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Working definitions split into three families. *Task-based* definitions specify jobs a system must do — the Turing test is the ancestor, and modern versions substitute economically valuable work or a benchmark suite. *Capability-based* definitions specify cognitive functions such as transfer learning, long-horizon planning, or acquiring new skills without retraining. *Autonomy-based* definitions require operation over extended periods without human scaffolding. [[nick-bostrom]] sidesteps the threshold in *Superintelligence*, defining superintelligence as intellect greatly exceeding human performance in virtually all domains and treating human-level capability as a waypoint.Working definitions split into three families. *Task-based* definitions specify jobs a system must do — the Turing test is the ancestor, and modern versions substitute economically valuable work or a benchmark suite. *Capability-based* definitions specify cognitive functions such as transfer learning, long-horizon planning, or acquiring new skills without retraining. *Autonomy-based* definitions require operation over extended periods without human scaffolding. [[nick-bostrom]] sidesteps the threshold in *Superintelligence*, defining superintelligence as intellect greatly exceeding human performance in virtually all domains and treating human-level capability as a waypoint. A framework from Google DeepMind researchers replaces the binary with a grid, scoring systems on performance (from "emerging" through "competent", "expert", "virtuoso" and "superhuman") crossed with generality.[^morris2024] On that scheme, mid-2020s systems are plausibly "emerging AGI" and clearly superhuman narrow AI in several domains — more informative than either "AGI is here" or "AGI is not here".A framework from [Google DeepMind](https://aiwiki.ai/wiki/google_deepmind) researchers replaces the binary with a grid, scoring systems on performance (from "emerging" through "competent", "expert", "virtuoso" and "superhuman") crossed with generality.[^morris2024] On that scheme, mid-2020s systems are plausibly "emerging AGI" and clearly superhuman narrow AI in several domains — more informative than either "AGI is here" or "AGI is not here". > [!note] Terminology> [!note] Terminology> "AGI" is used inconsistently even within single organisations: human-level competence across tasks, full automation of remote work, or the ability to do novel science. These are different claims with different timelines, and arguments equivocate between them.> "AGI" is used inconsistently even within single organisations: human-level competence across tasks, full automation of remote work, or the ability to do novel science. These are different claims with different timelines, and arguments equivocate between them.lines 50–56 → 50–562 unchanged lines not shown
Alan Turing's 1950 paper posed machine thinking in behavioural terms and proposed the imitation game to sidestep definitional argument.[^turing1950] The 1956 Dartmouth proposal assumed generality as the default goal, expecting machines to use language, form abstractions and improve themselves within a summer.Alan Turing's 1950 paper posed machine thinking in behavioural terms and proposed the imitation game to sidestep definitional argument.[^turing1950] The 1956 Dartmouth proposal assumed generality as the default goal, expecting machines to use language, form abstractions and improve themselves within a summer. The narrower ambitions of expert systems and, later, statistical machine learning displaced that goal for decades. The phrase "artificial general intelligence" appears in a 1997 paper by Mark Gubrud on the security implications of advanced technology, and was independently adopted in the early 2000s by Shane Legg, Ben Goertzel and Peter Voss, becoming established with a 2007 edited volume of that title. The phrase then distinguished a small community from a mainstream that regarded the goal as disreputable; several of the largest laboratories now state it as their objective.The narrower ambitions of expert systems and, later, statistical machine learning displaced that goal for decades. The phrase "artificial general intelligence" appears in a 1997 paper by Mark Gubrud on the security implications of advanced technology, and was independently adopted in the early 2000s by [Shane Legg](https://aiwiki.ai/wiki/shane_legg), Ben Goertzel and Peter Voss, becoming established with a 2007 edited volume of that title. The phrase then distinguished a small community from a mainstream that regarded the goal as disreputable; several of the largest laboratories now state it as their objective. ## Measuring progress## Measuring progress lines 58–64 → 58–641 unchanged line not shown
Saturation is weaker evidence than it appears. Contamination is hard to exclude when training corpora are web-scale and benchmarks are published; benchmarks measure what is easy to score, biasing them toward short checkable answers; and Goodhart's law applies with force when the benchmark is also the optimisation target for the labs reporting the scores.Saturation is weaker evidence than it appears. Contamination is hard to exclude when training corpora are web-scale and benchmarks are published; benchmarks measure what is easy to score, biasing them toward short checkable answers; and Goodhart's law applies with force when the benchmark is also the optimisation target for the labs reporting the scores. The most-cited contamination-resistant test is François Chollet's Abstraction and Reasoning Corpus, built around tasks requiring a novel rule to be inferred from a handful of examples.[^chollet2019] It resisted large language models for years. In late 2024 a preview of a model trained to spend far more computation at inference time reported private-set scores in the range of typical human performance, at costs orders of magnitude above ordinary use; a harder successor set was released in 2025 on which scores fell sharply again. The gap closes, then is redefined, and which of those moves is more informative remains unclear.The most-cited contamination-resistant test is [François Chollet](https://aiwiki.ai/wiki/francois_chollet)'s Abstraction and Reasoning Corpus, built around tasks requiring a novel rule to be inferred from a handful of examples.[^chollet2019] It resisted large language models for years. In late 2024 a preview of a model trained to spend far more computation at inference time reported private-set scores in the range of typical human performance, at costs orders of magnitude above ordinary use; a harder successor set was released in 2025 on which scores fell sharply again. The gap closes, then is redefined, and which of those moves is more informative remains unclear. ## The case for nearness## The case for nearness removed, struck through added, underlinedLine numbers count the serialised markdown of each revision, frontmatter included.
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