$Qubic is a groundbreaking #crypto platform built on the innovative concept of decentralized Quorum consensus, rooted in 13 years of visionary thinking and based on Leslie Lamport’s seminal work on the Byzantine generals problem. This consensus mechanism ensures secure and reliable coordination among independent validators, overcoming the challenges of deception and collusion.

Key Features of Qubic’s #Quorum Consensus Mechanism:Historical Foundation: Inspired by the 1982 paper by Marshall Pease, Robert Shostak, and Leslie Lamport, addressing the Byzantine generals problem, and incorporating ideas from Nick Szabo on coalition protocols.Validator Network: Comprising 676 validators, competing non-stop for their positions based on their performance in mining solutions, a process we term as ‘useful Proof of Work.’

Dynamic Validator Replacement: Every epoch (week), not more than 225 validators are replaced by higher-scoring candidates, ensuring continuous optimization and performance improvement.
Tick-Based Processing: Instead of blocks, #Qubic operates on ‘ticks,’ where each validator performs the same computations. Consensus is reached when more than 2/3 of validators agree on the global state per tick.

Incentive Structure: A fixed supply of 1 trillion tokens per epoch is distributed among validators, with potential earnings up to 1.5 billion tokens per validator, based on performance.
Execution: Each tick has a leader responsible for picking for execution by Quorum. Performance directly influences their revenue, promoting efficiency and reliability.

Qubic’s unique approach combines robust security, economic incentives, and cutting-edge technology to create a secure, scalable, and efficient decentralized network. We invite crypto researchers and enthusiasts to join us in exploring and refining this innovative project.

Join Qubic today and be part of the next generation of decentralization.

https://github.com/qubic

All code is opensource and free available at our github link above.

$BTC #US_Inflation_Easing_Alert

8 fakta singkat tentang #QUBIC

1. Qubic didukung oleh 676 Komputer untuk menjalankan #SmartContracts
2. Bukti Kerja Qubic yang Berguna#POWdigunakan untuk pelatihan #AI
3. Didirikan oleh Sergei Ivancheglo
4. Kontrak pintar penyelesaian Turing tercepat yang ditulis dalam C++
5. POW terdesentralisasi yang menawarkan keamanan, efisiensi, dan skalabilitas
6. DApps dan kontrak pintar sudah siap.
7. Finalitas Subdetik
8. 1 juta TPS

Ringkasan:
$Qubic = gabungan $KAS + $TAO.

Periksa di sini:

qubic.org

@_Qubic_

8 fakta singkat tentang #QUBIC

1. Qubic didukung oleh 676 Komputer untuk menjalankan #SmartContracts

2. Qubic merupakan Proof-of-Work#POWyang berguna untuk pelatihan #AI

3. Didirikan oleh Sergey Ivancheglo

4. Kontrak pintar Turing-complete tercepat yang ditulis dalam C++

5. POW terdesentralisasi yang menawarkan keamanan, efisiensi, dan skalabilitas

6. Siap untuk DApps dan kontrak pintar.

7. Finalitas dalam waktu kurang dari satu detik

8. 1 juta TPS

Ringkasan:
$Qubic = $KAS + $TAO digabungkan.

Lihat di sini:

qubic.org

@_Qubic_

#Qubic : An AI Tech Aimed At Revolutionalising The Crypto Space..

What Is The Main Goal Of A Natural Neural Network?..

Looking at how brain cells are trying to form connections with each other, one may ask if solving some task required for survival of any natural neural network (NNN) is really what that very NNN is trying to do. What if all (and only) goals of an NNN is to form connections? Emerged ability to tell cats and dogs apart, or memorize multiplication table, or whatever human beings do, can be just a side effect, because those who didn’t know that 2 * 2 = 4 didn’t live long enough to produce offsprings?

In approximately a month Qubic is changing its mining algorithm. The current one generates artificial neural networks (ANNs), having static structure, trying to solve some artificial task. The new algorithm aims to generate ANNs which change their structure while trying to solve a task. What do we expect from this? The first of all, we’ll see if “fine-tuning” of such ANNs will be improved with the existing approach mentioned in https://medium.com/@comefrombeyond/teacher-of-teachers-or-on-the-way-to-ai-singularity-f984da03679c. The second, we’ll get some insight about the hypothesis of NNN not worrying about anything except forming connections. And the third, we’ll make a step closer to hypercomputations conducted by self-transforming neural networks, which is probably the key to self-awareness.

The reference implementation of the new mining algorithm is still being developed, we expect to have it ready in a couple of weeks, so others will have some time to write highly optimized versions.

Introducing #Qubic. AI Revolution Inevitable

Aigarth's Journey Towards A True AI Singularity

The first trillionaire will be an artificial intelligence entrepreneur. The majority of people either agree or disagree with this prediction and only a small fraction sees the third option — the whole mankind being the beneficiary. And what is not less important, the AI belonging to noone/everyone could not be misused easily. In Qubic we are developing a platform where AI won’t be constrained at all.

Aigarth is the AI running on top of Qubic and is aiming to make the third option come true. The name consists of two words, “AI” meaning “artificial intelligence” and “garth” meaning “yard” or “garden”.

How does Aigarth works?
It is an AI-software that get it´s information from nearly hundred thousand (number is rapidly increasing) of Qubic-miners which are creating billions artificial neural networks (ANNs) with their computational power that is supposed to compress and decompress random data. This simple task, which the ANNs are trying to accomplish, is just food for another ANN created and run by Aigarth. The latter ANN (or rather its many variations), called Teacher, modifies the former ANNs so they solve their task of compression-decompression more efficiently.

If somebody asked what methods Teacher used to modify the “pupils” noone would be able to answer (not even the creator of Aigarth). Because Teacher applies simple operations encoded in the own neurons and synapses, hoomans don’t understand how all that works, we don’t see the forest for the trees. Why such strange approach? Because one day Teacher will be ordered to teach another ANN to be more efficient in teaching and that Teacher 2 will do the same on Teacher 3 and so on. And eventually we may face something called “singularity”… or „true AI“

1/1

#BinanceWish #BinanceSquareGenerosity #BinacePay keinginan saya dibawah 👇

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@山顶的汤仔 @AB4133

#BinanceWish