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Philosophy
Philosophy of Religion

The State Space of Artificial Intelligence

Profile image of Holger LyreHolger Lyre

2020, Minds and Machines

https://doi.org/10.1007/S11023-020-09538-3
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Abstract

The goal of the paper is to develop and propose a general model of the state space of AI. Given the breathtaking progress in AI research and technologies in recent years, such conceptual work is of substantial theoretical interest. The present AI hype is mainly driven by the triumph of deep learning neural networks. As the distinguishing feature of such networks is the ability to self-learn, self-learning is identified as one important dimension of the AI state space. Another dimension is recognized as generalization, the possibility to go over from specific to more general types of problems. A third dimension is semantic grounding. Our overall analysis connects to a number of known foundational issues in the philosophy of mind and cognition: the blockhead objection, the Turing test, the symbol grounding problem, the Chinese room argument, and use theories of meaning. It shall finally be argued that the dimension of grounding decomposes into three sub-dimensions. And the dimension o...

Figures (3)
We already saw in Sect. 4 that the grounding dimension in fact decomposes nto three sub-dimensions: functional role grounding, causal grounding and social srounding. Therefore, the full AI space has more than three dimensions. It is nev- srtheless instructive to look at the simplified three-dimensional model for a first orientation and to locate the systems discussed in this paper in this space: A clas- sic GOFAI system like DeepBlue is close to the origin (see Fig. 1). Such sys- ems are rule-based rather than learn-based, and almost all of them are narrow AT systems (e.g. DeepBlue is confined to chess). At best, a typical GOFAI system 1as an internal FRS (as DeepBlue captures the functional roles of chess pieces). AlphaGo sits at a much higher position in the self-learning dimension. From here we reach AlphaGoZero and AlphaZero by successive shifts parallel to the zeneralization axis. But none of the mentioned systems has a semantic grounding seyond FRS. At best, AI assistance systems such as Google Duplex move into his dimension, albeit still weakly at present. It would be desirable to proceed from the state space topology (dimensionality
We already saw in Sect. 4 that the grounding dimension in fact decomposes nto three sub-dimensions: functional role grounding, causal grounding and social srounding. Therefore, the full AI space has more than three dimensions. It is nev- srtheless instructive to look at the simplified three-dimensional model for a first orientation and to locate the systems discussed in this paper in this space: A clas- sic GOFAI system like DeepBlue is close to the origin (see Fig. 1). Such sys- ems are rule-based rather than learn-based, and almost all of them are narrow AT systems (e.g. DeepBlue is confined to chess). At best, a typical GOFAI system 1as an internal FRS (as DeepBlue captures the functional roles of chess pieces). AlphaGo sits at a much higher position in the self-learning dimension. From here we reach AlphaGoZero and AlphaZero by successive shifts parallel to the zeneralization axis. But none of the mentioned systems has a semantic grounding seyond FRS. At best, AI assistance systems such as Google Duplex move into his dimension, albeit still weakly at present. It would be desirable to proceed from the state space topology (dimensionality
dimensionality. As we already saw, the three-dimensional model offers a first ori- entation only, but is strictly speaking an approximation. It amounts to a simplified dimensionality reduction. While the generalization dimension is already correctly identified, we saw that the grounding dimension in fact decomposes into three fur- ther sub-dimensions. It could be dubbed a “main dimension” and actually represents a subspace of the AI state space. Let us first, however, consider self-learning.
dimensionality. As we already saw, the three-dimensional model offers a first ori- entation only, but is strictly speaking an approximation. It amounts to a simplified dimensionality reduction. While the generalization dimension is already correctly identified, we saw that the grounding dimension in fact decomposes into three fur- ther sub-dimensions. It could be dubbed a “main dimension” and actually represents a subspace of the AI state space. Let us first, however, consider self-learning.
Fig. 3 The full AI state space with three main dimensions, two of which decomposing into sub-dimen- sions
Fig. 3 The full AI state space with three main dimensions, two of which decomposing into sub-dimen- sions

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