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Subnet 24

Omega

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ABOUT

What exactly does it do?

The OMEGA Labs Bittensor subnet, is an innovative initiative focused on creating the world’s largest decentralized multimodal dataset to advance research and development in Artificial General Intelligence (AGI). Their mission is to democratize access to an extensive and diverse dataset that encompasses human knowledge and creativity, empowering researchers and developers to push the boundaries of AGI.

Harnessing the Bittensor network and a global community of miners and validators, they are constructing a dataset that exceeds the scale and diversity of existing resources. Featuring over 1 million hours of footage and 30 million+ 2-minute video clips, the OMEGA Labs dataset will facilitate the development of robust AGI models and drive transformation across multiple industries.

The OMEGA Labs Bittensor subnet, is an innovative initiative focused on creating the world’s largest decentralized multimodal dataset to advance research and development in Artificial General Intelligence (AGI). Their mission is to democratize access to an extensive and diverse dataset that encompasses human knowledge and creativity, empowering researchers and developers to push the boundaries of AGI.

Harnessing the Bittensor network and a global community of miners and validators, they are constructing a dataset that exceeds the scale and diversity of existing resources. Featuring over 1 million hours of footage and 30 million+ 2-minute video clips, the OMEGA Labs dataset will facilitate the development of robust AGI models and drive transformation across multiple industries.

PURPOSE

What exactly is the 'product/build'?

Key Features

  • Unmatched Scale and Variety: Over 1 million hours of footage and 30 million video clips spanning 50+ scenarios and 15,000+ action phrases.
  • Latent Representations: Utilizing cutting-edge models to translate video components into a unified latent space for efficient processing.
  • Incentivized Data Collection: Rewarding miners for contributing high-quality, diverse, and innovative videos through a decentralized network.
  • Empowering Digital Agents: Facilitating the development of intelligent agents capable of navigating complex workflows and supporting users across platforms.
  • Immersive Gaming Experiences: Enabling the creation of realistic gaming environments with rich physics and interactive elements.

 

Miner

Conducts searches on YouTube and retrieves up to 8 videos per query. Specifies a clip range (up to 2 minutes) and provides a description (catch) including video title, tags, and description. Obtains ImageBind embeddings for video, audio, and caption components. Returns video ID, caption, ImageBind embeddings (video, audio, caption), and start and end times for clips (up to 2 minutes).

 

Validator

Randomly selects one video from those submitted by miners for validation. Calculates ImageBind embeddings for all modalities (video, audio, caption) of the selected video. Compares embeddings to ensure consistency with miner submissions. If validated, assumes all eight videos from the miner are valid. Scores videos based on relevance, novelty, and detail richness:

  • Relevance: Uses cosine similarity between topic embeddings and each of the eight videos.
  • Novelty: Calculates 1 – similarity to the closest video in the Pinecone index.
  • Detail Richness: Determines similarity between text and video embeddings. Collects 1024 validated video entries and submits them as a concatenated file to Hugging Face. Adjusts file accumulation limits if a miner submits too frequently. Submits remaining validated entries in case of API shutdowns.

Key Features

  • Unmatched Scale and Variety: Over 1 million hours of footage and 30 million video clips spanning 50+ scenarios and 15,000+ action phrases.
  • Latent Representations: Utilizing cutting-edge models to translate video components into a unified latent space for efficient processing.
  • Incentivized Data Collection: Rewarding miners for contributing high-quality, diverse, and innovative videos through a decentralized network.
  • Empowering Digital Agents: Facilitating the development of intelligent agents capable of navigating complex workflows and supporting users across platforms.
  • Immersive Gaming Experiences: Enabling the creation of realistic gaming environments with rich physics and interactive elements.

 

Miner

Conducts searches on YouTube and retrieves up to 8 videos per query. Specifies a clip range (up to 2 minutes) and provides a description (catch) including video title, tags, and description. Obtains ImageBind embeddings for video, audio, and caption components. Returns video ID, caption, ImageBind embeddings (video, audio, caption), and start and end times for clips (up to 2 minutes).

 

Validator

Randomly selects one video from those submitted by miners for validation. Calculates ImageBind embeddings for all modalities (video, audio, caption) of the selected video. Compares embeddings to ensure consistency with miner submissions. If validated, assumes all eight videos from the miner are valid. Scores videos based on relevance, novelty, and detail richness:

  • Relevance: Uses cosine similarity between topic embeddings and each of the eight videos.
  • Novelty: Calculates 1 – similarity to the closest video in the Pinecone index.
  • Detail Richness: Determines similarity between text and video embeddings. Collects 1024 validated video entries and submits them as a concatenated file to Hugging Face. Adjusts file accumulation limits if a miner submits too frequently. Submits remaining validated entries in case of API shutdowns.

WHO

Team Info

Ben-Zion Benkhin – Founder and CEO

Ben founded WOMBO in 2020, aiming to simplify cutting-edge technology for everyday use.

Salman Shahid – Machine Learning Engineer

Salman, fascinated by autonomous AI since a young age, viewed AI as a tool for exploring groundbreaking concepts.

Parshant Loungani – Founder and Head of AI

Parshant, with a physics background, transitioned into AI due to its potential for innovation, leading to the creation of the successful WOMBO app.

Ben-Zion Benkhin – Founder and CEO

Ben founded WOMBO in 2020, aiming to simplify cutting-edge technology for everyday use.

Salman Shahid – Machine Learning Engineer

Salman, fascinated by autonomous AI since a young age, viewed AI as a tool for exploring groundbreaking concepts.

Parshant Loungani – Founder and Head of AI

Parshant, with a physics background, transitioned into AI due to its potential for innovation, leading to the creation of the successful WOMBO app.

FUTURE

Roadmap

Phase 1: Foundation (Q1 2024)

  • Launch the OMEGA Labs subnet on the Bittensor testnet (COMPLETE)
  • Achieve 100,000 hours of footage and 3 million video clips (COMPLETE)

 

Phase 2: Expansion (Q2 2024)

  • Reach 250,000 hours of footage and 15 million video clips (COMPLETE)
  • Train and demonstrate any-to-any models using the dataset (COMPLETE)
  • Establish synthetic data pipelines to improve dataset quality
  • Publish a research paper on the Bittensor-powered Ω AGI dataset
  • Expand operations to include running inference for advanced any-to-any multimodal models

 

Phase 3: Refinement (Q3 2024)

  • Achieve 500,000+ hours of footage and over 30 million video clips
  • Utilize the dataset to train robust unified representation models
  • Fine-tune any-to-any models for sophisticated audio-video synchronized generation
  • Launch an auction page for companies and groups to bid on validation topics using various currencies (in addition to TAO)
  • Develop advanced video processing models for applications such as:
  • Transcription
  • Motion analysis
  • Object detection and tracking
  • Emotion recognition

 

Phase 4: Application (Q4 2024)

  • Train action prediction models for desktop and mobile platforms using the dataset
  • Develop an MVP for cross-platform digital agents

 

Phase 5: Democratization (Q1 2025)

  • Generalize the subnet to allow miners to upload videos from any data source
  • Incentivize individuals to record and annotate their own data using non-deep learning methods

Phase 1: Foundation (Q1 2024)

  • Launch the OMEGA Labs subnet on the Bittensor testnet (COMPLETE)
  • Achieve 100,000 hours of footage and 3 million video clips (COMPLETE)

 

Phase 2: Expansion (Q2 2024)

  • Reach 250,000 hours of footage and 15 million video clips (COMPLETE)
  • Train and demonstrate any-to-any models using the dataset (COMPLETE)
  • Establish synthetic data pipelines to improve dataset quality
  • Publish a research paper on the Bittensor-powered Ω AGI dataset
  • Expand operations to include running inference for advanced any-to-any multimodal models

 

Phase 3: Refinement (Q3 2024)

  • Achieve 500,000+ hours of footage and over 30 million video clips
  • Utilize the dataset to train robust unified representation models
  • Fine-tune any-to-any models for sophisticated audio-video synchronized generation
  • Launch an auction page for companies and groups to bid on validation topics using various currencies (in addition to TAO)
  • Develop advanced video processing models for applications such as:
  • Transcription
  • Motion analysis
  • Object detection and tracking
  • Emotion recognition

 

Phase 4: Application (Q4 2024)

  • Train action prediction models for desktop and mobile platforms using the dataset
  • Develop an MVP for cross-platform digital agents

 

Phase 5: Democratization (Q1 2025)

  • Generalize the subnet to allow miners to upload videos from any data source
  • Incentivize individuals to record and annotate their own data using non-deep learning methods

MEDIA

Huge thanks to Keith Singery (aka Bittensor Guru) for all of his fantastic work in the Bittensor community. Make sure to check out his other video/audio interviews by clicking HERE.

In this audio interview, Keith chats with Parshant and Salman from Omega Labs who are focused on building the largest open-source multimodal dataset with Subnet 24, while Ben-Zion plans to leverage WOMBO (previous Subnet 30) to incentivize virality. This podcast episode highlights their big brains and even bigger ideas.

NEWS

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