Thinking Fast and Slow in AI

Abstract

This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making. The premise is that if we gain insights about the causes of some human capabilities that are still lacking in AI (for instance, adaptability, generalizability, common sense, and causal reasoning), we may obtain similar capabilities in an AI system by embedding these causal components. We hope that the high-level description of our vision included in this paper, as well as the several research questions that we propose to consider, can stimulate the AI research community to define, try and evaluate new methodologies, frameworks, and evaluation metrics, in the spirit of achieving a better understanding of both human and machine intelligence.

Dual-process theories of thought as potential architectures for developing neuro-symbolic AI models

“…Assuming a correspondence between System 1 and subsymbolic processing on one hand, and System 2 and symbolic processing on the other (as stated by Bengio, 2019; see also Booch et al, 2021), we auspicate that different architectures explored within the dual-process theory may help to develop and test the feasibility of specific instances of symbolic/subsymbolic systems integration. AI literature refers to this hybrid approach as neurosymbolic models (Gulwani et al, 2017).…”

Planning and Acting While the Clock Ticks

“…In future work, we intend to develop a way to identify whether a current problem instance has strong time pressure, thus automatically switching between concurrent planning and execution, situated planning, and offline planning. More generally, we will explore the connection to other types of thinking fast and slow in AI (Booch et al 2021).…”

Dual-process theories, cognitive architectures, and hybrid neural-symbolic models

“…Furthermore, according to this account, a highly plausible reason for having the two different levels is that these different levels (with different representations and different mechanisms) can potentially work together synergistically, complementing and supplementing each other (as argued, in detail, in Sun [38,39,41], and Sun et al [51]; see also Booch et al [2] and Kautz [21] for more recent views). This is probably, at least in part, the reason why there are these two levels -This likely contributes to why nature has chosen this design (in an evolutionary sense, of course).…”

“…To put it in a different way, the complexity of the matter should be adequately understood and should be properly taken into account, especially in any mechanistic models or theories that purport to value cognitive-psychological realism (Sun [46]). 2 Hybrid neural-symbolic models belong (or, at least, should belong) to this category of models.…”