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🚀 Introducing KumoRFM — the world’s first Relational Foundation Model purpose-built for enterprise prediction tasks! KumoRFM reasons over complex relational data to deliver instant, accurate, in-context predictions — no task-specific model training required. A true game-changer for solving key business problems like: ✅ Product recommendations ✅ Fraud detection ✅... show more
63,601 просмотров • 1 год назад •via X (Twitter)
Комментарии: 4
Awesome! 🙌
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Pretty neat. How does this compare at scale vs. text-to-sql?
Text-to-SQL make it easy for analyst to ask about the past ("How much product X sold last month?"). This is about predicting the future ("How much of the product X will we sell next month?"). Importantly, prediction is all done in-context at inference time (no model training is needed)
![How do you teach a robot to handle complex, multi-step tasks, without training it for each one? [Github ⬇️] The team behind ReKep shows that robots can perform bimanual, in-the-wild tasks by reasoning over keypoint constraints: Generated on the fly using vision and language models. No task-specific data, no environment modeling. Why it matters ✅ Encodes tasks as simple Python functions over 3D keypoints ✅ Uses VLMs to generate keypoint constraints from instructions ✅ Plans and replans in real time with a 10 Hz perception-action loop ✅ Works for bimanual, multi-stage tasks without task-specific training Built on open tools like SciPy and BEHAVIOR, ReKep brings reactive, general-purpose reasoning closer to real-world robot control. Project website: Paper: Code: Walkthrough video: Thank you, Wenlong Huang for sharing 🫶](https://image.24vids.com/tw-1912401403093393811/amplify_video_thumb/1912401341156065280/img/4bMnA4qRtaUvdlQb.jpg)
![A new robot policy just cleaned up a kitchen it had never seen before [watch what happens. paper included ⬇️] Pi-0.5 builds on top of Pi-0 and shows how smart co-training with diverse data can unlock real generalization in the home. It doesn’t just learn from one setup but adapts to many, including homes it’s never seen. What it does ✅ Handles new homes without training in them ✅ Follows complex language instructions ✅ Cleans, places dishes, handles spills ✅ Matches in-home training models using cross-embodiment and web data Robots that understand tasks and adapt to new spaces are finally within reach. More in the blog: Read the paper ⬇️ Physical Intelligence, co-founded by UC Berkeley professor Sergey Levine, is a robotics startup developing general-purpose AI foundation models that enable robots to perform a wide variety of real-world tasks with human-like adaptability, recently raising $400 million to advance this vision.](https://image.24vids.com/tw-1914938183675953474/amplify_video_thumb/1914938115292082176/img/DgEOnRIEYmX83ed_.jpg)
