Useful Proof of Work (UPoW) and Artificial Intelligence (AI) in the Qubic Ecosystem

Proof of Work (PoW) is a fundamental concept employed across various computer sciences and particularly in the realm of cryptocurrencies, where it ensures the security and reliability of decentralized networks like Bitcoin. It accomplishes this by making the process of altering or creating fraudulent transactions computationally expensive and time-consuming. However, in the innovative Qubic ecosystem, we've introduced an exciting twist on the traditional PoW by integrating AI training as a means of achieving the same consensus, giving rise to a novel consensus mechanism: Useful Proof of Work (UPoW).

Traditional Proof of Work

Proof of Work is essentially a competitive puzzle-solving process among computers. Whenever someone intends to make a transaction on the network, their computer must solve a complex mathematical puzzle. The first computer to solve the puzzle, or 'mine' the solution, gets the opportunity to validate the transaction and add it to a 'block' – a collection of transactions. This mining process is instrumental in preventing any single entity from controlling the network and ensures decentralised security.

The Qubic Approach: AI Training as Proof of Work

The Qubic ecosystem pushes the boundaries of the traditional PoW by employing AI training tasks in its consensus mechanism. Here, validators, known as Computors, backed by miners, often referred to as "AI miners," utilise AI models to solve intricate training tasks instead of conventional mathematical puzzles.

Functioning of AI Training for Proof of Work

In this approach, the network presents AI models with intricate training tasks, such as processing large datasets or training machine learning models on specific problems. This paradigm shift ensures that the work done by the Computors is not just for maintaining network security but also contributes to real-world applications and services.

Advantages of AI Training as Proof of Work

AI training as Proof of Work within the Qubic ecosystem presents numerous benefits. It's more energy-efficient as AI training can be optimised and run on normal hardware, consuming less energy per computational unit compared to traditional PoW. Additionally, AI training maintains the security and decentralisation of the network, with each AI miner competing to solve tasks, thus avoiding centralisation.

Useful Proof of Work (UPoW)

Useful Proof of Work (UPoW) within the Qubic ecosystem transforms the computational energy expended in the mining process into valuable, beneficial outcomes. The UPoW protocol directs this computational power towards the training of Artificial Neural Networks (ANNs), thereby harnessing the network's immense computational capacity for the progression of machine learning.

In the UPoW protocol of Qubic, the ranking of Computors hinges on the effectiveness of their AI miners in solving these complex problems. The primary objective of this mining operation is not merely the validation of transactions or creation of new blocks, but it's for establishing a Computor's ranking for each epoch, a time period of one week. The better a Computor's miner performs, the higher its ranking, and subsequently, its potential earnings.

The integration of PoUW adds a layer of energy efficiency to the Qubic network. It ensures that the energy used in mining is channeled towards real-world problem-solving, such as machine learning tasks. This strategy increases the overall utility of the network, making it beneficial not only for network maintenance but also for external applications and services.

A Technical Exploration of the Mechanics of ANN-Driven UPoW in Qubic

In Qubic, we take a unique approach to mining through our 'Useful Proof of Work' (UPoW) system. A fundamental component of this approach is the utilization of artificial neural networks (ANNs), a model inspired by the human brain's own network of neurons.

Since the inception of ANNs, the objective has been to replicate the complexity and functionality of the human brain as closely as possible. While some researchers have chosen to mimic the sophisticated neuron activation function, which requires intricate mathematical models, we have focused on replicating the structural changes that occur in a developing brain.

If you think back to the early years of life, you'll realize that while the basic functionality of neurons remained the same, your mental abilities greatly improved. This development can largely be attributed to the increase in connections between neurons.

Research suggests that initialising an ANN with random parameters results in an entity possessing some primitive cognitive function. In fact, an ANN where all neurons are interconnected already has some degree of memory and intellect. The process of improving an ANN is actually a process of eliminating connections - up to a point. There's a 'sweet spot' where an ANN of a certain size demonstrates the best intellectual abilities. Beyond this point, further elimination of connections leads to degradation.

In Qubic, miners don't follow a path of destruction but instead generate ANNs with a random structure of connections. These parameters are changed, and Aigarth analyses the properties of the ANNs. The current stage involves collecting samples and trying to discern patterns that may provide insight into the future direction of development. This process reflects Qubic's unique approach to utilising ANNs and creates a mining process that is not only computationally challenging but also contributes to the development of these neural networks.

Conclusion

The Qubic ecosystem's unique approach to PoW and the introduction of UPoW presents a promising and eco-friendly alternative to traditional consensus mechanisms. By employing AI models to solve complex tasks, Qubic maintains network security and decentralisation while reducing energy consumption and providing a valuable contribution to the advancement of machine learning and artificial intelligence.