What is artificial general intelligence (AGI)?

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1. 1

   Artificial general intelligence (Wikipedia)

   WikipediaPublished Oct 10, 2026Checked Oct 10, 2026

   “Artificial general intelligence (AGI) is a hypothetical type of artificial intelligence that matches or surpasses human capabilities across virtually all cognitive tasks. Unlike artificial narrow intelligence (ANI), whose competence is confined to well‑defined tasks, an AGI system can generalise knowledge, transfer skills between domains, and solve novel problems without task‑specific reprogramming. Creating AGI is a stated goal of technology companies such as OpenAI, Google, SpaceXAI, and Meta. A 2020 survey identified 72 active AGI research and development projects across 37 countries. AGI is a common topic in science fiction and futures studies. Contention exists over whether AGI represents an existential risk. Some AI experts and industry figures have stated that mitigating the risk of human extinction posed by AGI should be a global priority. Others find the development of AGI to be in too remote a stage to present such a risk.”

2. 2

   Sparks of Artificial General Intelligence: Early experiments with GPT-4

   arXiv (Cornell University) (Bubeck et al.)Published Mar 22, 2023Checked Oct 10, 2026

   “We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.”

3. 3

   Generative AI vs. AGI: The Cognitive Strengths and Weaknesses of Modern LLMs

   arXiv (Cornell University) (Goertzel)Published Sep 19, 2023Checked Oct 10, 2026

   “It is found that many of the practical weaknesses of these AI systems can be tied specifically to lacks in the basic cognitive architectures according to which these systems are built. It is argued that incremental improvement of such LLMs is not a viable approach to working toward human-level AGI, in practical terms given realizable amounts of compute resources. This does not imply there is nothing to learn about human-level AGI from studying and experimenting with LLMs, nor that LLMs cannot form significant parts of human-level AGI architectures that also incorporate other ideas. Social and ethical matters regarding LLMs are very briefly touched from this perspective, which implies that while care should be taken regarding misinformation and other issues, and economic upheavals will need their own social remedies based on their unpredictable course as with any powerfully impactful technology, overall the sort of policy needed as regards modern LLMs is quite different than would be the case if a more credible approximation to human-level AGI were at hand.”

4. 4

   Artificial General Intelligence: Concept, State of the Art, and Future Prospects

   Journal of Artificial General Intelligence (Goertzel)Published Jun 19, 2014Checked Oct 10, 2026

   “In recent years broad community of researchers has emerged, focusing on the original ambitious goals of the AI field - the creation and study of software or hardware systems with general intelligence comparable to, and ultimately perhaps greater than, that of human beings. This paper surveys this diverse community and its progress. Approaches to defining the concept of Artificial General Intelligence (AGI) are reviewed including mathematical formalisms, engineering, and biology inspired perspectives. The spectrum of designs for AGI systems includes systems with symbolic, emergentist, hybrid and universalist characteristics. Metrics for general intelligence are evaluated, with a conclusion that, although metrics for assessing the achievement of human-level AGI may be relatively straightforward (e.g. the Turing Test, or a robot that can graduate from elementary school or university), metrics for assessing partial progress remain more controversial and problematic.”

5. 5

   Stop treating `AGI' as the north-star goal of AI research

   arXiv (Cornell University) (Blili-Hamelin et al.)Published Feb 6, 2025Checked Oct 10, 2026

   “The AI research community plays a vital role in shaping the scientific, engineering, and societal goals of AI research. In this position paper, we argue that focusing on the highly contested topic of `artificial general intelligence' (`AGI') undermines our ability to choose effective goals. We identify six key traps -- obstacles to productive goal setting -- that are aggravated by AGI discourse: Illusion of Consensus, Supercharging Bad Science, Presuming Value-Neutrality, Goal Lottery, Generality Debt, and Normalized Exclusion. To avoid these traps, we argue that the AI research community needs to (1) prioritize specificity in engineering and societal goals, (2) center pluralism about multiple worthwhile approaches to multiple valuable goals, and (3) foster innovation through greater inclusion of disciplines and communities. Therefore, the AI research community needs to stop treating `AGI' as the north-star goal of AI research.”

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   Understanding Artificial General Intelligence: Defining Characteristics and Benchmarks

   Journal of Artificial Intelligence & Control Systems (Muhsen & Sadiq)Published Jun 26, 2025Checked Oct 10, 2026

   “Artificial General Intelligence (AGI) is an advanced form of Artificial Intelligence (AI) that aims to perform any intellectual task that a human can, surpassing the narrow scope of Artificial Narrow Intelligence (ANI). Unlike ANI, which is designed to handle specific, well-defined tasks, AGI has the potential to generalize knowledge and adapt across a variety of domains. This research examines AGI's learning and adaptability, common-sense thinking, autonomous decision-making, transfer learning, creativity, and problem-solving. It examines AGI history using cognitive science, neuroscience, machine learning, and other disciplines. This topic requires trustworthy criteria to measure AGI research progress. This research paper serves as a review of AGI, summarizing its defining traits, challenges, and evaluation benchmarks. It also surveys key recent advancements in AGI systems, integrating perspectives from cognitive science, machine learning, and neuroscience. Evaluation methods such as the Turing Test, Winograd Schema Challenge, Coffee Test, and Lovelace 2.0 Test are assessed for their relevance to AGI.”

7. 7

   Artificial intelligence (Wikipedia)

   WikipediaPublished Oct 9, 2026Checked Oct 10, 2026

   “Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making. It is a field of research in engineering, mathematics, and computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximise their chances of achieving defined goals. High-profile applications of AI include advanced web search engines, chatbots, virtual assistants, autonomous vehicles, play and analysis in strategy games (e.g., chess and Go), and content generation (e.g., text, images, audio, and videos). The traditional goals of AI research include learning, reasoning, knowledge representation, planning, natural language processing, and perception, as well as support for robotics. To reach these goals, AI researchers use techniques including state space search and mathematical optimisation, formal logic, artificial neural networks, and methods based on statistics, operations research, and economics.”