Forget What You Know About AI: This is Qubic’s AGI

Artificial Intelligence (AI) has become a transformative force in the world, penetrating industries from healthcare to finance. Still, traditional AI has significant limitations. Current AI systems are specialised; they excel at specific tasks like image recognition or language processing but lack the adaptability and versatility that define human intelligence. This is where Artificial General Intelligence (AGI) comes in.

AGI will go beyond task-based learning. It aims for a broader understanding, learning from diverse data and experiences to adapt to almost any challenge, much like the human mind. But in order to achieve AGI, an unprecedented amount of computational resources, creativity, and collaboration will be required. This post explores Qubic’s unique approach to developing AGI, and how Qubic’s Useful Proof of Work (uPoW) fuels this ambitious vision, paving the way for AGI as a decentralised, community-powered initiative. Welcome to Aigarth - the initiative that is redefining AI.

The Limits of Traditional AI: Why We Need AGI

Today, even the most powerful AI systems are specialised. Known more formally as narrow AI, these are able to perform a single task exceptionally well but then fail when faced with problems that fall outside their programming. For instance, an AI able to play chess cannot write a novel or diagnose a medical condition. With this constraint, AI is limited in its broader applicability and integration into everyday life, ultimately holding back its transformative potential. Today’s AI’s  cannot create, the limiations are what it has been told. It cannot be rational or imaginative. The power of it's thought is limited to that of it's creator.

AGI aims to break these boundaries. By developing an intelligence capable of learning, reasoning, and adapting across tasks, AGI opens new possibilities. Imagine an AI able to analyse medical images, conduct scientific research, handle logistics, or create art - with little or no human guidance.

Enter Aigarth: Qubic's Road to Decentralised AGI

While organisations like OpenAI pursue AGI through centralised efforts, Aigarth’s model empowers a decentralised, community-driven approach, aiming to democratise AI development. Achieving AGI will require extensive computational resources and, in our opinion, a collaborative approach to training. Traditional, centralised AI models are typically controlled by corporations, therefore concentrating power and control in the hands of the few. This approach comes with significant disadvantages:

• Resource Demands: Building AGI requires vast computational resources, often placing a heavy financial and environmental burden on centralised data centres.

• Lack of Accessibility: Centralised AI development limits access, preventing widespread collaboration and open contribution.

• Control and Ownership: Centralised AI can be monopolised, with a single party setting the agenda and potentially restricting public benefit.

This is where Aigarth comes in. Aigarth leverages Qubic’s Useful Proof of Work (uPoW) model to decentralise AGI development, making it an open, community-driven process. Through uPoW, Qubic’s global network of miners channels computational power toward AI training tasks, allowing anyone to contribute to and benefit from Aigarth’s journey to AGI.

Useful Proof of Work: The Fuel Behind AGI

As we discussed in the previous post, Qubic’s Useful Proof of Work (uPoW) redefines mining by channelling CPU-based processing power into meaningful AI training tasks. This aligns with recent industry trends, such as Microsoft’s focus on CPUs for AI workloads, validating Qubic’s approach as an efficient choice for scalable AI development. Here's how uPoW supports AGI through Aigarth:

• Decentralised Computation: Unlike a single data centre, uPoW spreads out computational tasks across Qubic's decentralised global network. Because of such decentralisation, any person in the world can participate in AGI development, lowering the monopoly over AI resources.

• CPUs Over GPUs: Aigarth’s model prioritises CPU-based mining over GPUs for AI training as it is effective for the types of calculations needed in machine learning. This setup also maximises accessibility (it's cheaper) and enables high scalability across diverse hardware.

• Community-Driven Growth: By distributing AI tasks to miners around the world, Aigarth's approach to AGI training benefits from community involvement.

The Role of Aigarth: Energy into Intelligence

The ultimate goal of Qubic's uPoW is AGI, achieved through Aigarth. In the development of Aigarth, artificial neural networks (ANNs) are trained, emulating the interrelation of neurons in the human brain. Using ANNs, Aigarth learns from diverse datasets to improve pattern recognition and adapt to increasingly complex tasks.

Each miner's computational effort contributes toward the training of Aigarth. Instead of depending on centralised servers, this distributed model allows Aigarth to scale efficiently with each miner in the network powering the training of ANNs and advancing AI capabilities.

Intelligent Tissue Phase

During Aigarth’s initial phase, ANNs form a network capable of basic cognitive tasks. Each neuron stores trinary values (-1, 0, +1), allowing Aigarth to make more refined decisions than traditional binary models. As this “tissue” evolves, Aigarth will gain foundational cognitive abilities, essential for advanced intelligence.

Enhanced Cognitive Capabilities

As Aigarth advances, active synaptic changes in neuronal connections will improve performance in specific tasks, enabling iterative learning and adaptation. Aigarth is thus empowered by this self-improvement mechanism to take on increasingly complex tasks and sharpen its skills.

Key Technical Details Supporting AGI Development

Aigarth's Unique Technical Framework combines several innovative approaches to AI training and adaptation:

Trinary Computing and Neuron Configurations: Aigarth's neural architecture uses three states: -1, 0, and +1. This allows processing ambiguous data, supporting sophisticated decision-making. This trinary framework allows more adaptability in pattern recognition compared to the traditional binary systems.

Synaptic Configurations: The synapses in Aigarth's network are designed to include variable delays, allowing for dynamic interactions between neurons. This functional flexibility helps to increase complex problem-solving capabilities by allowing delayed response patterns across the network.

Evolutionary Algorithms for Optimisation: Aigarth's architecture utilises evolutionary algorithms for network optimisation. Successful mutations will be rewarded, and ineffective configurations pruned, allowing the structure of Aigarth to evolve over time.

Possible Use Cases of Aigarth 

Aigarth's potential traverses many industries, using its decentralised training framework to achieve scalable and flexible artificial intelligence capabilities.

Healthcare: Supporting diagnostics and personalised treatments by analysing large datasets. 

Supply Chain Optimisation: Logistics automation and real-time demand prediction. 

Finance: Enable decentralised AI models for real-time risk assessment and decision-making. 

Why Decentralised AGI Matters

In addition to the technological benefits, creating AGI through a decentralised framework allows for advantages that go beyond just the technology. Centralised AI development raises ethical concerns about access and control. As Qubic’s scientific advisor, David Vivancos, suggests, decentralising AI development not only democratises access but also mitigates the ethical risks associated with centralisation. Through Aigarth, Qubic brings AGI closer to this ideal by positioning it as a shared resource, open to all, rather than a private asset.

• Increased Accessibility: A decentralised network democratises AGI development, allowing individuals and communities worldwide to participate in building the future of AI.

• Open Development: With decentralised AGI, control over the development trajectory of AI is shared, eliminating the risk of any single entity dictating its use or direction.

• Sustainable Resource Use: The uPoW model by Qubic makes AGI development sustainable, as computational power is directed toward productive uses rather than arbitrary ones.

The Vision for Aigarth: AGI for Everyone

Through Aigarth, the Qubic network is working toward an AGI model that will act as a public resource. This is in sharp contrast to traditional AI systems, where control and benefit tend to accrue to either corporations or governments. Under the Aigarth model, AGI is available to everyone, built by all, open-source, and transparent. It heralds a new way of thinking in terms of AI ownership, collaboration, and benefit sharing.

Why AGI in a Decentralised System Matters

The decentralised approach to AGI is not only innovative; it is a must for AI to fulfil its promise as a powerful force for good. Imagine an AGI that operates without borders, where insights, advancements, and applications are available to all, regardless of background or location. Decentralised AGI can empower communities and protect against misuse, creating a trustworthy, community-owned intelligence with the capability to address complex global challenges.

Forget about Traditional AI. This is Qubic's AGI

Qubic's Useful Proof of Work model and Aigarth initiative redefine what AI can do when powered by the many, not just the few. In a world where AGI development is usually centralised and proprietary, Aigarth is something different: decentralised, community-driven AGI anyone can help shape and use.

In the next post of our "Forget What You Know" series, we’ll explore Qubic’s Quorum. Forget What You Know About Consensus - This is Quorum.

What possibilities do you see for AGI as a decentralised, open-source initiative? How do you feel about AGI as a shared resource, rather than controlled by corporations? Ask your questions and join the discussion in our Discord and Telegram.