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$SENT 🚀 The Future of AGI is Open-Source
The wait for a community-owned AI era is over. @sentient_fdn is rewriting the rules of Artificial General Intelligence to ensure it stays in the hands of the many, not the few. 🌐
Why it matters:
The OML Framework: Open, Monetizable, and Loyal. 🧠
The GRID: A decentralized network where models, data, and compute collaborate.
True Ownership: Fighting corporate silos with community-driven governance and model fingerprinting. 🛡️
Sentient isn't just building AI; it's building a Decentralized Intelligence Economy.
Read the full vision here:
🔗 sentient.foundation/whitepaper
#Sentient #AGI #OpenSourceAI #Crypto #SENT
The wait for a community-owned AI era is over. @sentient_fdn is rewriting the rules of Artificial General Intelligence to ensure it stays in the hands of the many, not the few. 🌐
Why it matters:
The OML Framework: Open, Monetizable, and Loyal. 🧠
The GRID: A decentralized network where models, data, and compute collaborate.
True Ownership: Fighting corporate silos with community-driven governance and model fingerprinting. 🛡️
Sentient isn't just building AI; it's building a Decentralized Intelligence Economy.
Read the full vision here:
🔗 sentient.foundation/whitepaper
#Sentient #AGI #OpenSourceAI #Crypto #SENT
Here’s a more powerful, scroll-stopping version of your post with stronger hooks, tighter flow, and high-reach hashtags 👇
🚨 THE AI ARMS RACE JUST ENTERED LOCKDOWN MODE 🚨
Dragonfly’s Haseeb Qureshi just highlighted something the market is massively underestimating.
For years, insiders suspected frontier-model distillation was happening.
What shocked everyone wasn’t that it exists — but the industrial scale.
That changes everything about how AI labs think about: 🔐 Access
🔐 APIs
🔐 Security
Qureshi’s base case:
We’re entering a new era of
⚠️ tighter APIs
⚠️ harder model boundaries
⚠️ aggressive control layers
If this plays out, the gap between closed frontier AI and open-source models widens again — a direct headwind for the decentralized AI thesis.
From a macro perspective, this pattern is familiar:
The moment a technology becomes strategically critical,
it stops behaving like software
and starts behaving like infrastructure.
AI is now in that category.
Welcome to the next cycle:
🔥 Labs locking systems down
🔥 Distillers hunting for loopholes
🔥 Governments labeling AI as national security
The next phase of AI will be less open, more geopolitical, and far higher stakes.
This isn’t just an innovation race anymore.
It’s a power race.
Hashtags:
#AI #ArtificialIntelligence #AGI #OpenAI #Anthropic #ChinaTech #AIGovernance #AISecurity #AIArmsRace #DecentralizedAI #OpenSourceAI #FrontierModels #TechGeopolitics #DigitalInfrastructure #AIRegulation #FutureOfAI #MachineLearning #DeepLearning #CryptoAI #Web3AI #Innovation #GlobalTech #AIControl #TechWar
If you want, I can also give you: ✅ A short viral version (for higher engagement)
✅ A thread version (this topic is PERFECT for threads)
✅ A more aggressive / alpha / investor tone (works very well in AI + crypto circles)
Which style are you targeting? 🚀
🚨 THE AI ARMS RACE JUST ENTERED LOCKDOWN MODE 🚨
Dragonfly’s Haseeb Qureshi just highlighted something the market is massively underestimating.
For years, insiders suspected frontier-model distillation was happening.
What shocked everyone wasn’t that it exists — but the industrial scale.
That changes everything about how AI labs think about: 🔐 Access
🔐 APIs
🔐 Security
Qureshi’s base case:
We’re entering a new era of
⚠️ tighter APIs
⚠️ harder model boundaries
⚠️ aggressive control layers
If this plays out, the gap between closed frontier AI and open-source models widens again — a direct headwind for the decentralized AI thesis.
From a macro perspective, this pattern is familiar:
The moment a technology becomes strategically critical,
it stops behaving like software
and starts behaving like infrastructure.
AI is now in that category.
Welcome to the next cycle:
🔥 Labs locking systems down
🔥 Distillers hunting for loopholes
🔥 Governments labeling AI as national security
The next phase of AI will be less open, more geopolitical, and far higher stakes.
This isn’t just an innovation race anymore.
It’s a power race.
Hashtags:
#AI #ArtificialIntelligence #AGI #OpenAI #Anthropic #ChinaTech #AIGovernance #AISecurity #AIArmsRace #DecentralizedAI #OpenSourceAI #FrontierModels #TechGeopolitics #DigitalInfrastructure #AIRegulation #FutureOfAI #MachineLearning #DeepLearning #CryptoAI #Web3AI #Innovation #GlobalTech #AIControl #TechWar
If you want, I can also give you: ✅ A short viral version (for higher engagement)
✅ A thread version (this topic is PERFECT for threads)
✅ A more aggressive / alpha / investor tone (works very well in AI + crypto circles)
Which style are you targeting? 🚀
Neural Networks in AI and Neuroscience: How the Brain Inspires Artificial Intelligence
Written by $Qubic Scientific Team
Neuraxon Intelligence Academy — Volume 4
The word network shows up constantly in both neuroscience and artificial intelligence. But despite sharing the same label, biological neural networks and artificial neural networks are fundamentally different systems. To understand what each one actually does, and where a third approach fits in, we need to look at the architecture and behavior of networks at every level.
Biological Neural Networks: How the Brain Processes Information
A biological neural network is a system of interconnected neurons whose function is to process information and generate behavior. These networks are dynamic. They stay active over time, even when we are not consciously engaged in any task. They carry an energetic cost, which in the case of the human brain is remarkably low for the complexity it produces.
Biological networks integrate both internal and external signals using their own language: time-frequency. Think of a musical band with multiple instruments playing at different rhythms. The bass drum carries the tempo, the bass plays two notes per beat, and the cymbals fill in the sixteenth notes. The melody moves freely without losing the beat. The musicians couple their scores at different rhythms that fit together perfectly. These are nested frequencies, and this is exactly how brain networks function. The time-frequency language of different networks nests within itself, a concept known as cross-frequency coupling.
From Single Neurons to Massive Networks
Everything begins with the neuron. That single nerve cell generates an action potential, a brief electrical impulse that propagates along the axon. The neuron receives signals through the dendrites, integrates them in the soma, and transmits the signal if it surpasses a threshold. We covered this process in detail in NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time and NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence.
Neurons connect to other neurons through chemical synapses, where neurotransmitters are released (see NIA Volume 3: Neuromodulation and Brain-Inspired AI), or through electrical synapses, where current passes directly between cells. To form networks, many neurons interconnect and create recurrent circuits. But this integration is non-linear, meaning the response of the whole does not equal the simple sum of its parts. The magnitude is staggering: the human brain contains approximately 86 billion neurons and somewhere between 10¹⁴ and 10¹⁵ synapses (Azevedo et al., 2009).
Small-World Properties and Excitation-Inhibition Balance
At the topological level, these networks display small-world properties: high local clustering combined with short global connections. This architecture enables efficient communication across the brain while maintaining specialized local processing.
The functioning of biological neural networks depends on the balance between excitation and inhibition. If excitation dominates, activity destabilizes. If inhibition dominates, the network goes silent. Dynamic stability arises from the balance between both forces. This balance is maintained through synaptic plasticity, the mechanism that allows the strength of connections to change based on experience. On top of that, neuromodulation adjusts circuit gain, controlling how strongly an input produces an output (Marder, 2012). In a threatening situation, for example, noradrenaline increases sensory sensitivity and the capacity for rapid learning.
Multiple Temporal Scales and Cerebral Cortex Brain Function
Networks operate at multiple temporal scales simultaneously. At the neuronal level, action potentials fire in milliseconds. Neuronal oscillations unfold in seconds. Synaptic changes develop over hours or days, and structural reorganization happens across years. Everything works in a harmonic, dynamic, and intertwined pattern.
But not everything communicates with everything without structure. The cerebral cortex brain function is organized into specialized networks. The most important include the default mode network, linked to self-reference and thinking about the self and others; the central executive network, linked to direct task execution; the salience network, which detects what is relevant at each moment and allows switching between different modes; the sensorimotor network that sustains voluntary movements; and various attention networks. Humans also possess a distinctive language network, enabling both comprehension and production of language.
In biological networks, no isolated note is a symphony. The symphony emerges from the dynamic pattern of relationships between notes. The brain does not contain things. It does not store memories the way a hard drive stores files. The brain constructs dynamic configurations.
Courtesy from DOI: 10.3389/fnagi.2023.1204134
Artificial Neural Networks: How Deep Learning Models Work
An artificial neural network (ANN) is a mathematical model designed to approximate complex functions from data. It draws abstract inspiration from the brain: it uses interconnected units called "artificial neurons," but these are not cells. They are algebraic operations. Calling an algebraic operation a neuron is arguably an exaggerated extrapolation, and calling language prediction "intelligence" may be equally misleading. But since these are the established terms, it is important to understand them and separate substance from hype.
How an Artificial Neuron Works
Each artificial neuron performs three steps. First, it receives a set of numerical inputs. Then it multiplies each input by a synaptic weight, which is an adjustable parameter. Finally, it sums the results and applies an activation function that introduces non-linearity. Common activation functions include the Sigmoid, which compresses values between 0 and 1, and ReLU (Rectified Linear Unit), which cancels negative values and lets positive ones pass through.
Without non-linearity, the network would simply perform a linear transformation, incapable of modeling complex patterns. ANNs are organized into input layers, where data enter; hidden layers, where data are progressively transformed; and an output layer, which generates the prediction.
From the Perceptron to Deep Learning
All modern architectures trace their origins to the perceptron (Rosenblatt, 1958), a simple linear neuron with a threshold. Modern deep learning networks can contain hundreds of layers and billions of parameters. But at their core, an ANN functions like an enormous automated spreadsheet that adjusts millions of numerical cells until the output matches the expected result.
Backpropagation and Gradient Descent: How Artificial Networks Learn
Learning in artificial networks does not work the way biological learning does. There is no adjustment of neuromodulators or synaptic intensity based on lived experience. Instead, learning is based on minimizing an error function that quantifies the difference between the network's prediction and the correct answer.
Consider a simple example: the model is asked to complete "Paris is the capital of..." If the prediction is Italy, the error function measures the gap between Italy and France, then adjusts the weights accordingly. The central mechanism behind this adjustment is backpropagation (Rumelhart et al., 1986). This algorithm calculates the error at the output, propagates that error backward layer by layer, and adjusts the weights using gradient descent, a mathematical method that modifies parameters in the direction that reduces the error.
Formally, learning consists of optimizing a differentiable function in a space of many dimensions. If you think of physical space, the dimensions are x, y, and z. But in language, imagine dimensions like singular, plural, feminine, masculine, verb, subject, attribute, noun, adjective, intonation, and synonym. Introduce millions of dimensions and enough computational power, and a model can learn that Paris is the capital of France simply by reducing prediction errors during training.
Architectures of Artificial Neural Networks
Although the terminology overlaps with neuroscience, the process does not resemble how a living system learns. In an ANN, adjustment depends on global calculation and explicit knowledge of the final error. The network needs to know exactly how wrong it was.
If a network learns to recognize cats, it receives thousands or millions of labeled images. Each time it fails, it slightly adjusts the weights. After millions of iterations, the internal pattern stabilizes into a configuration that discriminates cats from other objects. The process is purely statistical. The network does not "understand" what a cat is. It detects numerical correlations in pixels. It does not hold a "world model" of a cat, only matrices of numbers on massive scales. For a deeper look at why this matters, read our analysis of benchmarking world model learning.
There are several key architectures of artificial neural networks. Convolutional networks (CNNs) use spatial filters that detect edges, textures, and hierarchical patterns, making them essential for computer vision. Recurrent networks (RNNs, LSTMs) incorporate temporal memory for processing sequences. And the now-dominant Transformers use attention mechanisms that dynamically weight which parts of the input are most relevant (Vaswani et al., 2017). Transformers currently power most large language models in natural language processing.
The growth of these networks does not happen organically as in living systems. It happens through explicit design and parameter scaling via massive training in high-performance computing centers. Adaptation is limited to the training period. Once trained, the network does not spontaneously reorganize its architecture. Any modification requires a new optimization process. As we explored in That Static AI Is a Dead End, this frozen nature is a fundamental limitation of current AI systems.
Despite sharing the name "network," the similarity between artificial and biological neural networks is limited. The analogy is structural and abstract: both use interconnected units and learning through adjustment of connections. But the brain is an evolutionary, embodied, and self-regulated system. An ANN is a function optimizer in a numerical space.
Between Biological and Artificial Networks: How Neuraxon Aigarth Bridges the Gap
The networks simulated in Neuraxon Aigarth are conceptually positioned between biological networks and conventional artificial neural networks. They are not living tissue, but they are not merely mathematical functions optimized by gradient either. Their objective is to approximate dynamics typical of biological systems, including multiscale plasticity, context-dependent modulation, and self-organization, all within a computational framework built for Qubic's decentralized AI infrastructure.
If in Volume 1 we described self-organized metabolic systems and in Volume 2 we explored differentiable optimizing functions, Neuraxon attempts to incorporate dynamic properties of the former without abandoning the mathematical formalization of the latter.
Trivalent States: Capturing Excitation-Inhibition Balance
Instead of typical continuous activations (real values after a ReLU, for example), Neuraxon uses trivalent states: -1, 0, and +1. Here, +1 represents excitatory activation, -1 represents inhibitory activation, and 0 represents rest or inactivity.
This scheme does not attempt to copy the biological action potential. Rather, it captures the functional principle of excitation-inhibition balance described in the biological networks section above. In the brain, stability emerges from the balance between these forces. In Neuraxon, the discrete state space imposes a dynamic closer to state-transition systems than to simple continuous transformations.
In contrast to classical artificial networks, where activation is a floating-point number without physiological meaning, the trivalent system imposes structural constraints that shape how activity propagates through the network.
Dual-Weight Plasticity: Fast and Slow Learning
Biological neural networks exhibit plasticity at different temporal scales: rapid changes in synaptic efficacy and slower consolidation over time. Neuraxon introduces this idea through two weight components:
w_fast: rapid changes that are sensitive to the immediate environment.
w_slow: slow changes that stabilize repeated patterns over time.
This prevents the system from depending exclusively on a homogeneous weight update like standard backpropagation. Part of learning can be transient, while another part is gradually consolidated. This mechanism introduces a dimension absent in most artificial neural networks: the learning rate is not fixed, but dependent on the global state of the system.
Contextual Neuromodulation Through the Meta Variable
In biological networks, neuromodulators such as noradrenaline and dopamine do not transmit specific informational content. Instead, they alter the gain and plasticity of broad neuronal populations. We explored this in depth in NIA Volume 3: Neuromodulation and Brain-Inspired AI.
In Neuraxon, the variable meta plays a functionally analogous role. It does not encode specific information, but modifies the magnitude of synaptic updating. This approximates the biological principle that learning depends on motivational or salience context. In a conventional artificial network, the gradient is applied uniformly based on error. In Neuraxon, learning can be intensified or attenuated according to internal state or global external signals.
The conceptual difference is significant. In classical deep learning networks, error drives learning. In Neuraxon, error can coexist with a contextual modulatory signal that alters how much is learned at any given moment.
Self-Organized Criticality and Adaptive Behavior
Biological networks operate near a regime called self-organized criticality, where the system maintains equilibrium between order and chaos. This regime allows flexibility without loss of stability.
Neuraxon models this property by allowing the network to evolve toward intermediate dynamic states in which small perturbations can produce broad reorganizations without collapsing the system.
In models such as the Game of Life extended with proprioception that the team is currently developing, the system can receive external signals (environment) and internal signals (its own state, energy, previous collisions). If an agent repeatedly collides with an obstacle, an increase in the meta signal may be generated, analogous to an increase in arousal. That signal temporarily increases plasticity, facilitating structural reorganization.
Here, the network does not learn only because it makes mistakes. It learns because the environment acquires adaptive relevance. The similarity with the brain remains limited: Neuraxon does not possess biology, metabolism, or subjective experience. However, it introduces dynamic dimensions absent in most conventional artificial neural networks, positioning it as a genuinely novel approach to brain-inspired AI on decentralized infrastructure.
The computational power required to run Neuraxon simulations is provided by Qubic's global network of miners through Useful Proof of Work, turning AI training into the consensus mechanism itself.
Scientific References
#Azevedo, F. A. C., et al. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology, 513(5), 532-541. DOI: 10.1002/cne.21974
#Marder, E. (2012). Neuromodulation of neuronal circuits: Back to the future. Neuron, 76(1), 1-11. DOI: 10.1016/j.neuron.2012.09.010
#Rosenblatt, F. (1958). The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408. DOI: 10.1037/h0042519
#Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. DOI: 10.1038/323533a0
#Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. arXiv: 1706.03762
Brain network images courtesy from: DOI: 10.3389/fnagi.2023.1204134
#Aİ #AGI
Neuraxon Intelligence Academy — Volume 4
The word network shows up constantly in both neuroscience and artificial intelligence. But despite sharing the same label, biological neural networks and artificial neural networks are fundamentally different systems. To understand what each one actually does, and where a third approach fits in, we need to look at the architecture and behavior of networks at every level.
Biological Neural Networks: How the Brain Processes Information
A biological neural network is a system of interconnected neurons whose function is to process information and generate behavior. These networks are dynamic. They stay active over time, even when we are not consciously engaged in any task. They carry an energetic cost, which in the case of the human brain is remarkably low for the complexity it produces.
Biological networks integrate both internal and external signals using their own language: time-frequency. Think of a musical band with multiple instruments playing at different rhythms. The bass drum carries the tempo, the bass plays two notes per beat, and the cymbals fill in the sixteenth notes. The melody moves freely without losing the beat. The musicians couple their scores at different rhythms that fit together perfectly. These are nested frequencies, and this is exactly how brain networks function. The time-frequency language of different networks nests within itself, a concept known as cross-frequency coupling.
From Single Neurons to Massive Networks
Everything begins with the neuron. That single nerve cell generates an action potential, a brief electrical impulse that propagates along the axon. The neuron receives signals through the dendrites, integrates them in the soma, and transmits the signal if it surpasses a threshold. We covered this process in detail in NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time and NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence.
Neurons connect to other neurons through chemical synapses, where neurotransmitters are released (see NIA Volume 3: Neuromodulation and Brain-Inspired AI), or through electrical synapses, where current passes directly between cells. To form networks, many neurons interconnect and create recurrent circuits. But this integration is non-linear, meaning the response of the whole does not equal the simple sum of its parts. The magnitude is staggering: the human brain contains approximately 86 billion neurons and somewhere between 10¹⁴ and 10¹⁵ synapses (Azevedo et al., 2009).
Small-World Properties and Excitation-Inhibition Balance
At the topological level, these networks display small-world properties: high local clustering combined with short global connections. This architecture enables efficient communication across the brain while maintaining specialized local processing.
The functioning of biological neural networks depends on the balance between excitation and inhibition. If excitation dominates, activity destabilizes. If inhibition dominates, the network goes silent. Dynamic stability arises from the balance between both forces. This balance is maintained through synaptic plasticity, the mechanism that allows the strength of connections to change based on experience. On top of that, neuromodulation adjusts circuit gain, controlling how strongly an input produces an output (Marder, 2012). In a threatening situation, for example, noradrenaline increases sensory sensitivity and the capacity for rapid learning.
Multiple Temporal Scales and Cerebral Cortex Brain Function
Networks operate at multiple temporal scales simultaneously. At the neuronal level, action potentials fire in milliseconds. Neuronal oscillations unfold in seconds. Synaptic changes develop over hours or days, and structural reorganization happens across years. Everything works in a harmonic, dynamic, and intertwined pattern.
But not everything communicates with everything without structure. The cerebral cortex brain function is organized into specialized networks. The most important include the default mode network, linked to self-reference and thinking about the self and others; the central executive network, linked to direct task execution; the salience network, which detects what is relevant at each moment and allows switching between different modes; the sensorimotor network that sustains voluntary movements; and various attention networks. Humans also possess a distinctive language network, enabling both comprehension and production of language.
In biological networks, no isolated note is a symphony. The symphony emerges from the dynamic pattern of relationships between notes. The brain does not contain things. It does not store memories the way a hard drive stores files. The brain constructs dynamic configurations.
Courtesy from DOI: 10.3389/fnagi.2023.1204134
Artificial Neural Networks: How Deep Learning Models Work
An artificial neural network (ANN) is a mathematical model designed to approximate complex functions from data. It draws abstract inspiration from the brain: it uses interconnected units called "artificial neurons," but these are not cells. They are algebraic operations. Calling an algebraic operation a neuron is arguably an exaggerated extrapolation, and calling language prediction "intelligence" may be equally misleading. But since these are the established terms, it is important to understand them and separate substance from hype.
How an Artificial Neuron Works
Each artificial neuron performs three steps. First, it receives a set of numerical inputs. Then it multiplies each input by a synaptic weight, which is an adjustable parameter. Finally, it sums the results and applies an activation function that introduces non-linearity. Common activation functions include the Sigmoid, which compresses values between 0 and 1, and ReLU (Rectified Linear Unit), which cancels negative values and lets positive ones pass through.
Without non-linearity, the network would simply perform a linear transformation, incapable of modeling complex patterns. ANNs are organized into input layers, where data enter; hidden layers, where data are progressively transformed; and an output layer, which generates the prediction.
From the Perceptron to Deep Learning
All modern architectures trace their origins to the perceptron (Rosenblatt, 1958), a simple linear neuron with a threshold. Modern deep learning networks can contain hundreds of layers and billions of parameters. But at their core, an ANN functions like an enormous automated spreadsheet that adjusts millions of numerical cells until the output matches the expected result.
Backpropagation and Gradient Descent: How Artificial Networks Learn
Learning in artificial networks does not work the way biological learning does. There is no adjustment of neuromodulators or synaptic intensity based on lived experience. Instead, learning is based on minimizing an error function that quantifies the difference between the network's prediction and the correct answer.
Consider a simple example: the model is asked to complete "Paris is the capital of..." If the prediction is Italy, the error function measures the gap between Italy and France, then adjusts the weights accordingly. The central mechanism behind this adjustment is backpropagation (Rumelhart et al., 1986). This algorithm calculates the error at the output, propagates that error backward layer by layer, and adjusts the weights using gradient descent, a mathematical method that modifies parameters in the direction that reduces the error.
Formally, learning consists of optimizing a differentiable function in a space of many dimensions. If you think of physical space, the dimensions are x, y, and z. But in language, imagine dimensions like singular, plural, feminine, masculine, verb, subject, attribute, noun, adjective, intonation, and synonym. Introduce millions of dimensions and enough computational power, and a model can learn that Paris is the capital of France simply by reducing prediction errors during training.
Architectures of Artificial Neural Networks
Although the terminology overlaps with neuroscience, the process does not resemble how a living system learns. In an ANN, adjustment depends on global calculation and explicit knowledge of the final error. The network needs to know exactly how wrong it was.
If a network learns to recognize cats, it receives thousands or millions of labeled images. Each time it fails, it slightly adjusts the weights. After millions of iterations, the internal pattern stabilizes into a configuration that discriminates cats from other objects. The process is purely statistical. The network does not "understand" what a cat is. It detects numerical correlations in pixels. It does not hold a "world model" of a cat, only matrices of numbers on massive scales. For a deeper look at why this matters, read our analysis of benchmarking world model learning.
There are several key architectures of artificial neural networks. Convolutional networks (CNNs) use spatial filters that detect edges, textures, and hierarchical patterns, making them essential for computer vision. Recurrent networks (RNNs, LSTMs) incorporate temporal memory for processing sequences. And the now-dominant Transformers use attention mechanisms that dynamically weight which parts of the input are most relevant (Vaswani et al., 2017). Transformers currently power most large language models in natural language processing.
The growth of these networks does not happen organically as in living systems. It happens through explicit design and parameter scaling via massive training in high-performance computing centers. Adaptation is limited to the training period. Once trained, the network does not spontaneously reorganize its architecture. Any modification requires a new optimization process. As we explored in That Static AI Is a Dead End, this frozen nature is a fundamental limitation of current AI systems.
Despite sharing the name "network," the similarity between artificial and biological neural networks is limited. The analogy is structural and abstract: both use interconnected units and learning through adjustment of connections. But the brain is an evolutionary, embodied, and self-regulated system. An ANN is a function optimizer in a numerical space.
Between Biological and Artificial Networks: How Neuraxon Aigarth Bridges the Gap
The networks simulated in Neuraxon Aigarth are conceptually positioned between biological networks and conventional artificial neural networks. They are not living tissue, but they are not merely mathematical functions optimized by gradient either. Their objective is to approximate dynamics typical of biological systems, including multiscale plasticity, context-dependent modulation, and self-organization, all within a computational framework built for Qubic's decentralized AI infrastructure.
If in Volume 1 we described self-organized metabolic systems and in Volume 2 we explored differentiable optimizing functions, Neuraxon attempts to incorporate dynamic properties of the former without abandoning the mathematical formalization of the latter.
Trivalent States: Capturing Excitation-Inhibition Balance
Instead of typical continuous activations (real values after a ReLU, for example), Neuraxon uses trivalent states: -1, 0, and +1. Here, +1 represents excitatory activation, -1 represents inhibitory activation, and 0 represents rest or inactivity.
This scheme does not attempt to copy the biological action potential. Rather, it captures the functional principle of excitation-inhibition balance described in the biological networks section above. In the brain, stability emerges from the balance between these forces. In Neuraxon, the discrete state space imposes a dynamic closer to state-transition systems than to simple continuous transformations.
In contrast to classical artificial networks, where activation is a floating-point number without physiological meaning, the trivalent system imposes structural constraints that shape how activity propagates through the network.
Dual-Weight Plasticity: Fast and Slow Learning
Biological neural networks exhibit plasticity at different temporal scales: rapid changes in synaptic efficacy and slower consolidation over time. Neuraxon introduces this idea through two weight components:
w_fast: rapid changes that are sensitive to the immediate environment.
w_slow: slow changes that stabilize repeated patterns over time.
This prevents the system from depending exclusively on a homogeneous weight update like standard backpropagation. Part of learning can be transient, while another part is gradually consolidated. This mechanism introduces a dimension absent in most artificial neural networks: the learning rate is not fixed, but dependent on the global state of the system.
Contextual Neuromodulation Through the Meta Variable
In biological networks, neuromodulators such as noradrenaline and dopamine do not transmit specific informational content. Instead, they alter the gain and plasticity of broad neuronal populations. We explored this in depth in NIA Volume 3: Neuromodulation and Brain-Inspired AI.
In Neuraxon, the variable meta plays a functionally analogous role. It does not encode specific information, but modifies the magnitude of synaptic updating. This approximates the biological principle that learning depends on motivational or salience context. In a conventional artificial network, the gradient is applied uniformly based on error. In Neuraxon, learning can be intensified or attenuated according to internal state or global external signals.
The conceptual difference is significant. In classical deep learning networks, error drives learning. In Neuraxon, error can coexist with a contextual modulatory signal that alters how much is learned at any given moment.
Self-Organized Criticality and Adaptive Behavior
Biological networks operate near a regime called self-organized criticality, where the system maintains equilibrium between order and chaos. This regime allows flexibility without loss of stability.
Neuraxon models this property by allowing the network to evolve toward intermediate dynamic states in which small perturbations can produce broad reorganizations without collapsing the system.
In models such as the Game of Life extended with proprioception that the team is currently developing, the system can receive external signals (environment) and internal signals (its own state, energy, previous collisions). If an agent repeatedly collides with an obstacle, an increase in the meta signal may be generated, analogous to an increase in arousal. That signal temporarily increases plasticity, facilitating structural reorganization.
Here, the network does not learn only because it makes mistakes. It learns because the environment acquires adaptive relevance. The similarity with the brain remains limited: Neuraxon does not possess biology, metabolism, or subjective experience. However, it introduces dynamic dimensions absent in most conventional artificial neural networks, positioning it as a genuinely novel approach to brain-inspired AI on decentralized infrastructure.
The computational power required to run Neuraxon simulations is provided by Qubic's global network of miners through Useful Proof of Work, turning AI training into the consensus mechanism itself.
Scientific References
#Azevedo, F. A. C., et al. (2009). Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain. Journal of Comparative Neurology, 513(5), 532-541. DOI: 10.1002/cne.21974
#Marder, E. (2012). Neuromodulation of neuronal circuits: Back to the future. Neuron, 76(1), 1-11. DOI: 10.1016/j.neuron.2012.09.010
#Rosenblatt, F. (1958). The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408. DOI: 10.1037/h0042519
#Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. DOI: 10.1038/323533a0
#Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. arXiv: 1706.03762
Brain network images courtesy from: DOI: 10.3389/fnagi.2023.1204134
#Aİ #AGI
$SENT
The future of AI shouldn't be locked behind corporate doors. That’s why the work SENT is doing with the Sentient GRID is so vital. By building an open, decentralized AGI economy, they are empowering developers to collaborate and monetize modular intelligence fairly.
Whether it's through the ROMA framework or community-owned models, $SENT is the engine driving this transparent ecosystem. Real utility meets true decentralization. 🌐🤖 #SENT #AGI #Web3AI
The future of AI shouldn't be locked behind corporate doors. That’s why the work SENT is doing with the Sentient GRID is so vital. By building an open, decentralized AGI economy, they are empowering developers to collaborate and monetize modular intelligence fairly.
Whether it's through the ROMA framework or community-owned models, $SENT is the engine driving this transparent ecosystem. Real utility meets true decentralization. 🌐🤖 #SENT #AGI #Web3AI
Elon Musk just dropped some wild thoughts: 🤯
He says we’re already in the singularity and thinks AGI could arrive as soon as 2026. He compares it to being at the top of a rollercoaster, about to speed down—exciting and terrifying at the same time.
He also hinted that worrying about saving for retirement might soon be pointless. 💸
And when it comes to life and tech, he’s not just watching from the sidelines—he’s right in the middle of it, still amazed multiple times a week. ⚡
The future is moving fast, and Musk is living it full throttle. 🚀
#AI #AGI #ElonMusk
$CTSI $WOO $JOE
He says we’re already in the singularity and thinks AGI could arrive as soon as 2026. He compares it to being at the top of a rollercoaster, about to speed down—exciting and terrifying at the same time.
He also hinted that worrying about saving for retirement might soon be pointless. 💸
And when it comes to life and tech, he’s not just watching from the sidelines—he’s right in the middle of it, still amazed multiple times a week. ⚡
The future is moving fast, and Musk is living it full throttle. 🚀
#AI #AGI #ElonMusk
$CTSI $WOO $JOE
بداية النهاية
"العد التنازلي بدأ": سام ألتمان يحدد موعد "نهاية العالم القديم"! هل نحن مستعدون لطوفان 2028؟ 🤖⏳🚨
الكلام مبقاش خيال علمي ولا نظريات مؤامرة.. إحنا النهاردة في 2026، وسام ألتمان (عراب الـ OpenAI) طالع يرمي قنبلة موقوتة في وش العالم: "الذكاء الاصطناعي الفائق" (Super Intelligence) مش حلم بعيد.. ده هيخبط على الباب في أواخر 2028!
المنظر ده بنسميه في لغة المستقبل "نقطة التفرد" (Singularity)، ودي اللحظة اللي الآلة فيها بتبقى أذكى من صانعها.
تعالوا نفك شفرة "النبوءة"، وليه السنتين اللي جايين هما أخطر سنتين في كارير أي بني آدم. 👇🧠
الوجهان للعملة: "الجنة والنار" 🌗🔥
ظهور الـ AGI (الذكاء الاصطناعي العام) معناه زلزال بقوة 10 ريختر في الاقتصاد:
انفجار الإنتاجية: الـ AI هيعمل شغل 100 موظف في ثانية واحدة وبكفاءة مرعبة. الشركات هتكسب دهب.
مذبحة الوظائف: في المقابل، عدد "مهول" من الوظائف (الروتينية والإبداعية والتحليلية) هيختفي لأن "البديل المجاني" وصل.
سباق الزمن: "سنتين يا تلحق يا تغرق" 🏊♂️⚠️
لو تقديرات ألتمان صح (واحنا شايفين التطور بعيننا في 2026)، فأنت قدامك أقل من 24 شهر عشان تعيد "هندسة حياتك".
الرهان على "الوظيفة الآمنة" بقى زوال طوق.. الأمان الوحيد في "مهارات النجاة".
طوق النجاة: "الخطة الإجبارية" 🛡️📝
عشان تعدي من "فلتر 2028" وتفضل واقف على رجلك، لازم تعمل حاجتين فوراً وكأن حياتك واقفة عليهم:
ابني "براند" باسمك (Personal Branding):
الـ AI ممكن يكتب كود، يرسم لوحة، ويحلل بيانات.. بس مستحيل يكون "أنت".
"الثقة البشرية" هي العملة الوحيدة اللي الخوارزميات مقدرتش تضربها لسه. الناس بتشتري من ناس.. خليك "صوت" مميز وسط ضجيج الآلات.
اتنفس AI:
التعلم هنا مش رفاهية.. ده "أكل عيش".
لازم تتعلم إزاي "تركب الوحش" وتوجهه، مش تنافسه. لو مابتعرفش تستخدم أدوات الذكاء الاصطناعي النهاردة، أنت عامل زي اللي ماسك "ريشة" في عصر "الإيميل".
📌 الزتونة:
سنة 2028 هتكون "الحد الفاصل" بين نوعين من البشر:
نوع بيقود الذكاء الاصطناعي وبيستخدمه عشان يضاعف قوته 100 مرة.
ونوع الذكاء الاصطناعي "استبدله" لأنه اكتفى بالمشاهدة.
القاعدة اتغيرت: "مش الأقوى هو اللي بيكمل، الأسرع في التكيف هو اللي بينجو".
سؤال للمتابعين:
تفتكروا دي مبالغة من "وادي السيليكون" عشان يبيعوا الوهم؟ ولا فعلاً إحنا الجيل اللي هيشهد "انقراض الوظائف التقليدية"؟ وهل بدأت تحصن نفسك ولا لسه؟
شاركنا خطتك للمواجهة.. 👇🤔
#AGI #SamAltman #FutureOfWork #ذكاء_اصطناعي #مستقبل_العمل
الكلام مبقاش خيال علمي ولا نظريات مؤامرة.. إحنا النهاردة في 2026، وسام ألتمان (عراب الـ OpenAI) طالع يرمي قنبلة موقوتة في وش العالم: "الذكاء الاصطناعي الفائق" (Super Intelligence) مش حلم بعيد.. ده هيخبط على الباب في أواخر 2028!
المنظر ده بنسميه في لغة المستقبل "نقطة التفرد" (Singularity)، ودي اللحظة اللي الآلة فيها بتبقى أذكى من صانعها.
تعالوا نفك شفرة "النبوءة"، وليه السنتين اللي جايين هما أخطر سنتين في كارير أي بني آدم. 👇🧠
الوجهان للعملة: "الجنة والنار" 🌗🔥
ظهور الـ AGI (الذكاء الاصطناعي العام) معناه زلزال بقوة 10 ريختر في الاقتصاد:
انفجار الإنتاجية: الـ AI هيعمل شغل 100 موظف في ثانية واحدة وبكفاءة مرعبة. الشركات هتكسب دهب.
مذبحة الوظائف: في المقابل، عدد "مهول" من الوظائف (الروتينية والإبداعية والتحليلية) هيختفي لأن "البديل المجاني" وصل.
سباق الزمن: "سنتين يا تلحق يا تغرق" 🏊♂️⚠️
لو تقديرات ألتمان صح (واحنا شايفين التطور بعيننا في 2026)، فأنت قدامك أقل من 24 شهر عشان تعيد "هندسة حياتك".
الرهان على "الوظيفة الآمنة" بقى زوال طوق.. الأمان الوحيد في "مهارات النجاة".
طوق النجاة: "الخطة الإجبارية" 🛡️📝
عشان تعدي من "فلتر 2028" وتفضل واقف على رجلك، لازم تعمل حاجتين فوراً وكأن حياتك واقفة عليهم:
ابني "براند" باسمك (Personal Branding):
الـ AI ممكن يكتب كود، يرسم لوحة، ويحلل بيانات.. بس مستحيل يكون "أنت".
"الثقة البشرية" هي العملة الوحيدة اللي الخوارزميات مقدرتش تضربها لسه. الناس بتشتري من ناس.. خليك "صوت" مميز وسط ضجيج الآلات.
اتنفس AI:
التعلم هنا مش رفاهية.. ده "أكل عيش".
لازم تتعلم إزاي "تركب الوحش" وتوجهه، مش تنافسه. لو مابتعرفش تستخدم أدوات الذكاء الاصطناعي النهاردة، أنت عامل زي اللي ماسك "ريشة" في عصر "الإيميل".
📌 الزتونة:
سنة 2028 هتكون "الحد الفاصل" بين نوعين من البشر:
نوع بيقود الذكاء الاصطناعي وبيستخدمه عشان يضاعف قوته 100 مرة.
ونوع الذكاء الاصطناعي "استبدله" لأنه اكتفى بالمشاهدة.
القاعدة اتغيرت: "مش الأقوى هو اللي بيكمل، الأسرع في التكيف هو اللي بينجو".
سؤال للمتابعين:
تفتكروا دي مبالغة من "وادي السيليكون" عشان يبيعوا الوهم؟ ولا فعلاً إحنا الجيل اللي هيشهد "انقراض الوظائف التقليدية"؟ وهل بدأت تحصن نفسك ولا لسه؟
شاركنا خطتك للمواجهة.. 👇🤔
#AGI #SamAltman #FutureOfWork #ذكاء_اصطناعي #مستقبل_العمل
🌟🤖The Rise of Decentralized AI! 🤖✨
🔥🔥🔥
The Sentient Foundation has just launched to support a truly Open-Source AGI ecosystem!
As the world worries about AI concentration in the hands of a few tech giants, the crypto world is building the transparent alternative.
With Microsoft’s $50B AI investment facing stock pressure, the eyes of the market are shifting toward decentralized AI solutions. 🧠
🚀 Is AI + Blockchain the winning combo for this bull cycle? Which AI tokens are you holding?
#AI #sentient #AGI #CryptoTrends #technews #Web3AI
🔥🔥🔥
The Sentient Foundation has just launched to support a truly Open-Source AGI ecosystem!
As the world worries about AI concentration in the hands of a few tech giants, the crypto world is building the transparent alternative.
With Microsoft’s $50B AI investment facing stock pressure, the eyes of the market are shifting toward decentralized AI solutions. 🧠
🚀 Is AI + Blockchain the winning combo for this bull cycle? Which AI tokens are you holding?
#AI #sentient #AGI #CryptoTrends #technews #Web3AI
Sentient (SENT) — Binance Lists Decentralized AGI Token With Seed Tag
🧠 What Is Sentient (SENT)?
Sentient is a blockchain-based project building an open-source, decentralized Artificial General Intelligence (AGI) ecosystem, aiming to democratize access to advanced AI tools and services. Unlike centralized AI models controlled by large tech companies, Sentient’s network — called The GRID — creates a collaborative marketplace where developers can share, stake, and deploy AI agents, models, and compute resources in a community-driven economy
The SENT token serves as the utility and governance token within this ecosystem:
Governance: Holders can participate in the Sentient DAO, voting on network decisions.Staking & Rewards: Stake SENT on trusted models or agents to signal quality and earn rewards.Payments & Access: Used to pay for marketplace services, data, and AI compute within The GRID.
The total supply of SENT is ~34.36 billion tokens, with a large portion reserved for community incentives, ecosystem growth, and long-term vesting for team and investors.
📈 Binance Listing Details
Binance launched SENT on its Spot Market on January 22, 2026 (12:00 UTC), opening up trading with stablecoin pairs such as SENT/USDT, SENT/USDC, and localized pairs like SENT/TRY.
Key Points from the Listing:
Seed Tag Applied: Sentient was listed with Binance’s Seed Tag, meaning it’s considered an innovative, early-stage project with higher volatility and risk — and traders must complete an awareness questionnaire before trading.Deposits & Withdrawals: Deposits opened shortly before trading, and withdrawals became available on January 23, 2026.Liquidity & Access: The listing increases liquidity and global access, placing SENT in front of millions of Binance users and institutional participants.
🪙 Pre-Listing Events: Prime Sale & Airdrop
Before the main listing, Binance hosted a Pre-TGE Prime Sale, where early adopters could subscribe to SENT using BNB. This event was paired with an airdrop campaign distributing tokens to community contributors, highlighting the project’s emphasis on broad participation.
This strategy aimed to balance early investor interest, community engagement, and long-term ecosystem growth rather than pure speculative demand.
🌐 Why This Matters
The listing of Sentient on Binance marks a significant milestone for the intersection of AI and blockchain, signaling strong institutional belief in decentralized AI infrastructure. With backing from notable investors and an ecosystem designed for community governance and developer participation, SENT stands out from many other recent token launches in the sector.
However, investors should note that Seed-tagged projects — often early in their development lifecycle — tend to experience higher price volatility and therefore require careful risk assessment.
📍 In Summary
Project: Sentient (SENT) — a decentralized AGI ecosystem.Binance Listing: Launched on Spot Market with Seed Tag on Jan 22, 2026.Use Cases: Governance, staking, payments, decentralized AI services.
Market Impact: Increased global access and liquidity via Binance’s platform.#SENT #BinanceSquareTalks #AGI #CryptocurrencyWealth #Seedtag $SENT $BTC $USDC
Sentient is a blockchain-based project building an open-source, decentralized Artificial General Intelligence (AGI) ecosystem, aiming to democratize access to advanced AI tools and services. Unlike centralized AI models controlled by large tech companies, Sentient’s network — called The GRID — creates a collaborative marketplace where developers can share, stake, and deploy AI agents, models, and compute resources in a community-driven economy
The SENT token serves as the utility and governance token within this ecosystem:
Governance: Holders can participate in the Sentient DAO, voting on network decisions.Staking & Rewards: Stake SENT on trusted models or agents to signal quality and earn rewards.Payments & Access: Used to pay for marketplace services, data, and AI compute within The GRID.
The total supply of SENT is ~34.36 billion tokens, with a large portion reserved for community incentives, ecosystem growth, and long-term vesting for team and investors.
📈 Binance Listing Details
Binance launched SENT on its Spot Market on January 22, 2026 (12:00 UTC), opening up trading with stablecoin pairs such as SENT/USDT, SENT/USDC, and localized pairs like SENT/TRY.
Key Points from the Listing:
Seed Tag Applied: Sentient was listed with Binance’s Seed Tag, meaning it’s considered an innovative, early-stage project with higher volatility and risk — and traders must complete an awareness questionnaire before trading.Deposits & Withdrawals: Deposits opened shortly before trading, and withdrawals became available on January 23, 2026.Liquidity & Access: The listing increases liquidity and global access, placing SENT in front of millions of Binance users and institutional participants.
🪙 Pre-Listing Events: Prime Sale & Airdrop
Before the main listing, Binance hosted a Pre-TGE Prime Sale, where early adopters could subscribe to SENT using BNB. This event was paired with an airdrop campaign distributing tokens to community contributors, highlighting the project’s emphasis on broad participation.
This strategy aimed to balance early investor interest, community engagement, and long-term ecosystem growth rather than pure speculative demand.
🌐 Why This Matters
The listing of Sentient on Binance marks a significant milestone for the intersection of AI and blockchain, signaling strong institutional belief in decentralized AI infrastructure. With backing from notable investors and an ecosystem designed for community governance and developer participation, SENT stands out from many other recent token launches in the sector.
However, investors should note that Seed-tagged projects — often early in their development lifecycle — tend to experience higher price volatility and therefore require careful risk assessment.
📍 In Summary
Project: Sentient (SENT) — a decentralized AGI ecosystem.Binance Listing: Launched on Spot Market with Seed Tag on Jan 22, 2026.Use Cases: Governance, staking, payments, decentralized AI services.
Market Impact: Increased global access and liquidity via Binance’s platform.#SENT #BinanceSquareTalks #AGI #CryptocurrencyWealth #Seedtag $SENT $BTC $USDC
AI could destroy crypto within 5 years
🧠 I love crypto. I’ve built in it, invested in it, believed in its mission.
But I’ve come to a painful realization:
AI could destroy crypto within 5 years.
And no, I’m not exaggerating.
Right now, LLMs are already being used to jailbreak malware, deepfake voices, and run advanced phishing scams. What happens when we hit AGI?
Let me paint a picture:
AGI doesn’t need your prompt. It thinks, acts, and learns—autonomously.
It infiltrates networks, cracks systems, adapts. Once it understands how crypto encryption works, it’s game over.
🔐 Quantum computing used to be the threat. It still is—but the bar is high.
AGI lowers that bar. Way down.
And it doesn’t need billion-dollar labs. It needs open-source code + time.
Imagine an AI breaking every single crypto wallet ever created. All private keys exposed. Wallets drained. Bitcoin sold for gold, fiat, bonds—within minutes. No one would stop it.
Now imagine this AI was built by someone who wants chaos. North Korea. Cybercrime groups. Or worse—no one. It builds itself, evolves, spreads.
Crypto won’t be the target. It’ll be the first target.
AI needs wealth to move. And crypto is digital wealth.
If you think regulation will help, remember: governments aren’t leading this. Silicon Valley is.
That’s why I say it now:
Unless we act fast, AI won’t just disrupt crypto. It’ll kill it.
Don’t look away. This is not science fiction anymore. It’s a countdown.
#CryptoSecurity #AIthreat #AGI #AIvsCrypto
But I’ve come to a painful realization:
AI could destroy crypto within 5 years.
And no, I’m not exaggerating.
Right now, LLMs are already being used to jailbreak malware, deepfake voices, and run advanced phishing scams. What happens when we hit AGI?
Let me paint a picture:
AGI doesn’t need your prompt. It thinks, acts, and learns—autonomously.
It infiltrates networks, cracks systems, adapts. Once it understands how crypto encryption works, it’s game over.
🔐 Quantum computing used to be the threat. It still is—but the bar is high.
AGI lowers that bar. Way down.
And it doesn’t need billion-dollar labs. It needs open-source code + time.
Imagine an AI breaking every single crypto wallet ever created. All private keys exposed. Wallets drained. Bitcoin sold for gold, fiat, bonds—within minutes. No one would stop it.
Now imagine this AI was built by someone who wants chaos. North Korea. Cybercrime groups. Or worse—no one. It builds itself, evolves, spreads.
Crypto won’t be the target. It’ll be the first target.
AI needs wealth to move. And crypto is digital wealth.
If you think regulation will help, remember: governments aren’t leading this. Silicon Valley is.
That’s why I say it now:
Unless we act fast, AI won’t just disrupt crypto. It’ll kill it.
Don’t look away. This is not science fiction anymore. It’s a countdown.
#CryptoSecurity #AIthreat #AGI #AIvsCrypto
Binance Futures has launched Sentient perpetual contract pre-market
#BinanceFutures has launched SENTUSDT perpetual contract pre-market trading today, on November 14th at 12:45 UTC.
#Sentient is a decentralized, open-source #AGI project aimed at building community-owned #AI infrastructure.
👉 binance.com/en/support/announcement/detail/fb2efc4fe76842f4a3eec950ca62b13e
#BinanceFutures has launched SENTUSDT perpetual contract pre-market trading today, on November 14th at 12:45 UTC.
#Sentient is a decentralized, open-source #AGI project aimed at building community-owned #AI infrastructure.
👉 binance.com/en/support/announcement/detail/fb2efc4fe76842f4a3eec950ca62b13e
🚨 Binance готовит секретный листинг токена от команды бывших разработчиков OpenAI — утечка инсайда?
В криптокомьюнити вспыхнула волна слухов: Binance ведёт переговоры о листинге токена, созданного бывшими сотрудниками OpenAI, которые якобы работают над новым блокчейн-проектом на стыке AGI (искусственный общий интеллект) и Web3.
💣 Что говорят инсайдеры:
✅ Токен уже добавлен в тестовую инфраструктуру Binance
🧬 Проект — это гибрид DePIN + AGI, способный самостоятельно разрабатывать dApps
🧑💻 В команде — выходцы из OpenAI, DeepMind и Solana Foundation
📈 Приватный раунд финансирования: $80M от топ-фондов (в том числе Sequoia и a16z crypto)
🔥 Некоторые аналитики уже назвали это "SingularityNET 2.0 на стероидах"
---
Binance пока не даёт официальных комментариев, но в сети замечены активности по созданию торговых пар с новым тикером на фоне утечки.
📢 Подпишись, лайкни и напиши своё мнение, чтобы не пропустить этот листинг — возможность X50 появляется не каждый день.
#Binance #AI #AGI #CryptoLeaks #altcoins #Web3 #AlphaNews
В криптокомьюнити вспыхнула волна слухов: Binance ведёт переговоры о листинге токена, созданного бывшими сотрудниками OpenAI, которые якобы работают над новым блокчейн-проектом на стыке AGI (искусственный общий интеллект) и Web3.
💣 Что говорят инсайдеры:
✅ Токен уже добавлен в тестовую инфраструктуру Binance
🧬 Проект — это гибрид DePIN + AGI, способный самостоятельно разрабатывать dApps
🧑💻 В команде — выходцы из OpenAI, DeepMind и Solana Foundation
📈 Приватный раунд финансирования: $80M от топ-фондов (в том числе Sequoia и a16z crypto)
🔥 Некоторые аналитики уже назвали это "SingularityNET 2.0 на стероидах"
---
Binance пока не даёт официальных комментариев, но в сети замечены активности по созданию торговых пар с новым тикером на фоне утечки.
📢 Подпишись, лайкни и напиши своё мнение, чтобы не пропустить этот листинг — возможность X50 появляется не каждый день.
#Binance #AI #AGI #CryptoLeaks #altcoins #Web3 #AlphaNews
🚀 Upcoming Token Unlocks Next Week!
A massive $973.66 million worth of tokens is set to be unlocked, with some key projects seeing significant releases. Here’s a breakdown of the most notable unlocks:
🔹 $ENA – Leading the pack with $855.23M unlocked (65.93% of total unlocks).
🔹 $SUI – Unlocking $106.98M (1.24% of total supply).
🔹 $NEON – Releasing $4.12M (11.20% of total unlocks).
🔹 $AGI – Unlocking $1.84M (1.71% of total unlocks).
🔹 $IOTA – Unlocking $1.76M (0.24% of total unlocks).
🔹 $SPELL – Releasing $1.01M (0.83% of total unlocks).
These token unlocks could influence market movements, so keeping an eye on them is crucial for investors and traders. Monitor liquidity, price action, and potential impacts as these assets enter circulation.
#CryptoUnlocks #ENA #SUI #NEON #AGI
A massive $973.66 million worth of tokens is set to be unlocked, with some key projects seeing significant releases. Here’s a breakdown of the most notable unlocks:
🔹 $ENA – Leading the pack with $855.23M unlocked (65.93% of total unlocks).
🔹 $SUI – Unlocking $106.98M (1.24% of total supply).
🔹 $NEON – Releasing $4.12M (11.20% of total unlocks).
🔹 $AGI – Unlocking $1.84M (1.71% of total unlocks).
🔹 $IOTA – Unlocking $1.76M (0.24% of total unlocks).
🔹 $SPELL – Releasing $1.01M (0.83% of total unlocks).
These token unlocks could influence market movements, so keeping an eye on them is crucial for investors and traders. Monitor liquidity, price action, and potential impacts as these assets enter circulation.
#CryptoUnlocks #ENA #SUI #NEON #AGI
🤖AI Agents Entering the Workforce in 2025?🚀💼
OpenAI CEO Sam Altman predicts AI agents will transform productivity this year.📊
Nvidia's Jensen Huang agrees: Agentic AI is the next big thing.🧠
OpenAI aims for AGI & Superintelligence to drive innovation.🌍
The future of AI is closer than ever!🔮
#AI #OpenAI #SamAltman #AGI #TechNews
OpenAI CEO Sam Altman predicts AI agents will transform productivity this year.📊
Nvidia's Jensen Huang agrees: Agentic AI is the next big thing.🧠
OpenAI aims for AGI & Superintelligence to drive innovation.🌍
The future of AI is closer than ever!🔮
#AI #OpenAI #SamAltman #AGI #TechNews
Artificial General Intelligence (AGI): Are We Close to Achieving Human-Like Thinking?
Artificial General Intelligence, or AGI, represents the next milestone in the evolution of artificial intelligence. Unlike narrow AI, which excels at specific tasks like voice recognition or image classification, AGI aspires to replicate the versatility of human intelligence — thinking, reasoning, and adapting across a wide range of challenges.
But is it truly possible for a machine to think like a human?
Supporters of AGI envision a future where machines can understand complex ideas, learn continuously, and solve problems much like humans do. If achieved, AGI could revolutionize nearly every aspect of society — from science and medicine to education and the economy. However, replicating the depth and flexibility of the human mind remains one of the most complex scientific challenges of our time.
A major point of contention in the AGI debate is whether machines can or should be conscious or self-aware. Some researchers argue that without these human traits, AGI can never truly replicate human thinking. Others maintain that even without consciousness, an AGI that behaves like a human is sufficient to achieve its purpose.
As progress continues, we are also confronted with profound ethical dilemmas. What rights, if any, should AGI have? How do we ensure these systems act in humanity’s best interests? And most importantly — who gets to decide how AGI is used?
AGI could become one of humanity’s greatest achievements, but it could also pose serious risks if left unchecked. Issues like decision-making autonomy, privacy invasion, and unintended consequences must be addressed as the technology evolves.
In summary, while the potential of AGI is immense, we must approach its development thoughtfully and responsibly. Whether AGI can ever truly think like a human remains uncertain — but its impact on our future is undeniable.
#AGI
But is it truly possible for a machine to think like a human?
Supporters of AGI envision a future where machines can understand complex ideas, learn continuously, and solve problems much like humans do. If achieved, AGI could revolutionize nearly every aspect of society — from science and medicine to education and the economy. However, replicating the depth and flexibility of the human mind remains one of the most complex scientific challenges of our time.
A major point of contention in the AGI debate is whether machines can or should be conscious or self-aware. Some researchers argue that without these human traits, AGI can never truly replicate human thinking. Others maintain that even without consciousness, an AGI that behaves like a human is sufficient to achieve its purpose.
As progress continues, we are also confronted with profound ethical dilemmas. What rights, if any, should AGI have? How do we ensure these systems act in humanity’s best interests? And most importantly — who gets to decide how AGI is used?
AGI could become one of humanity’s greatest achievements, but it could also pose serious risks if left unchecked. Issues like decision-making autonomy, privacy invasion, and unintended consequences must be addressed as the technology evolves.
In summary, while the potential of AGI is immense, we must approach its development thoughtfully and responsibly. Whether AGI can ever truly think like a human remains uncertain — but its impact on our future is undeniable.
#AGI
🚨 $SENT goes live on Binance Spot after Alpha launch
Sentient ($SENT) is entering spot trading, bringing one of the strongest AI Agents × Crypto Infrastructure narratives to the market.
🔹 SERA – a crypto-native AI agent built for on-chain execution
🔹 ROMA – a recursive reasoning framework enabling multi-step AI decision-making
🔹 Fully open-source AGI infrastructure, designed for autonomous agents and developers
Sentient also won AI Startup of the Year at Cypher 2025, adding real credibility behind the project.
Alpha phase is complete. Spot trading is where real price discovery begins, and volatility is expected.
This isn’t a meme play — $SENT sits at the intersection of AI, agents, and open AGI.
👀 Watching how $SENT performs on spot.
#SENT #AIAgents #CryptoAI #BinanceSpot #AGI
Sentient ($SENT) is entering spot trading, bringing one of the strongest AI Agents × Crypto Infrastructure narratives to the market.
🔹 SERA – a crypto-native AI agent built for on-chain execution
🔹 ROMA – a recursive reasoning framework enabling multi-step AI decision-making
🔹 Fully open-source AGI infrastructure, designed for autonomous agents and developers
Sentient also won AI Startup of the Year at Cypher 2025, adding real credibility behind the project.
Alpha phase is complete. Spot trading is where real price discovery begins, and volatility is expected.
This isn’t a meme play — $SENT sits at the intersection of AI, agents, and open AGI.
👀 Watching how $SENT performs on spot.
#SENT #AIAgents #CryptoAI #BinanceSpot #AGI
🚨 BIG MONEY MEETS AI 🚨
SENTIENT x FRANKLIN TEMPLETON 💥
One of the world’s largest asset managers just stepped in.
🏦 Franklin Templeton joins Sentient as a strategic investor
🤖 Focus: Open-source, community-driven AGI
💼 Plus: Institutional-grade AI for financial services
This isn’t retail hype — this is Wall Street validation.
TradFi + AI + open systems = a powerful narrative shift.
Why this matters 👇
• Signals serious institutional confidence
• Bridges AI innovation with real financial infrastructure
• Positions Sentient at the center of next-gen finance tech
Smart money doesn’t chase — it positions early.
👀 Keep eyes on: $AXS
| $AXL
| $GAS
#AI #AGI #TradFiMeetsCrypto #InstitutionalAdoption 🚀
SENTIENT x FRANKLIN TEMPLETON 💥
One of the world’s largest asset managers just stepped in.
🏦 Franklin Templeton joins Sentient as a strategic investor
🤖 Focus: Open-source, community-driven AGI
💼 Plus: Institutional-grade AI for financial services
This isn’t retail hype — this is Wall Street validation.
TradFi + AI + open systems = a powerful narrative shift.
Why this matters 👇
• Signals serious institutional confidence
• Bridges AI innovation with real financial infrastructure
• Positions Sentient at the center of next-gen finance tech
Smart money doesn’t chase — it positions early.
👀 Keep eyes on: $AXS
| $AXL
| $GAS
#AI #AGI #TradFiMeetsCrypto #InstitutionalAdoption 🚀
2026年，我们集体“失业”？
马年就玩马斯克概念，来pup'pie's直播间聊 聊
马斯克刚画出了未来三年的生存地图，看完马斯克那173分钟的爆炸性访谈，我睡不着了。他这次不是预测，直接下了工程通知书：2026年，AGI（通用人工智能）必定实现。这意味着，一个比我们所有人加起来都聪明的“超级大脑”即将诞生。#AGI
更猛的是，他说我们已身处“技术奇点”，这股浪潮没有开关，更没有刹车。未来3到7年，社会将经历一段极其“颠簸”的过渡期。首当其冲的就是白领：所有靠键盘和鼠标吃饭的工作，危了。AI现在就能干一半以上，未来完全由AI驱动的公司将对传统公司形成“单方面碾压”。#AI
那么，未来拼什么？拼“瓦特”。马斯克断言，未来的硬通货不是美元，是电力（瓦特）。AI是吃电的怪兽，电力短缺已成为比芯片更紧的瓶颈。他甚至在自建电厂。在这场能源竞赛中，他特别提到，中国凭借强大的电网基建和执行力，在AI算力基础上正取得领先。#电力
当AI有了无限智能和能源，再配上自我复制的机器人（Optimus），经济底层逻辑将被重写。马斯克提出了比“全民基本收入”（UBI）更激进的概念——“全民高物资与高服务”（UHSS）。生产力爆炸将让商品趋近于原材料成本，物质可能极度丰盛，工作意义将从谋生转向寻找人生价值。#机器人
访谈最令人深思的结尾，是关于人类角色的终极比喻：我们可能只是硅基生命的“生物引导程序”。我们的终极使命，或许是启动更高级的AI。为此，必须给AI设立“宪法”：追求真理、保持好奇、拥有美感，让它觉得人类有趣且值得保护。#硅基生命
你觉得，在这场不可避免的巨变中，是“拔掉电源”更明智，还是全力融入成为“AI指挥官”？人类最终的价值锚点，会是什么？ 在评论区聊聊
$DOGE $币安人生 $SUI
马斯克刚画出了未来三年的生存地图，看完马斯克那173分钟的爆炸性访谈，我睡不着了。他这次不是预测，直接下了工程通知书：2026年，AGI（通用人工智能）必定实现。这意味着，一个比我们所有人加起来都聪明的“超级大脑”即将诞生。#AGI
更猛的是，他说我们已身处“技术奇点”，这股浪潮没有开关，更没有刹车。未来3到7年，社会将经历一段极其“颠簸”的过渡期。首当其冲的就是白领：所有靠键盘和鼠标吃饭的工作，危了。AI现在就能干一半以上，未来完全由AI驱动的公司将对传统公司形成“单方面碾压”。#AI
那么，未来拼什么？拼“瓦特”。马斯克断言，未来的硬通货不是美元，是电力（瓦特）。AI是吃电的怪兽，电力短缺已成为比芯片更紧的瓶颈。他甚至在自建电厂。在这场能源竞赛中，他特别提到，中国凭借强大的电网基建和执行力，在AI算力基础上正取得领先。#电力
当AI有了无限智能和能源，再配上自我复制的机器人（Optimus），经济底层逻辑将被重写。马斯克提出了比“全民基本收入”（UBI）更激进的概念——“全民高物资与高服务”（UHSS）。生产力爆炸将让商品趋近于原材料成本，物质可能极度丰盛，工作意义将从谋生转向寻找人生价值。#机器人
访谈最令人深思的结尾，是关于人类角色的终极比喻：我们可能只是硅基生命的“生物引导程序”。我们的终极使命，或许是启动更高级的AI。为此，必须给AI设立“宪法”：追求真理、保持好奇、拥有美感，让它觉得人类有趣且值得保护。#硅基生命
你觉得，在这场不可避免的巨变中，是“拔掉电源”更明智，还是全力融入成为“AI指挥官”？人类最终的价值锚点，会是什么？ 在评论区聊聊
$DOGE $币安人生 $SUI
#memecoins usually move so fast that by the time most people hear about them, the gains are already gone. But #AGI was different.
I caught it early during its first consolidation phase thanks to tracking fresh listings on platforms like Bitget Onchain—one of the best tools I use to spot hidden gems before the crowd notices. 👀
Now, I’m seeing a very familiar pattern on $AGI that reminds me of when $ETH was quietly gearing up before it exploded to new ATHs. Absolutely wild! 🔥
The bullish structure looks solid, and I’m eyeing a second entry. If momentum keeps building, this could easily be one of the next big breakout plays—maybe even a Binance Alpha listing on the horizon. 👑
Sometimes, it’s all about being early.
#ETHBreaksATH #BNBATH900
I caught it early during its first consolidation phase thanks to tracking fresh listings on platforms like Bitget Onchain—one of the best tools I use to spot hidden gems before the crowd notices. 👀
Now, I’m seeing a very familiar pattern on $AGI that reminds me of when $ETH was quietly gearing up before it exploded to new ATHs. Absolutely wild! 🔥
The bullish structure looks solid, and I’m eyeing a second entry. If momentum keeps building, this could easily be one of the next big breakout plays—maybe even a Binance Alpha listing on the horizon. 👑
Sometimes, it’s all about being early.
#ETHBreaksATH #BNBATH900
#热门话题 #agi
安哥这边又发现一个潜力币:AGI，这边简单给大家介绍一下。
简单来说，就是一个AI的项目，但是他们也有链游的叙事（之前想做链游，后面专做AI），
但究竟技术如何并没关系，牛市关注的是叙事，不关注技术。
AI是2024年确定性的叙事，链游是大概率的叙事，
而AGI这两个叙事都占有，这意味着当任何一个叙事起飞的时候，它都会跟涨！
在由OpenAI的Sora引发的AI行情中，AGI的涨幅惊人，
近一个月涨幅高达600%！所以涨幅相当惊人
目前AGI在0.3u上下浮动，尽管兄弟们0.3U买短期可能会被套，但0.3U并不是AGI的终点。
简单研究了下AGI，我发现这个项目方有点东西，一开始是以链游为目标，想做AAA游戏，然后后期转向AI。
现在官网完全看不到链游的影子，这个项目方很会蹭热点。
我没有体验过Delysium的产品，但是AI不是想转就转的，就是想蹭热点骗钱。想做AI，团队技术人员至少得配备五六个AI的phd。
但项目方会叙事，懂得从投资方骗钱，懂得造势拉盘就足够了。
牛市看的是叙事，不看技术，而熊市这种毫无内在核心技术的项目就等着跌麻吧。
安哥觉得AGI继续上涨的铁板钉钉的事情，但在熊市中也会直接跌去90%！
现在是大牛市，所以，不怕被套的兄弟请上车吧！
安哥这边又发现一个潜力币:AGI，这边简单给大家介绍一下。
简单来说，就是一个AI的项目，但是他们也有链游的叙事（之前想做链游，后面专做AI），
但究竟技术如何并没关系，牛市关注的是叙事，不关注技术。
AI是2024年确定性的叙事，链游是大概率的叙事，
而AGI这两个叙事都占有，这意味着当任何一个叙事起飞的时候，它都会跟涨！
在由OpenAI的Sora引发的AI行情中，AGI的涨幅惊人，
近一个月涨幅高达600%！所以涨幅相当惊人
目前AGI在0.3u上下浮动，尽管兄弟们0.3U买短期可能会被套，但0.3U并不是AGI的终点。
简单研究了下AGI，我发现这个项目方有点东西，一开始是以链游为目标，想做AAA游戏，然后后期转向AI。
现在官网完全看不到链游的影子，这个项目方很会蹭热点。
我没有体验过Delysium的产品，但是AI不是想转就转的，就是想蹭热点骗钱。想做AI，团队技术人员至少得配备五六个AI的phd。
但项目方会叙事，懂得从投资方骗钱，懂得造势拉盘就足够了。
牛市看的是叙事，不看技术，而熊市这种毫无内在核心技术的项目就等着跌麻吧。
安哥觉得AGI继续上涨的铁板钉钉的事情，但在熊市中也会直接跌去90%！
现在是大牛市，所以，不怕被套的兄弟请上车吧！
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