Kled V3 可让实验室指定所需数据，并在 72 小时内向 500,000+ 名自愿加入的贡献者部署采集任务。它提供覆盖图像、视频、音频、文本和标注的 108 个可配置模板，贡献者按说明用手机采集数据。该流程可把模型失败点转化为采集任务，再用真实世界新样本训练和评估，形成更紧密的反馈闭环。
You can't scrape a dataset that doesn't exist yet.
Think about training a model on people performing a specific task, in a specific environment, from a specific camera angle.
Someone has to go and capture those examples.
This is what makes Kled V3 interesting.
Labs can specify the data they need, and Kled can deploy collection tasks to its network of 500,000+ opt-in contributors within 72 hours.
108 configurable templates across image, video, audio, text, and annotation. Contributors capture the data on their phones, with instructions and examples of what qualifies.
The opportunity here is a much tighter feedback loop:
Identify where a model fails.
Turn that failure into a collection task.
Get new examples from the real world.
Train and evaluate again.
The ability to repeatedly collect the exact data a model is missing could be very powerful.
This is Kled V3. We've solved data collection for artificial general intelligence. Any consumer dataset that can exist, can now be collected from physical reality in under 72 hours. All powered by the largest and most comprehensive data application layer on the planet. (Thread)在 X 查看被引用的帖子
来源：elvis · x.com