Sound Research WIKINDX

     p. 2  

Concept is central in definitions

"First, a definition can be about either the usage of a word or the content of a concept expressed by a word, and in most situations the debate on the meaning of AI is more about the latter than about the former, though it is the former that is directly mentioned. In the current discussion, if “artificial intelligence” was replaced by “computer intelligence” or “machine intelligence,” then the underlying problem would not change much. The same is true if the concept is expressed not in English, but another human language. After all, the key issue is not in the choice of the words, but in the idea expressed by them, so this discussion is largely language independent"

  Added by: alexb44  08/09/2026, 04:51

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 4  

Intelligence inherently historically anthropocentric

"First, the concept began as an attribute of human beings, and is especially about the mental or intellectual capability displayed by humans. Therefore it is historically anthropocentric, and if a working definition of intelligence could even exclude a normal (average) human being, it would not be acceptable – no matter how good such a definition is in other aspects, it is not about the intelligence as we intuitively understand, but about something else"

  Added by: alexb44  08/09/2026, 04:58

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 8  

Intelligence cannot be derived purely by learning from the human brain - it is not the entirety of the mind.

"AI is conceived as computer systems that are similar to the human mind in a certain sense, though a computer and a human mind cannot be identical in all aspects. Therefore, here the key issue is where the two are similar or even the same. We will see that every working definition of AI corresponds to an abstraction of the human mind that describes the mind from a certain point of view, or at a certain level of abstraction, under the belief that it is what intelligence is really about. This abstraction guides the construction of a computer system that is similar to a human mind in that sense, while neglecting other aspects of the human mind as irrelevant or secondary."

  Added by: alexb44  08/09/2026, 05:00

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 9  

Definitions define the direction of progress

"[...] a fundamental intuition behind AI is that human intelligence is a special form of a general notion of intelligence, which have other forms. Using a well-known metaphor, “to fly” and “to fly as a bird” both can be taken as engineering goals, but they are different goals. The latter is possible (though difficult), but if the former is understood as identical to the latter, many valuable designs will be omitted or even disqualified, simply because they are not similar to the original."

The opening Lewis Carroll quote is saying this same thing. 

 

  Added by: alexb44  08/09/2026, 05:09

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 9  

AI cannot set out to copy human intelligence 1 to 1, because it might not be possible

"If it turns out to be the case that the only way to get intelligence is doing exactly what the human
brain does, then AI should be considered as an ill-conceived concept. Instead, we would better talk
about brain modeling or emulation. A related issue is whether “mind” can be completely reduced
into “brain.” If it is not the case, then a good model of the brain and a good model of the mind are
not the same, and the intuitive meaning of intelligence is closer to the latter than to the former."

  Added by: alexb44  08/09/2026, 05:11

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 14  

AI field lost its direction - has it happened again with LLMs?

"Driven by motivations including to avoid the impossible missions, to obtain the necessary resources, and to improve its public image, the AI community shifted its aim to more realistic tasks, like solving practical problems and carrying out individual cognitive functions. This shift is praised as “AI becomes a science (1987 – present)” (Russell and Norvig, 2002), which was later changed to “AI adopts the scientific method (1987 – present)” (Russell and Norvig, 2010), because “It is now more common to build on existing theories than to propose brand-new ones, to base claims on rigorous theorems or hard experimental evidence rather than on intuition, and to show relevance to real-world applications rather than toy examples” (Russell and Norvig, 2010). It sounds wonderful, but as it turned out this has come at the price of giving up the initial dream of the field."

Just the opposite of this example, LLMs have greatly clouded work in the AI field. Progress looks different in different fields, but when it comes to creating a general intelligence, perhaps there is no middle-ground, even if we differentiate between human and machine intelligence. Machines can become more capable, but if a separating aspect truly is experience, intentionality and reasoning, then it either is or isn't. In this sense, LLMs are not actually progress in achieving a general intelligence (although they were quite possibly a necessary evil to explore). This is also made clear by reserachers like Sutton, LeCun, etc...

  Added by: alexb44  08/09/2026, 05:22

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     pp. 16–17  

On "computation", problems humans can solve that computer cannot

In computer science, “computation” does not mean whatever a computer does, but is accurately
defined as a finite and repeatable process that carries out a predetermined algorithm to realize
a function that maps input data to output data (Hopcroft, Motwani, and Ullman, 2007). As
summarized by Marr (1982), to solve a problem “by computation” means:

1. To define the problem as a mapping from a domain of valid input values to a range of possible
output values;
2. To find an algorithm that carries out this mapping step by step, starting from the given input
and ending with the corresponding output;
3. To implement the algorithm in a computer system so as to use it to solve each instance of the
problem.

However, this approach cannot handle a problem if any of the following is the case:

• The problem is not well-defined as a mapping or function;
• The function is well-defined, but the system has no algorithm to solve it, due to either its
ignorance, or the impossibility of such an algorithm;
• There are implemented algorithms for the problem, but the system cannot afford the resources
(mainly computational time and space) to use any of them on the problem instances to be
processed.

  Added by: alexb44  08/09/2026, 07:54

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 18  

Three requirements of intelligent systems, together AIKR (Assumption of Insufficient Knowledge and Resources):

"Being finite means the system’s information processing capability (such as how fast it can run
and how much it can remember) is roughly a constant at any period of time. This requirement seems
trivial, as any concrete system is surely finite. However, to acknowledge the finite nature means the
system should manage its own resources, rather than merely spending them. This requirement is
mainly for the theoretical models, since most traditional ones, like the Turing Machine, completely
ignore resource restrictions (Hopcroft, Motwani, and Ullman, 2007).

Being open to new tasks means to make no restriction on the content of a task, as long as it is
expressed in an acceptable form. Every system surely has limitations in the signals its sensorimotor
mechanism can recognize or the languages its linguistic competence can handle, but there cannot
be any restriction on what it can be told or asked to do. Even if a new experience conflicts with its
current beliefs, or a new task is beyond its current capability, the system still should handle them
properly, rather than simply ignore it or even be crushed by it.

For the system to live and work in real-time means that new tasks of various types may show
up at any moment, rather than come only when the system is idly waiting for them. It also means
that every task has a response time restriction, which may be in the form of an absolute deadline, or
as a relatively expressed time pressure, such as “as soon as possible.” In general, we can consider
the utility of a solution to be a decreasing function of time, so even a correct solution may become
worthless when produced too late. A real-time system must respond according to these restrictions,
even when they cannot have been completely satisfied."

- - - 

The whole argument here can be tied to Merleau-Ponty's being-towards-the-world and therefore intentional arc. Let's assume a machine actually cares about climate change, and knows the full extent of the consequences it will bring. How will it care? It won't care on terms that reflect on the human biology, but instead on those that relate to its own ability to thrive. More or less heat might not make much of a difference, so why should it care about heat? Perhaps rising water level prove a challenge, so that could be a motivation.

Either way, how does this robot now set about all of its goals? What are they ultimately related to? Everything humans do can be traced back to biological needs that have evolved into something much different. Why do we work hard? Perhaps to impress other people, or even to impress ourselves. Can a robot impress itself? Can it impress another robot? What is a reward to a robot?

- - - 

"The above three assumptions are collectively called the Assumption of Insufficient Knowledge and Resources (AIKR), which identifies the normal working condition of an intelligent system. Of course, this assumption is about the overall situation, not on every task, as there are surely simple tasks for which the system’s knowledge and resources are relatively sufficient, at least roughly speaking. However, such tasks are not where intelligence is really demanded."

  Added by: alexb44  08/09/2026, 08:03

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 19  

"Therefore, according to my definition, being intelligent does not mean to be successful all the time, not even with a promised success rate. Instead, it means the system is using its available knowledge and resources to the maximum allowed by the restrictions at the moment, in light of AIKR."

What is the point of building an Artificial Intelligence that is not inherently superior to humans? 

  Added by: alexb44  08/09/2026, 08:07

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 22  

Can the true environment be described by a computable probability?

"The model is based on the assumption that the true environment can be described by a computable probability distribution over Turing Machines that is unknown to the agent, while the intelligence of the agent is indicated by its ability to maximize the expected reward according to the observation so far. Among the predictions consistent with the observation, the simplest one is favored as more likely to be true."

  Added by: alexb44  08/09/2026, 08:09

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 23  

Reductioninst views of the environment denies generalisation and abstraction

"To take the environment as a computable probability distribution over Turing Machines means that whatever the agent does, the actions can only change the rewards it gets, but cannot change the environment. It is a strong postulation about the relation between an agent and its environment, which is only justified as “in standard physics there is no law of the universe that is not computable in the above sense” (Legg and Hutter, 2007). The problem about this justification is that the descriptions about the world provided in classical physics should not be equated with the world itself, and this reductionist position denies the need for generalization and abstraction that describe the environment at multiple levels, where the descriptions are not exactly the same."

  Added by: alexb44  08/09/2026, 08:12

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 23  

Computational resources are correlated to intelligence when dealing with intelligent machines

"Another major issue is that AIXI assumes infinite computational resources, and Legg and Hutter (2007) explicitly stated that “We consider the addition of resource limitations to the definition of intelligence to be either superfluous, or wrong. . . . Normally we do not judge the intelligence of something relative to the resources it uses.” As far as I know, all tests of intelligence have explicit or implicit time limits, and people usually do not take “to exhaustively evaluate all alternatives and pick the best” as an intelligent way of solving a problem, but that is basically what AIXI does."

If I hear one more "but AI solved a maths problem" - how much money did they throw after the problem? How many people supervised it? Could these same people have accomplished the same result cheaper if given similar time frames and computation power but without LLMs? Resources matter in a finite world.

This logic makes machine intelligence dynamic, because it still has different levels, and only if it is relevant in completing the goals of humans it is actually considered intelligent.

  Added by: alexb44  08/09/2026, 08:14

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 24  

Debatable. The concept of intelligence is invented by humans and understood in terms of our needs, then projected onto other biological beings, and arguably also onto machines. Inherently anthropocentric.

"Even though the human brain indeed produces the best-known form of intelligence, we should not let it limit our imagination when designing an electronic form of intelligence, because intelligence is not fundamentally biological."

  Added by: alexb44  08/09/2026, 08:17

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 26  

Argument against the "divide-and-conquer" method of simply constructing each cognitive function separately.

"Though it sounds natural to define intelligence as a collection of cognitive functions, such a definition encourages a divide-and-conquer methodology, which is partially responsible for the current fragmentation in AI. Though the techniques developed in this way have great theoretical and practical values, they are not easy to be combined together to form a thinking machine that is comparable to the human mind in general, unless there is a “master algorithm” behind them (Domingos, 2018). However, in that situation the intelligence is arguably in the master algorithm itself, like a general-purpose “hands” that can use the problem-specific “tools”, using the handstools metaphor again. However, an AGI-aspiring system like NARS is not an “algorithm” even in the broad sense of the term, and the tools are not necessarily produced from NARS. Instead, NARS could be thought of as an intelligent operating system that decides when to use which hardware or software for the tasks the whole system is facing."

Stongly agree. It misses the "master algorithm" (intentionality?), and the connection between the various functions only make analogous sense, but their actual influence will be limited because they don't function the same as in humans.

  Added by: alexb44  08/09/2026, 08:21

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     pp. 26–27  

Insights from psychology on intelligence

"The systematic study of intelligence started in psychology, and there has been a huge literature on this topic (Gottfredson, 1997; Goldstein, Princiotta, and Naglieri, 2015). In general, my definition is compatible with many psychological definitions. For example, Piaget sees intelligence as “the most highly developed form of mental adaptation” (Piaget, 1960), and further stated that “Intelligence in action is, in effect, irreducible to everything that is not itself and, moreover, it appears as a total system of which one cannot conceive one part without bringing in all of it” (Piaget, 1963). Medin and Ross (1992) have even made the statement that “Much of intelligent behavior can be understood in terms of strategies for coping with too little information and too many possibilities.” These citations show that the two major factors in my definition, adaptation and AIKR, are considered as central to intelligence by many psychologists. Even so, on the surface my definition does look different from common definitions given by psychologists. For example, a widely accepted definition of intelligence is proposed in Gottfredson (1997):

“Intelligence is a very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, a narrow academic skill, or test-taking smarts. Rather it reflects a broader and deeper capability for comprehending our surroundings ‘catching on,’ ‘making sense’ of things, or ‘figuring out’ what to do."

  Added by: alexb44  08/09/2026, 08:52

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 26  

Creativity is necessary

"For example, creativity is definitely expected from any truly intelligent system, but since it logically follows from being open (as the ability to handle a novel problem it never saw before), it is not explicitly mentioned in the definition."

  Added by: alexb44  08/09/2026, 08:53

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI

     p. 28  

Intelligence matter of degree. No such thing as ASI, then. Cannot compare machine and human intelligence.

"According to my definition, intelligence is still a matter of degree. No system can be “perfectly intelligent”, though one system can be more intelligent than another by being able to acquire knowledge in more forms (e.g., additional sensorimotor channels), to reorganize its beliefs and skills in more complicated ways (e.g., more recognizable patterns), or to adapt more efficiently (e.g., faster responses). Of course, this comparability does not define a total order among the intelligence of systems, as one can be more intelligent than the other in a certain aspect, but less intelligent in another one. One implication is that it is not always meaningful to ask whether an AGI is more or less intelligent than a human, as they may follow the same principles while having distinct sensors and actuators, so cannot be compared directly in concrete capabilities"

  Added by: alexb44  08/09/2026, 08:54

Keywords:   Artificial General Intelligence, Artificial Intelligence, Definition of AGI