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A new brain-inspired AI model called TopoLM learns language by organizing neurons into clusters, just like the human brain. Developed by researchers at EPFL, this topographic language model shows clear patterns for verbs, nouns, and syntax using a simple spatial rule that mimics real cortical maps. TopoLM not only matches real brain scans but also opens new possibilities in AI interpretability, neuromorphic hardware, and language processing.

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 the best AI news without the noise 👉 https://airevolutionx.beehiiv.com/ 🔍 What’s Inside: ‱⁠ ⁠A brain-inspired AI model called TopoLM that learns language by building its own cortical map ‱⁠ ⁠Neurons are arranged on a 2D grid where nearby units behave alike, mimicking how the human brain clusters meaning ‱⁠ ⁠A simple spatial smoothness rule lets TopoLM self-organize concepts like verbs and nouns into distinct brain-like regions đŸŽ„ What You’ll See: ‱⁠ ⁠How TopoLM mirrors patterns seen in fMRI brain scans during language tasks ‱⁠ ⁠A comparison with regular transformers, showing how TopoLM brings structure and interpretability to AI ‱⁠ ⁠Real test results proving that TopoLM reacts to syntax, meaning, and sentence structure just like a biological brain 📊 Why It Matters: This new system bridges neuroscience and machine learning, offering a powerful step toward *AI that thinks like us. It unlocks better interpretability, opens paths for **neuromorphic hardware*, and reveals how one simple principle might explain how the brain learns across all domains. DISCLAIMER: This video covers topographic neural modeling, biologically-aligned AI systems, and the future of brain-inspired computing—highlighting how spatial structure could reshape how machines learn language and meaning. #AI #neuroscience #brainAI

Get the best AI news without the noise 👉 https://airevolutionx.beehiiv.com/

🔍 What’s Inside:
‱ ⁠ ⁠A brain-inspired AI model called TopoLM that learns language by building its own cortical map.
‱ ⁠ ⁠Neurons are arranged on a 2D grid where nearby units behave alike, mimicking how the human brain clusters meaning.
‱ ⁠ ⁠A simple spatial smoothness rule lets TopoLM self-organize concepts like verbs and nouns into distinct brain-like regions.

đŸŽ„ What You’ll See:
‱ ⁠ ⁠How TopoLM mirrors patterns seen in fMRI brain scans during language tasks.
‱ ⁠ ⁠A comparison with regular transformers, showing how TopoLM brings structure and interpretability to AI
‱ ⁠ ⁠Real test results proving that TopoLM reacts to syntax, meaning, and sentence structure just like a biological brain.

📊 Why It Matters:

Continue reading “This New AI Could Be the First Real ARTIFICIAL BRAIN!” | >

We are surrounded by a multiplicity of materials, from metals and alloys to crystals, glasses, and ceramics; from polymers and plastics to organic and living-derived substances; and let’s not forget natural materials like stone and exotic materials like aerogel.

The amazing thing to me is that all these materials are formed from different combinations of the same small group of elements. For example, while living organisms and other objects can contain traces of many elements, a core group does the heavy lifting; only six elements—carbon ©, hydrogen (H), oxygen (O), nitrogen (N), phosphorus ℗, and sulfur (S)—make up over 95% of the mass of most living things.

Similarly, only eight elements—oxygen (O), silicon (Si), aluminum (Al), iron (Fe), calcium (Ca), Sodium (Na), potassium (K), and magnesium (Mg)—make up more than 98% of the Earth’s crust.

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Prebiotic molecules central to life’s earliest metabolic processes—chemical reactions in cells that change food into energy—may have been born in deep space long before Earth existed, according to new research from the University of Hawaiʻi at Mānoa Department of Chemistry.

Scientists in the W. M. Keck Research Laboratory in Astrochemistry have recreated the found in dense interstellar clouds and discovered a way for the complete set of complex carboxylic acids—critical ingredients in modern metabolism—to form without life on timescales equivalent to a few million years.

The study, published in the Proceedings of the National Academy of Sciences, focused on molecules such as those in the Krebs cycle, a fundamental metabolic pathway used by nearly all living organisms. These molecules, which help break down nutrients to release energy, may have , forming in the icy, low-temperature environments of interstellar space.

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AI and Human Enhancement:

A groundbreaking new AI system is exploring the limits of human potential, developing technologies that can enhance our physical and cognitive abilities. đŸ€– By analyzing biological data and applying advanced engineering principles, the AI can identify ways to improve human performance.

How AI Enhances Human Abilities:

AI-powered human enhancement technologies can:

Enhance Physical Abilities: Increase strength, speed, and endurance.
Improve Cognitive Abilities: Enhance memory, intelligence, and creativity.
Extend Lifespan: Slow down the aging process and increase lifespan.
The Ethical Implications:

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Asymmetric interactions between molecules may serve as a stabilizing factor for biological systems. A new model by researchers in the Department of Living Matter Physics at the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) reveals the regulatory role of non-reciprocity.

The scientists aim to understand the physical principles based on which particles and molecules are able to form living beings, and eventually, organisms. The work is published in the journal Physical Review Letters.

Most organizations, including companies, societies, or nations, function best when each member carries out their assigned role. Moreover, this efficiency often relies on spatial organization, which arose due to rules or emerged naturally via learning and . At the , cells operate in a similar way, with different components handling .

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This essay advances a speculative yet empirically-grounded hypothesis: that microtubular cytoskeletal structures constitute proto-cognitive architectures in unicellular organisms, thereby establishing an evolutionary substrate for cognition that predates neural systems. Drawing upon converging evidence from molecular biology, quantum biophysics, phenomenological philosophy, and biosemiotic theory, I propose a cytoskeletal epistemology wherein cognition emerges not exclusively from neural networks, but from the dynamic, embodied information-processing capacities inherent in cellular organization itself. This framework challenges neurocentric accounts of mind while suggesting new avenues for investigating the biological foundations of knowing.

Contemporary cognitive science predominantly situates the genesis of mind within neural tissue, tacitly assuming that cognition emerges exclusively from the electrochemical dynamics of neurons and their synaptic interconnections. Yet this neurocentric paradigm, while experimentally productive, encounters both conceptual and empirical limitations when confronted with fundamental questions regarding the biological preconditions for epistemic capacities. As Thompson (2007) observes, “Life and mind share a set of basic organizational properties, and the organizational properties distinctive of mind are an enriched version of those fundamental to life” (p. 128). This suggests a profound continuity between biological and cognitive processes — a continuity that invites investigation into pre-neural substrates of cognition.

The present inquiry examines the hypothesis that the microtubule — a foundational cytoskeletal element ubiquitous across eukaryotic cells — functions not merely as mechanical infrastructure but as an evolutionary precursor to cognitive architecture, instantiating proto-epistemic capacities in unicellular and pre-neural multicellular organisms. This hypothesis emerges at the intersection of multiple research programs, including quantum approaches to consciousness (Hameroff & Penrose, 2014), autopoietic theories of cognition (Maturana & Varela, 1980), and recent advances in cytoskeletal biology (Pirino et al., 2022).

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Characterizing the intelligence of biological organisms is challenging yet crucial. This paper demonstrates the capacity of canonical neural networks to autonomously generate diverse intelligent algorithms by leveraging an equivalence between concepts from three areas of cognitive computation: neural network-based dynamical systems, statistical inference, and Turing machines.

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After confirming the potential historic observation, the results were evaluated for several possible errors. The work was also analyzed independently. Each time, the team came back to the conclusion that they may have found the first potential signs of life outside our solar system.

“It was an incredible realisation seeing the results emerge and remain consistent throughout the extensive independent analyses and robustness tests,” said co-author MĂ„ns Holmberg, a researcher at the Space Telescope Science Institute in Baltimore.

Notably, the concentrations of either DMS or DMDS spotted by JWST were thousands of times higher than concentrations found on Earth. According to the Cambridge astronomers, detecting high levels of either of these chemicals on Hycean (ocean) worlds due to large amounts of biological activity was previously predicted.

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