![\"Computer](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/04\/1776871191_721_0x0.jpg\")
<\/p>\n<\/p>\n
Artificial general intelligence (AGI) does not yet have a universally accepted definition, but we need one ASAP.<\/p>\n
getty<\/p>\n
In today\u2019s column, I examine an unresolved controversy in the AI field that hasn\u2019t received the attention it rightfully deserves, namely, what constitutes a sensible and universally agreed-upon definition for pinnacle AI, commonly and vaguely referred to as artificial general intelligence (AGI).<\/p>\n
This is a vital matter. At some point, we should be ready to agree whether the advent of AGI has been reached. There is also the matter of gauging AI progress and whether we are getting closer to AGI or veering away from AGI. All told, if there isn\u2019t a wholly accepted universal definition, we will be constantly battling over whether pinnacle AI is in our sights and whether it has truly been attained. This is the classic dilemma of apples versus oranges. A person who defines apples as though they are oranges will be forever in a combative mode when trying to discuss whether someone is holding an apple in their hands. <\/p>\n
As Socrates once pointed out, the beginning of wisdom is the definition of terms. There needs to be a concerted effort to properly define what AGI means.<\/p>\n
Let\u2019s talk about it.<\/p>\n
This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here<\/a>). <\/p>\n Heading Toward AGI And ASI<\/p>\n First, some fundamentals are required to set the stage for this discussion.<\/p>\n There is a great deal of research going on to further advance AI. The general goal is to either reach artificial general intelligence (AGI) or maybe even the outstretched possibility of achieving artificial superintelligence (ASI). <\/p>\n Overall, the definition of AGI generally consists of aiming for AI that is considered on par with human intellect and can seemingly match our intelligence. ASI is AI that has gone beyond human intellect and would be superior in many, if not all, feasible ways. The idea is that ASI would be able to run circles around humans by outthinking us at every turn. For more details on the nature of conventional AI versus AGI and ASI, see my analysis at the link here<\/a>.<\/p>\n We have not yet attained the generally envisioned AGI. <\/p>\n In fact, it is unknown whether we will reach AGI, or that maybe AGI will be achievable in decades or perhaps centuries from now. The AGI attainment dates that are floating around are wildly varying and wildly unsubstantiated by any credible evidence or ironclad logic. ASI is even more beyond the pale when it comes to where we are currently with conventional AI. <\/p>\n Controversy About AGI As Terminology<\/p>\n To the surprise of many in the media and the general public at large, there is no universally accepted standardized definition for what AGI consists of. <\/p>\n This lack of an across-the-board formalized definition for AGI spurs numerous difficulties and problems. For example, AI gurus referring to AGI can be making unspoken assumptions about what they believe AGI to be, and therefore stoke confusion since they aren\u2019t referring to the same thing. Discussions can occur at cross purposes due to each respective expert having their own idiosyncratic definition of what AGI is or ought to be.<\/p>\n An especially disquieting concern is that attaining AGI has become a preeminent directional focus for many in the AI industry, yet this is a bit of a mirage since the AI field does not have a said-to-be one-and-only-one North Star that represents what AGI is supposed to be:<\/p>\n \u201cRecent advances in large language models (LLMs) have sparked interest in \u2018achieving human-level \u2018intelligence\u2019 as a \u2018north-star goal\u2019 of the AI field. This goal is often referred to as \u2018artificial general intelligence\u2019 (\u2018AGI\u2019).\u201d \u201cYet rather than helping the field converge around shared goals, AGI discourse has mired it in controversies.\u201d\u201cResearchers diverge on what AGI is and on assumptions about goals and risks. Researchers further contest the motivations, incentives, values, and scientific standing of claims about AGI.\u201dFinally, the building blocks of AGI as a concept — intelligence and generality — are contested in their own right.\u201d (source: Borhane et al, \u201cStop Treating \u2018AGI\u2019 as the North-Star Goal of AI Research.\u201d arXiv, February 7, 2025).The Moving Of The Cheese<\/p>\n In a prior posting, I had noted that some AI luminaries have been opting to define AGI in a manner that suits their specific interests. I refer to this as moving the cheese (see my discussion at the link here<\/a>). You might be familiar with the movable cheese metaphor — it became part of our cultural lexicon due to a book published in 1998 entitled \u201cWho Moved My Cheese? An Amazing Way To Deal With Change In Your Work And In Your Life\u201d. The book identified that we are all, at times, akin to mice seeking a morsel of cheese in a maze.<\/p>\n OpenAI CEO Sam Altman is especially adept at loosely defining and then redefining AGI. In his personal blog posting entitled \u201cThree Observations\u201d of February 10, 2025, he provided a definition of AGI that said this: \u201cAGI is a weakly defined term, but generally speaking, we mean it to be a system that can tackle increasingly complex problems, at human level, in many fields.\u201d<\/p>\n This AGI definition contains a plethora of ambiguity and came under fierce arguments about being shaped to accommodate OpenAI\u2019s AI products. For example, by indicating that AGI would be at a human level in \u201cmany fields\u201d, this seemed to be an immense watering down from the earlier concepts that AGI would be versed in all fields. It is a lot easier to devise pinnacle AI that would be merely accomplished in many fields, versus having to reach a much taller threshold of doing so in all fields.<\/p>\n Still Messing Around<\/p>\n According to a reported interview with Sam Altman that took place recently, Altman made these latest remarks about the AGI moniker:<\/p>\n \u201cI think it\u2019s not a super useful term.\u201d\u201cI think the point of all of this is it doesn\u2019t really matter, and it\u2019s just this continuing exponential model capability that we\u2019ll rely on for more and more things.\u201d (source: \u201cSam Altman now says AGI is \u2018not a super useful term\u2019 \u2013 and he\u2019s not alone\u201d by Ryan Browne, CNBC, August 11, 2025).<\/p>\n Once again, this type of chatter about the meaning of AGI has sparked renewed controversy. The remarks seem to try and create distance from the AGI definitions that he and others have touted in the last several years.<\/p>\n Why so?<\/p>\n