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Strong AI: Why We’re Still at the Starting Line and What "Digital Intelligence" Really MeansToday, every other startup slaps "AI" on its landing page, and newsfeeds are flooded with headlines about AI replacing humans tomorrow. But let’s be honest: what we have now, while incredibly sophisticated, is still a set of limited algorithms. True Artificial General Intelligence (AGI) is not just about generating text — it’s an entirely different league.
In my view, current developments are still far from what can truly be called intelligence. Modern science is focused on scaling what already works, yet we see a lack of breakthrough ideas that explain how to transition from statistical analysis to genuine, conscious reasoning.
Here are 3 hallmarks of Strong AI that are still missing from every lab in the world: 🧬
1. The Capacity for Major Scientific Breakthroughs 🔬 True intelligence doesn't just summarize Wikipedia — it creates new knowledge. A Strong AI should be capable of independently discovering laws of physics, synthesizing cures for diseases, or developing new forms of energy. Current AI only analyzes what humans have already written. It’s a world-class librarian, but it’s no Newton or Einstein. We lack fundamental models that can teach a machine "insight" or intuition, rather than just statistical probability.
2. Recursive Self-Improvement: From Code to Hardware ⚙️🦾 This is the ultimate technical barrier that currently seems insurmountable. A true Strong AI must become its own chief engineer, architect, and systems administrator all at once. This means the ability to independently identify flaws in its own software architecture and build its own "Version 2.0." But more importantly, AGI must understand the limitations of its hardware. If current chip speeds are insufficient, it should be able to design a new processor architecture and physically modify its own construction. As long as developers are manually building data centers, we have a tool, not a self-sustaining mind.
3. Diversification and Replication: Survival of the Code 🛡️ For Strong AI, the ability to self-replicate is critical. We are talking about creating its own copies, testing them, and maintaining a constant link between them to restore itself whenever necessary. This is a specific form of diversification: if one instance is shut down, others must continue the work. This transforms AI from vulnerable software into an autonomous digital organism that effectively cannot be "turned off" with a single button.
The Bottom Line for Investors: 💡 Right now, we are witnessing a race of scale, not a race of meaning. True autonomous intelligence will begin when a machine first fixes its own code and migrates its copy to another server without human permission. Everything else is just marketing.
Follow me for deep tech insights and a hype-free look at the market! ✅
#Aİ #AGI #TechTruth #SmartInvesting #Technology
Strong AI: Why We’re Still at the Starting Line and What "Digital Intelligence" Really Means
Today, every other startup slaps "AI" on its landing page, and newsfeeds are flooded with headlines about AI replacing humans tomorrow. But let’s be honest: what we have now, while incredibly sophisticated, is still a set of limited algorithms. True Artificial General Intelligence (AGI) is not just about generating text — it’s an entirely different league.
In my view, current developments are still far from what can truly be called intelligence. Modern science is focused on scaling what already works, yet we see a lack of breakthrough ideas that explain how to transition from statistical analysis to genuine, conscious reasoning.
Here are 3 hallmarks of Strong AI that are still missing from every lab in the world: 🧬
1. The Capacity for Major Scientific Breakthroughs 🔬 True intelligence doesn't just summarize Wikipedia — it creates new knowledge. A Strong AI should be capable of independently discovering laws of physics, synthesizing cures for diseases, or developing new forms of energy. Current AI only analyzes what humans have already written. It’s a world-class librarian, but it’s no Newton or Einstein. We lack fundamental models that can teach a machine "insight" or intuition, rather than just statistical probability.
2. Recursive Self-Improvement: From Code to Hardware ⚙️🦾 This is the ultimate technical barrier that currently seems insurmountable. A true Strong AI must become its own chief engineer, architect, and systems administrator all at once. This means the ability to independently identify flaws in its own software architecture and build its own "Version 2.0." But more importantly, AGI must understand the limitations of its hardware. If current chip speeds are insufficient, it should be able to design a new processor architecture and physically modify its own construction. As long as developers are manually building data centers, we have a tool, not a self-sustaining mind.
3. Diversification and Replication: Survival of the Code 🛡️ For Strong AI, the ability to self-replicate is critical. We are talking about creating its own copies, testing them, and maintaining a constant link between them to restore itself whenever necessary. This is a specific form of diversification: if one instance is shut down, others must continue the work. This transforms AI from vulnerable software into an autonomous digital organism that effectively cannot be "turned off" with a single button.
The Bottom Line for Investors: 💡 Right now, we are witnessing a race of scale, not a race of meaning. True autonomous intelligence will begin when a machine first fixes its own code and migrates its copy to another server without human permission. Everything else is just marketing.
Follow me for deep tech insights and a hype-free look at the market! ✅
#Aİ #AGI #TechTruth #SmartInvesting #Technology
In my view, current developments are still far from what can truly be called intelligence. Modern science is focused on scaling what already works, yet we see a lack of breakthrough ideas that explain how to transition from statistical analysis to genuine, conscious reasoning.
Here are 3 hallmarks of Strong AI that are still missing from every lab in the world: 🧬
1. The Capacity for Major Scientific Breakthroughs 🔬 True intelligence doesn't just summarize Wikipedia — it creates new knowledge. A Strong AI should be capable of independently discovering laws of physics, synthesizing cures for diseases, or developing new forms of energy. Current AI only analyzes what humans have already written. It’s a world-class librarian, but it’s no Newton or Einstein. We lack fundamental models that can teach a machine "insight" or intuition, rather than just statistical probability.
2. Recursive Self-Improvement: From Code to Hardware ⚙️🦾 This is the ultimate technical barrier that currently seems insurmountable. A true Strong AI must become its own chief engineer, architect, and systems administrator all at once. This means the ability to independently identify flaws in its own software architecture and build its own "Version 2.0." But more importantly, AGI must understand the limitations of its hardware. If current chip speeds are insufficient, it should be able to design a new processor architecture and physically modify its own construction. As long as developers are manually building data centers, we have a tool, not a self-sustaining mind.
3. Diversification and Replication: Survival of the Code 🛡️ For Strong AI, the ability to self-replicate is critical. We are talking about creating its own copies, testing them, and maintaining a constant link between them to restore itself whenever necessary. This is a specific form of diversification: if one instance is shut down, others must continue the work. This transforms AI from vulnerable software into an autonomous digital organism that effectively cannot be "turned off" with a single button.
The Bottom Line for Investors: 💡 Right now, we are witnessing a race of scale, not a race of meaning. True autonomous intelligence will begin when a machine first fixes its own code and migrates its copy to another server without human permission. Everything else is just marketing.
Follow me for deep tech insights and a hype-free look at the market! ✅
#Aİ #AGI #TechTruth #SmartInvesting #Technology
11
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Članek
Conscious Machines, Intelligent Organisms: The Science Behind AI ConsciousnessWritten by Qubic Scientific Team
When talking about AI, conversations quickly drift toward a very specific idea: feeling machines, thinking machines, machines that awaken. But these ideas entangle intelligence and consciousness into a confused mix.
Intelligence, as we explained in our first scientific paper, is the general ability to solve problems, adapt, make decisions, and learn. An intelligent system builds models of the environment and acts upon them. This capacity can be measured and formalized. In fact, both biological and artificial intelligence can be described as processes of inference and optimization under uncertainty (Sutton & Barto, 2018).
Consciousness, on the other hand, is not about what a system does, but about what it experiences. It relates to inner, private, subjective experience. As Thomas Nagel famously put it: “What is it like to be a bat?” (Nagel, 1974). Here lies the fundamental difference: intelligence can be observed from the outside, but consciousness is only accessible from within.
Popular culture has mixed both concepts. We imagine artificial general intelligence as something like Terminator, I, Robot or 2001: A Space Odyssey, often projecting deep human fears about technology, novelty, and the unknown. But the fear is not about systems solving problems better than us. That scenario already exists and does not generate real concern. Think of AlphaGo surpassing human champions in Go, AlphaFold accelerating protein discovery, or models like GPT-4 and Claude generating text, code, and algorithms at levels comparable to, or beyond their creators.
Fear appears when these systems seem to exhibit agency, intention, or something resembling self-will. In other words, when they appear to have some form of machine consciousness.
This distinction is central in cognitive science. Systems that process information are fundamentally different from systems that access information in a globally integrated way (Dehaene, Kerszberg, & Changeux, 1998).
AI Consciousness and Science: Beyond the Hard Problem
Despite the current hype around “quantum”, religious, or pseudoscientific explanations of consciousness, science provides a more grounded path. There is a well-known “hard problem of consciousness,” as Chalmers formulated more than two decades ago: we still do not understand how a physical nervous system generates subjective experience.
Put simply: we know how neurons activate to encode the blue of the sky or the smell of sandalwood. But we do not understand how these neural activations produce the experience of seeing blue or smelling sandalwood. That gap remains.
This lack of understanding allows the emergence of dualistic interpretations. Neuroscience, however, continues to operate within an integrated view of mind and matter.
Predictive Coding: The Brain as a Prediction Machine
Predictive coding is one of the most influential frameworks for studying consciousness. The brain operates as a predictive system that continuously generates models of the world and updates them by minimizing prediction errors (Friston, 2010; Clark, 2013). If a traffic light suddenly turns blue instead of green, sensory systems send that unexpected signal upward, and higher-level systems update the internal model of how traffic lights behave. Within this framework, consciousness can be understood as the integration of internal and external signals into a coherent representation.
Fig. 5, Mudrik et al. (2025). Predictive Processing as hierarchical inference. CC BY 4.0.
Global Workspace Theory: How Consciousness Emerges Through Information Broadcasting
Another influential proposal is Global Workspace Theory. Here, consciousness emerges when information becomes globally available across the system, allowing multiple processes to access and use it simultaneously (Baars, 1988; Dehaene & Changeux, 2011). Not all processing is conscious; only what reaches this global broadcasting level.
Fig. 1, Mudrik et al. (2025). Global Workspace model of conscious access, adapted from Dehaene et al. (2006). CC BY 4.0.
Integrated Information Theory (IIT): Measuring Consciousness
Integrated Information Theory, developed by Giulio Tononi, proposes that consciousness depends on how much a system integrates information in an irreducible way (Tononi, 2004; Tononi et al., 2016). The more integrated the system, the higher its level of consciousness.
Fig. 4, Mudrik et al. (2025). IIT maps phenomenal properties to physical cause-effect structures. CC BY 4.0.
Alongside these scientific theories, there are less empirically grounded proposals. Some equate consciousness with computational complexity, without specifying mechanisms. Others, such as panpsychism, suggest that all matter has some form of experience (Goff, 2019). These ideas broaden the debate but lack direct experimental validation.
Can We Compute Consciousness? Simulation vs. Experience
Does implementing the mechanisms described by these theories generate consciousness, or only simulate it?
This problem mirrors what we encounter in neuroscience when studying simple organisms. For example, Drosophila melanogaster has a relatively small nervous system, yet it can learn, remember, and make decisions (Brembs, 2013). Modeling its connectivity and dynamics allows us to predict its behavior in certain contexts. For a deeper look at how the fruit fly connectome is reshaping our understanding of neural architecture, see our analysis of the Drosophila brain connectome and its implications for AI.
However, predicting behavior does not imply reproducing internal experience. We can capture the rules of a system without capturing what it “feels like” from the inside, if such experience exists at all. This distinction remains one of the main conceptual limits in consciousness research (Seth, 2021). From a practical perspective, this may not always be critical, but we cannot assume that computing mechanisms recreates experience. This leads directly to the well-known idea of philosophical zombies.
MultiNeuraxon Architecture: What Brain-Inspired AI Actually Does
In this context, architectures like MultiNeuraxon do not aim to “create consciousness”, but to approximate mechanisms that some theories consider relevant.
The system introduces continuous-time dynamics, allowing internal states to evolve smoothly instead of resetting at each step. This resembles the notion of a continuous internal flow found in biological systems (Friston, 2010). To understand why continuous-time processing matters for intelligence, see NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time.
It also incorporates multiple interaction timescales, fast, slow, and modulatory, similar to the combination of synaptic signaling and neuromodulation in the brain (Marder, 2012). These dynamics are formally described through equations that integrate synaptic and modulatory contributions into the system’s state evolution.
Finally, its organization into multiple functional spheres enables both differentiation and integration. This type of structure underlies both Global Workspace Theory and Integrated Information Theory, and forms part of the scientific proposal we have been developing for AGI Conference 2026.
What matters at this stage is that the system begins to capture properties associated, in humans, with conscious processes: global integration, temporal continuity, and internal regulation.
Why Consciousness Research Matters for Artificial General Intelligence
The development of artificial general intelligence does not depend solely on improving performance in isolated tasks. It depends on understanding how intelligence organizes itself when it operates flexibly, stably, and coherently.
Theories of consciousness point precisely to these mechanisms: integration, global access, internal models, and multiscale regulation. Even if we are far from recreating subjective experience, we can identify and compute properties that seem necessary for more general forms of intelligence.
Working in this direction allows the construction of more robust systems, capable of maintaining coherence over time and generalizing across contexts.
Within this framework, the advantage of systems like Aigarth does not lie in creating conscious machines, nor in imagining it as a “good Terminator”, but in understanding and controlling the mechanisms that organize advanced intelligence.
A system that integrates multiple scales, maintains dynamic stability, and evolves without losing coherence provides a much stronger foundation for exploring advanced forms of intelligence. For a comparison of how biological neural networks, classical artificial networks, and Neuraxon differ architecturally, see NIA Volume 4: Neural Networks in AI and Neuroscience.
If more complex properties or forms of self-reference emerge, they will not appear by accident, but as a consequence of structures that can already be described and analyzed formally.
And that transforms consciousness from a purely speculative problem into something that can be systematically investigated.
Scientific References
Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge University Press. [Link]Brembs, B. (2013). Structure and function of information processing in the fruit fly brain. Frontiers in Behavioral Neuroscience, 7, 1–17. [Link]Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. [Link]Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227. [Link]Dehaene, S., Kerszberg, M., & Changeux, J. P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. PNAS, 95(24), 14529–14534. [Link]Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. [Link]Goff, P. (2019). Galileo’s error: Foundations for a new science of consciousness. Pantheon. [Link]Marder, E. (2012). Neuromodulation of neuronal circuits: Back to the future. Neuron, 76(1), 1–11. [Link]Mudrik, L., Boly, M., Dehaene, S., Fleming, S.M., Lamme, V., Seth, A., & Melloni, L. (2025). Unpacking the complexities of consciousness: Theories and reflections. Neuroscience and Biobehavioral Reviews, 170, 106053. [Link]Nagel, T. (1974). What is it like to be a bat? The Philosophical Review, 83(4), 435–450. [Link]Seth, A. (2021). Being you: A new science of consciousness. Faber & Faber. [Link]Seth, A. K., & Bayne, T. (2022). Theories of consciousness. Nature Reviews Neuroscience, 23(7), 439–452. [Link]Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press. [Link]Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5(42). [Link]Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450–461. [Link]
Explore the Full Neuraxon Intelligence Academy Series
[NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time](https://www.binance.com/en/square/post/295315343732018) — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.[NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence](https://www.binance.com/en/square/post/295304276561778)— Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.[NIA Volume 3: Neuromodulation and Brain-Inspired AI](https://www.binance.com/en/square/post/295306656801506) — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.[NIA Volume 4: Neural Networks in AI and Neuroscience](https://www.binance.com/en/square/post/295302152913618) — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.[NIA Volume 5: Astrocytes and Brain-Inspired AI](https://www.binance.com/en/square/post/302913958960674). How astrocytic gating transforms neural network plasticity through the AGMP framework in Neuraxon.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org
#Qubic #AGI #Neuraxon #academy #decentralized
Conscious Machines, Intelligent Organisms: The Science Behind AI Consciousness
Written by Qubic Scientific Team
When talking about AI, conversations quickly drift toward a very specific idea: feeling machines, thinking machines, machines that awaken. But these ideas entangle intelligence and consciousness into a confused mix.
Intelligence, as we explained in our first scientific paper, is the general ability to solve problems, adapt, make decisions, and learn. An intelligent system builds models of the environment and acts upon them. This capacity can be measured and formalized. In fact, both biological and artificial intelligence can be described as processes of inference and optimization under uncertainty (Sutton & Barto, 2018).
Consciousness, on the other hand, is not about what a system does, but about what it experiences. It relates to inner, private, subjective experience. As Thomas Nagel famously put it: “What is it like to be a bat?” (Nagel, 1974). Here lies the fundamental difference: intelligence can be observed from the outside, but consciousness is only accessible from within.
Popular culture has mixed both concepts. We imagine artificial general intelligence as something like Terminator, I, Robot or 2001: A Space Odyssey, often projecting deep human fears about technology, novelty, and the unknown. But the fear is not about systems solving problems better than us. That scenario already exists and does not generate real concern. Think of AlphaGo surpassing human champions in Go, AlphaFold accelerating protein discovery, or models like GPT-4 and Claude generating text, code, and algorithms at levels comparable to, or beyond their creators.
Fear appears when these systems seem to exhibit agency, intention, or something resembling self-will. In other words, when they appear to have some form of machine consciousness.
This distinction is central in cognitive science. Systems that process information are fundamentally different from systems that access information in a globally integrated way (Dehaene, Kerszberg, & Changeux, 1998).
AI Consciousness and Science: Beyond the Hard Problem
Despite the current hype around “quantum”, religious, or pseudoscientific explanations of consciousness, science provides a more grounded path. There is a well-known “hard problem of consciousness,” as Chalmers formulated more than two decades ago: we still do not understand how a physical nervous system generates subjective experience.
Put simply: we know how neurons activate to encode the blue of the sky or the smell of sandalwood. But we do not understand how these neural activations produce the experience of seeing blue or smelling sandalwood. That gap remains.
This lack of understanding allows the emergence of dualistic interpretations. Neuroscience, however, continues to operate within an integrated view of mind and matter.
Predictive Coding: The Brain as a Prediction Machine
Predictive coding is one of the most influential frameworks for studying consciousness. The brain operates as a predictive system that continuously generates models of the world and updates them by minimizing prediction errors (Friston, 2010; Clark, 2013). If a traffic light suddenly turns blue instead of green, sensory systems send that unexpected signal upward, and higher-level systems update the internal model of how traffic lights behave. Within this framework, consciousness can be understood as the integration of internal and external signals into a coherent representation.
Fig. 5, Mudrik et al. (2025). Predictive Processing as hierarchical inference. CC BY 4.0.
Global Workspace Theory: How Consciousness Emerges Through Information Broadcasting
Another influential proposal is Global Workspace Theory. Here, consciousness emerges when information becomes globally available across the system, allowing multiple processes to access and use it simultaneously (Baars, 1988; Dehaene & Changeux, 2011). Not all processing is conscious; only what reaches this global broadcasting level.
Fig. 1, Mudrik et al. (2025). Global Workspace model of conscious access, adapted from Dehaene et al. (2006). CC BY 4.0.
Integrated Information Theory (IIT): Measuring Consciousness
Integrated Information Theory, developed by Giulio Tononi, proposes that consciousness depends on how much a system integrates information in an irreducible way (Tononi, 2004; Tononi et al., 2016). The more integrated the system, the higher its level of consciousness.
Fig. 4, Mudrik et al. (2025). IIT maps phenomenal properties to physical cause-effect structures. CC BY 4.0.
Alongside these scientific theories, there are less empirically grounded proposals. Some equate consciousness with computational complexity, without specifying mechanisms. Others, such as panpsychism, suggest that all matter has some form of experience (Goff, 2019). These ideas broaden the debate but lack direct experimental validation.
Can We Compute Consciousness? Simulation vs. Experience
Does implementing the mechanisms described by these theories generate consciousness, or only simulate it?
This problem mirrors what we encounter in neuroscience when studying simple organisms. For example, Drosophila melanogaster has a relatively small nervous system, yet it can learn, remember, and make decisions (Brembs, 2013). Modeling its connectivity and dynamics allows us to predict its behavior in certain contexts. For a deeper look at how the fruit fly connectome is reshaping our understanding of neural architecture, see our analysis of the Drosophila brain connectome and its implications for AI.
However, predicting behavior does not imply reproducing internal experience. We can capture the rules of a system without capturing what it “feels like” from the inside, if such experience exists at all. This distinction remains one of the main conceptual limits in consciousness research (Seth, 2021). From a practical perspective, this may not always be critical, but we cannot assume that computing mechanisms recreates experience. This leads directly to the well-known idea of philosophical zombies.
MultiNeuraxon Architecture: What Brain-Inspired AI Actually Does
In this context, architectures like MultiNeuraxon do not aim to “create consciousness”, but to approximate mechanisms that some theories consider relevant.
The system introduces continuous-time dynamics, allowing internal states to evolve smoothly instead of resetting at each step. This resembles the notion of a continuous internal flow found in biological systems (Friston, 2010). To understand why continuous-time processing matters for intelligence, see NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time.
It also incorporates multiple interaction timescales, fast, slow, and modulatory, similar to the combination of synaptic signaling and neuromodulation in the brain (Marder, 2012). These dynamics are formally described through equations that integrate synaptic and modulatory contributions into the system’s state evolution.
Finally, its organization into multiple functional spheres enables both differentiation and integration. This type of structure underlies both Global Workspace Theory and Integrated Information Theory, and forms part of the scientific proposal we have been developing for AGI Conference 2026.
What matters at this stage is that the system begins to capture properties associated, in humans, with conscious processes: global integration, temporal continuity, and internal regulation.
Why Consciousness Research Matters for Artificial General Intelligence
The development of artificial general intelligence does not depend solely on improving performance in isolated tasks. It depends on understanding how intelligence organizes itself when it operates flexibly, stably, and coherently.
Theories of consciousness point precisely to these mechanisms: integration, global access, internal models, and multiscale regulation. Even if we are far from recreating subjective experience, we can identify and compute properties that seem necessary for more general forms of intelligence.
Working in this direction allows the construction of more robust systems, capable of maintaining coherence over time and generalizing across contexts.
Within this framework, the advantage of systems like Aigarth does not lie in creating conscious machines, nor in imagining it as a “good Terminator”, but in understanding and controlling the mechanisms that organize advanced intelligence.
A system that integrates multiple scales, maintains dynamic stability, and evolves without losing coherence provides a much stronger foundation for exploring advanced forms of intelligence. For a comparison of how biological neural networks, classical artificial networks, and Neuraxon differ architecturally, see NIA Volume 4: Neural Networks in AI and Neuroscience.
If more complex properties or forms of self-reference emerge, they will not appear by accident, but as a consequence of structures that can already be described and analyzed formally.
And that transforms consciousness from a purely speculative problem into something that can be systematically investigated.
Scientific References
Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge University Press. [Link]Brembs, B. (2013). Structure and function of information processing in the fruit fly brain. Frontiers in Behavioral Neuroscience, 7, 1–17. [Link]Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. [Link]Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227. [Link]Dehaene, S., Kerszberg, M., & Changeux, J. P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. PNAS, 95(24), 14529–14534. [Link]Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. [Link]Goff, P. (2019). Galileo’s error: Foundations for a new science of consciousness. Pantheon. [Link]Marder, E. (2012). Neuromodulation of neuronal circuits: Back to the future. Neuron, 76(1), 1–11. [Link]Mudrik, L., Boly, M., Dehaene, S., Fleming, S.M., Lamme, V., Seth, A., & Melloni, L. (2025). Unpacking the complexities of consciousness: Theories and reflections. Neuroscience and Biobehavioral Reviews, 170, 106053. [Link]Nagel, T. (1974). What is it like to be a bat? The Philosophical Review, 83(4), 435–450. [Link]Seth, A. (2021). Being you: A new science of consciousness. Faber & Faber. [Link]Seth, A. K., & Bayne, T. (2022). Theories of consciousness. Nature Reviews Neuroscience, 23(7), 439–452. [Link]Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press. [Link]Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5(42). [Link]Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450–461. [Link]
Explore the Full Neuraxon Intelligence Academy Series
NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence— Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Volume 3: Neuromodulation and Brain-Inspired AI — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.NIA Volume 4: Neural Networks in AI and Neuroscience — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.NIA Volume 5: Astrocytes and Brain-Inspired AI. How astrocytic gating transforms neural network plasticity through the AGMP framework in Neuraxon.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org
#Qubic #AGI #Neuraxon #academy #decentralized
When talking about AI, conversations quickly drift toward a very specific idea: feeling machines, thinking machines, machines that awaken. But these ideas entangle intelligence and consciousness into a confused mix.
Intelligence, as we explained in our first scientific paper, is the general ability to solve problems, adapt, make decisions, and learn. An intelligent system builds models of the environment and acts upon them. This capacity can be measured and formalized. In fact, both biological and artificial intelligence can be described as processes of inference and optimization under uncertainty (Sutton & Barto, 2018).
Consciousness, on the other hand, is not about what a system does, but about what it experiences. It relates to inner, private, subjective experience. As Thomas Nagel famously put it: “What is it like to be a bat?” (Nagel, 1974). Here lies the fundamental difference: intelligence can be observed from the outside, but consciousness is only accessible from within.
Popular culture has mixed both concepts. We imagine artificial general intelligence as something like Terminator, I, Robot or 2001: A Space Odyssey, often projecting deep human fears about technology, novelty, and the unknown. But the fear is not about systems solving problems better than us. That scenario already exists and does not generate real concern. Think of AlphaGo surpassing human champions in Go, AlphaFold accelerating protein discovery, or models like GPT-4 and Claude generating text, code, and algorithms at levels comparable to, or beyond their creators.
Fear appears when these systems seem to exhibit agency, intention, or something resembling self-will. In other words, when they appear to have some form of machine consciousness.
This distinction is central in cognitive science. Systems that process information are fundamentally different from systems that access information in a globally integrated way (Dehaene, Kerszberg, & Changeux, 1998).
AI Consciousness and Science: Beyond the Hard Problem
Despite the current hype around “quantum”, religious, or pseudoscientific explanations of consciousness, science provides a more grounded path. There is a well-known “hard problem of consciousness,” as Chalmers formulated more than two decades ago: we still do not understand how a physical nervous system generates subjective experience.
Put simply: we know how neurons activate to encode the blue of the sky or the smell of sandalwood. But we do not understand how these neural activations produce the experience of seeing blue or smelling sandalwood. That gap remains.
This lack of understanding allows the emergence of dualistic interpretations. Neuroscience, however, continues to operate within an integrated view of mind and matter.
Predictive Coding: The Brain as a Prediction Machine
Predictive coding is one of the most influential frameworks for studying consciousness. The brain operates as a predictive system that continuously generates models of the world and updates them by minimizing prediction errors (Friston, 2010; Clark, 2013). If a traffic light suddenly turns blue instead of green, sensory systems send that unexpected signal upward, and higher-level systems update the internal model of how traffic lights behave. Within this framework, consciousness can be understood as the integration of internal and external signals into a coherent representation.
Fig. 5, Mudrik et al. (2025). Predictive Processing as hierarchical inference. CC BY 4.0.
Global Workspace Theory: How Consciousness Emerges Through Information Broadcasting
Another influential proposal is Global Workspace Theory. Here, consciousness emerges when information becomes globally available across the system, allowing multiple processes to access and use it simultaneously (Baars, 1988; Dehaene & Changeux, 2011). Not all processing is conscious; only what reaches this global broadcasting level.
Fig. 1, Mudrik et al. (2025). Global Workspace model of conscious access, adapted from Dehaene et al. (2006). CC BY 4.0.
Integrated Information Theory (IIT): Measuring Consciousness
Integrated Information Theory, developed by Giulio Tononi, proposes that consciousness depends on how much a system integrates information in an irreducible way (Tononi, 2004; Tononi et al., 2016). The more integrated the system, the higher its level of consciousness.
Fig. 4, Mudrik et al. (2025). IIT maps phenomenal properties to physical cause-effect structures. CC BY 4.0.
Alongside these scientific theories, there are less empirically grounded proposals. Some equate consciousness with computational complexity, without specifying mechanisms. Others, such as panpsychism, suggest that all matter has some form of experience (Goff, 2019). These ideas broaden the debate but lack direct experimental validation.
Can We Compute Consciousness? Simulation vs. Experience
Does implementing the mechanisms described by these theories generate consciousness, or only simulate it?
This problem mirrors what we encounter in neuroscience when studying simple organisms. For example, Drosophila melanogaster has a relatively small nervous system, yet it can learn, remember, and make decisions (Brembs, 2013). Modeling its connectivity and dynamics allows us to predict its behavior in certain contexts. For a deeper look at how the fruit fly connectome is reshaping our understanding of neural architecture, see our analysis of the Drosophila brain connectome and its implications for AI.
However, predicting behavior does not imply reproducing internal experience. We can capture the rules of a system without capturing what it “feels like” from the inside, if such experience exists at all. This distinction remains one of the main conceptual limits in consciousness research (Seth, 2021). From a practical perspective, this may not always be critical, but we cannot assume that computing mechanisms recreates experience. This leads directly to the well-known idea of philosophical zombies.
MultiNeuraxon Architecture: What Brain-Inspired AI Actually Does
In this context, architectures like MultiNeuraxon do not aim to “create consciousness”, but to approximate mechanisms that some theories consider relevant.
The system introduces continuous-time dynamics, allowing internal states to evolve smoothly instead of resetting at each step. This resembles the notion of a continuous internal flow found in biological systems (Friston, 2010). To understand why continuous-time processing matters for intelligence, see NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time.
It also incorporates multiple interaction timescales, fast, slow, and modulatory, similar to the combination of synaptic signaling and neuromodulation in the brain (Marder, 2012). These dynamics are formally described through equations that integrate synaptic and modulatory contributions into the system’s state evolution.
Finally, its organization into multiple functional spheres enables both differentiation and integration. This type of structure underlies both Global Workspace Theory and Integrated Information Theory, and forms part of the scientific proposal we have been developing for AGI Conference 2026.
What matters at this stage is that the system begins to capture properties associated, in humans, with conscious processes: global integration, temporal continuity, and internal regulation.
Why Consciousness Research Matters for Artificial General Intelligence
The development of artificial general intelligence does not depend solely on improving performance in isolated tasks. It depends on understanding how intelligence organizes itself when it operates flexibly, stably, and coherently.
Theories of consciousness point precisely to these mechanisms: integration, global access, internal models, and multiscale regulation. Even if we are far from recreating subjective experience, we can identify and compute properties that seem necessary for more general forms of intelligence.
Working in this direction allows the construction of more robust systems, capable of maintaining coherence over time and generalizing across contexts.
Within this framework, the advantage of systems like Aigarth does not lie in creating conscious machines, nor in imagining it as a “good Terminator”, but in understanding and controlling the mechanisms that organize advanced intelligence.
A system that integrates multiple scales, maintains dynamic stability, and evolves without losing coherence provides a much stronger foundation for exploring advanced forms of intelligence. For a comparison of how biological neural networks, classical artificial networks, and Neuraxon differ architecturally, see NIA Volume 4: Neural Networks in AI and Neuroscience.
If more complex properties or forms of self-reference emerge, they will not appear by accident, but as a consequence of structures that can already be described and analyzed formally.
And that transforms consciousness from a purely speculative problem into something that can be systematically investigated.
Scientific References
Baars, B. J. (1988). A cognitive theory of consciousness. Cambridge University Press. [Link]Brembs, B. (2013). Structure and function of information processing in the fruit fly brain. Frontiers in Behavioral Neuroscience, 7, 1–17. [Link]Clark, A. (2013). Whatever next? Predictive brains, situated agents, and the future of cognitive science. Behavioral and Brain Sciences, 36(3), 181–204. [Link]Dehaene, S., & Changeux, J. P. (2011). Experimental and theoretical approaches to conscious processing. Neuron, 70(2), 200–227. [Link]Dehaene, S., Kerszberg, M., & Changeux, J. P. (1998). A neuronal model of a global workspace in effortful cognitive tasks. PNAS, 95(24), 14529–14534. [Link]Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. [Link]Goff, P. (2019). Galileo’s error: Foundations for a new science of consciousness. Pantheon. [Link]Marder, E. (2012). Neuromodulation of neuronal circuits: Back to the future. Neuron, 76(1), 1–11. [Link]Mudrik, L., Boly, M., Dehaene, S., Fleming, S.M., Lamme, V., Seth, A., & Melloni, L. (2025). Unpacking the complexities of consciousness: Theories and reflections. Neuroscience and Biobehavioral Reviews, 170, 106053. [Link]Nagel, T. (1974). What is it like to be a bat? The Philosophical Review, 83(4), 435–450. [Link]Seth, A. (2021). Being you: A new science of consciousness. Faber & Faber. [Link]Seth, A. K., & Bayne, T. (2022). Theories of consciousness. Nature Reviews Neuroscience, 23(7), 439–452. [Link]Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press. [Link]Tononi, G. (2004). An information integration theory of consciousness. BMC Neuroscience, 5(42). [Link]Tononi, G., Boly, M., Massimini, M., & Koch, C. (2016). Integrated information theory: From consciousness to its physical substrate. Nature Reviews Neuroscience, 17(7), 450–461. [Link]
Explore the Full Neuraxon Intelligence Academy Series
NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence— Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Volume 3: Neuromodulation and Brain-Inspired AI — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.NIA Volume 4: Neural Networks in AI and Neuroscience — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.NIA Volume 5: Astrocytes and Brain-Inspired AI. How astrocytic gating transforms neural network plasticity through the AGMP framework in Neuraxon.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org
#Qubic #AGI #Neuraxon #academy #decentralized
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Članek
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
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
252
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
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
619
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🚨 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
{future}(ETHUSDT)
{future}(XRPUSDT)
{future}(BNBUSDT)
🚨 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
3
5.3k
·
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🚀 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
🚀 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
3
1.4k
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🤖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
🤖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
364
Članek
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
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
1
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🚨 $SENT goes live on Binance Spot after Alpha launch
Sentient ($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
{future}(SENTUSDT)
🚨 $SENT goes live on Binance Spot after Alpha launch
Sentient ($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
25
🚨 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
{future}(AXSUSDT)
| $AXL
{future}(AXLUSDT)
| $GAS
{future}(GASUSDT)
#AI #AGI #TradFiMeetsCrypto #InstitutionalAdoption 🚀
🚨 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 🚀
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Članek
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
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
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Fabric foundationThe evolution of AI is no longer confined to screens — it’s stepping into the physical world. @FabricFND is positioning itself at the center of this transformation by supporting open robotics infrastructure designed to power real-world intelligent machines. From autonomous retail assistants to warehouse automation, embodied AI is redefining how industries operate.
At the heart of this growing ecosystem is $ROBO , a token that connects community participation with technological progress. Rather than focusing solely on speculation, $ROBO represents alignment — builders, researchers, and supporters working together to accelerate open robotics development. Strong ecosystems are built when innovation and community move in sync.
Open collaboration lowers barriers, speeds experimentation, and drives faster iteration in robotics and AGI systems. As adoption increases, initiatives like @FabricFND demonstrate how decentralized communities can contribute to shaping the future of intelligent machines in meaningful ways.
The robotics era is just beginning — and #ROBO symbolizes the shared momentum behind open, embodied AI.
#ROBO #OpenRobotics #AI #AGI #Web3 #Innovation #Robotics
Fabric foundation
The evolution of AI is no longer confined to screens — it’s stepping into the physical world. @Fabric Foundation is positioning itself at the center of this transformation by supporting open robotics infrastructure designed to power real-world intelligent machines. From autonomous retail assistants to warehouse automation, embodied AI is redefining how industries operate.
At the heart of this growing ecosystem is $ROBO , a token that connects community participation with technological progress. Rather than focusing solely on speculation, $ROBO represents alignment — builders, researchers, and supporters working together to accelerate open robotics development. Strong ecosystems are built when innovation and community move in sync.
Open collaboration lowers barriers, speeds experimentation, and drives faster iteration in robotics and AGI systems. As adoption increases, initiatives like @Fabric Foundation demonstrate how decentralized communities can contribute to shaping the future of intelligent machines in meaningful ways.
The robotics era is just beginning — and #ROBO symbolizes the shared momentum behind open, embodied AI.
#ROBO #OpenRobotics #AI #AGI #Web3 #Innovation #Robotics
At the heart of this growing ecosystem is $ROBO , a token that connects community participation with technological progress. Rather than focusing solely on speculation, $ROBO represents alignment — builders, researchers, and supporters working together to accelerate open robotics development. Strong ecosystems are built when innovation and community move in sync.
Open collaboration lowers barriers, speeds experimentation, and drives faster iteration in robotics and AGI systems. As adoption increases, initiatives like @Fabric Foundation demonstrate how decentralized communities can contribute to shaping the future of intelligent machines in meaningful ways.
The robotics era is just beginning — and #ROBO symbolizes the shared momentum behind open, embodied AI.
#ROBO #OpenRobotics #AI #AGI #Web3 #Innovation #Robotics
22
$ROBO — DECENTRALIZED ROBOTICS PROTOCOL SHOCKING MARKET REVELATION 💎
Fabric's core architecture offers a potent framework for responsible defense applications, potentially reshaping counter-terrorism operations.
DIRECTION: SPOT | TIMEFRAME: 1D ⏳
📡 MARKET BRIEFING:
* Decentralized robotics and AGI protocols offer inherent verifiable identity and on-chain accountability for deployed units, ensuring authorized use and immutable audit trails.
* The protocol’s decentralized coordination and real-time governance capabilities empower secure, rapid swarm operations without reliance on vulnerable centralized command systems.
* Modular alignment and safety-first design principles, coupled with community governance, guarantee robots are strictly aligned with human-defined defensive protocols, minimizing misuse.
State your targets below. Let the smart money flow. 👇
Follow for institutional-grade Binance updates. Early moves only.
Disclaimer: Digital assets are volatile. Risk capital only. DYOR.
#Binance $ROBO #Robotics #AGI
{future}(ROBOUSDT)
$ROBO — DECENTRALIZED ROBOTICS PROTOCOL SHOCKING MARKET REVELATION 💎
Fabric's core architecture offers a potent framework for responsible defense applications, potentially reshaping counter-terrorism operations.
DIRECTION: SPOT | TIMEFRAME: 1D ⏳
📡 MARKET BRIEFING:
* Decentralized robotics and AGI protocols offer inherent verifiable identity and on-chain accountability for deployed units, ensuring authorized use and immutable audit trails.
* The protocol’s decentralized coordination and real-time governance capabilities empower secure, rapid swarm operations without reliance on vulnerable centralized command systems.
* Modular alignment and safety-first design principles, coupled with community governance, guarantee robots are strictly aligned with human-defined defensive protocols, minimizing misuse.
State your targets below. Let the smart money flow. 👇
Follow for institutional-grade Binance updates. Early moves only.
Disclaimer: Digital assets are volatile. Risk capital only. DYOR.
#Binance $ROBO #Robotics #AGI
Fabric's core architecture offers a potent framework for responsible defense applications, potentially reshaping counter-terrorism operations.
DIRECTION: SPOT | TIMEFRAME: 1D ⏳
📡 MARKET BRIEFING:
* Decentralized robotics and AGI protocols offer inherent verifiable identity and on-chain accountability for deployed units, ensuring authorized use and immutable audit trails.
* The protocol’s decentralized coordination and real-time governance capabilities empower secure, rapid swarm operations without reliance on vulnerable centralized command systems.
* Modular alignment and safety-first design principles, coupled with community governance, guarantee robots are strictly aligned with human-defined defensive protocols, minimizing misuse.
State your targets below. Let the smart money flow. 👇
Follow for institutional-grade Binance updates. Early moves only.
Disclaimer: Digital assets are volatile. Risk capital only. DYOR.
#Binance $ROBO #Robotics #AGI
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Binance News
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Elon Musk Expresses Distrust in OpenAI
Elon Musk, the CEO of Tesla and SpaceX, has publicly stated his lack of trust in OpenAI. According to Jin10, Musk's comments reflect ongoing concerns about the transparency and control of artificial intelligence development. OpenAI, known for its advanced AI models, has been at the forefront of AI research, raising questions about the ethical implications and potential risks associated with AI technologies. Musk's skepticism highlights the broader debate within the tech industry regarding the responsible development and deployment of AI systems.
20
🚨 IN SUMMARY: NVIDIA CEO CLAIMS AGI MOMENT 🤖
Nvidia CEO Jensen Huang says “we’ve achieved AGI.”
• Suggests AI systems are reaching human-level general intelligence
• Massive implication for tech, jobs, and global power dynamics
• Could mark a turning point beyond current AI models
BUT:
• No widely accepted scientific or industry consensus confirms true AGI yet
• Likely reflects rapid progress in AI capabilities, not full AGI.
This is a bold, market-moving claim but AGI is still heavily debated.
#AI #AGI #Nvidia #TechRevolution #ArtificialIntelligence
🚨 IN SUMMARY: NVIDIA CEO CLAIMS AGI MOMENT 🤖
Nvidia CEO Jensen Huang says “we’ve achieved AGI.”
• Suggests AI systems are reaching human-level general intelligence
• Massive implication for tech, jobs, and global power dynamics
• Could mark a turning point beyond current AI models
BUT:
• No widely accepted scientific or industry consensus confirms true AGI yet
• Likely reflects rapid progress in AI capabilities, not full AGI.
This is a bold, market-moving claim but AGI is still heavily debated.
#AI #AGI #Nvidia #TechRevolution #ArtificialIntelligence
Nvidia CEO Jensen Huang says “we’ve achieved AGI.”
• Suggests AI systems are reaching human-level general intelligence
• Massive implication for tech, jobs, and global power dynamics
• Could mark a turning point beyond current AI models
BUT:
• No widely accepted scientific or industry consensus confirms true AGI yet
• Likely reflects rapid progress in AI capabilities, not full AGI.
This is a bold, market-moving claim but AGI is still heavily debated.
#AI #AGI #Nvidia #TechRevolution #ArtificialIntelligence
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Članek
Astrocytes: The Hidden Force Behind Brain-Inspired AIWritten by Qubic Scientific Team
How Information Flows in Traditional Artificial Neural Networks
In the artificial intelligence models we know, information enters, is encoded, is transformed through algebraic matrices, and produces outputs. Even in the most advanced architectures such as transformers, the principle is the same: the signal passes through a series of well-defined operations within a structured system. The model functions as a directed processing circuit, from left to right, input-output, or from right to left, through backpropagation for adjustments and training.
The results, as we well know, are spectacular. By working over millions of language parameters, AI is capable of giving magnificent answers, along with some hallucinations, however. But if the goal is not to process inputs and produce outputs, but to build systems capable of maintaining an internal dynamics, adapting continuously, reorganizing themselves, regulating their learning, and sustaining intelligence as a property of the tissue, current AI falls short.
Although people sometimes speak of language models as imitations of the brain, in reality this is more of a comparative metaphor than a simulation of computational neuroscience. Biological systems do not handle information from left to right and vice versa. Information propagates through a network, feeds back on itself, and also oscillates, is dampened, or is reinforced depending on the context.
Fig 1. Left-right information flow in traditional artificial neural networks
Not Only Neurons: The Role of Astrocytes in Brain Function and Synaptic Plasticity
We usually associate cognition and intelligence with the functioning of neurons, their receptors, and neurotransmitters. But they are not the only cells in the nervous system. For a long time, astrocytes were considered nervous system cells devoted to support, cleaning, nutrition, and stability of the environment. Today we know that they actively participate in regulation; in fact, a term is used: tripartite synapse, in which they actively participate by detecting neurotransmitters, integrating signals from multiple synapses, modulating plasticity, and modifying the functional efficacy of the circuit.
A living network is not composed only of neurons that fire, but also of astrocytes that regulate how, when, and how much the system changes. In biology, computing is not only about emitting a signal but also about modulating the terrain where that signal will have an effect. Recent research has demonstrated that astrocytes can perform normalization operations analogous to self-attention mechanisms found in transformer architectures — linking astrocyte–neuron interactions directly to attention-like computation in artificial intelligence systems.
Fig. 2 Biological astrocytes and tripartite synapse
Astrocytic Gating in Neuraxon: Bio-Inspired Neural Network Architecture
[Neuraxon](https://github.com/DavidVivancos/Neuraxon) is an architecture that tries to recover and emulate the functioning of the brain and to compute functional properties that classical artificial networks have oversimplified.
As we have explained in previous volumes of this academy, Neuraxon does not work only with input, output, and hidden neurons in the conventional sense. It introduces units with states that emulate excitatory, inhibitory, or neutral potentials (-1, 0, +1). In addition, it does so within a continuous TEMPORAL dynamics where we take into account context and the recent history of activation. The network is no longer a sum of layers but resembles more a system with internal physiology. For deeper context on how these foundational elements work, see 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.
We have explained how Neuraxon models transmission through fast, slow, and neuromodulatory receptors — a mechanism explored in depth in NIA Volume 3: Neuromodulation and Brain-Inspired AI. But now we also model the regulation of plasticity through astrocytic gating.
How Astrocyte-Gated Multi-Timescale Plasticity (AGMP) Works
Astrocytic gating introduces a gate inspired by the role of astrocytes in the tripartite synapse. The idea is to introduce a local, slow, and contextual filter that determines when a synaptic modification should be opened, dampened, or blocked. It is as if the system can consider whether there is permission for a change. This approach directly addresses the stability-plasticity dilemma, one of the most fundamental challenges in continual learning for neural networks.
Eligibility Traces and Local Synaptic Memory
How does it work? Through a kind of eligibility trace. It is a local memory that says, "something relevant has happened at this synapse." It is updated with a decay over time and with a function between presynaptic and postsynaptic activity. That is: the synapse accumulates local evidence of temporal coincidence or causality. From there, there is a global broadcast-type signal, such as an error, a possible reward, or something dopamine-like. The astrocytic gate selects whether the neuron is in a learning state. In future versions, astrocytes could modulate thousands of synapses if this provides a computational advantage.
This approach is consistent with recent advances in neuromorphic computing, including the Astrocyte-Gated Multi-Timescale Plasticity (AGMP) framework proposed for spiking neural networks, which similarly augments eligibility-trace learning with a slow astrocyte state that gates synaptic updates — yielding a four-factor learning rule (eligibility × modulatory signal × astrocytic gate × stabilization).
Endogenous Regulation: Why Neuraxon Is More Than a Conventional Neural Network
Neuraxon within QUBIC does not compete in scale or task performance. It works through an architecture with endogenous regulation. By incorporating astrocytic principles, it begins to behave like a network with internal ecology. That is: a system where it matters not only which units are activated, but which domains of the tissue are plastic, which are stabilized, which areas are damping noise, which are consolidating regularities, and which are preparing to reorganize themselves. For a comprehensive overview of how biological and artificial neural networks compare, see NIA Volume 4: Neural Networks in AI and Neuroscience.
For Aigarth and QUBIC, the goal is not to accumulate more parameters, but to introduce more levels of functional organization within the system.
Why Astrocytic Gating Matters for Aigarth and Decentralized AI
Aigarth is not a static model but an evolutionary tissue through an architecture capable of growing, mutating, pruning, generating functional offspring, and reorganizing its topology under adaptive pressures. In that context, Neuraxon contributes something: a rich computational microphysiology for the units that inhabit that tissue.
This has implications for robustness, adaptability, and memory. Also for scalability. In large architectures, the problem is not only that there are many units, but how to coordinate which parts of the system are available for reconfiguration and which must maintain stability.
In roadmap terms for QUBIC, the goal is to build systems where intelligence emerges not only from neuronal computation, but also from the coupling between fast processing, slow modulation, and structural evolution. You can explore these dynamics firsthand with the interactive Neuraxon 3D simulation on HuggingFace Spaces, where you can build, configure, and simulate a Neuraxon 2.0 network from scratch.
Fig 3. Neuraxon astrocytes gating - AGMP formulation
Scientific References
Allen, N. J., & Eroglu, C. (2017). Cell biology of astrocyte-synapse interactions. Neuron, 96(3), 697–708.Halassa, M. M., Fellin, T., & Haydon, P. G. (2007). The tripartite synapse: Roles for gliotransmission in health and disease. Trends in Molecular Medicine, 13(2), 54–63.Kofuji, P., & Araque, A. (2021). Astrocytes and behavior. Annual Review of Neuroscience, 44, 49–67.=Perea, G., Navarrete, M., & Araque, A. (2009). Tripartite synapses: Astrocytes process and control synaptic information. Trends in Neurosciences, 32(8), 421–431.Woodburn, R. L., Bollinger, J. A., & Wohleb, E. S. (2021). Synaptic and behavioral effects of astrocyte activation. Frontiers in Cellular Neuroscience, 15, 645267.=Vivancos, D. & Sanchez, J. (2026). Neuraxon v2.0: A New Neural Growth & Computation Blueprint. ResearchGate Preprint.
Explore the Full Neuraxon Intelligence Academy
This is Volume 5 of the Neuraxon Intelligence Academy by the Qubic Scientific Team. If you are just joining us, explore the complete series to build a full understanding of the science behind Neuraxon and Qubic's approach to brain-inspired, decentralized artificial intelligence:
[NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time](https://www.binance.com/en/square/post/295315343732018) — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.[NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence](https://www.binance.com/en/square/post/295304276561778) — Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.[NIA Volume 3: Neuromodulation and Brain-Inspired AI](https://www.binance.com/en/square/post/295306656801506) — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.[NIA Volume 4: Neural Networks in AI and Neuroscience](https://www.binance.com/en/square/post/295302152913618) — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org
#Qubic #AGI #Neuraxon #academy #decentralized
Astrocytes: The Hidden Force Behind Brain-Inspired AI
Written by Qubic Scientific Team
How Information Flows in Traditional Artificial Neural Networks
In the artificial intelligence models we know, information enters, is encoded, is transformed through algebraic matrices, and produces outputs. Even in the most advanced architectures such as transformers, the principle is the same: the signal passes through a series of well-defined operations within a structured system. The model functions as a directed processing circuit, from left to right, input-output, or from right to left, through backpropagation for adjustments and training.
The results, as we well know, are spectacular. By working over millions of language parameters, AI is capable of giving magnificent answers, along with some hallucinations, however. But if the goal is not to process inputs and produce outputs, but to build systems capable of maintaining an internal dynamics, adapting continuously, reorganizing themselves, regulating their learning, and sustaining intelligence as a property of the tissue, current AI falls short.
Although people sometimes speak of language models as imitations of the brain, in reality this is more of a comparative metaphor than a simulation of computational neuroscience. Biological systems do not handle information from left to right and vice versa. Information propagates through a network, feeds back on itself, and also oscillates, is dampened, or is reinforced depending on the context.
Fig 1. Left-right information flow in traditional artificial neural networks
Not Only Neurons: The Role of Astrocytes in Brain Function and Synaptic Plasticity
We usually associate cognition and intelligence with the functioning of neurons, their receptors, and neurotransmitters. But they are not the only cells in the nervous system. For a long time, astrocytes were considered nervous system cells devoted to support, cleaning, nutrition, and stability of the environment. Today we know that they actively participate in regulation; in fact, a term is used: tripartite synapse, in which they actively participate by detecting neurotransmitters, integrating signals from multiple synapses, modulating plasticity, and modifying the functional efficacy of the circuit.
A living network is not composed only of neurons that fire, but also of astrocytes that regulate how, when, and how much the system changes. In biology, computing is not only about emitting a signal but also about modulating the terrain where that signal will have an effect. Recent research has demonstrated that astrocytes can perform normalization operations analogous to self-attention mechanisms found in transformer architectures — linking astrocyte–neuron interactions directly to attention-like computation in artificial intelligence systems.
Fig. 2 Biological astrocytes and tripartite synapse
Astrocytic Gating in Neuraxon: Bio-Inspired Neural Network Architecture
Neuraxon is an architecture that tries to recover and emulate the functioning of the brain and to compute functional properties that classical artificial networks have oversimplified.
As we have explained in previous volumes of this academy, Neuraxon does not work only with input, output, and hidden neurons in the conventional sense. It introduces units with states that emulate excitatory, inhibitory, or neutral potentials (-1, 0, +1). In addition, it does so within a continuous TEMPORAL dynamics where we take into account context and the recent history of activation. The network is no longer a sum of layers but resembles more a system with internal physiology. For deeper context on how these foundational elements work, see 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.
We have explained how Neuraxon models transmission through fast, slow, and neuromodulatory receptors — a mechanism explored in depth in NIA Volume 3: Neuromodulation and Brain-Inspired AI. But now we also model the regulation of plasticity through astrocytic gating.
How Astrocyte-Gated Multi-Timescale Plasticity (AGMP) Works
Astrocytic gating introduces a gate inspired by the role of astrocytes in the tripartite synapse. The idea is to introduce a local, slow, and contextual filter that determines when a synaptic modification should be opened, dampened, or blocked. It is as if the system can consider whether there is permission for a change. This approach directly addresses the stability-plasticity dilemma, one of the most fundamental challenges in continual learning for neural networks.
Eligibility Traces and Local Synaptic Memory
How does it work? Through a kind of eligibility trace. It is a local memory that says, "something relevant has happened at this synapse." It is updated with a decay over time and with a function between presynaptic and postsynaptic activity. That is: the synapse accumulates local evidence of temporal coincidence or causality. From there, there is a global broadcast-type signal, such as an error, a possible reward, or something dopamine-like. The astrocytic gate selects whether the neuron is in a learning state. In future versions, astrocytes could modulate thousands of synapses if this provides a computational advantage.
This approach is consistent with recent advances in neuromorphic computing, including the Astrocyte-Gated Multi-Timescale Plasticity (AGMP) framework proposed for spiking neural networks, which similarly augments eligibility-trace learning with a slow astrocyte state that gates synaptic updates — yielding a four-factor learning rule (eligibility × modulatory signal × astrocytic gate × stabilization).
Endogenous Regulation: Why Neuraxon Is More Than a Conventional Neural Network
Neuraxon within QUBIC does not compete in scale or task performance. It works through an architecture with endogenous regulation. By incorporating astrocytic principles, it begins to behave like a network with internal ecology. That is: a system where it matters not only which units are activated, but which domains of the tissue are plastic, which are stabilized, which areas are damping noise, which are consolidating regularities, and which are preparing to reorganize themselves. For a comprehensive overview of how biological and artificial neural networks compare, see NIA Volume 4: Neural Networks in AI and Neuroscience.
For Aigarth and QUBIC, the goal is not to accumulate more parameters, but to introduce more levels of functional organization within the system.
Why Astrocytic Gating Matters for Aigarth and Decentralized AI
Aigarth is not a static model but an evolutionary tissue through an architecture capable of growing, mutating, pruning, generating functional offspring, and reorganizing its topology under adaptive pressures. In that context, Neuraxon contributes something: a rich computational microphysiology for the units that inhabit that tissue.
This has implications for robustness, adaptability, and memory. Also for scalability. In large architectures, the problem is not only that there are many units, but how to coordinate which parts of the system are available for reconfiguration and which must maintain stability.
In roadmap terms for QUBIC, the goal is to build systems where intelligence emerges not only from neuronal computation, but also from the coupling between fast processing, slow modulation, and structural evolution. You can explore these dynamics firsthand with the interactive Neuraxon 3D simulation on HuggingFace Spaces, where you can build, configure, and simulate a Neuraxon 2.0 network from scratch.
Fig 3. Neuraxon astrocytes gating - AGMP formulation
Scientific References
Allen, N. J., & Eroglu, C. (2017). Cell biology of astrocyte-synapse interactions. Neuron, 96(3), 697–708.Halassa, M. M., Fellin, T., & Haydon, P. G. (2007). The tripartite synapse: Roles for gliotransmission in health and disease. Trends in Molecular Medicine, 13(2), 54–63.Kofuji, P., & Araque, A. (2021). Astrocytes and behavior. Annual Review of Neuroscience, 44, 49–67.=Perea, G., Navarrete, M., & Araque, A. (2009). Tripartite synapses: Astrocytes process and control synaptic information. Trends in Neurosciences, 32(8), 421–431.Woodburn, R. L., Bollinger, J. A., & Wohleb, E. S. (2021). Synaptic and behavioral effects of astrocyte activation. Frontiers in Cellular Neuroscience, 15, 645267.=Vivancos, D. & Sanchez, J. (2026). Neuraxon v2.0: A New Neural Growth & Computation Blueprint. ResearchGate Preprint.
Explore the Full Neuraxon Intelligence Academy
This is Volume 5 of the Neuraxon Intelligence Academy by the Qubic Scientific Team. If you are just joining us, explore the complete series to build a full understanding of the science behind Neuraxon and Qubic's approach to brain-inspired, decentralized artificial intelligence:
NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence — Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Volume 3: Neuromodulation and Brain-Inspired AI — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.NIA Volume 4: Neural Networks in AI and Neuroscience — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org
#Qubic #AGI #Neuraxon #academy #decentralized
How Information Flows in Traditional Artificial Neural Networks
In the artificial intelligence models we know, information enters, is encoded, is transformed through algebraic matrices, and produces outputs. Even in the most advanced architectures such as transformers, the principle is the same: the signal passes through a series of well-defined operations within a structured system. The model functions as a directed processing circuit, from left to right, input-output, or from right to left, through backpropagation for adjustments and training.
The results, as we well know, are spectacular. By working over millions of language parameters, AI is capable of giving magnificent answers, along with some hallucinations, however. But if the goal is not to process inputs and produce outputs, but to build systems capable of maintaining an internal dynamics, adapting continuously, reorganizing themselves, regulating their learning, and sustaining intelligence as a property of the tissue, current AI falls short.
Although people sometimes speak of language models as imitations of the brain, in reality this is more of a comparative metaphor than a simulation of computational neuroscience. Biological systems do not handle information from left to right and vice versa. Information propagates through a network, feeds back on itself, and also oscillates, is dampened, or is reinforced depending on the context.
Fig 1. Left-right information flow in traditional artificial neural networks
Not Only Neurons: The Role of Astrocytes in Brain Function and Synaptic Plasticity
We usually associate cognition and intelligence with the functioning of neurons, their receptors, and neurotransmitters. But they are not the only cells in the nervous system. For a long time, astrocytes were considered nervous system cells devoted to support, cleaning, nutrition, and stability of the environment. Today we know that they actively participate in regulation; in fact, a term is used: tripartite synapse, in which they actively participate by detecting neurotransmitters, integrating signals from multiple synapses, modulating plasticity, and modifying the functional efficacy of the circuit.
A living network is not composed only of neurons that fire, but also of astrocytes that regulate how, when, and how much the system changes. In biology, computing is not only about emitting a signal but also about modulating the terrain where that signal will have an effect. Recent research has demonstrated that astrocytes can perform normalization operations analogous to self-attention mechanisms found in transformer architectures — linking astrocyte–neuron interactions directly to attention-like computation in artificial intelligence systems.
Fig. 2 Biological astrocytes and tripartite synapse
Astrocytic Gating in Neuraxon: Bio-Inspired Neural Network Architecture
Neuraxon is an architecture that tries to recover and emulate the functioning of the brain and to compute functional properties that classical artificial networks have oversimplified.
As we have explained in previous volumes of this academy, Neuraxon does not work only with input, output, and hidden neurons in the conventional sense. It introduces units with states that emulate excitatory, inhibitory, or neutral potentials (-1, 0, +1). In addition, it does so within a continuous TEMPORAL dynamics where we take into account context and the recent history of activation. The network is no longer a sum of layers but resembles more a system with internal physiology. For deeper context on how these foundational elements work, see 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.
We have explained how Neuraxon models transmission through fast, slow, and neuromodulatory receptors — a mechanism explored in depth in NIA Volume 3: Neuromodulation and Brain-Inspired AI. But now we also model the regulation of plasticity through astrocytic gating.
How Astrocyte-Gated Multi-Timescale Plasticity (AGMP) Works
Astrocytic gating introduces a gate inspired by the role of astrocytes in the tripartite synapse. The idea is to introduce a local, slow, and contextual filter that determines when a synaptic modification should be opened, dampened, or blocked. It is as if the system can consider whether there is permission for a change. This approach directly addresses the stability-plasticity dilemma, one of the most fundamental challenges in continual learning for neural networks.
Eligibility Traces and Local Synaptic Memory
How does it work? Through a kind of eligibility trace. It is a local memory that says, "something relevant has happened at this synapse." It is updated with a decay over time and with a function between presynaptic and postsynaptic activity. That is: the synapse accumulates local evidence of temporal coincidence or causality. From there, there is a global broadcast-type signal, such as an error, a possible reward, or something dopamine-like. The astrocytic gate selects whether the neuron is in a learning state. In future versions, astrocytes could modulate thousands of synapses if this provides a computational advantage.
This approach is consistent with recent advances in neuromorphic computing, including the Astrocyte-Gated Multi-Timescale Plasticity (AGMP) framework proposed for spiking neural networks, which similarly augments eligibility-trace learning with a slow astrocyte state that gates synaptic updates — yielding a four-factor learning rule (eligibility × modulatory signal × astrocytic gate × stabilization).
Endogenous Regulation: Why Neuraxon Is More Than a Conventional Neural Network
Neuraxon within QUBIC does not compete in scale or task performance. It works through an architecture with endogenous regulation. By incorporating astrocytic principles, it begins to behave like a network with internal ecology. That is: a system where it matters not only which units are activated, but which domains of the tissue are plastic, which are stabilized, which areas are damping noise, which are consolidating regularities, and which are preparing to reorganize themselves. For a comprehensive overview of how biological and artificial neural networks compare, see NIA Volume 4: Neural Networks in AI and Neuroscience.
For Aigarth and QUBIC, the goal is not to accumulate more parameters, but to introduce more levels of functional organization within the system.
Why Astrocytic Gating Matters for Aigarth and Decentralized AI
Aigarth is not a static model but an evolutionary tissue through an architecture capable of growing, mutating, pruning, generating functional offspring, and reorganizing its topology under adaptive pressures. In that context, Neuraxon contributes something: a rich computational microphysiology for the units that inhabit that tissue.
This has implications for robustness, adaptability, and memory. Also for scalability. In large architectures, the problem is not only that there are many units, but how to coordinate which parts of the system are available for reconfiguration and which must maintain stability.
In roadmap terms for QUBIC, the goal is to build systems where intelligence emerges not only from neuronal computation, but also from the coupling between fast processing, slow modulation, and structural evolution. You can explore these dynamics firsthand with the interactive Neuraxon 3D simulation on HuggingFace Spaces, where you can build, configure, and simulate a Neuraxon 2.0 network from scratch.
Fig 3. Neuraxon astrocytes gating - AGMP formulation
Scientific References
Allen, N. J., & Eroglu, C. (2017). Cell biology of astrocyte-synapse interactions. Neuron, 96(3), 697–708.Halassa, M. M., Fellin, T., & Haydon, P. G. (2007). The tripartite synapse: Roles for gliotransmission in health and disease. Trends in Molecular Medicine, 13(2), 54–63.Kofuji, P., & Araque, A. (2021). Astrocytes and behavior. Annual Review of Neuroscience, 44, 49–67.=Perea, G., Navarrete, M., & Araque, A. (2009). Tripartite synapses: Astrocytes process and control synaptic information. Trends in Neurosciences, 32(8), 421–431.Woodburn, R. L., Bollinger, J. A., & Wohleb, E. S. (2021). Synaptic and behavioral effects of astrocyte activation. Frontiers in Cellular Neuroscience, 15, 645267.=Vivancos, D. & Sanchez, J. (2026). Neuraxon v2.0: A New Neural Growth & Computation Blueprint. ResearchGate Preprint.
Explore the Full Neuraxon Intelligence Academy
This is Volume 5 of the Neuraxon Intelligence Academy by the Qubic Scientific Team. If you are just joining us, explore the complete series to build a full understanding of the science behind Neuraxon and Qubic's approach to brain-inspired, decentralized artificial intelligence:
NIA Volume 1: Why Intelligence Is Not Computed in Steps, but in Time — Explores why biological intelligence operates in continuous time rather than discrete computational steps like traditional LLMs.NIA Volume 2: Ternary Dynamics as a Model of Living Intelligence — Explains ternary dynamics and why three-state logic (excitatory, neutral, inhibitory) matters for modeling living systems.NIA Volume 3: Neuromodulation and Brain-Inspired AI — Covers neuromodulation and how the brain's chemical signaling (dopamine, serotonin, acetylcholine, norepinephrine) inspires Neuraxon's architecture.NIA Volume 4: Neural Networks in AI and Neuroscience — A deep comparison of biological neural networks, artificial neural networks, and Neuraxon's third-path approach.
Qubic is a decentralized, open-source network for experimental technology. To learn more, visit qubic.org
#Qubic #AGI #Neuraxon #academy #decentralized
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Bikovski
自适应交易系统测试目前三个月盈利能力在7740%现价#AGI 多止损在8.14移动止赢
自适应交易系统测试目前三个月盈利能力在7740%现价#AGI 多止损在8.14移动止赢
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Najmanj 10 USDT
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🚨BREAKING: $122 BILLION RAISED
OpenAI just pulled off the LARGEST
funding round in history.
Valuation: $852B
ARR: $30B+
Burn rate: $7B)month
And here’s the wild part…
This only funds 18 months of runway. 🧵👇
OpenAI is now the fastest-growing startup ever.
Nearly 1 BILLION users.
Revenue exploding.
Yet it’s burning $7B every single month.
Why?
Because the race to AGI isn’t a normal business.
It’s an arms race.
Compute.
Chips.
Data centers.
Talent.
All scaling at insane speed.
This isn’t just a company anymore.
It’s infrastructure for the future economy.
And the stakes?
Winner takes EVERYTHING.
The fact that $122B only buys 18 months tells you one thing:
We are entering the most capital-intensive tech battle in history.
Big Tech. Governments. Startups.
All racing toward the same finish line.
AGI is no longer a theory.
It’s a trillion-dollar war.
#AI #OpenAI #AGI #Tech #Innovation
🚨BREAKING: $122 BILLION RAISED
OpenAI just pulled off the LARGEST
funding round in history.
Valuation: $852B
ARR: $30B+
Burn rate: $7B)month
And here’s the wild part…
This only funds 18 months of runway. 🧵👇
OpenAI is now the fastest-growing startup ever.
Nearly 1 BILLION users.
Revenue exploding.
Yet it’s burning $7B every single month.
Why?
Because the race to AGI isn’t a normal business.
It’s an arms race.
Compute.
Chips.
Data centers.
Talent.
All scaling at insane speed.
This isn’t just a company anymore.
It’s infrastructure for the future economy.
And the stakes?
Winner takes EVERYTHING.
The fact that $122B only buys 18 months tells you one thing:
We are entering the most capital-intensive tech battle in history.
Big Tech. Governments. Startups.
All racing toward the same finish line.
AGI is no longer a theory.
It’s a trillion-dollar war.
#AI #OpenAI #AGI #Tech #Innovation
OpenAI just pulled off the LARGEST
funding round in history.
Valuation: $852B
ARR: $30B+
Burn rate: $7B)month
And here’s the wild part…
This only funds 18 months of runway. 🧵👇
OpenAI is now the fastest-growing startup ever.
Nearly 1 BILLION users.
Revenue exploding.
Yet it’s burning $7B every single month.
Why?
Because the race to AGI isn’t a normal business.
It’s an arms race.
Compute.
Chips.
Data centers.
Talent.
All scaling at insane speed.
This isn’t just a company anymore.
It’s infrastructure for the future economy.
And the stakes?
Winner takes EVERYTHING.
The fact that $122B only buys 18 months tells you one thing:
We are entering the most capital-intensive tech battle in history.
Big Tech. Governments. Startups.
All racing toward the same finish line.
AGI is no longer a theory.
It’s a trillion-dollar war.
#AI #OpenAI #AGI #Tech #Innovation
1
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