Artificial general intelligence (AGI) is a kind of expert system (AI) that matches or surpasses human cognitive abilities throughout a large range of cognitive jobs. This contrasts with narrow AI, which is restricted to specific jobs. [1] Artificial superintelligence (ASI), on the other hand, describes AGI that significantly goes beyond human cognitive capabilities. AGI is considered among the definitions of strong AI.
Creating AGI is a main objective of AI research study and of companies such as OpenAI [2] and Meta. [3] A 2020 study identified 72 active AGI research and development tasks across 37 nations. [4]
The timeline for accomplishing AGI remains a topic of continuous debate among scientists and specialists. Since 2023, some argue that it may be possible in years or years; others keep it might take a century or longer; a minority believe it might never ever be attained; and another minority claims that it is already here. [5] [6] Notable AI researcher Geoffrey Hinton has revealed concerns about the rapid progress towards AGI, recommending it might be accomplished earlier than numerous expect. [7]
There is dispute on the specific meaning of AGI and relating to whether contemporary large language models (LLMs) such as GPT-4 are early kinds of AGI. [8] AGI is a common subject in science fiction and futures research studies. [9] [10]
Contention exists over whether AGI represents an existential danger. [11] [12] [13] Many experts on AI have actually stated that reducing the risk of human termination postured by AGI must be a worldwide top priority. [14] [15] Others discover the development of AGI to be too remote to present such a danger. [16] [17]
Terminology
AGI is also called strong AI, [18] [19] full AI, [20] human-level AI, [5] human-level intelligent AI, or general intelligent action. [21]
Some academic sources book the term "strong AI" for computer programs that experience life or awareness. [a] In contrast, weak AI (or narrow AI) is able to solve one particular problem however does not have general cognitive capabilities. [22] [19] Some scholastic sources use "weak AI" to refer more broadly to any programs that neither experience awareness nor have a mind in the exact same sense as humans. [a]
Related concepts include synthetic superintelligence and transformative AI. A synthetic superintelligence (ASI) is a hypothetical type of AGI that is much more typically intelligent than humans, [23] while the concept of transformative AI connects to AI having a large effect on society, for instance, similar to the farming or commercial transformation. [24]
A framework for classifying AGI in levels was proposed in 2023 by Google DeepMind researchers. They define 5 levels of AGI: emerging, skilled, specialist, virtuoso, and superhuman. For example, a proficient AGI is defined as an AI that outperforms 50% of knowledgeable adults in a wide variety of non-physical tasks, and a superhuman AGI (i.e. a synthetic superintelligence) is likewise specified however with a threshold of 100%. They think about big language designs like ChatGPT or LLaMA 2 to be circumstances of emerging AGI. [25]
Characteristics
Various popular definitions of intelligence have actually been proposed. One of the leading proposals is the Turing test. However, there are other well-known meanings, and some researchers disagree with the more popular techniques. [b]
Intelligence traits
Researchers typically hold that intelligence is needed to do all of the following: [27]
reason, usage strategy, resolve puzzles, yewiki.org and make judgments under unpredictability
represent understanding, consisting of good sense knowledge
plan
discover
- interact in natural language
- if necessary, incorporate these abilities in conclusion of any provided goal
Many interdisciplinary methods (e.g. cognitive science, computational intelligence, wiki.tld-wars.space and decision making) consider additional characteristics such as imagination (the ability to form novel mental images and principles) [28] and autonomy. [29]
Computer-based systems that display a number of these capabilities exist (e.g. see computational creativity, automated reasoning, choice support group, robot, evolutionary calculation, smart representative). There is argument about whether contemporary AI systems possess them to an appropriate degree.
Physical qualities
Other capabilities are considered desirable in intelligent systems, as they may affect intelligence or help in its expression. These consist of: [30]
- the capability to sense (e.g. see, hear, etc), and
- the capability to act (e.g. move and manipulate items, modification area to check out, and so on).
This consists of the ability to identify and react to danger. [31]
Although the ability to sense (e.g. see, hear, and so on) and the capability to act (e.g. relocation and manipulate items, modification location to check out, and so on) can be desirable for some intelligent systems, [30] these physical capabilities are not strictly needed for an entity to qualify as AGI-particularly under the thesis that big language designs (LLMs) may currently be or end up being AGI. Even from a less optimistic point of view on LLMs, there is no company requirement for an AGI to have a human-like type; being a silicon-based computational system is enough, offered it can process input (language) from the external world in place of human senses. This interpretation aligns with the understanding that AGI has actually never been proscribed a particular physical embodiment and therefore does not demand a capacity for mobility or traditional "eyes and ears". [32]
Tests for human-level AGI
Several tests indicated to confirm human-level AGI have actually been thought about, consisting of: [33] [34]
The idea of the test is that the maker needs to try and pretend to be a male, by answering concerns put to it, and it will just pass if the pretence is fairly persuading. A considerable portion of a jury, who ought to not be professional about machines, need to be taken in by the pretence. [37]
AI-complete problems
An issue is informally called "AI-complete" or "AI-hard" if it is thought that in order to fix it, one would need to carry out AGI, because the solution is beyond the capabilities of a purpose-specific algorithm. [47]
There are numerous problems that have actually been conjectured to need general intelligence to fix in addition to human beings. Examples consist of computer system vision, natural language understanding, and dealing with unforeseen situations while fixing any real-world problem. [48] Even a specific task like translation requires a machine to check out and write in both languages, follow the author's argument (factor), comprehend the context (understanding), and faithfully reproduce the author's initial intent (social intelligence). All of these problems require to be solved at the same time in order to reach human-level device efficiency.
However, a number of these jobs can now be performed by contemporary large language models. According to Stanford University's 2024 AI index, AI has reached human-level efficiency on lots of standards for checking out comprehension and visual reasoning. [49]
History
Classical AI
Modern AI research started in the mid-1950s. [50] The first generation of AI scientists were convinced that synthetic general intelligence was possible and that it would exist in simply a few years. [51] AI leader Herbert A. Simon wrote in 1965: "devices will be capable, within twenty years, of doing any work a male can do." [52]
Their forecasts were the motivation for Stanley Kubrick and Arthur C. Clarke's character HAL 9000, who embodied what AI scientists thought they could produce by the year 2001.