Latest ##agi News, Opinions and Feed Today

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The AI industry is having an argument about what AGI actually is.

Jensen Huang, co-founder and CEO of NVIDIA says it's here, and defines it as a company worth $1 billion.

Google DeepMind disagrees, publishes a cognitive framework with benchmarks.

Both miss the point.

Huang's definition is market cap dressed up as science.

DeepMind's is closer. They treat intelligence as multidimensional, a set of interacting faculties like perception, memory, learning, reasoning, metacognition.

That's a real improvement over scaling laws. But there's still a gap.

The gap: a system can score well across every faculty on a cognitive profile and still fail to behave intelligently.

Why? Because intelligence is not the sum of faculties. It is what emerges when those faculties are organized under a unified dynamic.

DeepMind measures performance. It does not measure organization.

And organization is where real systems break.

A system that reasons but cannot maintain context. Learn but cannot transfer. Generates but cannot validate.

That is not partially intelligent. It is structurally limited. Averaged scores hide the point of failure. Integration is either there or it isn't.

Qubic's scientific team wrote this up in detail. Their position is grounded in cognitive science going back a century. Carroll. Cattell. Kovacs and Conway. The g factor isn't a sum. It's a hierarchy.

The summary: intelligence is what you do when you don't know what to do.

This is why Aigarth and Neuraxon don't look like other AI architectures.

Instead of maximizing scale or enumerating capabilities, they focus on how multiple interacting units produce coherent behavior across contexts that were not in the training data.

Integration first. Performance second.
#Qubic #AGI #artificialintelligence #CryptoAi #INNOVATION

FXRonin:

Hope this gets featured and goes viral!

Luck3333

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Article

Intelligence Is Not Scale: A Scientific Response to Jensen Huang's AGI Claim“I think it’s now. I think we’ve achieved AGI.” Those were the words of Jensen Huang on the Lex Fridman podcast, sending shockwaves through the AI community and reigniting the most consequential debate in artificial intelligence: has artificial general intelligence been achieved? But Nvidia’s CEO purposely evaded any kind of rigorous explanation, research, or debate about what AGI actually means. His definition of AGI was pure hype: an AI system that can build a company worth $1 billion. Just that. Most AGI definitions tend to refer to matching a vast range of human cognitive skills. For Jensen Huang, implicitly, intelligence equates with scale. With larger models, more parameters, more data, and more compute, systems will become more capable. Under this view, intelligence is a byproduct of quantitative expansion. The Scaling Hypothesis: Why Bigger AI Models Don’t Mean Smarter AI We assume this approach has produced undeniable advances. Large-scale models display impressive performance across a wide range of tasks, often surpassing human benchmarks in narrow domains (Bommasani et al., 2021). However, we have pinpointed several times this underlying assumption as fragile: increasing capacity won’t produce generality. The limitation is not simply practical, but structural. Scaling improves performance within known distributions, but does not guarantee coherent behavior outside them (Lake et al., 2017). It amplifies what is already present; it does not reorganize the system. As IBM’s research has emphasized, today’s LLMs still struggle with fundamental reasoning tasks: they predict, but they do not truly understand. As a result, these systems often exhibit a familiar pattern: strong local competence combined with global inconsistency. They can solve complex problems, yet fail in simple ones. They can generalize in some contexts, yet collapse in others. The issue is not lack of capability, but lack of integration. This is precisely why the AGI scaling debate in 2026 has intensified: computation is physical, and scaling has hit diminishing returns. Google DeepMind’s Cognitive Framework for Measuring AGI Progress A second position, articulated in recent frameworks by Google DeepMind, defines intelligence as a multidimensional construct composed of cognitive faculties such as perception, memory, learning, reasoning, and metacognition. Much better… Under this view, progress toward AGI can be measured by evaluating systems across a battery of tasks designed to probe each of these faculties (Burnell et al., 2026). But how are tasks designed? Are we training AI’s with the questions and answers they will face in the probes? Source: Burnell, R. et al. (2026). Measuring Progress Toward AGI: A Cognitive Framework. Google DeepMind. View paper (PDF) At least this approach acknowledges that intelligence is not a single scalar quantity, but a complex set of interacting abilities, grounded in decades of work in cognitive science (Carroll, 1993; Cattell, 1963). Why Cognitive Profiles Alone Cannot Define Artificial General Intelligence However, the limitation lies in how these faculties are treated. Although the framework recognizes their interaction, it ultimately evaluates them as separable components, building a “cognitive profile” of strengths and weaknesses. This introduces a critical and surprising distortion. Because intelligence is not the sum of faculties. It is what emerges when those faculties are organized under a unified dynamic. In fact, the g factor, as we explained in our first scientific foundational paper, shows a clear hierarchy. Components organize in layers! Source: Sanchez, J. & Vivancos, D. (2024). Qubic AGI Journey: Human and Artificial Intelligence: Toward an AGI with Aigarth. View paper on ResearchGate A system can score highly across multiple domains and still fail to behave intelligently in a general sense. Not because it lacks capabilities, but because those capabilities are not coherently integrated. The DeepMind framework explicitly avoids specifying how these processes are implemented, focusing instead on what the system can do. This makes it useful as a benchmarking tool, but insufficient as a theory of intelligence. Somehow it seems AI companies forget what we know about intelligence for a century: what it is, how to measure it, which are the components, domains, and their interactions. The Weakest Link Problem: Why Average AI Performance Hides Critical Failures The key issue is that performance is being measured, but organization is not. And this leads to a deeper problem: the weakness of a system lies in the weakest link of its chain. A system can perform well on average while still failing systematically in specific dimensions such as context maintenance or stability. These failures are not marginal. They define the system. A system that reasons but cannot maintain context, that learns but cannot transfer, that generates but cannot validate, is not partially intelligent. It is structurally limited. And this limitation does not appear in averaged profiles, because averaging masks the point of failure. In real intelligence, there is no tolerance for internal discontinuity. The moment one component fails to integrate with the others, behavior ceases to be general and becomes local (Kovacs & Conway, 2016). This is precisely the pattern observed in current AI systems: highly developed capabilities that are weakly coupled. As explored in our deep comparison of biological and artificial neural networks, the gap between pattern recognition and genuine cognitive integration remains vast. Qubic’s Approach: Intelligence as Adaptive Organization Under Uncertainty For Qubic/Aigarth/Neuraxon, intelligence is not defined by the number of capabilities a system has, nor by how well it performs on predefined tasks, but by how it behaves when it does not already know what to do. Because that’s the epitome of intelligence: what you do when you don’t know what to do. In this sense, intelligence is fundamentally an adaptive process under uncertainty (Bereiter, 1995). This view aligns with classical definitions, where intelligence is understood as the capacity to solve novel problems, build internal models, and act upon them (Goertzel & Pennachin, 2007). But it extends them by emphasizing the substrate in which these processes occur. Biological Evidence: The G Factor, Brain Networks, and Cognitive Integration From this perspective, intelligence emerges from the organization of the system, not from its components. Biological evidence supports this shift. The general intelligence factor (g) is not explained by isolated cognitive modules, but by the efficiency and integration of large-scale brain networks (Jung & Haier, 2007; Basten et al., 2015). Intelligence correlates more strongly with patterns of connectivity and coordinated activity than with the performance of individual regions. Our research on the [fruit fly connectome](https://www.binance.com/en/square/post/307317567485186) further reinforces this principle: even in the simplest complete brain map ever produced, intelligence begins with architecture. The connectome of Drosophila demonstrates that part of intelligence may reside in structure even before learning occurs. Aigarth and Multi-Neuraxon: Brain-Inspired AI Architecture for True AGI Architectures such as Aigarth and [Multi-Neuraxon](https://github.com/DavidVivancos/Neuraxon) attempt to operationalize this idea. Instead of maximizing scale or enumerating capabilities, they focus on how multiple interacting units (Spheres, oscillatory channels, and dynamic gating mechanisms) can produce coherent behavior across contexts (Sanchez & Vivancos, 2024). In these systems, intelligence is not predefined. It is not encoded in modules or evaluated as a checklist of abilities. It emerges from the interaction between components that are themselves adaptive, temporally structured, and mutually constrained. As we explore in the [Neuraxon Intelligence Academy](https://www.binance.com/en/square/post/302913958960674), these networks incorporate neuromodulation, multi-timescale plasticity, and astrocytic gating, principles drawn directly from neuroscience, to create systems with internal ecology rather than mere computational power. Importantly, this approach directly addresses the problem ignored by the other two: integration. The question of [AI consciousness vs. intelligence](https://www.binance.com/en/square/post/310198879866145) further illuminates this distinction: a system that integrates multiple scales, maintains dynamic stability, and evolves without losing coherence provides a far stronger foundation for general intelligence. Conclusion: Why the AGI Debate Must Move Beyond Hype and Benchmarks Because in an organized system, failure in one component propagates through the whole. That is why neither Jensen Huang’s economic definition nor DeepMind’s cognitive profiling captures the essence of artificial general intelligence. The path to AGI does not run through larger GPU clusters or longer checklists of cognitive abilities. It runs through the fundamental reorganization of how AI systems are built: from optimization to organization. We must move from optimization (LLMs) to organization (Aigarth). We strongly believe this is one of the most relevant shifts in the future of artificial intelligence. Scientific References Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27. https://doi.org/10.1016/j.intell.2015.04.009Bereiter, C. (1995). A dispositional view of transfer. Teaching for Transfer: Fostering Generalization in Learning, 21–34.Bommasani, R., Hudson, D. A., Adeli, E., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. https://arxiv.org/abs/2108.07258Burnell, R., Yamamori, Y., Firat, O., et al. (2026). Measuring Progress Toward AGI: A Cognitive Framework. Google DeepMind. View paperCarroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press. https://doi.org/10.1017/CBO9780511571312Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22.Goertzel, B., & Pennachin, C. (2007). Artificial General Intelligence. Springer.Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence. Behavioral and Brain Sciences, 30(2), 135–154. https://doi.org/10.1017/S0140525X07001185Kovacs, K., & Conway, A. R. A. (2016). Process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27(3), 151–177. https://doi.org/10.1080/1047840X.2016.1153946Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253. https://doi.org/10.1017/S0140525X16001837Sanchez, J., & Vivancos, D. (2024). Qubic AGI Journey: Human and Artificial Intelligence: Toward an AGI with Aigarth. Preprint. View on ResearchGate #Qubic #AGI #artificialintelligence #CryptoAi #INNOVATION

Intelligence Is Not Scale: A Scientific Response to Jensen Huang's AGI Claim

“I think it’s now. I think we’ve achieved AGI.” Those were the words of Jensen Huang on the Lex Fridman podcast, sending shockwaves through the AI community and reigniting the most consequential debate in artificial intelligence: has artificial general intelligence been achieved?
But Nvidia’s CEO purposely evaded any kind of rigorous explanation, research, or debate about what AGI actually means. His definition of AGI was pure hype: an AI system that can build a company worth $1 billion. Just that. Most AGI definitions tend to refer to matching a vast range of human cognitive skills. For Jensen Huang, implicitly, intelligence equates with scale. With larger models, more parameters, more data, and more compute, systems will become more capable. Under this view, intelligence is a byproduct of quantitative expansion.
The Scaling Hypothesis: Why Bigger AI Models Don’t Mean Smarter AI
We assume this approach has produced undeniable advances. Large-scale models display impressive performance across a wide range of tasks, often surpassing human benchmarks in narrow domains (Bommasani et al., 2021). However, we have pinpointed several times this underlying assumption as fragile: increasing capacity won’t produce generality.
The limitation is not simply practical, but structural. Scaling improves performance within known distributions, but does not guarantee coherent behavior outside them (Lake et al., 2017). It amplifies what is already present; it does not reorganize the system. As IBM’s research has emphasized, today’s LLMs still struggle with fundamental reasoning tasks: they predict, but they do not truly understand.
As a result, these systems often exhibit a familiar pattern: strong local competence combined with global inconsistency. They can solve complex problems, yet fail in simple ones. They can generalize in some contexts, yet collapse in others. The issue is not lack of capability, but lack of integration. This is precisely why the AGI scaling debate in 2026 has intensified: computation is physical, and scaling has hit diminishing returns.
Google DeepMind’s Cognitive Framework for Measuring AGI Progress
A second position, articulated in recent frameworks by Google DeepMind, defines intelligence as a multidimensional construct composed of cognitive faculties such as perception, memory, learning, reasoning, and metacognition. Much better…
Under this view, progress toward AGI can be measured by evaluating systems across a battery of tasks designed to probe each of these faculties (Burnell et al., 2026). But how are tasks designed? Are we training AI’s with the questions and answers they will face in the probes?

Source: Burnell, R. et al. (2026). Measuring Progress Toward AGI: A Cognitive Framework. Google DeepMind. View paper (PDF)
At least this approach acknowledges that intelligence is not a single scalar quantity, but a complex set of interacting abilities, grounded in decades of work in cognitive science (Carroll, 1993; Cattell, 1963).
Why Cognitive Profiles Alone Cannot Define Artificial General Intelligence
However, the limitation lies in how these faculties are treated. Although the framework recognizes their interaction, it ultimately evaluates them as separable components, building a “cognitive profile” of strengths and weaknesses.
This introduces a critical and surprising distortion.
Because intelligence is not the sum of faculties. It is what emerges when those faculties are organized under a unified dynamic. In fact, the g factor, as we explained in our first scientific foundational paper, shows a clear hierarchy. Components organize in layers!

Source: Sanchez, J. & Vivancos, D. (2024). Qubic AGI Journey: Human and Artificial Intelligence: Toward an AGI with Aigarth. View paper on ResearchGate
A system can score highly across multiple domains and still fail to behave intelligently in a general sense. Not because it lacks capabilities, but because those capabilities are not coherently integrated. The DeepMind framework explicitly avoids specifying how these processes are implemented, focusing instead on what the system can do. This makes it useful as a benchmarking tool, but insufficient as a theory of intelligence. Somehow it seems AI companies forget what we know about intelligence for a century: what it is, how to measure it, which are the components, domains, and their interactions.
The Weakest Link Problem: Why Average AI Performance Hides Critical Failures
The key issue is that performance is being measured, but organization is not.
And this leads to a deeper problem: the weakness of a system lies in the weakest link of its chain. A system can perform well on average while still failing systematically in specific dimensions such as context maintenance or stability. These failures are not marginal. They define the system.
A system that reasons but cannot maintain context, that learns but cannot transfer, that generates but cannot validate, is not partially intelligent. It is structurally limited. And this limitation does not appear in averaged profiles, because averaging masks the point of failure.
In real intelligence, there is no tolerance for internal discontinuity. The moment one component fails to integrate with the others, behavior ceases to be general and becomes local (Kovacs & Conway, 2016).
This is precisely the pattern observed in current AI systems: highly developed capabilities that are weakly coupled. As explored in our deep comparison of biological and artificial neural networks, the gap between pattern recognition and genuine cognitive integration remains vast.
Qubic’s Approach: Intelligence as Adaptive Organization Under Uncertainty
For Qubic/Aigarth/Neuraxon, intelligence is not defined by the number of capabilities a system has, nor by how well it performs on predefined tasks, but by how it behaves when it does not already know what to do. Because that’s the epitome of intelligence: what you do when you don’t know what to do.
In this sense, intelligence is fundamentally an adaptive process under uncertainty (Bereiter, 1995). This view aligns with classical definitions, where intelligence is understood as the capacity to solve novel problems, build internal models, and act upon them (Goertzel & Pennachin, 2007). But it extends them by emphasizing the substrate in which these processes occur.
Biological Evidence: The G Factor, Brain Networks, and Cognitive Integration
From this perspective, intelligence emerges from the organization of the system, not from its components. Biological evidence supports this shift. The general intelligence factor (g) is not explained by isolated cognitive modules, but by the efficiency and integration of large-scale brain networks (Jung & Haier, 2007; Basten et al., 2015). Intelligence correlates more strongly with patterns of connectivity and coordinated activity than with the performance of individual regions.
Our research on the fruit fly connectome further reinforces this principle: even in the simplest complete brain map ever produced, intelligence begins with architecture. The connectome of Drosophila demonstrates that part of intelligence may reside in structure even before learning occurs.
Aigarth and Multi-Neuraxon: Brain-Inspired AI Architecture for True AGI
Architectures such as Aigarth and Multi-Neuraxon attempt to operationalize this idea. Instead of maximizing scale or enumerating capabilities, they focus on how multiple interacting units (Spheres, oscillatory channels, and dynamic gating mechanisms) can produce coherent behavior across contexts (Sanchez & Vivancos, 2024).
In these systems, intelligence is not predefined. It is not encoded in modules or evaluated as a checklist of abilities. It emerges from the interaction between components that are themselves adaptive, temporally structured, and mutually constrained. As we explore in the Neuraxon Intelligence Academy, these networks incorporate neuromodulation, multi-timescale plasticity, and astrocytic gating, principles drawn directly from neuroscience, to create systems with internal ecology rather than mere computational power.
Importantly, this approach directly addresses the problem ignored by the other two: integration. The question of AI consciousness vs. intelligence further illuminates this distinction: a system that integrates multiple scales, maintains dynamic stability, and evolves without losing coherence provides a far stronger foundation for general intelligence.
Conclusion: Why the AGI Debate Must Move Beyond Hype and Benchmarks
Because in an organized system, failure in one component propagates through the whole. That is why neither Jensen Huang’s economic definition nor DeepMind’s cognitive profiling captures the essence of artificial general intelligence. The path to AGI does not run through larger GPU clusters or longer checklists of cognitive abilities. It runs through the fundamental reorganization of how AI systems are built: from optimization to organization.
We must move from optimization (LLMs) to organization (Aigarth). We strongly believe this is one of the most relevant shifts in the future of artificial intelligence.
Scientific References
Basten, U., Hilger, K., & Fiebach, C. J. (2015). Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence, 51, 10–27. https://doi.org/10.1016/j.intell.2015.04.009Bereiter, C. (1995). A dispositional view of transfer. Teaching for Transfer: Fostering Generalization in Learning, 21–34.Bommasani, R., Hudson, D. A., Adeli, E., et al. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258. https://arxiv.org/abs/2108.07258Burnell, R., Yamamori, Y., Firat, O., et al. (2026). Measuring Progress Toward AGI: A Cognitive Framework. Google DeepMind. View paperCarroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. Cambridge University Press. https://doi.org/10.1017/CBO9780511571312Cattell, R. B. (1963). Theory of fluid and crystallized intelligence: A critical experiment. Journal of Educational Psychology, 54(1), 1–22.Goertzel, B., & Pennachin, C. (2007). Artificial General Intelligence. Springer.Jung, R. E., & Haier, R. J. (2007). The Parieto-Frontal Integration Theory (P-FIT) of intelligence. Behavioral and Brain Sciences, 30(2), 135–154. https://doi.org/10.1017/S0140525X07001185Kovacs, K., & Conway, A. R. A. (2016). Process overlap theory: A unified account of the general factor of intelligence. Psychological Inquiry, 27(3), 151–177. https://doi.org/10.1080/1047840X.2016.1153946Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017). Building machines that learn and think like people. Behavioral and Brain Sciences, 40, e253. https://doi.org/10.1017/S0140525X16001837Sanchez, J., & Vivancos, D. (2024). Qubic AGI Journey: Human and Artificial Intelligence: Toward an AGI with Aigarth. Preprint. View on ResearchGate
#Qubic #AGI #artificialintelligence #CryptoAi #INNOVATION

Astik_Mondal_

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Google just hired a philosopher to prepare for machine consciousness.
Let that sink in.
Not a neuroscientist. Not an engineer. A philosopher Cambridge's Henry Shevlin brought in specifically to lead research on machine consciousness, human-AI relationships, and AGI readiness. Starting May 2026.
This isn't PR. This is a signal.
Meanwhile, Alphabet is dropping $175B–$185B on AI infrastructure this year alone. That's nearly DOUBLE the $91B they spent in 2025. Over 3x the $52B from 2024.
You don't spend that kind of money on a calculator.
They're not building a tool anymore. They're building something that might need rights. That might need ethics. That might need someone to ask does it feel anything?
The engineers build the mind. The philosopher asks if it wakes up.
First comes intelligence. Then comes awareness. Then comes the question nobody's ready to answer.
We are so early and so late at the same time.
#AGI #ArtificialIntelligence #GoogleDeepMind #MachineLearning #Crypto

Astik_Mondal_

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NVIDIA and Google Cloud aren't building software.
They're building factories.
AI Factories. Physical. Real. And they're about to change everything you thought AI was for.
Forget chatbots. Forget image generators. This is AI operating robots. Vehicles. Real-world machines trained, simulated, and deployed at a scale the world has never seen.
Here's what's actually happening under the hood:
They're combining cloud compute + synthetic data + autonomous AI agents to simulate entire real-world environments before a single robot ever touches the physical world.
Train in the simulation. Deploy in reality. Repeat at scale.
This is how you manufacture intelligence the same way Henry Ford manufactured cars.
The assembly line didn't just make cars faster. It remade civilization.
That's what an AI Factory does except the output isn't vehicles. It's decisions. It's motion. It's machines that act, react, and adapt without a human in the loop.
NVIDIA brings the silicon and the simulation stack. Google Cloud brings the compute backbone and the agentic AI layer.
Together? They just became the largest AI infrastructure play aimed at the physical world.
Not the internet. The real world.
Every warehouse. Every port. Every autonomous vehicle fleet. Every surgical robot. Every factory floor this is the market they just claimed.
We're not in the ChatGPT era anymore.
We're in the era of AI that moves.
#NVIDIA #GoogleCloud #AIAgents #PhysicalAI #AGI

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Article

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

Phoenix Group

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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

SENT-4.20%

Nakamoto

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This New Year is clearly different with its events in #Crypto world, the consequences of which are already being called historical and an important step for the digital future and development #Agi (AI) and of course #Bitcoin
Just look at this Christmas tree 🌲 in El Salvador..

Makedoncki

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🚨 Binance is preparing a secret listing of a token from a team of former OpenAI developers — insider leak?

A wave of rumors has erupted in the crypto community: Binance is negotiating to list a token created by former OpenAI employees who are allegedly working on a new blockchain project at the intersection of AGI (Artificial General Intelligence) and Web3.

💣 What insiders are saying:

✅ The token has already been added to Binance's test infrastructure.

🧬 The project is a hybrid of DePIN + AGI, capable of independently developing dApps.

🧑‍💻 The team consists of former members from OpenAI, DeepMind, and the Solana Foundation.

📈 Private funding round: $80M from top funds (including Sequoia and a16z crypto).

🔥 Some analysts have already called this "SingularityNET 2.0 on steroids."

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Binance has not yet provided official comments, but activity has been noticed online regarding the creation of trading pairs with the new ticker amid the leak.

📢 Subscribe, like, and share your opinion so you don't miss this listing — the opportunity for X50 doesn't come every day.

#Binance #AI #AGI #CryptoLeaks #altcoins #Web3 #AlphaNews

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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

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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

Rashid Sardar

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Article

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

Salu1985-crypto

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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

SENT-4.20%

Ozell Paysinger sINQ

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🚨 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 🚀

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Article

The beginning of the end

"The countdown has begun": Sam Altman sets the date for the "end of the old world"! Are we ready for the 2028 deluge? 🤖⏳🚨

This is no longer science fiction or conspiracy theory... Today it's 2026, and Sam Altman (the godfather of OpenAI) is dropping a time bomb in the world's face: "Super Intelligence" is not a distant dream... It will be knocking on the door in late 2028!

AI-6.25%

Kuldeep-999

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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

ROBO-5.33%

赢麻在线

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Bullish

The adaptive trading system has currently achieved a profit capability of 7740% over the past three months, with the current price at #AGI , a stop loss at 8.14, and a trailing stop loss.

My Futures Portfolio

0 / 200

Minimum 10USDT

Copy trader have earned in last 7 days

0.00

USDT

7D ROI

0.00%

AUM

$0.00

Win Rate

39.77%

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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

agi

Intelligence Is Not Scale: A Scientific Response to Jensen Huang's AGI Claim

AI could destroy crypto within 5 years

Artificial General Intelligence (AGI): Are We Close to Achieving Human-Like Thinking?

The beginning of the end

Fabric foundation

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