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Come l'IA elabora l'incertezza (Binario vs. Trinario)
Nella ricerca dell'Intelligenza Artificiale Generale (AGI), l'industria tecnologica ha ossessivamente fornito più dati e più potenza ai sistemi binari tradizionali. Ma la vera intelligenza non riguarda solo avere tutte le risposte: riguarda possedere l'umiltà intellettuale di riconoscere quando non si sa.
Questo è il difetto filosofico e architettonico fondamentale dell'IA moderna. Ed è esattamente il difetto che Qubic, attraverso il suo progetto di IA evolutiva #Aigarth, risolve introducendo un terzo stato nella sua architettura neurale: L' "Sconosciuto" (0).

Nel mondo della tecnologia, T3chFest non è un luogo per hype vuoti. Ospitato presso l'Universidad Carlos III di Madrid, funge da uno dei test tecnici più rigorosi per i progetti, scrutinati da oltre 1.800 sviluppatori e architetti di sistema d'élite. Tra dozzine di relatori, Qubic è emerso come il nome più atteso—non solo per il suo massiccio supporto della comunità, ma grazie a un framework architettonico capace di ridefinire l'intersezione tra Blockchain e Intelligenza Artificiale.

Over the past few years, AI and crypto have begun to converge into one of the most powerful narratives in technology.
Projects promise:
decentralized AI infrastructureautonomous agentsdistributed machine learningtokenized data economies
Names like SingularityNET, Fetch.ai, and Bittensor are frequently mentioned as pioneers of decentralized AI.
But if we look deeper, most of these projects are not fundamentally reinventing artificial intelligence.
They are building economic or infrastructure layers around existing AI models.
One project, however, is attempting something much more radical.
Qubic is trying to rethink how intelligence itself should be built.
At the center of that vision lies a new architecture called Neuraxon.
If their ideas prove viable, the implications could extend far beyond crypto — potentially touching the future path toward Artificial General Intelligence (AGI).
The Two Competing Paths Toward AGI
Right now, the global AI industry appears to be converging on two very different philosophies of intelligence.
Path 1: Scaling Intelligence
This is the approach followed by major AI labs:
OpenAIGoogle DeepMindAnthropicMeta
The strategy is simple:
Scale everything.
more datamore computemore parameters
This approach has produced astonishing results:
GPT modelsGeminiClaude
These systems can write, code, reason, summarize, and converse with remarkable fluency.
But despite their power, they share a fundamental limitation.
They are frozen intelligence.
The Problem with Frozen AI
Modern AI systems follow a familiar lifecycle:
Train → Freeze → Deploy
During training:
massive datasets are ingestedbillions of parameters are optimized
After training ends:
the model is deployedthe weights are fixed
From that point onward, the system performs inference only.
It no longer learns from its experiences.
In other words:
The AI you interact with today is a snapshot of intelligence frozen in time.
No matter how many conversations it has, it does not accumulate experience in the way biological intelligence does.
If a human brain stopped learning the moment it began interacting with the world, we would not call it intelligent.
We would call it dysfunctional.
Why Continuous Learning Matters
Biological intelligence is fundamentally different.
Humans and animals learn continuously through:
experienceexplorationenvironmental interaction
Every moment reshapes neural connections through synaptic plasticity.
Learning and living are not separate processes.
They are the same process.
Many researchers believe that true AGI will require similar properties, including:
lifelong learningadaptive memoryenvironmental interactionself-directed exploration
This is where a different paradigm begins to emerge.
The Second Path: Evolutionary Intelligence
Instead of building bigger models, some researchers are exploring whether intelligence might emerge from adaptive systems that evolve over time.
In this paradigm:
AI is not a finished product.
It is a continuously evolving system.
Multiple agents interact, adapt, and compete, gradually improving through experience.
Rather than a static model, intelligence becomes something closer to an ecosystem.
This idea is much closer to how biological intelligence emerged on Earth.
And it is the philosophical foundation behind Qubic's AGI research direction.
What Makes Qubic Different
Most AI-crypto projects focus on creating markets or infrastructure for AI services.
Qubic takes a different approach.
Instead of asking:
How can we monetize AI?
Qubic asks a deeper question:
How should intelligence itself be built?
The Qubic ecosystem explores three major components:
1. Distributed Compute
A decentralized network of nodes provides computational resources that can run AI systems continuously.
Instead of relying on centralized GPU data centers, computation can be distributed across a network.
2. Evolutionary AI Frameworks
Within the ecosystem, frameworks like Aigarth explore evolutionary AI concepts.
Multiple AI agents can:
interactcompeteevolve
In some ways, this resembles natural selection in digital form.
3. Neuraxon
The most intriguing component of the ecosystem is Neuraxon, a bio-inspired neural architecture designed to support continuous learning and adaptive intelligence.
Instead of mimicking conventional neural networks, Neuraxon attempts to model aspects of biological neural systems.
Neuraxon: A Different Kind of Neural Architecture
Traditional neural networks are essentially static computational graphs.
Weights are adjusted during training but remain fixed afterward.
Neuraxon proposes a more dynamic approach inspired by neuroscience.
Tri-State Neurons
Most artificial neurons operate in a binary-like activation pattern.
Neuraxon introduces three possible states:
+1 — Excitatory0 — Neutral−1 — Inhibitory
This structure mirrors biological neural activity, where neurons can either stimulate or suppress activity within a network.
The neutral state also allows the system to represent hesitation, uncertainty, or dormant potential activity.
Continuous Neural Dynamics
Traditional AI operates through discrete computations:
Input → Compute → Output

Neuraxon instead maintains a continuous internal neural state.
Even when no external input arrives, the system’s internal state continues evolving over time.
This mirrors the behavior of biological brains, where neural activity never truly stops.
Instead of isolated computations, the system behaves more like an ongoing cognitive process.
Synaptic Plasticity
One of the defining characteristics of biological intelligence is plasticity.
Connections between neurons strengthen or weaken depending on experience.
Neuraxon introduces mechanisms where synapses can:
strengthen through repeated activationweaken when unusedreorganize network structures
This allows the system to adapt dynamically during operation rather than only during training.
Multi-Timescale Learning
Neuraxon also explores learning across multiple timescales.
Short-term adjustments can coexist with slower structural changes.
This is similar to biological systems where:
the hippocampus supports short-term learningthe cortex stabilizes long-term knowledge
Such mechanisms may help balance the classic problem of AI learning:
plasticity vs stability.
Neuromodulation
Another feature inspired by biology is neuromodulation.
In the human brain, chemicals like dopamine and serotonin regulate when neural circuits become more plastic or more stable.
Neuraxon explores similar mechanisms that determine when learning should occur.
This may help address one of the biggest challenges in continuous learning systems:
catastrophic forgetting.
Comparing Major AI-Crypto Projects
To better understand Qubic’s position, it helps to compare it with other major projects.

What stands out is that Qubic is one of the few projects attempting to design a new architecture for intelligence itself.
Dead AI vs Living AI
One way to think about this shift is the difference between Dead AI and Living AI.
Dead AI
Most modern systems fall into this category.
They are:
trained oncedeployedfixed afterward
They are incredibly powerful but fundamentally static.
Living AI
Living AI refers to systems that:
operate continuouslylearn from experienceadapt their internal structure over time
Instead of static models, they behave more like organisms.
If architectures like Neuraxon succeed, AI could gradually transition from software tools to adaptive cognitive systems.
The Hidden AGI Race
The AI race we usually hear about involves companies like OpenAI and Google.
But beneath that visible competition lies a deeper question:
Where does intelligence actually come from?
Is it the result of:
bigger modelsmore datamore compute?
Or does it emerge from:
adaptationinteractionevolutionary dynamics?
If the second view turns out to be correct, the path toward AGI might not come from ever-larger language models.
It might come from systems capable of continuous learning and evolution.
A Possible “Cambrian Explosion” of AI
In biological history, the Cambrian Explosion marked a period where life diversified rapidly into countless new forms.
Some researchers believe that if AI systems gain the ability to:
evolvelearn continuouslyinteract with complex environments
we could witness something similar in artificial intelligence.
Instead of a few dominant models, we might see an explosion of diverse AI systems evolving across decentralized networks.
The Big Question of the Next Decade
The future of AI may ultimately depend on one question:
Does intelligence emerge from larger models… or from adaptive systems that evolve over time?
If the answer lies in evolution and continuous learning, projects like Qubic and Neuraxon could represent early experiments in a completely new direction for artificial intelligence.
It is far too early to know whether this approach will succeed.
But the ideas being explored raise one of the most fascinating possibilities in modern technology:
AI that does not merely run algorithms, but evolves through experience.
Further Reading
Readers interested in exploring these concepts further can consult the following resources:
#Qubic Ecosystem
https://github.com/qubic
#Neuraxon Research Paper
https://www.researchgate.net/publication/397331336_Neuraxon
Qubic #AGI Journey
https://www.researchgate.net/publication/387364505_Qubic_AGI_Journey_Human_and_Artificial_Intelligence_Toward_an_AGI_with_Aigarth
Neuraxon GitHub Repository
https://github.com/DavidVivancos/Neuraxon
Neuraxon Interactive Demo
https://huggingface.co/spaces/DavidVivancos/Neuraxon
Research Website
https://vivancos.com
#artificialintelligence
#CryptoAi

The global technology sector is crashing into a brutal structural barrier. With NVIDIA’s latest Rubin GPUs demanding a staggering 260% increase in high-bandwidth memory (HBM4) compared to the H100, we are no longer facing a simple "chip shortage." We have entered the era of the "Memory Wall."
The math is ruthless: the spot price of 16GB DDR4 RAM has skyrocketed from $5 to $77 in just eight months. Every HBM4 module produced for a centralized data center is a memory chip stripped from the consumer supply chain. As hardware costs spiral out of control, the traditional method of training Artificial Intelligence—throwing massive amounts of RAM and compute at inefficient algorithms—is becoming economically unsustainable.
Amidst this crisis, Qubic stands out not just as a blockchain project, but as a foundational infrastructure revolution. By combining Trinary Logic, Bare Metal execution, and Useful Proof-of-Work (uPoW), Qubic is redefining how machines think.
1. The Dead End of "Binary Brute Force"
Today’s centralized AI models rely on binary logic (0s and 1s) and massive datasets. To compensate for the inherent limitations of binary architecture, tech giants use "Brute Force"—scaling up GPU clusters and memory pools to astronomical sizes.
However, when memory becomes the ultimate bottleneck, this approach fails. Qubic tackles this problem at its root: We don't need more memory; we need smarter computation.
2. Trinary Logic: The IEEE-Validated Strategic Weapon
The core DNA of Qubic is its shift from Binary to Trinary Logic (-1, 0, 1).
Academic Validation: This isn't just a crypto whitepaper concept. The recent acceptance of Qubic’s Trinary Logic research paper by the IEEE for the AMLDS 2026 conference in Japan proves that Neuraxon (Qubic's AI) is a serious computer science breakthrough.Brain-Like Efficiency: Trinary logic closely mimics human neural patterns (Excitation, Inhibition, Neutral states). This allows Neuraxon to process complex information—like the newly released 1.12TB Neuraxon2LifeTS dataset—with a significantly smaller memory footprint than traditional binary models.
3. The Power of "Bare Metal" Execution

Running an AI on Windows or Linux is like running a marathon in a heavy winter coat. Operating Systems (OS) act as middlemen, consuming precious memory bandwidth and processing power just to keep themselves running.
Qubic removes the middleman entirely by running on Bare Metal.
100% Hardware Utilization: By executing directly on the hardware without an OS, Qubic maximizes the bandwidth of every RAM stick and GPU.Zero Latency & Physical AI: This ultra-low latency environment is exactly what allowed Qubic to successfully demonstrate Neuraxon controlling a physical robot (Sphero Mini) in real-time. We are bridging the gap between digital simulation and the physical world.
4. Critical Q&A: Addressing the Skepticism
To truly understand Qubic's hegemony, we must address the hardest questions from the community:
Q1: "If Trinary Logic is so superior, why aren't Google, NVIDIA, or Intel using it?"
Answer: The Legacy Cost. The entire $10 trillion global tech ecosystem is built on 70 years of Binary infrastructure. For a tech giant to switch to Trinary, they would have to scrap everything from software compilers to silicon foundries and start over. Qubic has the "First Mover Advantage" because we built a Trinary ecosystem from the ground up, unchained by legacy debt.
Q2: "Doesn't running Bare Metal hurt decentralization since it's harder for average users to set up?"
Answer: It actually protects decentralization. As RAM prices explode, average consumer PCs will soon struggle to run complex AI models inside a bloated OS. Qubic’s Bare Metal optimization strips away the software bloat, allowing older or consumer-grade hardware to punch above its weight and remain competitive in the network.
Q3: "Is the Dogecoin ASIC mining integration just a hype gimmick?"
Answer: It is a strategic economic engine. Mining is a business, and miners need stability. By integrating Dogecoin ASIC mining (Mainnet launching April 1, 2026), Qubic creates a "Dual-Engine" economy. ASIC hardware secures the network and provides steady revenue to miners, while our uPoW consensus directs GPU/CPU power toward training the AI. This ensures our infrastructure survives bear markets and hardware price shocks.
Q4: "How can a decentralized network ever beat OpenAI's massive supercomputers?"
Answer: Efficiency beats Scale in a resource-constrained world. OpenAI wins by having the most resources. Qubic is designed to win by having the most efficient architecture. Through Trinary Logic and Bare Metal, we achieve neural complexity using a fraction of the hardware. We aren't trying to out-spend the giants; we are out-thinking them.
The Verdict
The 2026 memory crisis will act as a brutal market filter. It will wipe out the "AI wrappers" and GPU-rental platforms that lack fundamental innovation. As capital flees the hype, it will seek purpose-built, full-stack infrastructure.
With its IEEE-validated Trinary core, Bare Metal execution, and self-sustaining uPoW economy, Qubic is building the ultimate sanctuary for the future of Decentralized Intelligence.
Dive into our open-source infrastructure here: https://github.com/qubic
Read the latest Qubic All-Hands recap
#Qubic #DeAI #trinary #IEEE #UPoW

L'articolo recente di CoinDesk, "L'IA decentralizzata è in un momento difficile, ma stanno emergendo opportunità reali," cattura perfettamente il sentimento attuale dei capitalisti di rischio: l'hype sta svanendo e gli investitori ora richiedono sostanza rispetto alle parole d'ordine. Mentre molti progetti DeAI stanno combattendo per dimostrare la loro utilità, Qubic’s March 2026 All-Hands updates forniscono una lezione su come passare da "crypto-hype" a "infrastruttura AI di livello industriale."
Ecco come Qubic sta affrontando le preoccupazioni principali sollevate dai VC globali:

Il riepilogo di marzo 2026 di All-Hands evidenzia un enorme salto per la Qubic Network in AI, Core Tech e Ricerca.
1. AI & Scienza: Dalla Simulazione alla Realtà 🤖
AI Fisica: Il team scientifico ha dimostrato Neuraxon che controlla un robot fisico (Sphero Mini). Questo dimostra che l'AI di Qubic può colmare il divario tra reti neurali digitali e hardware del mondo reale.
Massive Dataset: Rilasciato Neuraxon2LifeTS (1.12 TB), un dataset 190 volte più grande delle versioni precedenti, alimentando la fase successiva della ricerca AGI decentralizzata.

Dall'inizio del 2026, il mercato crypto in Vietnam sta entrando in una fase molto interessante: da una crescita spontanea a uno sviluppo più orientato e gestito in modo più chiaro.
📊 Il Vietnam rimane una delle comunità crypto più grandi del mondo
Secondo il rapporto Global Crypto Adoption Index, il Vietnam si trova attualmente tra i TOP 4 paesi con il più alto tasso di accettazione delle crypto nel mondo.
Alcune statistiche degne di nota:

Tin tức Binance dự định xin thêm 5 giấy phép hoạt động tại châu Á dimostra una tendenza molto chiara: la regione Asia-Pacifico sta diventando sempre più centrale nello sviluppo del mercato crypto globale.
Secondo numerosi rapporti recenti, il volume degli scambi di crypto in questa regione ha raggiunto circa 2,36 trilioni di USD, con una forte crescita rispetto all'anno precedente. In particolare, molti paesi con il più alto tasso di accettazione delle crypto al mondo si trovano in Asia, tra cui il Vietnam è sempre tra i leader.

Qubic sta portando il mining di Dogecoin nella sua rete. Non come una funzione secondaria o un pensiero posticipato, ma come un'espansione fondamentale di ciò che il protocollo può fare. L'architettura è finalizzata. I test iniziano a marzo. L'obiettivo per il lancio della mainnet è il 1 aprile 2026.
Questa è l'immagine completa di come funzionerà il mining di Dogecoin su Qubic, cosa significa per la rete e perché i miner esistenti dovrebbero prestare attenzione.
Qual è il modello di mining di Qubic?
Qubic non mina per il gusto di farlo. La rete funziona su un concetto chiamato Useful Proof of Work, dove la potenza computazionale serve a uno scopo reale: addestrare Aigarth, l'iniziativa di ricerca AI di Qubic. I miner contribuiscono con potenza di elaborazione per far avanzare i modelli di AI, e la rete li ricompensa con QUs per quel lavoro.

Ciao comunità @Binance Vietnam ! Essendo un nuovo membro della grande famiglia di Binance Square, sono molto entusiasta di poter condividere la mia visione approfondita sulle tendenze più esplosive al momento: L'intersezione tra Intelligenza Artificiale (AI) e Blockchain. Nel primo articolo di questa campagna #CreatorpadVN , analizzeremo il trampolino di lancio che $BNB sta creando per l'era dell'AI.

1. Progresso strategico della BNB Chain con MCP

Mentre il mondo delle criptovalute è spesso distratto dal clamore a breve termine, le vere rivoluzioni si costruiscono nei laboratori della Scienza Aperta. Oggi, l'ecosistema Qubic ha raggiunto un traguardo storico che colma il divario tra la matematica teorica e l'Intelligenza Generale Decentralizzata (AGI).
🏆 La Scoperta Accademica a Osaka
Siamo entusiasti di annunciare che i ricercatori Jose Sanchez e David Vivancos hanno avuto il loro articolo innovativo, "The Neutral Buffer State: Trinary Logic Advantage in Branching Ratio Stability for Continuous-Time Networks," ufficialmente accettato per la pubblicazione e presentazione alla Conferenza Internazionale AMLDS 2026 a Osaka, Giappone.

Mentre il mondo è ossessionato da "Blockchain vs. Finanza Tradizionale", una rivoluzione più profonda sta avvenendo sotto il cofano di Qubic. Non è solo un altro registro; è una riprogettazione fondamentale di come i computer elaborano le informazioni. Abbandonando lo standard binario di 80 anni per la logica ternaria bilanciata, Qubic ha sbloccato un livello di efficienza che Big Tech sta appena iniziando a comprendere.
1. La Ribellione Visionaria: "Fidati della Matematica, Non dei CEO"
La storia di Qubic è iniziata nell'aprile 2022, guidata da Sergey Ivancheglo (CFB). La sua soluzione per raggiungere una vera AGI? Programmazione bare-metal. Qubic interagisce direttamente con l'hardware, eliminando il sovraccarico dei sistemi operativi. Questo garantisce zero latenza e massima capacità, permettendo alla rete di raggiungere un teorico 15 milioni di Transazioni Al Secondo (TPS).

Mentre i giganti della Silicon Valley continuano a costruire "giardini recintati" attorno alla loro intelligenza artificiale, si è verificato un cambiamento tettonico nella comunità open-source. Neuraxon 2.0—il più grande dataset di dinamiche neurali nella storia—è stato ufficialmente rilasciato su Hugging Face.
Questo è più di un semplice traguardo tecnico; è una dichiarazione che Aigarth (il sistema di intelligenza artificiale di Qubic) è evoluto da un quadro teorico nella forza trainante dell'era dell'IA decentralizzata.
1. Aigarth: Dalla Teoria a un Potente Data Center