Abstract
After introductory remarks, we share our two-part theoretical position, viz. that: (P1) The best overarching approach to suitably defining GI, and obtaining AGI, is via formal logic, including specifically via logic-based learning that is academic in nature; and (P2) AI/AGI is best pursued by seeking artificial agents that pass determinate cognitive tests. We note that in striking harmony with this position is work on AGI by Goertzel et al. that has inspired us; this is work in which PreSchool for would-be AGIs provides an attractive route toward AGI itself. While Goertzel et al. envisage a virtual academic environment, we have in mind physical classrooms, for physical robots. We describe the robot PERI.2, which we have started to send to school.
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Notes
- 1.
We note here one vocal objection to that consensus: Yann LeCun has claimed that humans do not have general intelligence [24]. He discusses a hypothetical scenario wherein a human’s visual field is permuted as an example of our lack of general intelligence, arguing (it seems) that the ability to learn this permutation is required of anything which could be considered “general” intelligent. While an attempted refutation of LeCun’s position is out of scope for this paper, we do volunteer here that this “permutation skill” is clearly not particularly intelligent by any reasonable definition of the word (let alone by any reputable test of intelligence/cognitive ability we are aware of), and hence any definition of general intelligence which requires it as a prerequisite is not one we find at all plausible.
- 2.
If some of our readers are artificial, and not human persons, then they have AGI.
- 3.
On the other hand, among prominent AGI researchers, we are incidentally not alone in our emphasis on logic-based r &d; see e.g. [30], to which we return below.
- 4.
- 5.
ShadowProver has long been used to engineer logic-based intelligent artificial agents in our lab. A robust example can be found e.g. in [19]. While ShadowProver’s reasoning is deductive, it is the basis for types of reasoning we believe are key to AGI r &d, e.g. nonmonotonic/defeasible reasoning. See [10] for an example of an inductive logic and an inductive automated reasoner (ShadowAdjudicator).
- 6.
Since all symbolic information and processing in AI/AGI can carried out in a formal logic, feel free to replace ‘Symbolic’ here with ‘Logicist’ or ‘Logic-based.’
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Acknowledgements
We are grateful to: AFOSR for a DURIP award to Bringsjord & Govindarajulu that has brought PERI.2 to physical life, to ONR for sponsorship of r &d devoted to meta-cognitively perceptive artificial agents (award # N00014-22-1-2201), and to both AFOSR (currently award #FA9550-17-1-0191) and ONR for longstanding support of r &d in automated reasoning, planning, and logic-based learning.
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Bringsjord, S. et al. (2023). PERI.2 Goes to PreSchool and Beyond, in Search of AGI. In: Goertzel, B., Iklé, M., Potapov, A., Ponomaryov, D. (eds) Artificial General Intelligence. AGI 2022. Lecture Notes in Computer Science(), vol 13539. Springer, Cham. https://doi.org/10.1007/978-3-031-19907-3_17
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