What physics offers for artificial intelligence? Lessons from the brain's inner time and its dynamics.

Clinical Snapshot

Key Findings

• Effect Size: Not applicable - theoretical work

• Primary Outcome: Theoretical framework proposing brain's 'inner time' and spontaneous dynamics as key missing elements in current AI

• Nnt Or Sensitivity: Conceptual sensitivity to temporal dynamics as distinguishing feature between biological and artificial intelligence

Clinical Application

Theoretical framework requires significant technological development for practical implementation Relevant to Australian AI research initiatives and neurotechnology development programs, though no specific regulatory implications AI researchers, computational neuroscientists, clinicians interested in brain-computer interfaces

Abstract

Computing can take advantage of physics-that is, 'whatever physics offers' (Jaeger et al. 2023 Nat. Commun. 14, 4911 (doi:10.1038/s41467-023-40533-1)). One of the key features physics provides is time: it describes rules and makes predictions about how activity patterns change over time, as governed by dynamics. We argue that computing can learn from physics in how it conceives time-namely, in terms of dynamics, i.e. the changing patterns of activity unfolding over time. In particular, we focus on the brain's intrinsic neural dynamics of spontaneous activity and how it 'uses' them for dynamic input processing and encoding in order to 'participate' in the world's physical time. By 'participate', we mean becoming part of the input dynamics: for instance, when listening or dancing to music, neural activity (via entrainment) and, consequently, mental activity follows the rhythm and dynamics of the music. This shapes the listener's experience, such as consciousness-the brain, and thus the subject, actively participates in music rhythms through temporal alignment (Northoff et al. 2023 Interface Focus13, 20220076 (doi:10.1098/rsfs.2022.0076)). Drawing on recent empirical evidence, we show that dynamic features such as the brain's scale-free activity and variability-which reflect an intrinsic temporal structure, or the brain's 'inner time'-are central to tracking and encoding input dynamics. Importantly, this processing is actively modulated rather than passively received, through the brain's own 'hidden dynamic repertoire'. Extending earlier discussions by Dreyfus and others, we argue that current computing devices, whether classical or natural (i.e. non-von Neumann machines), lack spontaneous activity and an inner time that can exert an active, rather than purely passive, influence on processing. As a result, they can neither actively process and encode input dynamics through their own inner time, nor 'use' or 'participate' in the dynamics of the world's physical time. Instead of 'being in time' and 'being in the world', current computing devices-and, by extension, artificial intelligence-are effectively 'locked out of time and world', meaning they are not directly connected to physical time. Unlike humans, they therefore cannot be characterized as 'being in time' or 'being in the world', which in turn prevents them from acquiring tacit or implicit knowledge, including the ability to navigate and behave flexibly within a continuously changing world. This article is part of the theme issue 'World models in natural and artificial intelligence'.

References

1. 1.Jaeger, H. M., et al. (2023). Physical computing beyond von Neumann. Nature Communications, 14, 4911. https://doi.org/10.1038/s41467-023-40533-1
2. 2.Northoff, G., et al. (2023). Temporo-spatial theory of consciousness. Interface Focus, 13, 20220076. https://doi.org/10.1098/rsfs.2022.0076