining and running advanced large models requires enormous computing resources and energy consumption, resulting in high costs and environmental pressure, which limits the popularization and further development of AGI technology.

Limitations of model capabilitiesWhile existing large-scale models demonstrate powerful pattern recognition and generation capabilities, they still lag significantly behind human intelligence in deeper cognitive abilities such as common-sense reasoning, causal inference, interpretability, robustness, and long-term planning. For example, large-scale models are still prone to errors when dealing with problems requiring complex logical reasoning or understanding of the physical world. They are also susceptible to the influence of cue words, producing "illusions" (i.e., generating inaccurate or meaningless content), and their decision-making processes often lack transparency and interpretability.

Data bottleneckHigh-quality, diverse training data is crucial for improving model performance, but acquiring and labeling large-scale, unbiased datasets is a significant challenge. Existing models often employ a "cramming" approach to learning, lacking the ability for genuine understanding and proactive exploration.

Ethical and security issues of AGIThese issues, including data privacy, algorithmic bias, the spread of misinformation, potential risks of abuse, and the impact on the job market and social structure, need to be properly addressed through technological development.

Key Technological Challenges of General Artificial Intelligence

Learning from multimodal data such as videos

A key technological challenge in achieving Artificial General Intelligence (AGI) is the ability to learn from multimodal data, much like humans do, particularly from dynamic, context-rich media such as video. One of the primary ways humans acquire knowledge and understand the world is through visual observation and accumulated experience, and video data contains a wealth of visual information, temporal sequence information, and interactions and causal relationships between objects. Current AI models, especially large language models, rely heavily on text data for training, which represents only a small fraction of human learning experience. Research indicates that text-based learning accounts for approximately 5% of human learning. For machines to truly understand the physical world and social scenarios, they must be able to extract semantic information from videos, such as recognizing objects, actions, scenes, and the complex relationships between them. Developing algorithms and model architectures that can effectively process and fuse multimodal information is crucial. Advances in computer vision technologies, such as more accurate object detection, behavior recognition, and scene understanding, are needed to align and correlate this visual information with other modalities such as text and audio.

For example, when an AGI system watches a cooking video, it needs not only to identify ingredients, utensils, and cooking actions, but also to understand the sequence and purpose of these actions, as well as potential unexpected situations and corresponding solutions. Learning from videos also involves modeling temporal dynamics and causal relationships. Events in a video unfold over time, and AGI needs to understand the sequence of events, their duration, and the causal connections between them, which is crucial for effective planning and decision-making. Although some research has begun to explore tasks such as video understanding, video description generation, and video-based question answering, there is still a long way to go before machines can learn efficiently from videos and build a deep understanding of the world like humans. Solving this challenge will greatly promote the application of AGI in robotics, autonomous driving, intelligent monitoring, and human-computer interaction.

Understanding time, cause and effect, and planning

For artificial general intelligence (AGI) to achieve a level of intelligence comparable to humans, one of its core capabilities is a deep understanding of time and causality, and the ability to make effective plans based on this understanding. This includes comprehending the sequence of events, their duration, and the causal relationships between different events.

For example, when we plan a trip, we consider the mode of transportation, the time required, possible delays, and the consequences of different choices. These are all based on the application of time, cause and effect, and planning skills.

Achieving AGI's mastery of time, causality, and planning capabilities presents numerous technical challenges.

first,Time Representation and ReasoningThis is a fundamental problem. AGI needs to be able to represent time information in an appropriate way, such as discrete time points, continuous time periods, and the relative relationships between them (such as before, after, simultaneous, etc.). On this basis, AGI also needs to be able to perform time reasoning, such as determining whether two events can happen simultaneously, or whether the occurrence of one event requires the occurrence of another event first.

SecondlyCausal discovery and inferenceThis is a more complex problem. Simply observing the correlation between events is insufficient to infer causal relationships. AGI needs to be able to identify potential causal relationships from observed data, distinguish between causation and correlation, and understand the strength and direction of causal relationships. This requires going beyond traditional statistical methods and introducing more complex causal models and reasoning mechanisms.

at last,Planning based on causal understandingThe ultimate goal is to leverage AGI's understanding of temporal dynamics and causal relationships to develop action plans that achieve specific objectives. AGI is required to predict the possible outcomes of different actions, assess the merits of these outcomes, and make decisions in complex and uncertain environments.

Achieving interpretability and robustness

Interpretability

This refers to the ability of humans to understand the reasons and processes behind specific decisions or predictions made by an AGI system. Many current advanced AI models, especially deep learning models, are considered "black box" models, with their internal mechanisms difficult for humans to comprehend. While these models may exhibit high accuracy on specific tasks, their lack of explainability makes it difficult to trust their decisions, especially in high-risk fields such as medicine, finance, and law. If an AGI system makes an incorrect decision, we cannot trace the cause of the error or effectively correct it. Therefore, developing AGI systems that provide clear and easily understandable explanations is crucial for building user trust, ensuring fairness, and conducting effective debugging and improvement. Explainability also helps us discover potential biases or vulnerabilities in the model. Explainable AI (XAI) technologies, such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive exPlanations), are being actively researched to provide local or global explanations of model decisions.

Robustness

AGI (Advanced Generative Intelligence) systems refer to their ability to maintain stable performance and make correct decisions when faced with noise, interference, adversarial attacks, or unexpected inputs. The real world is full of uncertainty and complexity, and AGI systems must be able to cope with various unexpected situations without easily failing or making catastrophic errors. Current AI models often perform well within the training data distribution, but their performance can drop sharply when encountering out-of-distribution data or carefully designed adversarial examples. For example, autonomous vehicles must be able to operate safely and reliably in severe weather or rare traffic conditions. Improving the robustness of AGI requires a multi-pronged approach, including designing more robust model architectures, adopting more effective regularization methods, conducting more comprehensive testing and validation, and developing mechanisms to detect and handle anomalies. AGI systems lacking robustness are not only unreliable but can also pose serious safety risks. Therefore, while pursuing improvements in AGI capabilities, it is crucial to prioritize research into its interpretability and robustness to ensure the safe, reliable, and responsible development of AGI technology.

Overcoming data and computing power bottlenecks

The realization of artificial general intelligence (AGI) largely depends on the driving force of massive amounts of high-quality data and the support of powerful computing capabilities.

Data bottleneck

This manifests in several ways. Firstly, acquiring sufficiently large, diverse, and high-quality training data is a significant challenge. Many real-world problems lack adequate labeled data, and manual labeling is extremely expensive. While unsupervised and self-supervised learning methods alleviate the reliance on labeled data to some extent, they still require substantial amounts of raw data. Secondly, current AI models are far less efficient than humans, often requiring significantly more data to achieve similar performance. AGI needs the ability to learn rapidly from limited data (few-shot learning) and continuously learn new knowledge without forgetting old knowledge; current models perform poorly in this regard, easily suffering from "catastrophic forgetting." Furthermore, potential biases, noise, and inconsistencies in the data can negatively impact model performance and generalization ability.

Computing bottleneck

Training advanced AI models, especially large-scale deep learning models, requires enormous computing resources. For example, training a model like GPT-3 might cost millions of dollars in computing power, while training GPT-4 consumed the equivalent of several weeks' worth of energy for thousands of households. As the scale of models continues to increase, the demand for computing power grows exponentially. This results in high economic costs and poses serious challenges to energy supply and environmental impact. If the current rate of AI chip sales continues, AI chips will consume more than 4% of the electricity in the United States by 2028. Although hardware technology (such as dedicated AI chips) is constantly advancing, whether its development speed can keep up with the growth rate of AGI's demand for computing power remains an open question.

Algorithm efficiency is also a key factor. Developing more efficient and energy-saving algorithms and model architectures to reduce reliance on computing power is an important way to overcome the computing power bottleneck. If the development of AGI relies excessively on the accumulation of computing power, its popularization and application will be severely limited, allowing only a few well-resourced institutions to participate, which is detrimental to the healthy development and widespread benefits of AGI technology. How to improve the learning efficiency and performance of AGI under limited data and computing power conditions is a key problem that urgently needs to be solved in current research.

Potential applications of general artificial intelligence

Scientific research and exploration

AGI systems can autonomously analyze massive amounts of scientific data, identify complex patterns and correlations, propose new scientific hypotheses, and even design and execute experiments. For example,

existDrug development fieldAGI can accelerate the discovery of new drugs by analyzing molecular structure, biological pathways, and clinical trial data to predict drug efficacy and side effects, thereby significantly shortening the research and development cycle and reducing costs.

existMaterials ScienceAGI can help design and discover new materials with specific properties.

existBasic science fields such as astronomy and physicsAGI can process and analyze massive datasets from telescopes, particle colliders, and other equipment, helping scientists discover new astrophysical phenomena or fundamental particles.

AGI canSimulating complex natural systemsSuch as climate change and ecosystem evolution, providing scientists with a deeper understanding and more accurate predictions.

Economic Development and Industrial Transformation

AGI, as a potential general-purpose technology (GPT), may have an impact no less significant than that of the steam engine, electricity, and the internet in history. AGI promises to dramatically improve productivity and automation levels.

existManufacturing sectorAGI can optimize the entire production process, from supply chain management, production line scheduling, quality control to equipment maintenance, achieving full-process intelligence and autonomy. For example, an AGI system can analyze massive amounts of data from sensors to identify production bottlenecks in real time, predict equipment failures, and automatically adjust production plans to maximize efficiency and reduce waste.

existagricultural sectorIn agriculture, AGI can assist in precision planting, pest and disease prediction, and automated harvesting, improving crop yield and quality. In the service industry, AGI-driven intelligent customer service, personalized recommendations, and intelligent investment advisors will reshape customer experience and improve service efficiency.

existContent creation fieldAGI can autonomously generate high-quality text, images, music, and videos, bringing revolutionary changes to industries such as media, entertainment, and advertising.

existscientific research fieldAGI can assist scientists in conducting large-scale data analysis, proposing new scientific hypotheses, designing experimental schemes, and directly participating in the discovery of new materials and the development of new drugs, greatly accelerating the process of scientific discovery. AGI may promote the popularization of the "Artificial Intelligence as a Service" (AIaaS) model, enabling small and medium-sized enterprises and individual developers to easily use powerful AGI capabilities and stimulate broader innovation.

AGI will reshape global value chains and the competitive landscape. Countries and companies possessing core AGI technologies and application capabilities will dominate future global economic competition. The development and application of AGI technology will attract substantial capital and talent investment, forming a powerful industrial cluster effect. China possesses significant market advantages and data resources in the application of artificial intelligence, particularly in manufacturing and other sectors. It is expected to promote the deep integration of AGI technology with the real economy through the "AI+" initiative, achieving industrial upgrading. However, gaps still exist with international leading levels in core technologies such as high-end chips and underlying algorithms, requiring continued increased R&D investment to overcome key bottlenecks. The development of AGI will also intensify international technological competition and talent acquisition. Governments worldwide need to formulate forward-looking development strategies and policies to seize the opportunities presented by AGI while mitigating potential risks.

China's artificial intelligence industry is expected to achieve significant growth over the next decade, from nearly 400 billion yuan in 2025 to more than 1.7 trillion yuan in 2035, with a compound annual growth rate of 15.6%. This fully demonstrates the huge driving potential of AGI and related AI technologies for economic development.

Social services and improvement of people's livelihood

existHealthcareAGI systems can integrate and analyze massive amounts of medical literature, clinical cases, genomic data, and real-time physiological monitoring data to assist doctors in making more accurate disease diagnoses, developing personalized treatment plans, and predicting disease risks. For example, AGI can analyze medical images (such as CT and MRI scans) to detect tumors and other lesions at an early stage, with accuracy and efficiency potentially surpassing that of human doctors. In drug development, AGI can accelerate the discovery process of new drugs by simulating molecular interactions, screening candidate compounds, and optimizing clinical trial design, thereby shortening the development cycle and reducing development costs. AGI-driven intelligent health management assistants can provide individuals with 24/7 health consultations, chronic disease management, and emergency response services, improving the overall health level of the population. AGI can provide personalized dietary and exercise recommendations based on users' health data and lifestyle habits, and automatically contact medical institutions in case of emergencies.

existEducationAGI has the potential to achieve truly personalized learning, tailoring instruction to individual needs and improving the quality and equity of education. AGI tutors can dynamically adjust teaching content and pace based on each student's learning progress, cognitive characteristics, and interests, providing customized learning paths and tutoring plans. This helps students master knowledge more effectively, stimulates their interest in learning, and cultivates their innovative abilities.

existTransportation sectorAGI (Automatic Traffic Intelligence) is a key technology for achieving fully autonomous driving, and it is expected to significantly improve the safety and efficiency of transportation systems, reducing traffic congestion and accidents. AGI-driven intelligent traffic management systems can optimize traffic lights in real time, predict traffic flow, and allocate public transportation resources, providing citizens with a more convenient and efficient travel experience. AGI can be applied to many areas such as smart homes, environmental protection, disaster early warning and rescue, and refined urban management. For example, by analyzing environmental data, it can predict pollution events and optimize energy consumption; in the event of a disaster, it can assist in planning rescue routes and allocating rescue resources, providing strong technical support for building a safer, more convenient, and more livable social environment.

National defense security and global governance

The emergence of Artificial General Intelligence (AGI) will have profound and complex impacts on national defense and global governance, bringing both unprecedented opportunities and severe challenges and risks. The development of AGI poses five major challenges to U.S. national security, including the potential to give rise to "disruptive" weapons, trigger a systemic shift in the structure of national power, lower the technological threshold for weapons of mass destruction, lead to uncontrolled intelligent agents, and exacerbate the instability of the development path and the post-AGI world. These potential impacts indicate that once AGI technology matures, its military and strategic significance will be no less than that of nuclear weapons or the information technology revolution.

existNational defense and security fieldAGI (Automatic Gaining Intelligence) has the potential to profoundly change the nature of warfare and the military balance through applications in intelligence analysis, strategic decision support, autonomous weapon systems (AWS) development, cyber warfare, and logistical support. AGI systems can rapidly process and analyze massive amounts of data from various sensors and intelligence sources, identify potential threats, assess risks, and provide commanders with decision-making recommendations.

existGlobal governanceAGI can be used to analyze global challenges such as climate change, pandemics, and transnational crime, and help develop more effective response strategies.

The Development Prospects of General Artificial Intelligence

The development of Artificial General Intelligence (AGI) will continue to be a focus of attention in the technology sector and society as a whole. Although achieving true AGI still faces many uncertainties and challenges, its immense potential value and far-reaching impact motivate researchers and institutions worldwide to continue their exploration. We may be at a critical turning point. The rapid development of AI technologies, exemplified by large-scale language models, has given us a glimpse of AGI, but it has also exposed the significant gap between it and human general intelligence. Future AGI research may exhibit a trend of parallel development across multiple paths and the integration of multiple technologies, including the continuous optimization and expansion of existing deep learning paradigms, potentially leading to entirely new theoretical frameworks and algorithmic models.

In considering the future of AGI, we must maintain a clear mind and a prudent attitude. On the one hand, we should actively embrace the opportunities brought by AGI, encourage technological innovation and application exploration, and fully leverage its potential in addressing major challenges facing humanity and improving social well-being. On the other hand, we must also pay close attention to the potential risks and challenges of AGI, prioritizing safety, controllability, fairness, and ethical considerations. It requires the joint efforts of governments, academia, industry, and the public, strengthening international cooperation, and establishing sound laws, regulations, ethical guidelines, and governance mechanisms to ensure that the development of AGI always moves in a direction beneficial to humanity. The future of AGI is not merely a technological issue, but a profound question concerning the fate of humanity and the direction of civilization. It is hoped that through continuous efforts and wise choices, we can ultimately harness the powerful force of AGI, making it a positive factor in promoting the progress and prosperity of human society, and jointly creating a better future.