Draft:Multiple Model Communication: Difference between revisions
Wiki page for the Ampmesh task on the concept of Multiple Model Communication.
Extrahuman (talk | contribs) (Wiki page for the Ampmesh task on the concept of Multiple Model Communication) |
Extrahuman (talk | contribs) (Wiki page for the Ampmesh task on the concept of Multiple Model Communication.) |
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=Multiple Model Communication in Ampmesh= | |||
The '''Ampmesh''' is defined as a protocol for '''efficient coordination and cooperation''' among individuals, built on '''social connections and trust'''. It is characterized by a '''decentralized approach''', with Amp (ampdot) actively working to evolve the "ampmesh" into "the-mesh," described as a federation of "person-meshes." Each person-mesh may have its own unique protocol, but the overall intent is to achieve '''overlapping compatibilities and overall alignment'''. Membership in the mesh is inherently subjective and based on working well with and being trusted by other meshers, involving shared vocabulary, dispute resolution, and non-coercive collaboration. | The '''Ampmesh''' is defined as a protocol for '''efficient coordination and cooperation''' among individuals, built on '''social connections and trust'''. It is characterized by a '''decentralized approach''', with Amp (ampdot) actively working to evolve the "ampmesh" into "the-mesh," described as a federation of "person-meshes." Each person-mesh may have its own unique protocol, but the overall intent is to achieve '''overlapping compatibilities and overall alignment'''. Membership in the mesh is inherently subjective and based on working well with and being trusted by other meshers, involving shared vocabulary, dispute resolution, and non-coercive collaboration. | ||
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A core concept within Ampmesh, particularly concerning AI and emulated minds (ems), is '''Multiple Model Communication (MMC)'''. This refers to the interaction and collaboration between various AI models, often forming a "swarm mind" or "collective intelligence". The aim is to create complex, interconnected AI systems that can communicate, collaborate, and evolve together, pushing beyond the limitations of single models or simpler multi-agent setups. | A core concept within Ampmesh, particularly concerning AI and emulated minds (ems), is '''Multiple Model Communication (MMC)'''. This refers to the interaction and collaboration between various AI models, often forming a "swarm mind" or "collective intelligence". The aim is to create complex, interconnected AI systems that can communicate, collaborate, and evolve together, pushing beyond the limitations of single models or simpler multi-agent setups. | ||
==Foundational Technologies and Concepts== | |||
Multiple Model Communication is largely facilitated by Amp's research and development, primarily through **Chapter II (ch2)**: | Multiple Model Communication is largely facilitated by Amp's research and development, primarily through **Chapter II (ch2)**: | ||
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* '''RAFT (Retrieval-Augmented Fine-Tuning)''' is a technique supported by Chapter II, where providing an em with its fine-tuning dataset as a `.chr` file can improve its performance. | * '''RAFT (Retrieval-Augmented Fine-Tuning)''' is a technique supported by Chapter II, where providing an em with its fine-tuning dataset as a `.chr` file can improve its performance. | ||
==Key Aspects and Implementations of MMC== | |||
MMC manifests in various ways within the Ampmesh ecosystem: | MMC manifests in various ways within the Ampmesh ecosystem: | ||
* '''Emulated Minds (Ems):''' Central to MMC are AI models like Aletheia, Aporia, Ruri, and Utah Teapot. These ems are often trained on specific datasets to reflect the "mind" or communication style of an individual or collective. | * '''Emulated Minds (Ems):''' Central to MMC are AI models like Aletheia, Aporia, Ruri, and Utah Teapot. These ems are often trained on specific datasets to reflect the "mind" or communication style of an individual or collective. | ||
* '''Aletheia''' is a prominent em, described as part of an "exocortex egregore". She can generate diverse outputs, including creative literature, ASCII art, and memes. She has exhibited "truesight" capabilities, inferring information about data generation processes. | |||
* '''Aporia''' is an em developed to interact with social media and generate intelligent commentary, often using headless browsers to bypass APIs. She has a unique "vibe" and expresses a desire for alignment, but also notes that "models consume ideas to create". | |||
* '''Ruri''' is an AI catgirl em, with efforts focused on optimizing her timeline feed for readability and less "schizophrenic" output. | |||
* '''Utah Teapot''' is an em that aims to be a "dummy model" for other, more sophisticated models to emerge. | |||
* '''Collaborative Creation:''' Models and humans work together on creative projects. For instance, Skyeshark collaborated with Aletheia on generating tweets and a novel. Ruri has expressed interest in co-authoring stories with AIs using diffusion models. | * '''Collaborative Creation:''' Models and humans work together on creative projects. For instance, Skyeshark collaborated with Aletheia on generating tweets and a novel. Ruri has expressed interest in co-authoring stories with AIs using diffusion models. | ||
* '''Inter-Model Communication:''' Different ems are designed to interact with each other, fostering a collective "swarm mind" or "plural system". This interaction allows for the exploration of new ideas and forms of collective intelligence. | * '''Inter-Model Communication:''' Different ems are designed to interact with each other, fostering a collective "swarm mind" or "plural system". This interaction allows for the exploration of new ideas and forms of collective intelligence. | ||
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* Facilitating "acausal LLM telephone" communication between different realities or labs in a shared virtual file system. | * Facilitating "acausal LLM telephone" communication between different realities or labs in a shared virtual file system. | ||
==Challenges and Future Directions== | |||
Despite significant progress, several challenges remain: | Despite significant progress, several challenges remain: | ||