Abstract
This article confirms the definition of AGI and discusses unrealized functions of human-like AGI as of 2022, which include fluid intelligence, generative rule handling with case-based AI, making out in the real-world, social intelligence, language acquisition, and mathematics. (The article is an English version of a proceedings article in Japanese for a local workshop on AGI [2022-11-22].)
1. AGI and General Intelligence
General intelligence, which is a part of the term Artificial General Intelligence (AGI), is a psychological term, originally postulated as one or a few general problem-solving factors in the measurement of human intelligence [1]. Factors of intelligence are determined by statistically processing the results of intelligence tests. The CHC model is an attempt to comprehensively enumerate the factors.
While AGI has not been unanimously defined in the community, it is generally considered to be an attempt to provide artifacts with problem-solving abilities that can deal with problems beyond those assumed at the time of design. (AI that solves only the problems assumed at the time of design is called "narrow AI" as opposed to AGI.)
While, as indicated above, human general intelligence and the general intelligence for AGI are different by definition, this article gives examples of what the current AI has not achieved from the standpoint that AGI should achieve "at least" human intelligence or human problem-solving abilities.
2. Fluid Intelligence
Fluid intelligence, posited as one of the intelligence factors, is "the ability to solve novel, abstract problems that do not depend on task-specific knowledge" [2] and is often regarded as a central part of human intelligence. By this definition, fluid intelligence is closely related to the "problem-solving ability to deal with problems beyond design assumptions" required for AGI. (Note: A more general discussion of fluid intelligence as "policy generation" is given in [3] (Chapter 12)).
In a matrix reasoning task, subjects are presented with a matrix, where a cell in the last row is blank. Subjects discover a rule from the pattern shown in the other rows and apply the rule to the last row to fill in the blank cell.
Tasks to measure fluid intelligence require the ability to conduct an internal search from one or a small number of examples presented to find a solution while generating hypotheses (cf. my blog article). The Raven Progressive Matrices (RPMs; see Fig.1) are typical intelligence test tasks that measure fluid intelligence. A review article [4] summarizes attempts to solve RPMs using deep learning, and describes the problem of insufficient generalization with deep learning. Humans solve tasks without being given a large number of task examples in advance as they discover the regularities/rules while dealing with the tasks. Thus, to realize fluid intelligence in AGI, it would be important to implement the ability to discover rules (see my previous post).
3. A Theoretical Problem: Generative Rules and Case-Based AI
Current mainstream machine learning-based AI is basically case-based, which tries to solve problems with a large number of examples. Case-based AI cannot, in principle, solve problems that do not exist in the examples or in their generalization. Meanwhile, human languages use generative rules, which can generate an infinite number of patterns from a finite set of rules and vocabulary. A finite set of cases can not, in principle, cover the infinity to be generated by rules. Besides natural languages, computer languages, logic, and mathematics are examples of systems based on generative rules.
The inability of case-based AI to cover generative rule-based phenomena does not mean that AI in general cannot handle them; "good old" symbolic AI often handled generative rules. Given the success of case-based AI, it will be important to incorporate generative rule handling into case-based AI.
Notes: For a discussion rather favoring the symbolic approach to case-based AI, see [5]. cf. related conference: The Challenge of Compositionality for AI and a recent talk.
For a successful example of combining deep learning and symbolic search, see MuZero [6].
4. Dealing with the Real World
Intelligent robots that work in the real world like humans are not yet available. For example, we are yet to have a robot proposed by Wozniak, which can make coffee in a kitchen it enters for the first time. While the current mainstream ML-based AI is case-based as pointed out above, it lacks enough experience (cases) in the real-world. While data for learning is often collected from the Internet, data from interaction of agents with the real (3D) world or of "lived experience” is scarce. Note that research on real-world interactions of artificial agents has been made in the field of cognitive (developmental) robotics [7] [8].
5. Social Intelligence
Humans begin to infer the intentions of others as infants [9] and often acquire a "theory of mind" before reaching school age. Such intelligence has not been realized in AI. Because society is also part of the real world, lived experience is required to learn social intelligence. While data for social intelligence can be collected in cognitive developmental robotics and cognitive psychology, human social intelligence may require genetically wired mechanisms (or prior knowledge), which are studied in broader cognitive science such as neuroscience.
6. Language Acquisition
Linguistic competence is the ability to appropriately handle the phonological, morphological, syntactic, semantic, and pragmatic aspects of a language. As grammar is a set of generative rules, its appropriate handling requires the ability to handle generative rules (see above) [10]. Case-based AI can handle "meaning" hidden in the distribution of words and associations between words and images appearing in data sources (corpus). Since the meaning of a complex linguistic expression such as a sentence is synthesized from the meanings of its components by generative rules, the ability to handle generative rules is also necessary to handle compositional semantics. Meanwhile, "lived experience" (see above) is required to handle semantics grounded on real-world experience (cf. The symbol grounding problem has been partially solved [11]). Pragmatic competence is social intelligence acquired through the practice of linguistic exchange (language games) with others; so, again, lived experience is necessary. Linguistic competence requires the ability to handle generative rules and the lived experience of language practice, both of which have not yet been fully integrated to the current AI.
Human language acquisition begins in infancy. Infants are assumed to have an innate ability to handle generative rules in addition to statistical learning. Infants are also able to infer the intention of their caregivers to understand the relationship between words and their referents (see social intelligence above). Given these facts, AI's acquisition of linguistic abilities would profit from research on human language acquisition.
7. Mathematics
According to mathematical logic, mathematics can be viewed as a system of "generative rules" (see above). In fact, case-based AI cannot even handle addition [12][13]. On the other hand, the part of mathematics formulated in first-order predicate logic can be handled by the Good Old symbolic AI (e.g., quantifier elimination solvers).
If AI is to imitate human mathematical abilities, cognitive scientific research on human mathematical abilities (to handle numbers and quantity) would be necessary (cf. this is an area J. Piaget, et al. pioneered).
8. Summary
This article discussed the unrealized functions of current AI compared to human intelligence. Specifically, case-based AI cannot handle generative rules, so it cannot handle syntactic and compositional semantics of language nor mathematics. It was also pointed out that current AI suffers a paucity of lived experience.
As classical symbolic AI handled generative rules, it is important to make case-based AI handle generative rules (philosophically, it is a synthesis of empiricism and rationalism).
It was suggested that cognitive robotics research will be important to address the issue of lived experience for AI.
Finally, it is noted that the insights of cognitive science in general will be important for AGI research in terms of learning from human intelligence.
References
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