#agi mnenja skupnosti in razpoloženje na trgu

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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

Astik_Mondal_

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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

Crypto圣泽

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马斯克手撕Sora：每天亏100万刀还玩个屁！Grok已经赚钱了！

马斯克又开炮了！这次直接怼OpenAI：你们那个Sora每天烧100万美金，最后关停了吧？老子Grok视频生成已经盈利了！

兄弟们，马斯克这波操作真的狠。他解释了Sora为啥失败——每天亏100万刀，这谁扛得住？OpenAI烧钱烧到最后只能关停。但马斯克偏偏反着来，重押Grok视频生成，而且已经喊出“正盈利”了。
为啥他这么执着视频？马斯克自己说了：视频是最接近现实的高密度信息。你想实现AGI，光靠文本学习那点东西，跟让AI看说明书学开车差不多——纸上谈兵。必须让它看视频、生成视频，才能真正理解这个世界是怎么运转的。换句话说，Grok不是在做短视频工具，是在给AGI建“世界模型”。
我的看法很简单：这事儿对币圈其实有深层影响。以后AI相关的叙事，会从“文本生成”慢慢转向“多模态+现实交互”。那些只会讲故事、没有实际视频或物理世界落地能力的AI项目，可能会被市场冷落。反而像Grok这样直接奔着AGI去的，才是长期资金愿意蹲的方向。
当然，马斯克说盈利了，咱们也得留个心眼——他嘴里的“盈利”有时候口径挺灵活的。但方向是对的：谁能低成本搞定视频生成，谁就是下一阶段的王者。
你们觉得Grok能干翻Sora吗？评论区见。
#马斯克 #Grok视频生成 #Sora关停 #AGI #AI赛道

NightHawkTrader2

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ZHIZHI $02513 JUST STUNNED THE MARKET 🚀

Revenue surged to over 724 million yuan, up 132% year on year, while gross margin reached 41%, far above the industry average. The 83% API price hike did not kill demand; usage kept climbing, signaling real pricing power and persistent supply tightness.

This is the kind of setup that forces a rerating. When customers accept higher prices and still consume more, it usually means the product is becoming mission-critical fast.

Not financial advice. Manage your risk.

#AI #AGI #Stocks #HongKong #WhaleWatch 🚀

赢麻在线

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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

Moj portfelj terminskih pogodb

0 / 200

Najmanj 10 USDT

Trgovalec s kopiranjem je v zadnjih 7 dneh zaslužil

0.00

USDT

7D ROI

0.00%

AUM

$0.00

Delež zmagovalnih trgovanj

39.77%

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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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🚀 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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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

Nakamoto

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Этот Новый год явно отличается своими событиями в #Crypto мире , последствия которых уже называют историческими и важным шагом для цифрового будущего и развития #Agi (AI) и конечно #Bitcoin
Чего стоит только эта елка 🌲 в Сальвадоре..

Makedoncki

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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"

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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

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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

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

SENT0,00%

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

SENT0,00%

BNB stret DZ

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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

AI−4,92%

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+4,55%

PhoenixTraderpro

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$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

ROBO+4,55%

Luck3333

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Elon is right. Centralized AI is a trust trap. You can't regulate what stays hidden. $QUBIC solves this via a decentralized Layer 1. No "black box" secrets, just 676 Quorum Members & #uPoW evolving AGI transparently. Trust math, not CEOs. 🧠⚡️ #Qubic #AGI #ElonMusk #OpenAI

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.

agi

Conscious Machines, Intelligent Organisms: The Science Behind AI Consciousness

马斯克手撕Sora：每天亏100万刀还玩个屁！Grok已经赚钱了！

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

AI could destroy crypto within 5 years

بداية النهاية

Fabric foundation

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