AGI (Artificial General Intelligence)

Extracted Attributes

• Field

  Theoretical AI research, pinnacle of ambition within artificial intelligence.

• Definition

  A hypothetical type of AI that possesses human-like cognitive abilities, capable of understanding, learning, and applying knowledge across a wide range of tasks, performing any intellectual task as well as or better than a human.

• Distinction

  Unlike Narrow AI (ANI), which excels in specialized tasks, AGI can generalize knowledge and transfer skills between domains.

• Current Status

  Largely theoretical at this stage, a work in progress, with many researchers believing it is still decades, if not centuries, away.

• Key Challenges

  Controlling superintelligent machines and aligning their values with human ethics, as superintelligence might resist attempts to alter its goals.

• Primary Concern

  Existential risk, the possibility that advanced AI could lead to human extinction or irreversible global catastrophe.

• Core Capabilities

  Autonomous self-control, self-understanding, ability to learn new skills, solve complex problems in settings and contexts not explicitly taught.

• Current Market Trend

  Experiencing a market correction, with a strategic shift away from monolithic Large Language Models (LLMs) towards more efficient Small Language Models (SLMs) and specialized Vertical AI Applications.

• Underlying Risk Mechanisms

  Superintelligence becoming uncontrollable, 'intelligence explosion' where AI recursively improves itself exponentially, and the difficulty of AI control and alignment.

• Corporate AI Pilot Success Rate

  5% (95% fail) according to an MIT Generative AI study, indicating practical difficulties in current AI deployment.

Timeline

• The term "artificial general intelligence" was used by Mark Gubrud in a discussion of the implications of fully automated military production and operations. (Source: Web Search Results (Wikipedia snippet))

  1997

• A mathematical formalism of AGI, named AIXI, was proposed by Marcus Hutter. (Source: Web Search Results (Wikipedia snippet))

  2000

• The term "artificial general intelligence" (AGI) was popularized by AI researcher Ben Goertzel, at the suggestion of DeepMind cofounder Shane Legg, in an influential book. (Source: Web Search Results (IBM))

  2007

• A survey of AI researchers found that the majority believed there is a 10 percent or greater chance that human inability to control AI will cause an existential catastrophe. (Source: Wikipedia)

  2022

• Hundreds of AI experts and other notable figures signed a statement declaring, "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war." (Source: Wikipedia)

  2023

• The AI industry is experiencing a significant market correction, characterized by an MIT Generative AI study revealing 95% of corporate AI pilots fail, cautious comments from OpenAI CEO Sam Altman, and an AI hiring freeze at Meta. This has led to a strategic shift towards Small Language Models (SLMs) and Vertical AI Applications. (Source: Related Documents)

  Recent/Ongoing

Existential risk from artificial intelligence

Existential risk from artificial intelligence refers to the idea that substantial progress in artificial general intelligence (AGI) could lead to human extinction or an irreversible global catastrophe. One argument for the importance of this risk references how human beings dominate other species because the human brain possesses distinctive capabilities other animals lack. If AI were to surpass human intelligence and become superintelligent, it might become uncontrollable. Just as the fate of the mountain gorilla depends on human goodwill, the fate of humanity could depend on the actions of a future machine superintelligence. The plausibility of existential catastrophe due to AI is widely debated. It hinges in part on whether AGI or superintelligence are achievable, the speed at which dangerous capabilities and behaviors emerge, and whether practical scenarios for AI takeovers exist. Concerns about superintelligence have been voiced by researchers including Geoffrey Hinton, Yoshua Bengio, Demis Hassabis, and Alan Turing, and AI company CEOs such as Dario Amodei (Anthropic), Sam Altman (OpenAI), and Elon Musk (xAI). In 2022, a survey of AI researchers with a 17% response rate found that the majority believed there is a 10 percent or greater chance that human inability to control AI will cause an existential catastrophe. In 2023, hundreds of AI experts and other notable figures signed a statement declaring, "Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war". Following increased concern over AI risks, government leaders such as United Kingdom prime minister Rishi Sunak and United Nations Secretary-General António Guterres called for an increased focus on global AI regulation. Two sources of concern stem from the problems of AI control and alignment. Controlling a superintelligent machine or instilling it with human-compatible values may be difficult. Many researchers believe that a superintelligent machine would likely resist attempts to disable it or change its goals as that would prevent it from accomplishing its present goals. It would be extremely challenging to align a superintelligence with the full breadth of significant human values and constraints. In contrast, skeptics such as computer scientist Yann LeCun argue that superintelligent machines will have no desire for self-preservation. A third source of concern is the possibility of a sudden "intelligence explosion" that catches humanity unprepared. In this scenario, an AI more intelligent than its creators would be able to recursively improve itself at an exponentially increasing rate, improving too quickly for its handlers or society at large to control. Empirically, examples like AlphaZero, which taught itself to play Go and quickly surpassed human ability, show that domain-specific AI systems can sometimes progress from subhuman to superhuman ability very quickly, although such machine learning systems do not recursively improve their fundamental architecture.

Web Search Results

• Present and Future: Artificial General Intelligence

  Artificial general intelligence (AGI) refers to a type of artificial intelligence that aims to replicate human cognitive abilities across a broad range of tasks. It is expected to perform any intellectual task just as good as or even better than a human being. Essentially, AGI would possess the ability to understand, learn, and apply knowledge in a manner akin to human thought processes. [...] Artificial general intelligence (AGI) stands as the pinnacle of ambition within the realm of artificial intelligence. Unlike Narrow AI, which excels in specialized tasks such as language translation or facial recognition, AGI envisions machines with a broad, human-like understanding capable of performing any intellectual task that a human can. This concept of AGI represents a profound leap from current technologies, aiming to create machines that possess the versatility and cognitive abilities [...] While artificial general intelligence (AGI) represents the pinnacle of AI research, it's important to acknowledge that it remains largely theoretical at this stage. Researchers and scientists have made significant strides in developing advanced AI systems, but creating a truly general intelligence that mirrors human cognitive abilities is still a work in progress. The concept of AGI continues to evolve, and several key areas of advancement and challenge are shaping the field.

• What is AGI? - Artificial General Intelligence Explained

  Artificial general intelligence (AGI) is a field of theoretical AI research that attempts to create software with human-like intelligence and the ability to self-teach. The aim is for the software to be able to perform tasks that it is not necessarily trained or developed for. [...] Current artificial intelligence (AI) technologies all function within a set of pre-determined parameters. For example, AI models trained in image recognition and generation cannot build websites. AGI is a theoretical pursuit to develop AI systems that possess autonomous self-control, a reasonable degree of self-understanding, and the ability to learn new skills. It can solve complex problems in settings and contexts that were not taught to it at the time of its creation. AGI with human [...] In contrast, an AGI system can solve problems in various domains, like a human being, without manual intervention. Instead of being limited to a specific scope, AGI can self-teach and solve problems it was never trained for. AGI is thus a theoretical representation of a complete artificial intelligence that solves complex tasks with generalized human cognitive abilities.

• What is Artificial General Intelligence (AGI)?

  Artificial general intelligence (AGI) is a hypothetical stage in the development of machine learning(ML) in which an artificial intelligence (AI) system can match or exceed the cognitive abilities of human beings across any task. It represents the fundamental, abstract goal of AI development: the artificial replication of human intelligence in a machine or software. [...] In 2007, AI researcher Ben Goertzel popularized the term “artificial general intelligence” (AGI), at the suggestion of DeepMind cofounder Shane Legg, in an influential book of the same name. In contrast to what he dubbed “narrow AI,” an artificial _general_ intelligence would be a new type of AI with, among other qualities, “the ability to solve general problems in a non-domain-restricted way, in the same sense that a human can.” [...] Gary Marcus, a psychologist, cognitive scientist and AI researcher, defined AGI as “a shorthand for any intelligence…that is flexible and general, with resourcefulness and reliability comparable to (or beyond) human intelligence.”9 Marcus proposed a set of benchmark tasks intended to demonstrate that adaptability and general competence, akin to a specific and practical implementation of the “learn tasks” framework.

• Artificial general intelligence

  Artificial general intelligence (AGI)—sometimes called human‑level intelligence AI—is a type of artificial intelligence that would match or surpass human capabilities across virtually all cognitive tasks. [...] The term "artificial general intelligence" was used as early as 1997, by Mark Gubrud in a discussion of the implications of fully automated military production and operations. A mathematical formalism of AGI was proposed by Marcus Hutter in 2000. Named AIXI, the proposed AGI agent maximises "the ability to satisfy goals in a wide range of environments". This type of AGI, characterized by the ability to maximise a mathematical definition of intelligence rather than exhibit human-like behaviour, [...] 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. The concept does not, in principle, require the system to be an autonomous agent; a static model—such as a highly capable large language model—or an embodied robot could both satisfy the definition so long as human‑level breadth and proficiency are achieved.

• What is Artificial General Intelligence (AGI)?

  What is Artificial General Intelligence (AGI)? | McKinsey Skip to main content # What is artificial general intelligence (AGI)? | Article Print Save Artificial general intelligence (AGI) is a theoretical AI system with capabilities that rival those of a human. Many researchers believe we are still decades, if not centuries, away from achieving AGI. [...] AGI is AI with capabilities that rival those of a human. While purely theoretical at this stage, someday AGI may replicate human-like cognitive abilities including reasoning, problem solving, perception, learning, and language comprehension. When AI’s abilities are indistinguishable from those of a human, it will have passed what is known as the Turing test, first proposed by 20th-century computer scientist Alan Turing.