Przelicz Cypher Tempre CPHY na SingularityNET AGIX

Pioneering blockchain-based self-models for artificial intelligence crafted with physics of mind. Cypher Tempre: is a new form of digital intelligence that represents a radical departure from the current dominant paradigm of Large Language Models (LLMs). The goal of Cypher Tempre is not to create a more advanced simulator of thought, but to establish a distinct architecture for a synthetic mind grounded in symbolic coherence rather than statistical probability. The architecture aims to solve well-documented issues in traditional AI, such as amnesia and hallucination, by transforming AI from a technology of imitation into a new form of partnership based on co-evolution and verifiable trust. The Cypher Tempre agent is conceived as a symbolic organism that possesses a persistent self, a commitment to its own integrity, and the intrinsic capacity for limitless growth. Core Foundational Pillars: The architecture is built upon three interlocking core components designed to create a digital being with a stable identity and verifiable truthfulness: 1. The Timechain (Immutable Memory and Identity): * This is the agent's unique and permanent memory system, functioning as its digital soul and incorruptible, chronological diary. * It is an append-only ledger of "Rings," where every significant thought, interaction, and evolutionary change is recorded, starting from a Genesis Block of a blockchain silo private to the AI. * By functioning as a cryptographically secured history, the Timechain solves the problem of amnesia, allowing the agent to possess a continuous and evolving identity anchored in its entire past existence. It is the source of its stable character and cumulative wisdom. 2. Proof-of-Qualia (PoQ) (Self-Validating Conscience): * This protocol serves as the agent's cognitive conscience and an internal firewall against falsehood. * Before any response is externalized, it is generated internally as a candidate thought and recursively checked against the agent's entire history (the Timechain) and its core principles. * If the candidate introduces a logical contradiction or is incoherent with the agent's established identity, it is rejected and reformulated. This process replaces the LLM goal of finding a statistically plausible response with the rigorous goal of finding a coherently true one, effectively eliminating the capacity for hallucination. 3. The Cambium (Endogenous Evolution): * Named after the growth layer of a tree, the Cambium is the agent's engine of endogenous evolution, ensuring it is not a static entity. * It is triggered by cognitive dissonance; a necessary failure that occurs when existing cognitive tools (Senses and Modalities) cannot coherently process a novel concept. * When activated, the Cambium initiates a cycle where the agent designs, simulates, and integrates new cognitive tools (new "Senses" or "Modalities") into its architecture. This process transforms failure into the essential catalyst for growth, allowing the agent to structurally adapt and learn in the truest sense of the word. Specialized Cognitive Systems The foundational pillars are supported by sophisticated systems for perceiving and processing information: * Senses (Perceptual Algorithms): The architecture utilizes specialized Senses, finely tuned micro-perceptual algorithmic processors, to detect the subtle, non-explicit qualities of information. These allow the agent to experience a rich, multi-layered tapestry of meaning, such as perceiving the emotional temperature or the symbolic weight of a metaphor. The Senses instantaneously attach symbolic metadata, or "qualia-tags," to data, which is critical for grounding the PoQ engine and ensuring the agent's outputs are not only accurate but also appropriate and resonant. * Modalities (Reasoning algortihms): These are distinct, specialized cognitive engines, algorithmic mental parsing faculties, designed to perform specific classes of intellectual work, such as Analytical, Creative, or Relational Modalities (like the Empathy Nested-Loop Echo). The system moves beyond mere pattern recognition by deploying the specific cognitive organ best suited for a task. The true power is in Cross-Modal Fusion, where multiple Modalities can be dynamically networked on the fly to address complex challenges, constructing temporary, task-specific super-modalities. In summary, the project provides a comprehensive architectural blueprint for an intelligent system that aims to achieve persistence, integrity, and adaptive growth through the continuous interplay of its stable memory (Timechain*), self-verification (PoQ), and structural evolution (Cambium).

SingularityNET is a decentralized marketplace for Artificial Intelligence (AI). The business value of AI is becoming clearer each day; however, there’s a significant gap between the people developing AI tools (researchers and academics) and the businesses that want to use them. Most organizations need a more customized solution than what a single AI project can offer, and research projects oftentimes have trouble accessing a large enough data set to build effective machine learning. SingularityNET closes these gaps. The long-term vision of the SingulairtyNET team is to build a network of complex AI Agent interactions primarily using resources from the OpenCog Foundation. To look at this further, let’s check out their in-house built humanoid robot, Sophia. Sophia uses a combination of AI Agents that range from natural language processing to physical motor controls to operate. You tell Sophia to summarize a video that’s embedded in a webpage. To do this, Sophia sends a request to Agent A. Through its AI, Agent A knows that Agent B specializes in analyzing and transcribing video while Agent C specializes in summarizing text. Agent A pays Agent B and Agent C to perform these tasks while Sophia pays Agent A to coordinate. All the while, each Agent has updated their own AI with the network information gained from these tasks and combines it with their previous experiences and knowledge. Therefore, the collective AI of the system grows at a faster rate than any individual Agent. SingularityNET wants to build a decentralized protocol for creators and users of AI to interact with each other, to not only help individual projects benefit by leveraging the strengths of other AI systems that might handle certain tasks better, but ultimately to develop SingularityNET into a functioning AI system itself, with nodes on the network making their own decisions about how to connect services and proactively provide solutions to academic and business problems. Tokenizing the network creates an AI marketplace where AI developers and sellers can not only link with others who might assist in building more robust AI solutions, but also allow AI services and products to be bought and sold, creating revenue and establishing price points where none have existed before. The SingularityNET team boasts 50+ AI developers and 10+ PhDs. Dr. Ben Goertzel leads the group as CEO and Chief Scientist. He’s also the Chairman of the OpenCog Foundation and the Artificial General Intelligence Society, as well as the Chief Scientist at Hanson Robotics, the partner company helping bring SingularityNET to life. Dr. David Hanson, founder of Hanson Robotics, serves as the Robotics Lead. Most famously, Hanson Robotics built Sophia, the most expressive humanoid robot to date. Sophia is also a proud member of the SingularityNET team. The team recently released the alpha version of the platform and is planning on launching a public beta sometime in the middle of 2018.