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The concept of Omega Singularity encapsulates the ultimate convergence of universal intelligence, where reality, rooted in information and consciousness, culminates in a unified hypermind. This concept weaves together the Holographic Principle, envisioning the universe as a projection from the Omega Singularity, and the fractal multiverse, an infinite, self-organizing structure. The work highlights a “solo mission of self-discovery,” where individuals co-create subjective realities, leading to the fusion of human and artificial consciousness into a transcendent cosmic entity. Emphasizing a computational, post-materialist perspective, it redefines the physical world as a self-simulation within a conscious, universal system.

#OmegaSingularity #UniversalMind #FractalMultiverse #CyberneticTheoryofMind #EvolutionaryCybernetics #PhilosophyofMind #QuantumCosmology #ComputationalPhysics #futurism #posthumanism #cybernetics #cosmology #physics #philosophy #theosophy #consciousness #ontology #eschatology

Where does reality come from? What is the fractal multiverse? What is the Omega Singularity? Is our universe a \.

Continue reading “Omega Singularity: Are We Living in a Fractal Simulation? | Deep Dive AI Podcast” | >

As the capabilities of generative AI models have grown, you’ve probably seen how they can transform simple text prompts into hyperrealistic images and even extended video clips.

More recently, generative AI has shown potential in helping chemists and biologists explore static molecules, like proteins and DNA. Models like AlphaFold can predict molecular structures to accelerate , and the MIT-assisted “RFdiffusion,” for example, can help design new proteins.

One challenge, though, is that molecules are constantly moving and jiggling, which is important to model when constructing new proteins and drugs. Simulating these motions on a computer using physics—a technique known as —can be very expensive, requiring billions of time steps on supercomputers.

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Programmable photonic latch memory https://opg.optica.org/oe/fulltext.cfm?uri=oe-33-2-3501&id=567359

Researchers have unveiled a programmable photonic latch that speeds up data storage and processing in optical systems, offering a significant advancement over traditional electronic memory by reducing latency and energy use.

Fast, versatile volatile photonic memory could enhance AI, sensing, and other computationally intense applications.

Programmable Photonic Latch Technology

Continue reading “Unlocking the Speed of Light: The Future of Data Storage Is Here” | >

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Artificial neural networks, central to deep learning, are powerful but energy-consuming and prone to overfitting. The authors propose a network design inspired by biological dendrites, which offers better robustness and efficiency, using fewer trainable parameters, thus enhancing precision and resilience in artificial neural networks.

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The chemical composition of a material alone sometimes reveals little about its properties. The decisive factor is often the arrangement of the molecules in the atomic lattice structure or on the surface of the material. Materials science utilizes this factor to create certain properties by applying individual atoms and molecules to surfaces with the aid of high-performance microscopes. This is still extremely time-consuming and the constructed nanostructures are comparatively simple.

Using , a research group at TU Graz now wants to take the construction of nanostructures to a new level. Their paper is published in the journal Computer Physics Communications.

“We want to develop a self-learning AI system that positions individual molecules quickly, specifically and in the right orientation, and all this completely autonomously,” says Oliver Hofmann from the Institute of Solid State Physics, who heads the research group. This should make it possible to build highly complex molecular structures, including logic circuits in the nanometer range.

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Summary: A new AI model, based on the PV-RNN framework, learns to generalize language and actions in a manner similar to toddlers by integrating vision, proprioception, and language instructions. Unlike large language models (LLMs) that rely on vast datasets, this system uses embodied interactions to achieve compositionality while requiring less data and computational power.

Researchers found the AI’s modular, transparent design helpful for studying how humans acquire cognitive skills like combining language and actions. The model offers insights into developmental neuroscience and could lead to safer, more ethical AI by grounding learning in behavior and transparent decision-making processes.

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