Introduction: OMEGA Any-to-Any is a decentralized, open-source AI project built on the Bittensor blockchain by OMEGA Labs. Our mission is to create state-of-the-art (SOTA) multimodal any-to-any models by attracting the world's top AI researchers to train on Bittensor, taking advantage of Bittensor's incentivized intelligence platform. Our goal is to establish a self-sustaining, well-resourced research lab, where participants are rewarded for contributing compute and/or research insight.

MainNet UID: 21

TestNet UID: 157

Why Any-to-Any? 🧠📚🌃🎧🎥

Multimodal First: A2A jointly models all modalities (text, image, audio, video) at once, with the belief that true intelligence lies in the associative representations present at the intersection of all modalities.
Unified Fundamental Representation of Reality: The Platonic Representation Hypothesis suggests that as AI models increase in scale and capability, they converge towards a shared, fundamental representation of reality. A2A models, by jointly modeling all modalities, are uniquely positioned to capture this underlying structure, potentially accelerating the path towards more general and robust AI.
Decentralized Data Collection: Thanks to our SN24 data collection, we leverage a fresh stream of data that mimics real-world demand distribution for training and evaluation. By frequently refreshing our data collection topics based on gaps in the current data, we avoid the issue of underrepresented data classes. Through self-play, our SN's best checkpoints can learn from each other and pool their intelligence.
Incentivized Research: World class AI researchers and engineers already love open source. With Bittensor's model for incentivizing intelligence, researchers can be permissionlessly compensated for their efforts and have their compute subsidized according to their productivity.
Bittensor Subnet Orchestrator: Incorporates specialist models from other Bittensor subnets, acting as a high-bandwidth, general-purpose router. By being the best open source natively multimodal model, future AI projects can leverage our rich multimodal embeddings to bootstrap their own expert models.
Public-Driven Capability Expansion: Public demand dictates which capabilities the model learns first through the decentralized incentive structure.
Beyond Transformers: Integrate emerging state-of-the-art architectures like early fusion transformers, diffusion transformers, liquid neural networks, and KANs.

Roadmap 🚀

Phase 1: Foundation (Remainder of Q2 2024)

Design a hard-to-game validation mechanism that rewards deep video understanding
Produce the first checkpoint with SOTA image and video understanding capabilities with our ImageBind + Llama-3 architecture as a proof-of-concept starting point
Generalize the validation mechanism to enable broad architecture search and new multimodal tokenization methods
Onboard 20+ top AI researchers from frontier labs and open source projects
Expand SN24 data collection beyond YouTube to include multimodal websites (e.g. Reddit, blogposts) and synthetic data pipelines
Launch OMEGA Focus screen recording app, providing rich data for modelling long-horizon human workflows, combatting the hallucination and distraction problem found in top closed-source LLMs

Phase 2: Fully Multimodal (Q3 2024)

Produce the first any-to-any checkpoint natively modelling all modalities that can beat other OSS models on top multimodal and reasoning benchmarks
Develop a user-friendly interface for miners and validators to interact with the subnet's top models
Onboard 50 more top AI researchers from top labs and open source research collectives
Publish a research paper on A2A's architecture, incentive model, and performance
Release open source multimodal embedding models (based on our top A2A checkpoint's internal embedding space) for other labs to condition their models on
Integrate a framework that can auto-evaluate all the models & commodities produced by other subnets on Bittensor which our top models can then interact with, both through tool-use and through native communication in the latent-space via projection modules

Phase 3: Exponential Open Research Progress (Q4 2024)

Produce the first any-to-any OSS checkpoint that beats all closed-source SOTA general intelligence models
Establish partnerships with AI labs, universities, and industry leaders to drive adoption
Expand our one-stop-shop Bittensor model evaluation and router framework to arbitrary open source and closed-source checkpoints and APIs
Implement task-driven learning, with OMEGA Labs routinely curating high-signal tasks for model trainers to master
Start crafting an entirely new "online" validation mechanism that rewards miners for producing agentic models that can complete real-world tasks
Use our top checkpoint to power up the multimodal intelligence features of the OMEGA Focus app

Phase 4: Agentic Focus (Q1 2025)

Launch our agent-focused "online" validation mechanism centered around long-range task completion
Achieve SOTA performance on agent benchmarks
Use OMEGA Focus as an outlet to provide OMEGA digital twin companions to users
Launch an app store for A2A-powered applications leveraging our open source models
Reach 10M+ users with the OMEGA Focus app

Templar