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Subnet 32 on Bittensor Network for AI detection that incentivizes the development of distributed solutions aimed at identifying LLM-generated contentWas this text written by a human or AI?
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About Us
ProblemGiven the rapid growth of Large Language Models, there is an increasing amount of content generated by AI every dayChatGPT alone generates 100 billion words daily, compared to the 100 trillion words generated by humans. It's becoming increasingly difficult to determine whether a piece of text was written by a human or an AI
Check out podcast with one of the founders to find out more about our subnet
Solution
That’s why we decided to create system that will constantly incentivize the development of distributed solutions aimed at identifying LLM-generated contentTo address the problem below, we're developing a subnet on Bittensor Network that defines incentive mechanisms, validation processes, and a baseline model for miner. For more information check out our github repositoryUse cases
ML engineers
Use our solution to filter out AI-generated content from your training data, maintaining the integrity of your machine learning modelsTeachers
Our tool helps distinguish between student-created work and AI-generated text, safeguarding academic integrityBloggers
Easily identify and remove AI-generated spam comments on your posts with our efficient detection systemWriters
Improve the authenticity of your work by identifying and revising machine-generated text segments with our technologyRecruiting
Streamline your hiring process by detecting AI-generated job applications, ensuring you connect with genuine candidatesCybersecurity
Protect against AI-powered phishing attempts by verifying the origin of emails and documents with our detection solutionRoadmap
Our commercialization journey of SN32 is envisioned in four steps and each step will have it’s own subscription plans.Applications will be based on API, which will be run by validators to provide access for miners predictions.
Learn more in vision and roadmap document
Twitter extension
Build an extension for twitter, which will mark tweets and comments that you’re reading with AI-generated/human-written tags based on miners predictions from the subnetAPI
Provide an API service that can be used by developers for their own integrations or for making predictions on a big amount of text (for example by AI engineers to clean up their datasets)Web service
Develop a full version of web service, which can be used by users even outside bittensor community to detect AI-generated textsBrowser extension
Develop a browser extension, with which users can just highlight some text and see a probability of this text to be AI-generated