1.12 Key Claims of the Book 15

1.12 Key Claims of the Book 15 e inter-reflecting networks modeling self and others, reflecting a “mirrorhouse” design pattern 10. Given the strengths and weaknesses of current and near-future digital computers, a. A (loosely) neural-symbolic network is a good representation for directly storing many kinds of memory, and interfacing between those that it doesn’t store directly; b. Uncertain logic is a good way to handle declarative knowledge. To deal with the prob- lems facing a human-level AGI, an uncertain logic must integrate imprecise probability and fuzziness with a broad scope of logical constructs. PLN is one good realization. c. Programs are a good way to represent procedures (both cognitive and physical-action, but perhaps not including low-level motor-control procedures). d. Evolutionary program learning is a good way to handle difficult program learning prob- lems. Probabilistic learning on normalized programs is one effective approach to evolu- tionary program learning. MOSES is one good realization of this approach. e. Multistart hill-climbing, with a strong Occam prior, is a good way to handle relatively straightforward program learning problems. f. Activation spreading and Hebbian learning comprise a reasonable way to handle atten- tional knowledge (though other approaches, with greater overhead cost, may provide better accuracy and may be appropriate in some situations). e Artificial economics is an effective approach to activation spreading and Hebbian learning in the context of neural-symbolic networks; e ECAN is one good realization of artificial economics; e A good trade-off between comprehensiveness and efficiency is to focus on two kinds of attention: processor attention (represented in CogPrime by ShortTermImpor- tance) and memory attention (represented in CogPrime by LongTermImportance). g. Simulation is a good way to handle episodic knowledge (remembered and imagined). Running an internal world simulation engine is an effective way to handle simulation. h. Hybridization of one’s integrative neural-symbolic system with a spatiotemporally hier- archical deep learning system is an effective way to handle representation and learning of low-level sensorimotor knowledge. DeSTIN is one example of a deep learning system of this nature that can be effective in this context. i. One effective way to handle goals is to represent them declaratively, and allocate atten- tion among them economically. CogPrime’s PLN/ECAN based framework for handling intentional knowledge is one good realization. 11. It is important for an intelligent system to have some way of recognizing large-scale pat- terns in itself, and then embodying these patterns as new, localized knowledge items in its memory. Given the use of a neural-symbolic network for knowledge representation, a graph-mining based “map formation” heuristic is one good way to do this. 12. Occam’s Razor: Intelligence is closely tied to the creation of procedures that achieve goals in environments in the simplest possible way. Each of an AGI system’s cognitive algorithms should embody a simplicity bias in some explicit or implicit form. 13. An AGI system, if supplied with a commonsensically ethical goal system and an intentional component based on rigorous uncertain inference, should be able to reliably achieve a much higher level of commonsensically ethical behavior than any human being. 14. Once sufficiently advanced, an AGI system with a logic-based declarative knowledge ap- proach and a program-learning-based procedural knowledge approach should be able to HOUSE_OVERSIGHT_012931