Remaining Issues with AGI as of 2022

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)).

Fig.1 Raven Progressive Matrix

CC BY-SA 3.0 Life of Riley @Wikimedia

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

[1] Spearman, C.: General Intelligence, Objectively Determined and Measured, The American Journal of Psychology, Vol.15, No.2, pp.201—292.  doi:10.2307/1412107 (1904).

[2] Kievit, R.A., et al.: A watershed model of individual differences in fluid intelligence, Neuropsychologia, Vol.91, pp.186–198 (2016)　doi:10.1016/j.neuropsychologia.2016.08.008

[3] Hernández-Orallo, J.: The Measure of All Minds: Evaluating Natural and Artificial Intelligence, The Cambridge University Press (2017)

[4] Małkiński, M., et al.: Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven’s Progressive Matrices, arXiv,　doi:10.48550/arXiv.2201.12382 (2022)

[5] Marcus, G.: The Next Decade in AI: Four Steps Towards Robust Artificial Intelligence, arXiv, doi:10.48550/arXiv.2002.06177 (2020)

[6] Schrittwieser, J. et al.: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model, arXiv, doi:10.48550/arXiv.1911.08265 (2020)

[7] Pfeifer, R., Bongard, J.: How the Body Shapes the Way We Think: A New View of Intelligence, MIT Press (2006)

[8] Cangelosi, A., et al.: Developmental Robotics: From Babies to Robots, MIT Press (2015)

[9] Gergely, G., Bekkering, H. & Király, I.: Rational imitation in preverbal infants. Nature 415, 755 (2002). doi: 10.1038/415755a

[10] Delétang, G. et al.: Neural Networks and the Chomsky Hierarchy, arXiv, doi:10.48550/arXiv.2207.02098 (2022)

[11] Steels, L.: The symbol grounding problem has been solved, so what’s next?, in Symbols and Embodiment: Debates on meaning and cognition, doi:　10.1093/acprof:oso/9780199217274.003.0012 (2008)

[12] Brown, T., et al.: Language Models are Few-Shot Learners, ArXiv, doi: 10.48550/arXiv.2005.14165 (2020)

[13] Fujisawa, I., et al.: Logical Tasks for Measuring Extrapolation and Rule Comprehension, ArXiv, doi: 10.48550/arXiv.2211.07727 (2022)

A Model of Fluid Intelligence based on Examining Experienced Sequences

[日本語版]

Abstract

This article proposes a model of rule/policy discovery based on examining experienced sequences.  Fluid intelligence, as measured by intelligence tests, can be viewed as the ability to discover policies for solving problems from one or a small number of examples.  Agents with fluid intelligence examine a small number of experienced time series to discover common rules.  If the sequence is not present, memory recall (replay) would be used.  The proposed model ”goes over” experienced sequences and extracts elements such as attributes, relationships among the input elements, and agent actions, to generate hypothetical policies.

1. Introduction

AGI as engineering is an attempt to give artifacts a general problem-solving capability beyond design.  General intelligence was originally postulated as a factor of one or a few general problem-solving capabilities in the measurement of human intelligence [1].  Fluid intelligence was postulated as one of the factors that make up general intelligence. While the original definition of fluid intelligence [2] was "the ability to recognize relationships," the definitions in the community vary.  Kievit et al. summarize fluid intelligence as "the ability to solve novel, abstract problems that do not depend on task-specific knowledge" [3].  More generally, Hernández-Orallo [4](Chapter 12) addresses fluid intelligence as a policy-generating capability.  In an intelligence test, the subject is required to find a policy for solving the problem from one or a few examples.  This requires the ability to conduct an internal search, generate multiple hypotheses, and find a solution, and it would be the central ability of fluid intelligence.  In the following, fluid intelligence is regarded as the ability to discover policies for problem solving from one or a few examples.  Note that while there are attempts to solve fluid intelligence tasks such as Raven’s Progressive Matrices (see Appendix) with deep learning methods [5], if they have learned with ample task data similar to the task to be tested, they are using crystallized intelligence rather than fluid intelligence to solve it.

In intelligence test-like tasks (see "Appendix: Assessment Tasks"), abstraction is necessary, for the same situation is not normally repeated. The abstract elements of the solution include the attributes of the input elements, the relationships between the attributes, and the actions of the agent.  Policy discovery, including abstraction, is a process of induction.  While machine learning is also inductive, a difference lies in the number of samples.  Fluid intelligence in intelligence testing requires finding common structures from a small number of samples.  This ability is useful in devising solutions to problems encountered in a variety of situations.  In the following, a model of rule (policy) discovery based on examining experienced time series is proposed.

2. Model of the Discovery Process

Discovery of rules (policies) from experienced series is done by "going over" the the series:

• If the entire problem is not presented to the agent at once, a replay is performed, otherwise the agent goes over the presented scene.
• Elements (attributes of input elements, relations among the attributes, and actions of agents) are extracted from the success series to form a hypothetical policy series.
• Various heuristics can be used to determine which elements are prioritized in the hypothetical policy. (e.g., Emphasis is placed on elements in close temporal and spatial proximity and the relationships associated with them.)
• Elements in the failed series are discounted.
• The hypothetical policy is verified with one or more series.
• Hypothetical policies that fail verification are stored as rejected policies so that they will not be tested/used again.

3. Required Mechanisms

• Mechanism to go over spatial scenes by gaze (eye) movement for problems presented visually
• Mechanism to go over a temporal sequence – replay mechanism which recalls memorized sequences for policy generation and validation
• Mechanism to generate policy elements;  e.g., attribute extraction (abstraction) and discovery of relationships between elements
• Mechanism to give preference: preferences are useful for the search process to select policy elements.
• Mechanism to create a hypothetical policy series by adopting policy elements
• Mechanism to store hypothetical policies
• Mechanism to determine whether a hypothetical policy can be applied to a spatial scene or temporal (replayed) series
• Mechanism not to use rejected series
• Working memory – required for various tasks
• Mechanism to control the process as a whole

4. Policy Generation, Verification, and Application

Based on the required mechanisms, the process of policy generation, verification, and application can be summarized as follows:

• Policy generation
• Go over the successful series and create a series that reproduces the input elements.
• Attention is given preferentially to a specific attribute or relation in the sequence.
• Generate a hypothetical policy series from the sequence of attributes or relations extracted with the given attention.
• Policy Verification
• A series may be made by trial runs or by memory recall (replay).
• Recall the hypothetical policy from the (trial or recalled) series, and try to apply it (see below).
• If a (trial or recalled) success series matches the policy, retain it for further validation with other success series.
• If a (trial or recalled) failure series matches the policy, reject the policy.
• Applying policy to a series
• Apply the policy to the sequence, starting with the first element in the sequence and checking for a match to each recalled policy sequence element in turn.
• If the application of a policy element fails, then the policy fails.

5. Implementation with (Artificial) Neural Networks

If the elements (attributes and relations) are entirely symbolized and provided, the mechanism above could be implemented by a symbolic (GOFAI) algorithm.  If the elements are not clearly defined, it should be difficult to create a symbolic algorithm, and implementation would require fuzzy pattern matching and learning functions as found in (artificial) neural networks.  Note that problems must be solved without having been exposed to similar tasks even when learning is required.  In the following, hints for implementation with (artificial) neural networks are presented in line with the Required Mechanisms.

• Mechanism to go over spatial scenes
  The mechanism of saccade control by the brain can be imitated.
• Mechanism to go over temporal sequences
  Experienced sequences are recalled from other sequences and used for policy generation and validation.  Since no generalization is needed in the memory of series, a simple non-learning storage device could be used.
  Since replay is believed to occur in the hippocampus in the brain, the hippocampal mechanism can be imitated.  Meanwhile, as the phonological loop (working memory for speech) [6] is assumed to be located in the cortex, extra-hippocampal cortical circuits may also have replay-like functions.
• Mechanism for generate policy elements
• Attribute extraction (Abstraction)
  It is known that abstraction occurs in artificial neural networks through learning.
　　　
• Discovery of relations between elements
  Relations (e.g., co-occurrence of attributes) among elements can be extracted with artificial neural networks.  In order for a neural network to recognize transformation (such as rotation) of a figure, it must have learned the transformation.
　　　
• When policy elements are created during replay, it would be better to have a mechanism to control the timing of replay to create a time margin for processing. [Note]
• Mechanism to give preferences
  Preferences such as for spatial proximity can be incorporated into the structure of a neural network.
• Mechanism to create a hypothetical policy series by adopting policy elements/Mechanism to store hypothetical policies
• Policy elements are recalled and adopted by attention.  A certain mechanism (e.g., winner-takes-all) would be needed to select an element for attention.
• The series formed in the system can be stored in a mechanism similar to replay.
• Policy elements are pairs of attributes or relations to be selected and attention.
• Mechanism to determine whether a hypothetical policy can be applied to a spatial scene or replayed temporal series / Mechanism not to use rejected series
• Matching a hypothetical policy with memorized series could be implemented with the pattern matching function of a neural network.
• Policies that match the failed series are classified as rejected, and will not be used.
• Working memory – networks such as a bi-stable network could be used.
• Mechanism to control the process as a whole
  The process is repeated until a policy consistent with all the presented series is generated.

Note: Policy elements become the object of attention (i.e., made aware) when they are added to the policy. In this sense, policy generation involves System 2 in dual-process theory [7], which also makes policy verbalization possible.  However, other processes are not necessarily brought to attention.

6. Conclusion

This article has only suggested a model.  Future work would include its psychological validation and/or software implementation.  A literature survey on brain regions and functions corresponding to the model will be necessary to support it from the neuroscientific viewpoint.  Since policies discovered by the model include the actions (operations) of the agent, the mechanism is to discover at least one class of algorithms.  By examining how general the class of algorithms it discovers, it will be possible to evaluate it as a model of general intelligence.

References

[1] Spearman, C.: General Intelligence, Objectively Determined and Measured, The American Journal of Psychology, Vol.15, No.2, pp.201--292. doi:10.2307/1412107 (1904).

[2] Cattell, R.B.: The measurement of adult intelligence, Psychol. Bull., Vol.40, pp.153-–193. doi:10.1037/h0059973 (1943)

[3] Kievit, R.A., et al.: A watershed model of individual differences in fluid intelligence, Neuropsychologia, Vol.91, pp.186–198 (2016)
doi:10.1016/j.neuropsychologia.2016.08.008

[4] Hernández-Orallo, J.: The Measure of All Minds: Evaluating Natural and Artificial Intelligence, The Cambridge University Press (2017)

[5] Małkiński, M., et al.: Deep Learning Methods for Abstract Visual Reasoning: A Survey on Raven’s Progressive Matrices, arXiv, doi:10.48550/arXiv.2201.12382 (2022)

[6] Baddeley, A.D., Hitch, G.J.: Working Memory, In G.A. Bower (Ed.), Recent advances in learning and motivation (Vol. 8, pp. 47-90), New York: Academic Press (1974)

[7] Kahneman, D.: A perspective on judgement and choice, American Psychologist, Vol.58, No.9, pp.697-–720. doi:10.1037/0003-066x.58.9.697 (2003)

[8] Joyner, A., et al.: Using Human Computation to Acquire Novel Methods for Addressing Visual Analogy Problems on Intelligence Tests, ICCC (2015) [PDF]

[9] Carpenter, A., Just, A., Shell, P.: What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test, Psychological Review, 97(3) doi: 10.1037/ 0033-295X.97.3.404 (1990)