If you have ever wondered what all the noise about Qubic and its AI is about, when there are many projects out there building AI too. Let’s look at what sets Qubic’s Aigarth apart from the rest.
Key Differences: Qubic’s Aigarth vs. Traditional AI
| Category | Qubic’s Aigarth | Traditional AI (e.g., LLMs) |
| Computational Focus | CPU-based, Decentralized Distributed Computing | GPU-based, Centralized Data Centers |
| Information Processing | Ternary Logic (TRUE, FALSE, UNKNOWN) | Binary Logic (TRUE, FALSE / 1, 0) |
| Development Model | Decentralized, Evolutionary, Self-modifying | Centralized, Programmed/Trained (often on fixed data) |
| Goal | Emergent AGI, General Problem-Solving | Narrow AI (specific tasks), often pattern recognition |
Computational Focus: Traditional AI, like Large Language Models (LLMs), heavily relies on powerful Graphics Processing Units (GPUs) in centralized data centers. In contrast, Qubic’s Aigarth uses standard Central Processing Units (CPUs) in a decentralized, distributed way, making it more accessible and energy-efficient.
Information Processing: Traditional AI uses binary logic (True/False or 1/0). Aigarth introduces “ternary logic,” which includes a third state: UNKNOWN. This allows Aigarth to handle uncertainty and noise more effectively.
Development Model: Traditional AI is typically developed in a centralized manner, where models are programmed or trained on fixed datasets. Aigarth, however, is designed to be decentralized, self-modifying, and evolves through a process similar to natural selection.
Goal: The primary goal of Traditional AI is often to achieve narrow AI, meaning systems that excel at specific tasks like pattern recognition. Aigarth’s ambition is to achieve Artificial General Intelligence (AGI), which means general problem-solving capabilities that can adapt to many different situations.