“a new system of AI model that deeply resemble biological models.”
While the promise of LNNs is undeniable, their journey to widespread adoption is not without challenges. One significant hurdle lies in the inherent complexity of these biologically inspired systems. Researchers must navigate the intricacies of differential equations, dynamic systems, and causal modeling, which may require interdisciplinary collaborations and a deep understanding of neurobiology.
At the core of LNNs lies a fundamental shift in thinking — a departure from the traditional, purely algorithmic approach to AI. Researchers from MIT looked at biological systems and its efficient decision making process, seeking inspiration from the very source of intelligence, a flexible neuron.
Just as the intricate neural networks in our brains continuously adapt and evolve, processing information in a dynamic and fluid manner, LNNs mimic this behavior by incorporating principles derived from neurobiology. LNNs mimic the interlinked electrical connections or impulses of the worm to predict network behavior over time, expressing the system state at any given moment.
SOURCE MATERIAL : June 4, 2024 by Shibil https://medium.com/@shibilahammad/liquid-neural-networks-lnns-can-we-emulate-the-human-brain-neurons-c58c23f0264b
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