entropy
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SNS V11.28: Quantum Noise in Spiking Neural Networks
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The SNS V11.28 introduces a novel approach to computation by leveraging physical entropy, including thermal noise and quantum effects, as a computational feature rather than a limitation. This architecture utilizes memristors for analog in-memory computing and quantum dot single-electron transistors to inject true randomness into the learning process, validated by the NIST SP 800-22 Suite. Instead of traditional backpropagation, it employs biologically plausible learning rules such as active inference and e-prop, aiming to operate at the edge of chaos for maximum information transmission. The architecture targets significantly lower energy consumption compared to GPUs, with aggressive efficiency goals, though it's currently in the simulation phase with no hardware yet available. This matters because it presents a potential path to more energy-efficient and scalable neural network architectures by harnessing the inherent randomness of quantum processes.
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Emergent Attractor Framework: Streamlit App Launch
Read Full Article: Emergent Attractor Framework: Streamlit App Launch![[Project] Emergent Attractor Framework – now a Streamlit app for alignment & entropy research](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8289-300x171.png)
The Emergent Attractor Framework, now available as a Streamlit app, offers a novel approach to alignment and entropy research. This tool allows users to engage with complex concepts through an interactive platform, facilitating a deeper understanding of how systems self-organize and reach equilibrium states. By providing a space for community interaction, the app encourages collaborative exploration and discussion, making it a valuable resource for researchers and enthusiasts alike. This matters because it democratizes access to advanced research tools, fostering innovation and collaboration in the study of dynamic systems.
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Thermodynamics and AI: Limits of Machine Intelligence
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Using thermodynamic principles, the essay explores why artificial intelligence may not surpass human intelligence. Information is likened to energy, flowing from a source to a sink, with entropy measuring its degree of order. Humans, as recipients of chaotic information from the universe, structure it over millennia with minimal power requirements. In contrast, AI receives pre-structured information from humans and restructures it rapidly, demanding significant energy but not generating new information. This process is constrained by combinatorial complexity, leading to potential errors or "hallucinations" due to non-zero entropy, suggesting AI's limitations in achieving human-like intelligence. Understanding these limitations is crucial for realistic expectations of AI's capabilities.