Abstract: This article argues that the quest for brain-mimicking artificial intelligence is no longer limited by algorithms or transistor counts, but by the philosophical assumptions embedded in modern electronics. Today’s AI systems, despite their impressive performance, remain rooted in a worldview shaped by the historically inherited and largely unquestioned digital reductionism and Cartesian dualism: one that fragments signals, separates mind from matter, and treats intelligence as an abstract computation detached from physical embodiment. As a result, we have learned to scale processing power, but not to cultivate cognition. The article traces how these assumptions became encoded into signal processing and hardware design, examines their practical consequences in contemporary AI architectures, and shows why incremental refinements cannot bridge the gap to brain-like intelligence. It then advances neutral monism as a new philosophical anchor, reframing signals not as passive carriers of information but as active participants in perception, action, and meaning. From this foundation emerges Moore’s Law 2.0: a shift from computing power to signal intelligence, from static architectures to adaptive, feedback-rich, and temporally coherent systems. In this vision, electronics no longer merely compute: they sense, negotiate time, and co-evolve with their environments, opening a pathway toward machines that do not just process the world, but begin to understand it.
(Full citation if needed: L. Xiu and U. Braga-Neto, "Transcending Reductionism and Dualism: Philosophical Critique of Electronics and a Vision for Brain-Mimicking Artificial Intelligence Under Moore’s Law 2.0 [Perspectives]," in IEEE Signal Processing Magazine, vol. 43, no. 1, pp. 98-107, Jan. 2026, doi: 10.1109/MSP.2025.3635586.)
