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🔥 OpenGraph Private Access is live! Meet our decentralized data annotation platform Building as a decentralized Scale AI but made for Physical AI 🤖 🏷️Annotate the world with built-in verifiability and zero data drift Try now →
15,665 görüntüleme • 11 ay önce •via X (Twitter)
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Start with two tasks step by step 1. 🏷️ Labeling – assign 1 label per image 2. 📦 Bounding Boxes – draw 1 box per image
Complete both tasks and share your results! (we’re tracking activity… 👀 there might be something in it for you later 🫡)
It’s on testnet! Make sure to switch your wallet to the testnet network. If you need testnet SUI, you can grab some here:
@scale_AI
@scale_AI complete sir, confuse on dataset2 why i can crop only one? on example can crop all of them.
@scale_AI Sorry if it’s a bit confusing! for this prototype, we limited it to just one bounding box per image to speed up the validation process. we’ll add a note to make it clearer!
@scale_AI considering back to school✍️
@scale_AI done sir
@scale_AI Done✅LFG!
@scale_AI 👀
@scale_AI Looking into it ser 👀
![Studies have shown ChatGPT outperforms human annotators for Structured Data by about 25% and costs 30x less. 1 In just 2 months, miners on SN33 running ChatGPT without optimization can’t survive. Today we announce SN33 is now ReadyAI to fully align with our mission 👇 SN33 is building a more performant and significantly cheaper alternative to Scale AI Today structured data is performed primarily by human annotation services like Amazon’s Mechanical Turk and Scale AI It is now more important than ever for every business and individual to make their data AI Ready. However, taking unstructured data and making it Structured Data using today’s tools is extremely costly. SN33 revolutionizes this process, unlocking immense opportunities for commercialization. We lay out the vision for it in this detailed blog post: Validators TODAY can monetize access to this structured data pipeline independently, but we’re streamlining this process, launching a frontend soon that any validator can opt into to provide bandwidth. We've received great feedback from the community, recognizing that what we're building goes far beyond Conversational AI. Building the world's largest annotated conversational dataset (which we've already accomplished) is just one of countless real-world applications for SN33's Structured Data pipeline. We're building a decentralized Scale AI, offering a full suite of Structured Data commodities—from text metadata tagging (available today) to fully customizable queries for company-specific data annotation use cases and image metadata tagging coming soon 👀. Thanks for all the feedback! It has been invaluable so keep bringing it to us! 🙏$TAO Openτensor Foundaτion 1 “ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks” shows “The zero-shot accuracy of ChatGPT exceeds that of crowd-workers by about 25 percentage points on average [...] Moreover, the per-annotation cost of ChatGPT is less than $0.003—about thirty times cheaper than MTurk”](https://image.24vids.com/tw-1825631402781819359/ext_tw_video_thumb/1825631116633714689/pu/img/ihH0N-4TGHxyNXtb.jpg)
