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THE BIGGER PICTURE

Artificial neural networks (ANNs) are powerful tools and a foundational building block in today’s AI. However, they are typically engineered to solve specific tasks like image recognition or natural language processing. In contrast, biological systems, particularly the human brain, exhibit a kind of intelligence that is far more task responsive, resilient, and capable of long-term learning. This perspective argues that some of these differences stem from aspects that are, by design, omitted in the modeling of many current AI systems: biological features such as intrinsic randomness and complex internal dynamics, which are often ignored because they seem irrelevant to task-specific optimization. However, overlooking these features may limit how robust and generalizable artificial systems can ultimately become. By drawing on insights from neuroscience, biology, engineering, and philosophy, this perspective makes a case for rethinking which aspects of biology matter for building intelligent machines. It explores how components like ion channels and neuromorphic hardware challenge the standard blueprint of AI. Rather than simply mirroring nature, this perspective calls for a principled approach to identifying which biological structures are functionally significant and how they might be meaningfully adapted for technological use. Understanding these often-overlooked dimensions could guide the development of AI systems that are not only highly capable, resilient, and flexible but also more aligned with how real organisms learn, adapt, and persist.